The Pennsylvania State University

The Graduate School

College of Health and Human Development

A WITHIN-NETWORK VARIABILITY APPROACH TO UNDERSTANDING

“PROCESS” IN GROUP-BASED INTERVENTIONS

A Doctoral Dissertation in

Human Development and Family Studies

by

Lauren E. Molloy

© 2012 Lauren E. Molloy

Submitted in Partial Fulfillment

for the Requirements

for the Degree of

Doctor of Philosophy

December 2012

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The dissertation of Lauren E. Molloy was reviewed and approved* by the following: Scott D. Gest Associate Professor of Human Development & Family Studies Dissertation Co-Adviser Co-Chair of Committee Nilam Ram Associate Professor of Human Development & Family Studies Dissertation Co-Adviser Co-Chair of Committee Wayne Osgood Professor of Crime, Law, and Justice, Department of Sociology Rachel Smith Associate Professor of Communication Arts & Sciences Douglas Coatsworth Professor of Human Development and Family Studies Professor in Charge of Graduate Studies, Department of Human Development & Family Studies Steven Zarit Professor of Human Development and Family Studies Department Head of Human Development and Family Studies *Signatures are on file in the Graduate School.

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ABSTRACT

Group-based approaches to delivering interventions are common, cost effective,

and empirically-supported. Yet empirical evidence regarding the role of group process in

facilitating participant outcomes remains limited, in large part by the analytic approaches

most commonly used to assess it. In this dissertation, I propose and illustrate an

innovative analytic paradigm for studying group process that integrates social network

analysis (SNA) and methods sensitive to intra-individual change and variability over time

(IIV) to overcome past limitations and advance understanding of group process. Tools

from these two methods are applied to the collection of data from two settings:

undergraduate and graduate students enrolled in group therapy at a university counseling

center, and parents and youth participating in a parent and life skills training preventive

intervention.

Paper 1 is a conceptual paper describing the limitations of past approaches and

elaborating an integrated SNA and IIV approach to studying group process; illustrative

examples provide compelling evidence of the added value of this approach beyond

traditional measures. Paper 2 focuses on data from the 18 therapy groups (121

participating clients); network-based indices of group structure and individual

connectedness significantly predicted participants’ weekly reports of progress and

perceptions of session value, within and across time. Additionally, average levels and

linear and quadratic growth in individuals’ connectedness (as measured by SNA)

significantly related to pre- to post-treatment improvement in mental health. Finally,

Paper 3 focuses on data from 20 groups (106 parents, 86 youth) participating in the

Strengthening Families Program (SFP). Results indicated that weekly participant reports

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of self-efficacy and perceptions of session value were significantly predicted by network-

based measures of group structure and individuals’ positions within their groups within

and across sessions. Moreover, improvement in mother-youth relationship quality and

several indices of youths’ mental health, self-beliefs, and attitudes from pre- to post-

intervention were significantly predicted by average levels of group density and

individual connectedness across sessions (on both positive and negative relationships),

and by linear growth in individuals’ connectedness across sessions, with several relations

differing by age group (i.e., mother versus youth).

Taken together, results provide support for the value of an integrated SNA and

IIV approach to studying group process in preventive and treatment interventions.

Findings of each paper are discussed in terms of the value of this approach for

understanding how group process in psycho-education and therapy groups develops

across sessions and facilitates intervention effectiveness. The analytic approach tested

here and resulting substantive findings will allow researchers, program developers, and

clinicians to target and capitalize on specific social dynamics in evaluating, improving,

and developing preventive and treatment programs.

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TABLE OF CONTENTS

LIST OF TABLES ......................................................................................................... viii LIST OF FIGURES ......................................................................................................... ix ACKNOWLEDGEMENTS ............................................................................................. x Chapter 1: INTRODUCTION ......................................................................................... 1 Chapter 2: A WITHIN-NETWORK VARIABILITY APPROACH TO UNDERSTANDING “PROCESS” IN GROUP-BASED INTERVENTIONS: CONCEPTUAL DISCUSSION AND EMPIRICAL ILLUSTRATIONS ................... 3 INTRODUCTION .............................................................................................................. 3 Limitations of the Existing Group Process Literature.................................................... 4 Addressing the Limitations of Past Research................................................................. 6 The UNITE Study........................................................................................................... 7 METHODS SENSITIVE TO CHANGE AND VARIABILITY........................................ 8 Empirical Illustration of IEV Methods ......................................................................... 11 SOCIAL NETWORK ANALYSIS .................................................................................. 14 Empirical Illustration of SNA....................................................................................... 20 INTEGRATING TOOLS FROM SNA AND IEV METHODOLOGIES....................... 25 Empirical Illustration of an Integrated IEV & SNA Approach .................................... 26 APPLICATION TO “NON-PROCESS” GROUPS ......................................................... 33 Analysis & Results........................................................................................................ 34 DISCUSSION................................................................................................................... 35 Contributions of the Integrated IEV-SNA Approach ................................................... 35 Limitations and Future Directions ............................................................................... 36 Concluding Remarks..................................................................................................... 38 REFERENCES ................................................................................................................. 39 Chapter 3: MATCHING PSYCHOTHERAPY GROUP PROCESSES TO

METHODS ABLE TO ARTICULATE THOSE PROCESSES: CONTRIBUTIONS OF SOCIAL NETWORK ANALYSIS AND INTRAINDIVIDUAL VARIABILITY METHODOLOGIES............................... 59

INTRODUCTION ............................................................................................................ 59 Importance of Group Process ....................................................................................... 59 Limitations of the Existing Group Process Literature.................................................. 60 Addressing the Limitations of Past Research: Social Network Analysis..................... 61 Addressing the Limitations of Past Research: Methods Sensitive to Change and

Variability..................................................................................................................... 64 The Present Study......................................................................................................... 66 METHODS ....................................................................................................................... 67 Participants & Procedures............................................................................................. 68 Measures ....................................................................................................................... 69

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Preliminary Data Analytic Steps................................................................................... 75 RESULTS ......................................................................................................................... 77 Aim 1: Covariation between Group Process and Weekly Progress.............................. 77 Aim 2: Predicting Post-Therapy Outcomes from Average Levels and Change in

Process.......................................................................................................................... 82 DISCUSSION................................................................................................................... 85 Aim 1: Covariation between Group Process and Weekly Progress.............................. 86 Aim 2: Predicting Post-Therapy Outcomes from Average Levels and Change in

Process.......................................................................................................................... 89 Strengths, Limitations, & Future Directions ................................................................ 93 Concluding Remarks .................................................................................................... 95 REFERENCES ................................................................................................................. 96 APPENDIX..................................................................................................................... 112 Chapter 4: UNDERSTANDING PROCESS IN GROUP-BASED INTERVENTIONS: SOCIAL NETWORK ANALYSIS AND INTRAINDIVIDUAL METHODOLOGIES AS WINDOWS INTO THE “BLACK BOX”....................... 114 INTRODUCTION .......................................................................................................... 114 Importance of Understanding Process........................................................................ 114 Limitations of Existing Group Process Literature...................................................... 117 Addressing the Limitations of Past Research: Social Network Analysis................... 118 Addressing the Limitations of Past Research: Methods Sensitive to Change and

Variability................................................................................................................... 120 Other Moderating Factors........................................................................................... 121 The Present Study ........................................................................................................... 124 METHODS ..................................................................................................................... 125 Participants & Procedures........................................................................................... 126 Measures ..................................................................................................................... 126 Preliminary Data Analytic Steps................................................................................. 130 RESULTS ....................................................................................................................... 131 Aim 1: Covariation between Group Process and Weekly Progress............................ 132 Aim 2: Predicting Post-Intervention Outcomes from Average Levels and Change in

Group Process............................................................................................................. 136 DISCUSSION................................................................................................................. 139 Aim 1: Covariation between Group Process and Weekly Progress............................ 140 Aim 2: Predicting Post-Intervention Outcomes from Average Levels and Change in Group Process ................................................................................................................. 143 Contributions & Implications Moving Forward ......................................................... 148 Strengths & Limitations.............................................................................................. 150 Concluding Remarks................................................................................................... 152 REFERENCES ............................................................................................................... 153 APPENDICES ............................................................................................................... 167 !

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LIST OF TABLES Table 2.1: Predicting pre- to post-therapy improvement from between-person differences

in average level, slope, and lability in GCQ measures of group climate ..................... 46 Table 2.2: Measurement model and factor loadings ......................................................... 47 Table 2.3: Correlations between network indices and traditional indices of group process

in therapy groups .......................................................................................................... 48 Table 2.4: Multilevel models predicting weekly reports of progress and session value

from SNA indices ......................................................................................................... 49 Table 2.5: Multilevel models predicting weekly progress from between- and within-

person GCQ scores, between- and within-person SNA indices of individual connectedness, and between- and within-group SNA indices of group structure........ 50

Table 2.6: Predicting pre- to post-therapy improvement in from between-person

differences in average level, slope, and lability in GCQ scores and Outdegree .......... 51

Table 2.7: Multilevel models predicting SFP participants’ weekly reports of session value from between-person average levels and within-person variation in a) group leader ratings of engagement and b) number of reciprocal positive ties................................. 52

Table 2.8: Predicting pre- to post-SFP intervention improvement in frequency of mother-

youth shared activities from leader ratings of positive engagement and group-level density of positive connections .................................................................................... 53

Table 3.1: Multilevel models predicting weekly progress from between- and within-

group/ person variation in network indices of group process..................................... 103 Table 3.2: Multilevel models predicting weekly perceptions of session value from

between- and within-group/ person variation in network indices of group process... 104 Table 3.3: Correlations of linear and quadratic slopes in progress indices with linear and

quadratic slopes in SNA indices of individual-level connectedness .......................... 105 Table 3.4: Predicting pre- to post-therapy improvement from between-group differences

in average level, slope, and lability in group-level density of positive and negative connections ................................................................................................................. 106

Table 3.5: Predicting pre- to post-therapy improvement from between-person differences

in average level, slope, and lability in individual-level sources of connectedness and discomfort................................................................................................................... 107

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Table 3.6: Predicting pre- to post-therapy improvement from between-person differences in average level, slope, and lability in individual-level feelings of connectedness and discomfort................................................................................................................... 108

Table 3.7: Predicting pre- to post-therapy improvement from between-person differences

in average level, slope, and lability in individual-level number of reciprocal positive relationships................................................................................................................ 109

Table 4.1: Multilevel models predicting weekly self-efficacy from between- and within-

group/ person variation in network indices of group process..................................... 160 Table 4.2: Multilevel models predicting weekly session value from between- and within-

group/ person variation in network indices of group process..................................... 161 Table 4.3: Correlations of individual-specific growth trajectories of SNA indices to

individual-specific growth in session value and self-efficacy.................................... 162 Table 4.4: Regression analyses predicting pre- to post-intervention change in the quality

of mother-youth relationship from between-person/ group differences in average level and slope of network indices of process..................................................................... 163

Table 4.5: Regression analyses predicting pre- to post-intervention change in youths’

mental health and targeted self-beliefs and attitudes from between-person/ group differences in average level and slope of network indices of process........................ 164

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LIST OF FIGURES Figure 2.1. Participant ratings across sessions on two subscales of the GCQ from a sample therapy group........................................................................................................ 54 Figure 2.2: GCQ reports of group engagement across sessions for a subsample of highly variable and highly stable clients ...................................................................................... 55 Figure 2.3: Sample sociograms depicting between-person differences in centrality and between-group differences in density, reciprocity, clustering, and centralization ........... 56 Figure 2.4: Sample networks derived from participant responses to network items assessing caring, engagement, and negativity for one of the therapy groups at one measurement occasion ...................................................................................................... 57 Figure 2.5: Between- and within-group variability in the density of caring relationships across a subsample of groups............................................................................................ 58 Figure 3.1: Predicting weekly self-reports of “global progress” from interactions of school level with individual-level SNA indices.............................................................. 110 Figure 3.2: Predicting pre- to post-therapy improvement (on three mental health subscales) from interactions of school level with group-level density ........................... 111 Figure 4.1: Group size moderates the relations between average level source of connectedness and weekly reports of self-efficacy and session value............................ 165 Figure 4.2: Age group moderates the prediction of changes in parent-youth relationship quality by average levels and slopes in individuals’ connectedness............................... 166 !

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ACKNOWLEDGEMENTS

! I would like to thank the many people whose continued support, encouragement,

and guidance helped me to get to where I am today. First, to my co-chairs and co-

mentors, Drs. Scott Gest and Nilam Ram: I cannot thank you enough for all of the time

and effort you put into my graduate career.

To Scott: Thank you for your patience and guidance in helping me to navigate

the transition to graduate school, explore and settle upon a coherent set of research

interests, broaden and deepen those research interests in various ways, and clarify and

integrate my research and career goals in order to successfully conquer the various

milestones of graduate school, apply for jobs, and of course, complete this dissertation.

Thank you for always “watching out for” and encouraging my professional development,

and helping to keep me moving forward throughout the process. And lastly, thank you

for the invaluable feedback you’ve given me over the years on my writing, presentations,

etc. I am continually amazed by your insight and expertise, including (but not limited to)

your comprehensive knowledge of the peer relations literature (e.g., your ability to pull

up relevant authors, theories, and findings from memory at any time!) and your skill at

“seeing the forest through the trees” (e.g., hearing about a bunch of results, and being

able to pull out an interesting story on the spot!). I can only hope to emulate these

qualities one day. Oh – and thank you for helping me find this job with Youth-Nex!

To Nilam: Thank you for all of your encouragement and support – both

instrumental and emotional – that helped me to complete this dissertation and to “jump

through all the hoops” along the way. Your contributions to my research interests,

longer-term line of work, and broader thinking have been invaluable: searching for ways

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in which theory and methods can push each other forward has become ingrained in my

thinking and my approach to research. I truly admire your interdisciplinary thinking and

your ability to take complex statistical concepts and translate them into a language that is

both widely accessible and conveys their value to a more general audience. On a day to

day basis, thank you for all the times you were available and willing to sit with me and

answer a stats question, let me bounce an idea off of you, or help me think through a

logistical issue on my study – to name just a few examples. More broadly, thank you for

always treating me like a junior colleague and collaborator.

I would also like to thank the other members of my dissertation committee: Drs.

Doug Coatsworth, Rachel Smith, and Wayne Osgood. Thank you for all of the time,

expertise, and thoughtful feedback that you contributed to this process; I greatly value the

unique perspective that each of you brought to the table, and know that your suggestions

will substantially improve the quality and impact of my dissertation once it is ultimately

published. Thank you also for making my defense meetings (comps, proposal, and

dissertation defense) so productive, interesting, thought-provoking, and even enjoyable!

I am incredibly grateful to the many people who supported, encouraged, and

believed in the value of the UNITE study (the source of my dissertation data) and helped

it come to fruition. Thank you again to Scott and Nilam for encouraging me to pursue the

study and providing me with the skills and resources needed to execute it. A huge thank

you to Dr. Leann Terry for advocating on its behalf to the staff at CAPS, and to Dr. Doug

Coatsworth for advocating on its behalf to the SFP team despite some “push-back”; and

to both of you for all the time you invested in helping me to finalize the study design,

write items, and troubleshoot logistical issues. The UNITE study also would not have

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been possible without the help of several undergraduate research assistants who collected

the majority of the data and helped me to enter, clean, and analyze it: Carly Young, Karli

Lawson, Jon Reader, Sydney Fitzgerald, Gabi Janney, Alyse Ahn, Anna Long, Emily

Wilhite, and Hanna Mincemoyer. A special thank you to Hanna who consistently went

above and beyond in her work on the project, and who was a tremendous help during the

final push of my dissertation. And last but not least, a big thank you to all of the

participants who contributed data to this study.

I would also like to thank my lab-mates on the projects to which a bulk of my

time in graduate school was devoted: Kelly, Alice, and Deb on “MSTP”, and Kelly, Deb,

Sonja, Dan, April, and Lacey on PROSPER Peers. I am especially indebted to Kelly

Rulison, who had the patience of a saint in helping me get acquainted with the MSTP

dataset, excel templates, and R syntax during my first few years of graduate school.

Despite your incredibly busy schedule and amazing productivity, you always made time

to answer my questions and explain (or re-explain) things to me, for which I am

incredibly grateful. You were absolutely critical in helping to lay the foundation for my

future success with and interest in this dissertation project.

Of course, I wouldn’t be where I am today without the help of my friends and

family. To my parents: thank you for your unconditional love and support, for always

believing in me, for being my biggest champion, and for helping and encouraging me to

do my best since as far back as I can remember. Thanks for making me feel like an

academic superstar throughout all of my years of schooling, for instilling in me a good

work ethic, the motivation to achieve, and a love of learning, and for helping me think

through and decide on the career path that ultimately led to my doctoral degree at Penn

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State. To my sister: thank you for being my best friend, for constantly inspiring me, and

for being by my side through all of my ups and downs. Your love and emotional support

played as valuable a role in my success as did any of the academic support I got in

school. To Jana and Andrew, the two “honorary” members of my immediate family:

thank you for your love and loyalty throughout the ups and downs, and especially for

supporting me through (and distracting me from) the “downs”! To my nephew, Jack:

thanks for all the smiles and giggles – a picture of you in my inbox was enough to

brighten up any day, no matter how stressful. Thank you also to my companion and

work-at-home buddy for 8 years and counting, my cat Fiona, for the purrs and snuggles.

And to my dog, Gil, may he rest in peace: thank you for your unconditional love, loyalty,

and companionship; thank you for all the smiles you put on my face; thank you for all the

times you greeted my return from a long day at the office with a warm, wagging

welcome; thank you for enough happy memories to last a lifetime; and thank you for

seeing me through to the end of my dissertation. I miss you every day.

Beth Grisa Hunt: Thank you for welcoming me into your life and helping me

through the toughest years of graduate school. Thank you for all of the dinners, the

“homework parties”, your patience and advice, and the many long nights (e.g., in your

office, at your house, at my house, etc). Most importantly, thank you for making State

College feel like a home, and for six years of friendship with many more to come.

Harshini Shah: Thank you for your love, support, and a wonderful start to a

lifelong friendship. Thank you for the lunch dates, the study dates, and the countless fun

memories. Thank you for being by my side during some of the toughest classes, and

thank you for the pep talks – and other long talks – that helped get me through some of

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the most stressful phases of grad school (and life)!

Mary Lai Rose: Thank you for the unwavering support, optimism, and positive

energy you brought to every interaction, and for all of the fun events and outings you

organized that helped make Central PA a great place to live. And thank you for the

many, many study dates at Barnes & Noble: the comps and dissertation process were so

much more bearable – and even fun – because we got to claw our way through them

together! Your companionship and support were critical during those final hurdles.

And to the rest of my State College family – especially Chris, Joche, Steph, Petra,

Theo, Kristi, and Jake – thank you for making my years in graduate school such an

incredible chapter in my life!

And lastly, to my fiancé Dan, my best friend and the love of my life: Thank you

for being my rock during times of greatest stress; thank you for cheering me on and

cheering me up; thank you for your patience and for picking up the slack on cooking and

cleaning during crunch times; thank you for the hikes, the dinners, our Waffle Shop

brunches, and the trips around the country and the world; thank you for helping me grow

and inspiring me to be a better person; and thank you for your unwavering support and

confidence in me every step of the way. I love you so much and I truly cannot imagine

how I would have done this without you.

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CHAPTER 1:

INTRODUCTION

Group-based approaches to delivering interventions are common, cost effective,

and empirically-supported. A rich theoretical literature suggests numerous benefits of a

group-based approach to interventions (e.g., universality, cohesiveness). Yet empirical

evidence regarding the role of group process in facilitating participant outcomes remains

limited, in large part by the analytic approaches most commonly used to assess it. Most

of these approaches are a mismatch to the dynamic nature of group process, and lack the

precision needed to operationalize the complex social processes believed to underlie its

“curative” effects. Traditional measures over-simplify group process, overlooking

between-group, between-person, and across-session variation that may have important

implications for participant outcomes. In this dissertation, I propose and illustrate an

innovative analytic paradigm for studying group process that integrates social network

analysis (SNA) and methods sensitive to intra-individual change and variability over time

(IIV) to overcome past limitations and advance understanding of group process.

Paper 1 is a conceptual paper describing the limitations of past approaches and

elaborating an integrated SNA and IIV approach to studying group process, using

empirical examples to illustrate the key concepts.

In Paper 2, data were collected from 18 therapy groups (121 participating clients)

at the University’s counseling center; participants reported weekly (immediately

following each session) on their current relations to group-mates and perceptions of their

own progress and the session’s value, and completed pre- and post-treatment surveys

assessing their mental health symptoms. Social network measures of group-level

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structure and individual-level connectedness on both positive and negative ties among

group members are computed to represent group process at each session, and examined in

relation participants’ weekly reports of progress. Linear and quadratic growth in

individual-level connectedness are then examined in relation to linear and quadratic

growth in individuals’ progress and perceived session value. Lastly, average levels and

linear and quadratic growth in positive and negative connections are examined in relation

to pre- to post-treatment improvement in mental health. Implications of findings for

practices in group therapy are discussed.

In Paper 3, data were collected from 20 groups (106 parents, 86 youth)

participating in a parent and youth life skills training program, the Strengthening Families

Program (SFP). Immediately following each of seven weekly sessions, participants report

on their current relations to group members, and perceptions of session value and self-

efficacy. The integrated SNA and IIV approach is again applied to examine weekly

covariation of group process and participant progress, and to explore the implications of

development and fluctuations in group process for participant outcomes targeted by the

intervention. The value of this approach for understanding how group process in psycho-

education groups facilitates intervention effectiveness, and how this information will be

useful to program developers and group facilitators in the future, are discussed.

Together, these three papers illustrate and provide support for the value of an

integrated SNA and IIV approach to studying group process in preventive and treatment

interventions. The analytic approach tested here and resulting substantive findings will

allow program developers and clinicians to target and capitalize on specific social

dynamics in evaluating, improving, and developing preventive and treatment programs.

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CHAPTER 2:

A WITHIN-NETWORK VARIABILITY APPROACH TO UNDERSTANDING

“PROCESS” IN GROUP-BASED INTERVENTIONS: CONCEPTUAL

DISCUSSION AND EMPIRICAL ILLUSTRATIONS

Group-based approaches to delivering preventive and treatment interventions are

common, cost effective, and empirically supported. Group therapy is extremely common

in treating a range of mental health issues, and youth violence and drug prevention

programs are almost exclusively delivered in classrooms or to groups of parents. Group

delivery formats have the potential to facilitate positive outcomes in numerous ways; for

instance, they provide participants with feelings of belonging and acceptance,

opportunities to recognize that others struggle with the same feelings they do, a boost in

self-efficacy by helping one another, and a safe, non-judgmental environment in which to

test out a new repertoire of healthy interpersonal behaviors (Kivlighan & Holmes, 2004;

Yalom, 2005). Thus, the nature and quality of participant interactions – i.e., the group

process – is believed to play a critical role in the extent to which participants benefit from

the group. Yet the role of group process in facilitating positive outcomes remains largely

theoretical, having received surprisingly limited empirical attention. Moreover, the

studies that do exist to date typically over-simplify group process, employing assessment

and analytic approaches that are a mismatch to the inherently dynamic and complex

nature of intra-group relations (Morgan-Lopez & Fals-Stewart, 2006). Thus, the goal of

this paper is to demonstrate an innovative measurement and analytic approach that

integrates cutting-edge tools for the study of social relationships and change over time in

order to overcome previous limitations and fill the gaps in our understanding of how

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group process relates to outcomes.

Limitations of the Existing Group Process Literature

Forward progress toward a greater understanding of group processes demands

measurement strategies that are precise and effective at capturing those processes and

how they change over time. Yet the analytic complexities and methodological challenges

of studying group process have been a major hindrance to such progress (e.g., Morgan-

Lopez & Fals-Stewart, 2006). Thus, it is most common that participant responses to items

assessed at one or two occasions about the group as a whole (e.g., “members liked and

cared about each other”) are averaged to create an “overall group climate” score

(MacKenzie, 1983). Such global measures are severely limited in their ability to

articulate group process. In the following sections, I begin by outlining the ways in which

current approaches over-simplify group process, and discuss some of the key

methodological challenges faced in this type of research.

Change. First, most statistical models applied to “group process” research are

unrealistically static. As noted above, group process data is often collected at only one or

two measurement occasions, failing to account for the amount and frequency of change

constantly occurring simultaneously in both individuals and their relationships (Snijders,

2011). Across psychological research in general as well as group process research in

particular, there is a call for analytic methods more sensitive to the dynamic nature of

psychological phenomena to produce more of a “motion picture” of the change processes

instead of static “snapshots” of phenomena at individual occasions of measurement. For

many of the characteristics we study, including (or perhaps especially) interpersonal

relationships, there is a distribution of possible values or relationships that may be

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manifested at any given occasion depending on the specific circumstances surrounding

that assessment occasion. Thus, capturing one or two “snapshots” over the course of

treatment risks seriously misrepresenting participants’ true experiences (Bolger, Davis, &

Rafaeli, 2003; Nesselroade, 1991).

Moreover, failure to recognize or capture changes across time in group process

may mask noteworthy and psychologically meaningful ups and downs in intra-group

relationships. By treating individuals as “static packages of stable true scores that change

only relatively slowly according to developmental principles” (Nesselroade, 1991),

traditional approaches fail to capture more rapidly-occurring changes, characteristics of

those changes (e.g., directional or fluctuating, patterned or random), or the implications

of those changes for participant outcomes (Ram & Gerstorf, 2009). As such, many

researchers are calling for a greater focus on the dynamics of within-person (or within-

network) change and variability (e.g., Bolger et al., 2003; Snijders, 2011). In sum, more

frequent measurement occasions are critical for obtaining an accurate picture of the

overall group process, capturing important change processes, and examining the

implications of within-person/ network change over time for individual outcomes.

Group structure. Second, data interdependence among participants in groups is

nearly inevitable (and is actually is a desired consequence of a group treatment

approach), and is an important analytic challenge that must be dealt with when

investigating groups. Unfortunately, this challenge is often the reason that group research

is avoided, and other times is the reason that interdependence is simply ignored (i.e.,

individuals within groups are treated as though they are independent of one another).

Ignoring group-level nesting violates the standard assumptions of most traditional

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models. The result of this violation of assumptions is misleadingly small standard errors,

and in turn, serious risk of committing a type I error (i.e., spurious “significant” results;

Morgan-Lopez & Fals-Stewart, 2006). Moreover, there are conceptually important group-

level features that are a product of these interdependencies and deserve direct empirical

attention, but cannot be captured with the measurement tools commonly applied in the

literature: for instance, participant ratings of the “group as a whole” would not be able to

detect the formation of smaller sub-groups or “cliques” within the larger group.

Individual position. Lastly, most group process studies have overlooked important

aspects of how individuals contribute or respond to the group dynamics. For instance, a

meta-analysis of therapeutic factors in psychotherapy groups identified twenty-four

studies on this topic (Kivlighan & Holmes, 2004); every one of these studies aggregated

scores across participants to form group-level ratings, masking any variation across

members. This is problematic, as variation across members of a group is both expected

and likely to be psychologically meaningful. For instance, if a member feels rejected or

isolated from his or her group-mates, the program may be less useful to that individual. In

sum, more attention is needed to between-person differences that may impact the extent

to which particular members benefit.

Addressing the Limitations of Past Research

In order to overcome the limitations of past research and advance our

understanding of group process, the proposed approach draws upon and integrates current

best practices for studying social relationships and change over time: social network

analysis (“SNA”) and methods sensitive to intra-entity variability (“IEV methods”). In

this paper, I will walk through the importance of attending to change and variability in

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group process over time, and discuss methods for articulating these changes. I will then

move on to discuss the application of social network analysis to the study of group

process. Lastly, I will discuss and demonstrate the integration of these methods.

Throughout this paper, empirical examples are employed to demonstrate concepts, using

data from psychotherapy groups. Thus, I will first briefly introduce the UNITE study

(more extensive details can be found in the context of an empirical paper focused

specifically on analysis of these data; Molloy, 2012b).

The UNITE Study

The UNITE study was a longitudinal study of groups in therapy and intervention

settings. In brief, in the therapy setting, data were collected from 18 psychotherapy

groups run by the university’s center for counseling and psychological services (mean

group size = 6.61, 9 groups of all undergraduate students, 8 of all graduate students, and

1 mixed group). Participants completed surveys (administered on pre-programmed smart

phones) immediately following each weekly session (for 4 to 16 sessions) that included

three sets of items: 1) the Group Climate Questionnaire (GCQ; see MacKenzie, 1983), a

measure commonly used for assessing members’ perceptions of a group’s therapeutic

environment; 2) a series of items that were adapted from each GCQ item into a social

network format, described in detail later in this paper (see section on social network

analysis); 3) the Group Evaluation Scale (GES; Hess, 1996) to assess participants’

weekly progress. In addition, the OQ-30 (Burlingame & Lambert, 2004) was

administered at the first and last sessions of treatment, to assess mental health

improvement.

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Methods Sensitive to Change and Variability

Need for IEV Data. The intimate connections (e.g., caring, trust) assumed to

facilitate positive outcomes in intervention and therapy groups take time to develop and

may fluctuate over time. There is an extensive theoretical (and a growing empirical)

literature highlighting the dynamic nature of group process. For instance, it is frequently

noted that there are several distinct �developmental stages� of group process (e.g.,

Wheelan, 1997). Given expectations of a complex series of changes, studies examining

change in group climate with a simple pre- to post-test difference score have been

criticized for failing to capture the process of change (Kivlighan & Lilly, 1997). Clearly,

there is a push for greater empirical attention to change in the study of group process.

What I will refer to here as “intra-entity variability (IEV) methods” are a set of data

collection and analytic techniques purposively developed to capture, quantify, and

analyze change. IEV methods allow us to operationalize dynamic processes (e.g., do

group processes drive changes members’ symptoms?) and dynamic characteristics (e.g.,

how stable or variable are the social relationships in a given group?) (Ram, Conroy,

Pincus, Hyde, & Molloy, 2012).

Collection of IEV Data. Change processes are conceptually important, yet

impossible to capture unless measurement occasions match the timescale of change (e.g.,

Collins, 2006). Based on this logic, ecological momentary assessment (EMA) is a

measurement approach in which: a) data are collected within their natural setting; b)

assessment occasions are strategically selected to coincide in time with the phenomenon

of interest, and c) data focus on how participants currently feel, and are less prone to

recall biases (Schwartz & Stone, 1998). In order to understand group process within

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group-based interventions, then, the logic of EMA dictates that we should collect data: a)

within the intervention delivery setting, b) at every session, representing each time intra-

group relationships have an opportunity to change, and c) immediately following each

session, while the intra-group interactions and feelings are fresh in participants’ minds.

EMA has been used to track short-term changes in a wide range of psychological,

behavioral, and health constructs, such as mood (Silk et al., 2011), substance use

(Gwaltney, Bartolomei, Colby, & Kahler, 2008), symptoms of mental health disorders

(Haedt-Matt & Keel, 2011), and adherence to health regimens (Helgeson, Lopez, &

Kamarck, 2009).

Tools for the Analysis of IEV Data. To make use of IEV data, analytic tools must

be selected that are sensitive to intra-individual or intra-group variability (i.e., intra-

entity variability) across time, allowing for characterization of the changes observed (e.g.,

the amount or rate of change) and relations of these changes to participant progress and

outcomes (Ram & Gerstorf, 2009). Multilevel modeling (MLM) is a useful tool that is

frequently applied to EMA data A key benefit of MLM is the ability to tease apart

between-person from within-person associations: person-specific means and slopes can

be “extracted” from each individual’s data, leaving the “de-trended” residuals to

represent within-person fluctuation across sessions (Bolger et al., 2003; Schwartz &

Stone, 1998). With person-specific means and slopes accounting for all between-person

variance, the residuals are left to represent only within-person processes (Hoffman &

Stawski, 2009). Entered into multilevel models, we can test whether individuals who are

more well-connected than their group-mates tend to also experience better outcomes than

their group-mates (i.e., between-person associations), as well as whether on sessions

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when an individual is more well-connected than usual (i.e., above their own mean level

of connectedness) they experience more progress than usual during that session (i.e.,

within-person associations). When the latter association is significant, evidence is

strengthened that associations between predictor and outcome are not simply the result of

stable or “innate” dispositional differences between persons: the circumstances of a given

session may help participants become more engaged than usual and in turn experience

greater benefits. In other words, it helps us to identify processes in which we may be

able to intervene and make a difference for participants’ experiences.

Repeated measures data can also be used to quantify between-person differences

in change and variability. For instance, we can quantify an individual’s characteristic

amount of lability (i.e., unstructured “ups and downs”), operationalized as individually

calculated standard deviations; in turn, lability can be examined as a between-person

variable in relation to other constructs (Ram et al., 2012). For instance, higher lability in

self-concept and self-worth have been linked to lower competence, lower motivation, and

avoidance of challenges (e.g., Molloy, Ram, & Gest, 2011; Newman & Wadas, 1997),

and greater lability in affect and psychological well-being has been linked to poorer

mental health (e.g., Roberts & Kassel, 1997).

Based on these and similar findings, some researchers have suggested that lability

in particular psychological constructs may be indicative of “vulnerability”. In other

studies, lability has been used to quantify interpersonal flexibility (i.e., the ability to adapt

behavior across a wide range of contexts), and covariation of emotions has been used to

operationalize �poignancy�, or the ability to experience mixed emotions (Ram & Gerstorf,

2009). Along similar lines, it can also be useful to quantify between-person differences

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in trajectories of change: in one study, best therapeutic gains were observed among

groups demonstrating a U-shaped trajectory of change in engagement (Kivlighan & Lilly,

1997). More broadly, the key point is that theoretical constructs such as “psychological

vulnerability” and “interpersonal flexibility” can be operationalized using multiple

repeated measurements and individually-calculated standard deviations or slopes. Taken

together, studies employing these methods consistently suggest that the dynamic

characteristics and processes that IEV methods allow us to investigate are meaningful for

individuals’ adjustment and well-being across a range of domains (see Ram & Gerstorf,

2009 for a review). As such, IEV methods will be a valuable tool for informing and

evaluating interventions.

Empirical Illustration of IEV Methods

Figure 2.1 presents group climate data from a sample therapy group: scores on

the engagement subscale (Figure 2.1a) and conflict subscale (Figure 2.1b) of the GCQ are

plotted across all weeks (dotted line), while a second line (solid) demonstrates what the

plot would look like if these data had been collected once every four sessions (i.e., more

widely spaced intervals that are common in group process research). As these sample

plots highlight, a substantial amount of within-group variability is missed when

measurement occasions are widely spaced. While the plot of the “traditional” approach

would suggest an “inverted U-shape” trend for engagement (i.e., low early on, peaking in

the middle, and dipping back down), the EMA approach reveals no such pattern, but

instead suggests that engagement in this group “bounces around” from week to week.

Perhaps even more striking are the plots of conflict: the traditional approach would

suggest a steady, almost exactly linear increase in conflict across sessions. In contrast, the

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EMA approach reveals that this group experienced substantial “ups and downs” in

conflict, which would suggest that characterization of the group’s conflict at each session

was dependent upon the circumstances of that session. Weeks 4, 8, 12, and 16 were all

relatively low points in this group’s conflict; as such, the traditional approach

substantially misrepresents participants’ experiences of group process in this group. For

instance, noteworthy peaks in conflict during weeks 7, 9, 10, and 11 are completely

missed. The EMA plot, in contrast, allows us to detect what was likely a psychologically

significant therapeutic event mid-treatment.

The session-to-session fluctuation in engagement and conflict observed in Figure

2.1 were evident across our sample. What was also apparent, however, was the presence

of several distinct features of the evolution of group process that differed across groups

and persons, and might have implications for participant outcomes. For instance, Figure

2.2 shows a subsample of participant reports of group engagement; specifically, Figure

2.2a demonstrates five examples of participant reports of engagement that were highly

variable across sessions, and Figure 2.2b demonstrates five highly stable examples. In

addition to apparent differences in the “overall” or average level of engagement reported

by each participant, one could also observe distinctions across persons in the overall

growth or decline (i.e., linear slope) observed, the extent to which trajectories reflect

acceleration or deceleration in the growth of engagement (e.g., a quadratic slope), and the

amount of un-patterned “ups and downs” around those trajectories (i.e., residuals). Each

of these between-person differences can be “extracted” from the data, and examined in

relation to participants’ pre- to post-treatment improvement.

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For purposes of the current demonstration, growth curve models were fit to each

individual, allowing us to extract four aspects of change for each individual: a time-

centered person-specific intercept (used to represent between-person differences in level),

linear slope, quadratic slope, and occasion-specific residuals (unexplained by the general

trend). A standard deviation of the residuals was computed for each individual to

represent amount of intra-individual fluctuation or “lability” (Molloy et al., 2011). These

steps were applied to each of the three subscales of the GCQ (i.e., engagement, conflict,

and avoidance). The resulting individual-specific scores were entered into a step-wise

linear regression (level at step 1, linear slope at step 2, quadratic slope at step3, and

lability at step 4) as predictors of participants’ post-therapy mental health scores,

controlling for baseline mental health. Findings suggest that a U-shaped trajectory of

change in engagement (γ = -1.37, p = .02), and greater lability in avoidance (γ = -.43, p =

.03) predicted the greatest gains in mental health (see Table 2.1). The benefits of the “U-

shaped” trajectory observed here are consistent with past research and theory on group

process (Kivlighan & Lilly, 1997). As for lability in avoidance: perhaps this reflects the

group’s tendency to hit upon some uncomfortable interpersonal issues, but also for the

group to work through those issues. In other words, it may be indicative of participants

taking interpersonal risks, and gaining practice with resolving interpersonal issues as they

arise.

Conclusions about IEV Methods. More broadly, what this demonstration

highlights is the value of between-person differences in change and variability in helping

to account for between-person differences in therapy outcomes. The brief substantive

interpretations offered for these findings are intended to demonstrate how IEV methods

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can a) be matched to theoretical expectations about how group process operates, b) be

used in future research to directly test theories about the evolution of group process

across sessions, and c) expand our thinking about group process and the types of research

questions we can explore within an IEV framework. Neither a quadratic slope nor a

“lability” score could have been derived from a simple pre- and post-test measures of

“change” in group process. In sum, we hope to have made clear that group process should

be measured relatively often (at least each session) and that aspects of how groups evolve

across session may be useful for gaining additional understanding of how to better

facilitate success of the therapy.

Social Network Analysis

Need for social network data. As noted earlier, participant ratings about the group

as a whole are commonly aggregated across participants to obtain a picture of the

“overall group climate”. The two major problems with this approach are that: a)

structural features of the group cannot be captured, such as the formation of subgroups or

a hierarchical structure, and b) some members may be more well-embedded or engaged

in the group than others, but these types of between-person differences are overlooked.

Here, I propose and demonstrate the use of social network analysis (SNA) as a valuable

set of tools ideal for addressing these limitations and advancing our understanding of

group process and its role in facilitating outcomes. Key concepts of SNA are discussed

here, and brief substantive examples of SNA indices are provided; for more complete

literatures and discussions of applications of SNA relevant to group-based prevention or

treatment settings, see Molloy (2012b, 2012c).

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Social network analysis (SNA) is a set of measurement and statistical tools

designed to operationalize long-standing theories of group dynamics (e.g., Moody &

White, 2003). The network of interconnections among members of a group is assessed by

asking individuals to report on their own dyadic relationships to other individuals in the

group or “network” based on specified criteria. For instance, members of a therapy group

may be asked: “who do you like and care about?” Social network analytic tools can then

be applied to these data to quantify features of a group’s structure and individual’s

positions within those groups.

Group structure. Group structure refers broadly to the layout or distribution of

social ties within a group. Perhaps the most common group-level characteristic discussed

in the group process literature is how cohesive a group is (Yalom, 2005). Group-as-a-

whole measures give us a general sense of the “togetherness” and climate of a group; yet

the degree of a group’s cohesion can be measured and defined more precisely using

social network statistics. For instance, density and reciprocity are commonly used to

operationalize cohesion. Density gives us an “average” level of connectedness of the

group, and is computed as the total number of dyadic ties present within a group (e.g.,

how many “nominations” of liking were made by participants), divided by the total

number of possible dyadic ties (i.e., if every member was connected to ever other

member, density would be 1.0, or 100%; see Figure 2.3b for a visual example of

between-group differences in density). Reciprocity characterizes the extent of mutuality

within a group, and is defined as the proportion of ties within a group that are mutual or

bidirectional (see Figure 2.3c for a visual example of between-group differences in

reciprocity). These are just two examples of indices with which SNA allows us to

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precisely operationalize “group cohesion”. For demonstration purposes, these two are the

indices used.

SNA studies across a wide range of contexts have related greater network density

and reciprocity to more positive outcomes for members of those networks. For instance,

more cohesive work networks are associated with higher affect among employees

(Totterdell, Wall, Holman, Diamond, & Epitropaki, 2004); and greater cohesion within

social networks (e.g., online communities, friendship networks) is related to a greater

sense of social support (e.g., Barnett & Hwang, 2006), and better mental health among

members of those networks (e.g., Mitchell & Trickett, 1980). Together, these studies

provide strong empirical support for the value of density and reciprocity as measures with

implications for individual outcomes (see Molloy, 2012b, 2012c for additional examples

relevant to group-based prevention and treatment settings).

Beyond more precise operationalizations of cohesion, SNA allows us to quantify

group structural features that would be impossible to capture with traditional measures.

For instance, centralization is the extent to which social ties are unequally distributed

across members of a group, such that some members are more well-connected than others

(i.e., there is a “hierarchical” structure to the group; see Figure 2.3e for a visual example),

and is typically operationalized as how central the most central member is in relation to

other members. Differences in the centrality between the most central member and other

members are summed, then divided by the largest possible sum of centrality differences

(Freeman, 1979). The degree to which a network is centralized may affect a group’s

functioning: work groups with a hierarchical structure are found to be less productive

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(e.g., Cummings & Cross, 2003), and social status hierarchies in classrooms may

promote interpersonal conflict among peers (e.g., Farmer, 2000).

Alternatively, a group may be clustered into subgroups or “cliques” (see Figure

2.3d). Several indices of clustering can be computed; in the present example, we use an

index referred to as “transitivity”. The transitivity of a network or group is

operationalized as the proportion of all possible triads that are closed triads (i.e., if A is

connected to B, and B is connected to C, A is also connected to C). Groups with more

“closed triads” are considered more clustered or “cliquey”. Again, the evidence suggests

that clustered networks may be detrimental to their members. For instance, to the extent

that “in-group/ out-group” differentiation is made salient, clique structures may promote

exclusionary behaviors that are detrimental to members both within and outside cliques

(Adler & Adler, 1995). In sum, there is compelling evidence to suggest that network

indices capture psychologically meaningful group structural features, and thus, that SNA

may be a useful tool in the study of group process.

Individual position. SNA also allows us to identify between-person differences in

position within a group. Centrality operationalizes individuals’ levels of

“embeddedness” within their groups, calculated as the number of dyadic ties between a

given member and his or her group-mates. This is important because members who are

disconnected from or rejected by their group (i.e., those with low centrality) are unlikely

to benefit as much from the group format as those with high centrality (see Figure 2.3a

for examples of between-person differences in centrality). Indeed, a number of past SNA

studies show better outcomes among individuals with more social connections or those

who are more embedded in their social network, and poorer outcomes among those who

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are relatively isolated or rejected. For instance, youths’ centrality among inpatient peers

has been linked to poorer adjustment, more problem behaviors, and greater risk of longer-

term disorders (Blitz & Glenwick, 1990; Connolly, 1987).

In addition, it can be useful to identify the unique roles or contributions of

different members within a group. Outdegree centrality is defined as the number of

group-mates that a given participant selects or “nominates” in response to a relational

question. Thus, a participant who selects several group-mates in response to the question

“I felt like ___ understood my feelings”, for instance, would have a relatively high

outdegree, indicating that he or she felt well-understood that day. Conversely, indegree

centrality is defined as the number of times an individual was selected by his or her

group-mates in response to a relational question. Thus, a participant who receives several

nominations in response to the above question would have a high indegree, indicating

that he or she was an important source of understanding for the group that day. Though

both “roles” have the potential to be beneficial, the conceptual distinction that SNA

allows us to quantify is valuable for building our understanding of group process. With

SNA tools, we can test how each of these roles uniquely contributes to participant

outcomes.

Lastly, with each participant reporting on his or her own dyadic connections to

group-mates, we can quantify how many reciprocal relationships a given member has

within his or her group, or what proportion of his or her relationships are mutual. Once

again, there is evidence to suggest that reciprocity may be psychologically significant and

beneficial within a group-based intervention setting. Research on social support

consistently highlights the importance of both receiving and giving support, and of

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reciprocity in particular, for individuals’ psychological health and well-being. Several

studies have linked reciprocity of one’s support network to psychological well-being, and

have found that individuals benefit more from social and emotional support if they are

able to reciprocate that support (e.g., Tolsdorf, 1976). It is believed that those who

receive more social support than they give may experience guilt, distress, loneliness, and

low perceptions of self-competence as a result of the imbalance (e.g., Newsom & Schulz,

1998), while providing others with support has been found to enhance self-esteem and

self-perceived competence (e.g., Liang, Krause, & Bennett, 2001). Reciprocity in

particular has been linked to important psychological benefits such as reduced stress and

depressive symptoms, and enhanced social satisfaction, life satisfaction, self-esteem, and

well-being (e.g., Rook, 1987; Takizawa et al., 2006), while imbalanced relationships

(either over- or under-benefitting) are associated with negative affect, low self-efficacy,

and poorer mental and physical health (Jaeckel, Seiger, Orth, & Wiese, 2011; Jou &

Fukada, 1996).

In sum, SNA provides a rigorous extension of previous methodological

approaches to the study of group process. Not only does SNA address the

interdependencies of relational data, but it also provides methods of describing the

complex connections within groups of individuals, allowing us to precisely characterize

the structure of groups, the positions and roles of individual members within those

groups, and the contribution of intra-group relations to individual functioning (Koehly &

Shivy, 1998). Its use is gaining popularity across a wide range of disciplines, and the

social network methodology toolbox is rapidly expanding (Butts, 2009; Marsden, 1990).

Social network analysts now have a variety of unique ways of describing, quantifying,

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and visualizing the structure and processes characterizing networks, with the help of

statistical programs that are continuously seeing impressive advances (Bender-deMoll &

McFarland, 2006; Butts, 2008; Huisman & van Duijn, 2005; Snijders, 2011). To our

knowledge, the present dataset is the first to which an SNA approach to measurement and

analysis of process in group-based interventions has been applied.

Empirical Illustration of SNA

I will first describe the network measures of process and how network statistics

were derived from participant responses to these SNA process items. Next, I will briefly

describe factor analyses of the SNA data used to derive a small set of latent process

constructs for use in analyses. The reliability of the resulting factor scores will be

demonstrated, and validity of the scores will be tested by examining their concurrent

relations to traditional group process indices and predictive relations to participant

progress and longer-term outcomes.

SNA Process Items. Weekly surveys included a series of items that were adapted

from each GCQ item into a social network format: specifically, items were reworded to

prompt respondents to report on their own social ties (Hale, 2009). Thus, the GCQ item

“The members liked and cared about each other” was reworded as “I like and care about

___”, with the roster of group members as the response options. The 13 network items

assess both positive and negative aspects of the group climate (e.g., support, anger). !

Network Statistics Applied to the SNA Data. To create indices of group structure

and individual positions, we need a “map” of each group’s network of relationships (i.e.,

who is connected to whom). At each assessment, participant responses to the 13 network

items were compiled to create occasion-specific “social networks”. Consistent with

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standard SNA practices, each occasion-specific network corresponds to one network

item. Thus, participants’ responses to the item “I like and care about ___” are used to

build a map of the network of “liking” relationships that exists among members at each

assessment. This procedure is used to create occasion-specific networks from participant

responses to each of the 13 network items (e.g., caring, engagement, etc; see Figure 2.4

for sample networks). Network statistics were then computed on each of these networks,

including group-level density, reciprocity, clustering, and centralization, and individual-

level indegree, outdegree, and number of reciprocal ties. Outdegree and indegree were

standardized by the dividing the number of nominations “sent” or “received” by the total

number of possible nominations (i.e., group size-1). These seven network statistics were

computed for each “network” at each assessment.

Factor Analysis of Network Indices. Next, a factor analysis was used to develop a

measurement model for reducing the larger set of individual network indices described

above (i.e., 13 indegree scores, and 13 outdegree scores) into a smaller set of indices

(factors). After a confirmatory factor analysis (CFA) revealed that the network statistics

did not follow the original GCQ factor structure, an exploratory factor analysis (EFA)

was used to determine a more appropriate measurement model. Based on the scree plots

and theoretical interpretations of each potential factor solution, we determined that a

four-factor structure provided the best empirical and conceptual fit to the indegree and

outdegree data. The four factors were interpreted as source of connectedness (i.e.,

indegree of positive connections), felt connected (i.e., outdegree of positive connections),

source of discomfort (i.e., indegree of negative connections), and felt discomfort (i.e.,

outdegree of negative connections). See Table 2.2 for the items comprising each factor

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and their factor loadings. The resulting factor loadings were used in regression-based

calculations of individual-level centrality factor scores, and the conceptual grouping of

items suggested by the factor solution was used to guide the grouping of the remaining

network indices (i.e., reciprocity and group-level network indices), treating the manifest

indices for each factor as if each loaded equally.

Internal Consistency. Alphas computed on the network indices making up each

factor suggest good internal consistency. Source of connectedness showed an alpha of α

= .76; with correlations among the seven items aggregated to create this score ranging

from r = .07 to r = .63. Similarly, felt connected showed an alpha of α = .73, with

correlations among the six items aggregated to create this score ranging from r = .09 to r

= .77. Source of discomfort showed an alpha of α = .64, with correlations among the five

items aggregated to create this score ranging from r = .12 to r = .42. Lastly, felt

discomfort showed an alpha of α = .56, with correlations among the six items aggregated

to create this score ranging from r = .10 to r = .37. Internal consistency of the remaining

network indices to which this factor structure was applied were similarly strong. Density

of positive connections showed an alpha of .86 and density of negative connections

showed an alpha of .72, and, alpha levels for were .68 for group-level reciprocity of

positive connections, and .79 for individual-level number of reciprocal positive

connections. These alphas were comparable to or better than those of the original three

GCQ subscales in the present sample: α = .66 for the GCQ engagement subscale, α = .71

for the conflict subscale, and α = .27 for the avoidance subscale. In sum, alpha levels

suggest relatively strong internal consistency of the network-based factor scores used in

remaining analyses.

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Distinctiveness of Traditional and SNA measures. Correlations were computed

between the network indices of group process and the “traditional” indices of group

process (i.e., GCQ subscales; see Table 2.3 for complete set of correlations). All

individual-level centrality indices of group process as well as group-level density showed

significant correlations in the expected directions with the three GCQ subscales: source

of connectedness (i.e., indegree of positive connections) correlated r =.17 with the

engagement subscale, r = -.20 with conflict, and r = -.15 with avoidance. The

“outdegree” correlations with the GCQ subscales were slightly stronger: felt connected

correlated r = .32 with the engagement subscale, r = -.29 with conflict, and r = -.18 with

avoidance. Source of discomfort and felt discomfort showed similar associations with the

GCQ subscales, in the expected directions (i.e., negative correlation with the engagement

subscale, and positive correlations with the conflict and avoidance subscales), with

correlations ranging in strength from .13 < r < .39. In all, the low to moderate strength of

these correlations suggest conceptual distinctions in what the two different approaches

“tap into”, leaving room for the network indices to predict participant progress and

outcomes beyond prediction by the GCQ.

Concurrent Associations. Next, I move on to test the concurrent association of

the network indices with functioning and session value. Two sets of two-level multilevel

models (sessions nested within persons) are used to predict concurrent participant

progress scores1. In the first set, the three GCQ subscales as well as the four individual-

level network indices (i.e., source of connectedness, felt connected, source of discomfort,

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!1!Three-level multilevel models were tested first, in which persons were also nested within groups, but these models did not converge; thus, the group-level of nesting had to be removed.!

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and felt discomfort) were simultaneously tested as predictors of global progress and

session value. Next, the same two indices of participant “progress” are examined in

relation to six group-level indices: density, reciprocity, transitivity, and centralization of

positive connections, and density and centralization of negative connections.

Results of these models demonstrate the value of SNA indices of group process

for predicting concurrent weekly progress scores (see Table 2.4). After controlling for

prediction by the original three GCQ subscale scores, participants who were a greater

source of connectedness (i.e., higher indegree) tended to report greater global progress (β

= 48.35, p < .001) and perceive sessions as more valuable (β = 51.72, p < .001), as were

participants who felt more connected (β = 56.49 predicting global progress, p < .001; β =

126.63, p < .001 predicting session value). Similarly, participants who felt more

discomfort tended to report less global progress (β = -34.53, p < .01) and perceived

sessions as less valuable (β = -30.69, p < .05).

Results of the group-level models also provide support for the predictive value of

the network indices: members of groups that were more densely connected (in terms of

positive connections) reported greater global progress (β = 27.99, p < .001) and session

value (β = 41.28, p < .001), and members of groups with a greater prevalence of

“negativity” (i.e., greater density of negative connections) reported significantly less

global progress. Moreover, results provide some evidence of group-level centralization as

a predictor of participant progress: members of groups that were more centralized

perceived sessions as more valuable (β = 21.71, p < .05).

Conclusions about SNA Indices. Taken together, the findings presented here are

useful for demonstrating two preliminary conclusions. First, they contribute to the

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growing body of evidence and theoretical literature suggesting that group cohesion

facilitates more positive outcomes for members, and that negativity in groups may hinder

participant progress. Moreover, they highlight the need to attend to both group-level and

individual-level features of the group dynamics. As expected, participants who were

more engaged or well-embedded within the group reported greater progress, while those

who felt disconnected or uncomfortable were significantly less likely to report progress

gains in a given week. Second and more broadly, these findings provide important

evidence of the value of a social network approach to the study of group process. Even

after accounting for participants’ perceptions of the overall group climate, as assessed

with the field’s most common measure of group process (i.e., the GCQ), network indices

of the connectedness of individuals and groups were significantly predictive of

concurrent reports of participant progress. In other words, the present findings suggest

that the network indices illustrated here are tapping into significant and meaningful

features of the group process that are missed with the traditional measurement approach

(for instance, the distinction between members who are a source of connectedness versus

those who feel connected).

Integrating Tools from SNA and IEV Methodologies

As demonstrated above, IEV and SNA methodologies each have much to offer to

the study of group process. In this section, I demonstrate how these two sets of tools can

be usefully integrated, allowing us to ask and answer new questions with the potential to

substantially advance our understanding of group process and its relation to intervention

effectiveness. An integration of these tools will allow us to: a) tease apart features of the

group process (e.g., group-level vs. person-level processes, stable vs. time-varying

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features) that are associated with participant progress over the course of an intervention,

and b) examine whether participants’ experiences of change and lability in social

dynamics are associated with treatment outcomes.

The study described in this paper is among the first to apply an EMA framework

to capturing social network connections in “real time” (i.e., every time participants meet

and the networks of relationships have an opportunity to change). Thus, the empirical

illustration presented below provides a first glimpse into what can be learned from an

EMA approach to assessing how intra-group relationships develop, evolve, and shape

individual outcomes. Moreover, by applying an EMA approach to the collection of

group process data, network statistics to precisely operationalize those group processes,

and analytic tools sensitive to IEV, we have the potential to gain a far more precise

picture of how group process evolves over time, and whether between-person and group

differences in intervention effectiveness may in part be accounted for by the nature of

intra-group relations within and across time.

Empirical Illustration of an Integrated IEV & SNA Approach

A visualization of the data suggests the utility of both SNA and IEV tools in a

group treatment setting. As demonstrated by the sample therapy group shown in Figure

2.5, plots of the network data across sessions reveal substantial variability in relationships

and group structures across sessions, as well as substantial between-group and between-

person differences. Thus, growth curve models were fit to the weekly network indices to

derive several sets of scores. First, group-specific intercepts were used to represent

between-group differences in average level of density, reciprocity, transitivity, and

centralization. Similarly, person-specific intercepts were used to represent between-

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person differences in average level of connectedness: specifically, in the extent to which

individuals felt connected or discomfort (i.e., outdegree centrality), were a source of

connectedness or discomfort (i.e., indegree centrality), and experienced reciprocal

positive connections. Secondly, linear and quadratic slopes were used to represent group-

and person-specific trajectories of change in group structure and individual

connectedness scores. Thirdly, residuals were used to represent within-group and within-

person variation in group structure and individual connectedness. Lastly, the standard

deviation of the residuals was computed for each group and each individual, to represent

individual- and group-level lability scores – i.e., the amount of unstructured “ups and

downs” in intra-group connections.

In the following two empirical examples, I illustrate how IEV methods (described

in the first section of this paper) can be applied to SNA-derived measures of group

process (described in the second section of this paper) to answer new questions and

provide further clarity to old questions about group process. In the first example, the

scores computed above are used to examine how between- and within-entity processes –

at the individual and group levels – each uniquely contribute to participants’ progress. In

the second example, I test the predictive validity of between-person differences in level,

slope, and lability in SNA indices of group process: these indices are tested as predictors

of pre- to post-treatment improvement in mental health after controlling for level, slope,

and lability in GCQ scores.

Example 1: Covariation between Network Statistics and Weekly Progress

In this first example, the residuals derived above are used to represent within-

person (“WP”) and within-group (“WG”) variation across time, and intercepts are used to

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represent stable between-person (“BP”) and between-group (“BG”) differences in level.

To account for nesting of occasions within persons within groups, these scores were

tested as predictors of weekly progress scores in a series of three-level multilevel models

(MLM). Week number (centered at each group’s “middle” session) and occasion-

specific predictors (i.e., within-person and within-group variation in connectedness and

structure) comprise Level 1. These scores test whether session-to-session fluctuations in

individual connectedness and group structure coincide with weekly reports of progress.

To test the added value of the within-person network indices, within-person derivations

of GCQ scores are also tested. Person-level predictors (i.e., between-person differences in

levels of connectedness) represent level 2, and test whether stable between-person (“BP”)

differences in connectedness help to account for between-person differences in progress.

Again, to test the added value of between-person network indices, between-person GCQ

scores are also tested at this level, and school level is added as a control variable. Finally,

at Level 3, group-level predictors (i.e., between-group differences in level of density,

reciprocity, transitivity, and centralization) test whether stable between-group (“BG”)

structural differences may help to account for between-group differences in participant

reports of progress. Intercept and slope were allowed to vary across groups and persons

(under the usual multivariate normal assumptions). Given the large number of

parameters, the final model is trimmed down to highlight the significant fixed and

random effects (see Table 2.5).

Overall, results provide support for the predictive validity of the within- and

between-person/ group network indices. At the within-person level, after controlling for

within-person GCQ scores, participants reported greater global progress on weeks when

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they were a greater source of connectedness (i.e., indegree; γ = 60.20, p < .001) and felt

more connected than usual (i.e., outdegree; γ = 66.43, p < .001), and reported less

progress on weeks when they felt discomfort (γ = -34.11, p < .01). After controlling for

between-person GCQ scores, between-person differences in levels of source of

connectedness (i.e., indegree of positive connections; γ = 54.81, p < .01) and feeling

discomfort (i.e., outdegree of negative connections; γ = -72.74, p < .01) significantly

predicted participants’ reports of global progress. In other words, participants who were a

more frequent source of connectedness for their group-mates and those who felt less

discomfort within the group experienced greater benefits. At the group level, findings

were contrary to expectations: members of less densely connected (γ = -105.28, p < .05)

and more clustered groups (γ = 95.58, p < .01) tended to report greater progress. In

addition, members reported greater progress on weeks when their group was more

centralized (γ = 13.23, p < .10).

Based on previous social network studies and the existing literature on group

process, it was hypothesized that the best weekly progress would be reported by

participants who were a greater source of connectedness (i.e., indegree), felt more

connected (i.e., outdegree), were lower sources of discomfort (i.e., indegree), and felt less

discomfort (i.e., outdegree), and by those who were members of more cohesive (i.e., high

density and reciprocity), less clustered, and less centralized groups. This pattern of

associations was similarly expected at the within-group and within-person level. While

findings support several of these hypotheses, some unexpected results also emerged. For

instance, members reported more progress on weeks when their group was more

centralized than usual. Perhaps a focus on a few select members during a given session

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facilitates “curative” processes: by uniting around a single issue, group members may

experience a sense of “togetherness”, or a boost of self-efficacy from the experience of

helping each other. This is just one interpretation, but more research will be needed to

clarify this finding further.

In the context of this paper, the more prominent focus is on recognizing what this

approach (rather than these particular substantive findings) can contribute to our

understanding of group process. SNA tools allowed us to detect significant processes

that would be missed by traditional approaches, such as group-level centralization and

clustering, and the individual-level distinctions between participants who contribute to a

group’s connectedness and those who are “sensitive to” these contributions from other

members. Similarly, the numerous repeated measures that we obtained through an EMA

approach to data collection and the use of multilevel modeling allowed us to tease apart

the multiple different sources of variance involved in group process, including group-

level differences, person-level differences, and within-person and within-group

processes. These distinctions are important for understanding at what “level” group

process plays its most prominent roles, in turn giving us a sense of where it might be

most useful to intervene. For instance, significant between-person differences may have

implications for the process by which facilitators select members for enrollment into their

group, while within-person variation suggests that actions could perhaps be taken by the

facilitator during the course of treatment to improve a given member’s experiences within

the group.

Example 2: Predicting Outcomes from Average Levels, Slopes, and Lability

Scores

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In this second example, I demonstrate how between-person differences in level,

change, and lability may help to account for variation in participant outcomes, above and

beyond prediction by level and variability scores on the traditional measures of group

process. Individual-specific average level in network statistics (e.g., on average, how

well-connected was a given member), individual-specific linear and quadratic

trajectories of change (e.g., how quickly did participants gain new connections across

time), and individual-specific lability (e.g., how much did participants’ connectedness

fluctuate across sessions) will be used in regressions to predict pre- to post-therapy

improvement in mental health symptoms. Average levels, slopes, and lability in GCQ

scores will also be entered in these models, allowing us to again assess the added

predictive value of the network indices after controlling for traditional indices. This

analysis will provide new insight about how the dynamic nature of group process over

time – and in particular, of the unique features that network indices allow us to

operationalize – may contribute to a treatment’s effectiveness.

Average level, linear and quadratic slopes, and lability scores for person-level

feelings of connectedness and discomfort (i.e., outdegree) were entered into a model

predicting pre- to post-therapy change in total OQ score2. Once again, non-significant

terms were trimmed from the model; however, individual-specific average scores were

not trimmed unless or until all of their corresponding change scores (i.e., linear and

quadratic slopes and lability scores for a given term) had been trimmed, and linear slopes

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!2!Due to a small sample size (N=98) and high correlations among the indegree and outdegree network indices to be tested (e.g., r = .88 between the individual-specific intercepts used to represent average indegree and outdegree on positive connections), the two sets of centrality scores were not tested in the same model. For purposes of simplicity in this illustration, only outdegree scores (i.e., feeling connected or discomfort) are tested.!

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were not trimmed unless their corresponding quadratic slopes had been trimmed. Results

provide further support for the added value of the SNA and IEV indices of group process:

after controlling for average levels, slopes, and lability in the GCQ measures of group

climate, linear growth (B = -67.84, p < .10) and a U-shaped trajectory of growth (B = -

281.47, p < .01) in feelings of connectedness, and greater lability in feelings of discomfort

(B = -95.12, p < .10) predicted improvement in mental health (see Table 2.6).

As noted earlier, the benefits of the U-shaped trajectory of growth in

connectedness are consistent with past research: it is during the “middle stages” of

treatment that interpersonal problems tend to surface and participants help one another

confront unhealthy patterns of behavior, contributing to progress but also often

manifesting as lower warmth among members. The present findings regarding lability,

however, provide new and interesting insight into the role of change processes in group

therapy: lability in feelings of discomfort predicted better outcomes. Fluctuations in

feeling discomfort likely represent a tendency for some participants to face interpersonal

challenges during the course of treatment (accounting for occasional peaks in

discomfort), but also for those issues to be resolved (accounting for the dips in

discomfort). These opportunities to confront and then resolve interpersonal difficulties

within the group are believed to be an important benefit of group-based treatment (e.g.,

Borden, Schultz, Herman, & Brooks, 2010); thus, it is not surprising that lability in

discomfort would predict better outcomes.

Again, the interpretations of our results presented here are only educated guesses,

and would require more research to be further clarified. Instead, the purpose of these

analyses is to illustrate the use of an integrated SNA and IEV framework for studying

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group process, and the valuable opportunity that these indices provide to operationalize

theoretically important features of the group process and its evolution across sessions.

Thus, more broadly, the above findings suggest that change trajectories and lability in

network indices of group process appear to have predictive value above and beyond the

traditional GCQ measures. It is clear that the network indices are not simply replicating

what is captured with traditional measures, but that they are capturing unique features of

the group process that are missed with the traditional approach. In addition, the

significance of linear and non-linear trajectories of change and lability in group processes

for predicting participant outcomes highlights the value of measures and analytic tools

that are sensitive to the dynamic nature of group process.

Application to “Non-Process” Groups

To demonstrate applicability of this approach across a broader range of settings,

the same assessment and analytic approach that was applied to group therapy data was

also applied to groups of parents and youth in a parent and youth life skills training

program, the Strengthening Families Program (SFP). Although the focus and role of

group process differs between the two settings, group process is expected to have some

similar benefits in each (e.g., participants can still benefit from the sense of universality

provided by a group). Thus, the integrated SNA and IEV approach has similar potential

for value applied to this and other “non-process” groups.

Sample 2. Data were collected from 20 SFP groups (10 parent groups, 10 youth

groups), yielding a total of 120 parents and 94 youth. Beginning at the first or second of

seven weekly sessions, group facilitators administered brief paper and pencil surveys

immediately following each session. Network indices of process tapped broadly caring,

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acceptance, and distaste among members. In addition, weekly surveys assessed

perceptions of session value and current feelings of self-efficacy. Leaders provided

external ratings of participants’ engagement and resistance at each session. Pre- and post-

intervention surveys were mailed to participants’ homes; in the present example, we

focus on quality of the mother-youth relationship.

Analysis & Results

Like in the group therapy setting, correlations of network indices applied to SFP

with the traditional leader-rated SFP engagement measure suggest concurrent

associations as well as distinctiveness of each approach and what it “taps into”. Leader

ratings of participant’s positive engagement showed weak to moderate correlations with

source of connectedness (r = .30, p < .001), feeling connected (r = .23, p < .001) and

group-level density of positive connections (r = .31, p < .001). In addition, leader ratings

of a participant’s resistance within the group weakly but significantly correlated with

source of discomfort (r = .10, p < .01).

To demonstrate the use of IEV tools, two models were tested to parallel analyses

run on the group therapy data. In the first model, participant weekly reports of session

value were predicted from within- and between-person indices of group process, as

measured by leader ratings and SNA indices. Leader-rated engagement significantly

predicted participant ratings of session value at the between-person level (γ = .25, p <

.001), but at the within-person level, participants reported higher session value on weeks

when they experienced more reciprocity than usual (γ = .70, p < .05; see Table 2.7). In a

second model, average level and slope of group-level density were tested as predictors of

pre- to post- improvement in mother-youth shared activities, after controlling for average

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level and slope in leader ratings of engagement. Only slope in the density of positive

connections significantly predicted improvement in participant outcomes (B = 3.39, p <

.05). Members of groups that became increasingly cohesive across sessions experienced

the greatest boosts in the mother-child relationship (see Table 2.8).

With these empirical illustrations from two distinct group intervention settings

(e.g., treatment vs. prevention, distinct delivery formats, and different age groups), I take

a first step toward demonstrating the viability of applying this approach across a range of

settings (see Molloy, 2012b, 2012c). Evidence of the predictive value of these indices are

a promising sign of the potential for this approach to help researchers and program

facilitators in operating, evaluating, and maximizing the effectiveness of group-based

interventions.

Discussion

Contributions of the Integrated IEV-SNA Approach

In this paper, I have described and illustrated how tools from SNA and IEV

methodologies can be used to precisely operationalize and examine conceptually

important features of group process and their role in group-based intervention settings.

An EMA approach to the collection of group process data provides us far clearer and

more accurate information about what went on during the course of an intervention than

do widely spaced measurement intervals common to the existing group process literature.

The subsequent application of analytic tools sensitive to IEV allows us to make the

greatest use of these data. In addition to providing us with a more accurate picture of the

“overall” climate, IEV tools allow us to map out prototypical as well as person- and

group-specific trajectories of change in group dynamics, quantify the amount of

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variability observed across sessions, and tease apart the unique contributions of between-

group differences, between-person differences, within-group processes, and within-

person processes to participant outcomes.

The application of SNA tools to the collection and examination of group process

data provides yet another layer of precision and insight. The ability of SNA to

operationalize features of the group process is inherent to the method: SNA tools were

specifically developed to operationalize long-standing theories of group dynamics.

Moreover, the majority of SNA indices – e.g., being a source of connectedness (i.e.,

indegree), reciprocity of relationships – are not based solely on participant self-reports.

Individuals who are sources of connectedness for their group-mates are identified as such

by their group-mates, and reciprocal ties require “confirmation” by both members of the

dyad. Thus, the observed relations are not simply the product of same reporter biases.

Using the SNA statistics described in this paper, or choosing from a variety of other

options within the SNA “toolbox”, we can precisely operationalize and track the

formation of a group’s structure and the unique roles that members play within that

group. Researchers, program developers, and group facilitators can use these tools for

evaluation and examination of a range of important issues: for instance, the extent to

which intended or expected group dynamics are present within a group, or whether leader

perceptions of the group process are in line with those of participants.

Limitations & Future Directions

As the first to apply measurement and analytic tools from SNA and IEV

methodologies to group process research, this study was largely exploratory, serving as

both a limitation and strength of this research. The SNA and IEV statistics applied in the

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present study are just a few examples of the individual, structural, and change dynamics

that these techniques allow us to capture. For instance, an alternate definition of group

cohesion that we could have applied is the extent to which a group functions as a single

unit, operationalized as the number of dyadic ties within the group that would need to be

“broken” before the group would be split apart into smaller subgroups (Moody & White,

2003). Without clear precedent guiding selection of indices for this context, it is possible

that other indices not employed in the present study would have mapped more closely

onto the processes operating in group-based interventions; more research will be needed

to identify the “best” indices for this and other contexts. Yet this research serves as a

useful test and illustration of the viability of this approach and the value of the SNA and

IEV “toolbox” for operationalizing and studying the complex social processes operating

in group-based intervention settings.

The analytic approach presented here opens the door to a number of future studies

with great potential value to the field. Two logical next steps for future research will be a)

to replicate these examples across other samples in order to more definitively identify

features of the group process that are most beneficial to members, and b) in turn, to

examine how specific actions by group facilitators may influence session-level group

process or its trajectories of change, and foster the social dynamics found to be most

beneficial for participant outcomes. These are just two examples of the important

research questions that can and should be addressed with the analytic approach

demonstrated in this paper.

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Concluding Remarks

While a large research base demonstrates the effectiveness of group-based

delivery of prevention and treatment programs, it is also well-known that not all

participants benefit equally. By attending closely to features of the group process and

their evolution across sessions, we may be able to reduce those gaps and strengthen the

impact of our interventions for a broader range of individuals. These tools will be an

important step toward furthering our knowledge of group process and its relation to

outcomes. As we continue to hone in on benefits of specific group dynamics, researchers

and program developers can apply this information to consider whether and how existing

programs ought to be modified or future programs developed so as to explicitly attend to,

target, and encourage the most beneficial group dynamics.

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Wheelan, S. A. (1997). Group development and the practice of group psychotherapy.

Group Dynamics: Theory, Research, and Practice, 1(4), 288-293. doi:

10.1037/1089-2699.1.4.288

Yalom, I. D. (2005). The Theory and Practice of Group Psychotherapy (5th ed.). New

York, NY: Basic Books.

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Table 2.1: Predicting pre- to post-therapy improvement from between-person differences in average level, slope, and lability in GCQ measures of group climate

Predicting Pre- to Post-Therapy Improvement in

Mental Health

Fixed Effects B SE Intercept 24.21* 10.62 Baseline .53*** .08 Individual-Level Average GCQ Engagement -.15 .11 GCQ Conflict GCQ Avoidance .12 .09 Individual-Level Linear Slope GCQ Engagement .65* .25 GCQ Conflict GCQ Avoidance Individual-Level Quadratic Slope GCQ Engagement -1.37* .57 GCQ Conflict GCQ Avoidance Individual-Level Lability GCQ Engagement GCQ Conflict GCQ Avoidance -.43* .20 Random Effects

Between Person Intercept 4.38 7.43 Residual Variance 92.89*** 15.05

***p < .001, **p < .01, *p < .05, +p < .10 Note. In the above analyses, positive values represent a worsening of the number or severity of psychological symptoms, and negative values indicate improvement in symptoms.

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Table 2.2: Measurement model and factor loadings

Network Items Source of Connection

Felt Connected

Source of Discomfort

Felt Discomfort

Indegree: 1. I felt like __ understood my experiences and feelings today. .42 Indegree: 2. Who did you feel supported by today? .68 Indegree: 3. __ and I engaged in a productive interaction during today's session. .72 Indegree: 4. __ helped me achieve a greater understanding of why I do the things I do. .63 Indegree: 5. I understand what __ is going through. .76 Indegree: 6. Who do you like and care about? .36 Indegree: 7. Today, I felt distant from __. -.49 Outdegree: 1. I felt like __ understood . . . .75 Outdegree: 2. Who did you feel supported by today? .75 Outdegree: 3. __ and I engaged in a productive interaction . . . .70 Outdegree: 4. . . . helped me to achieve a greater understanding. . . .49 Outdegree: 5. I understand what __ is going through. .45 Outdegree: 6. Who do you like and care about? .33 Indegree: 8. I had a hard time trusting __ today. .61 Indegree: 9. Did anybody make you feel tense or anxious today? If so, who? .55 Indegree: 10. Sum of: 1) Did anybody make you feel unwelcome in the group today? If so, who?; 2) Today, I felt like __ judged or disapproved of my feelings or experiences. .60 Indegree: 11. Did anybody anger or upset you today? If so, who? .54 Indegree: 12. I avoided looking at important issues going on between myself and __. .32 Outdegree: 8. I had a hard time trusting __ today. .38 Outdegree: 9. Did anybody make you feel tense or anxious today? If so, who? .56 Outdegree: 10. Sum of: 1) feel unwelcome . . . 2) judged or disapproved . . . .58 Outdegree: 11. Did anybody anger or upset you today? If so, who? .50 Outdegree: 12. Today, I avoided looking at important issues . . . .20 Outdegree: 7. Today, I felt distant from __. .46

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Table 2.3: Correlations between network indices and traditional indices of group process in therapy groups GCQ Measures of Group Climate

SNA Indices of Group Process Engagement Conflict Avoidance

Networks of Positive Connections

Source of Connections (Individual-Level Indegree Centrality) .17** -.20** -.15**

Felt Connected (Individual-Level Outdegree Centrality) .32** -.29** -.18**

Group-Level Density of Pos Connections .18** -.24** -.16**

Networks of Negative Connections

Source of Discomfort (Individual-Level Indegree Centrality) -.13** .27** .17**

Felt Discomfort (Individual-Level Outdegree Centrality) -.20** .39** .25**

Group-Level Density of Neg Connections -.19** .30** .19**

**p < .01 Note. N’s for each correlation ranged from 771 to 785, representing the number of person-weeks in this sample: 125 client participants, with 1 to 16 occasions of data per person.

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Table 2.4: Multilevel models predicting weekly reports of progress and session value from Individual-Level (Left) and Group-Level (Right) SNA Indices, after controlling from GCQ Indices of Group Climate Individual-Level Predictors Global Progress Session Value

Group-Level Predictors Global Progress Session Value

Fixed Effects B SE B SE Fixed Effects B SE B SE

Intercept 49.94*** 3.91 35.97*** 4.89 Intercept 31.05*** 4.31 -6.11 7.87 Week -.25+ .13 -.23 .17 Week -.21 .14 -.03 .21 GCQ Subscales GCQ Subscales Engagement .26*** .04 .50*** .05 Engagement .37*** .05 .70*** .06 Conflict -.16*** .04 -.17*** .05 Conflict -.23*** .04 -.28*** .05 Avoidance <.01 .03 <.01 .04 Avoidance -.02 .05 Network of Positive Connections Network of Positive Connections

Source of Connections

(Indegree) 48.35*** 10.28 51.72*** 13.03 Density 27.99*** 4.85 50.93*** 7.52

Felt Connected

(Outdegree) 56.49*** 9.28 126.63*** 11.67 Centralization 21.71* 10.97 Network of Negative Connections Network of Negative Connections

Source of Discomfort (Indegree) .39 17.03 26.06 21.76 Density -40.04* 17.49

Felt Discomfort

(Outdegree) -34.53** 11.22 -30.69* 14.56 Centralization Random Effects Random Effects

Between Person Between Person Intercept 62.80*** 10.52 44.11*** 9.86 Intercept 70.01*** 11.88 47.06*** 12.06 Residual Variance 103.73*** 5.87 194.19*** 10.63 Residual Variance 127.74*** 7.07 274.44*** 15.16

***p < .001, **p < .01, *p < .05, +p < .10

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Table 2.5: Multilevel models predicting weekly progress from between- and within-person GCQ scores, between- and within-person SNA indices of individual connectedness, and between- and within-group SNA indices of group structure

Predicting Weekly Self-

Reports of Progress

Fixed Effects B SE Intercept 49.82*** 8.03 Session Number .23+ .13

GCQ Indices: Average Individual-Level Ratings Engagement .27** .10 Conflict -.21* .08 SNA Indices: Average Individual-Level Connectedness

Source of Connectedness (indegree) 54.81** 20.55 Felt Discomfort (outdegree) -72.74** 23.71

SNA Indices: Average Group-Level Structure of Positive Connections Density -105.28* 47.13 Reciprocity 1.92 25.43 Transitivity 95.58** 29.41

GCQ Indices: Within-Person Variation Engagement .20*** .06 Conflict -.16*** .04 SNA Indices: Within-Person Variation in Connectedness

Source of Connectedness (indegree) 60.20*** 13.68 Felt Connected (outdegree) 66.43*** 12.19 Felt Discomfort (outdegree) -34.12** 12.50

SNA Indices: Within-Group Variation in Structure of Positive Connections Density -4.05 7.27 Centralization 13.23+ 7.91 Random Effects

Between Person Intercept 55.57*** 10.06 Residual Variance 117.62*** 6.53

***p < .001, **p < .01, *p < .05, +p < .10

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Table 2.6: Predicting pre- to post-therapy improvement from between-person differences in average level, slope, and lability in GCQ Scores and Outdegree

Predicting Mental Health

Improvement B SE Intercept 29.04* 11.02 Baseline .59*** .07

GCQ: Average Individual-Level Ratings Engagement -.20+ .12 Conflict Avoidance .12 .09

SNA: Average Ind-Level Outdegree (Feelings of): Connectedness 33.65 22.20 Discomfort 42.67 31.36 GCQ: Individual-Level Linear Slope in Ratings Engagement .47* .22 Conflict Avoidance .50* .24

SNA: Ind-Level Linear Slope in Outdegree (Feelings of): Connectedness -67.84+ 40.16 Discomfort GCQ: Ind-Level Quadratic Slope in Ratings Engagement Conflict Avoidance SNA: Ind-Level Quadratic Slope in Outdegree (Feelings of): Connectedness -281.47** 104.05 Discomfort

GCQ: Individual-Level Lability in Ratings Engagement Conflict Avoidance -.41+ .21 SNA: Individual-Level Lability in Outdegree (Feelings of): Engagement Conflict Discomfort -95.12+ 53.51

***p < .001, **p < .01, *p < .05, +p < .10 Note: Negative values indicate improvement in symptoms

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Table 2.7: Multilevel models predicting SFP participants’ weekly reports of session value from between-person average levels and within-person variation in a) group leader ratings of engagement and b) number of reciprocal positive ties

Predicting Weekly Perceptions

of Session Value in SFP

Fixed Effects B SE Intercept 2.05*** .61

Session Number .04 .03 Age Group (Parents vs Youth) -.19 .18

Individual-Level Average

Leader-Rated Positive Engagement .25*** .04

Number of Reciprocal Positive Connections .73 .52

Within-Person Variation

Leader-Rated Positive Engagement .03 .02

Number of Reciprocal Positive Connections .70* .34

Random Effects

Between Person Intercept .87*** .13

Week .04*** .01 Residual Variance .75*** .06

***p < .001, **p < .01, *p < .05, +p < .10

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Table 2.8: Predicting pre- to post-SFP intervention improvement in frequency of mother-youth shared activities from leader ratings of positive engagement and group-level density of positive connections

Predicting Improvement in Mother-Youth Shared

Activities (SFP) Fixed Effects B SE

Intercept -.02 .33 Baseline .62*** .09

Average Level

Leader Ratings of Positive Engagement .04*** .03 Group-Level Density of Positive

Connections .18 .26 Slope

Leader Ratings of Positive Engagement .10 .11 Group-Level Density of Positive

Connections 3.39+ 1.71 ***p < .001, **p < .01, *p < .05, +p < .10

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Figure 2.1: Participant ratings across sessions on two subscales of the GCQ from a sample therapy group

This figure depicts GCQ indices of group engagement (top) and conflict (bottom), as measured on a session-by-session basis (dotted lines) versus once every four weeks (solid lines). This figure demonstrates the noteworthy variability in group climate that is missed when measurement occasions are widely spaced, as is common in previous studies. The “EMA” approach juxtaposed the more “traditional” approach highlights how ratings once every four weeks may substantially misrepresent clients’ true experiences of group climate.

a) GCQ Engagement across Sessions

b) GCQ Conflict across Sessions

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Figure 2.2: GCQ reports of engagement across sessions for a subsample of highly variable (left) and highly stable (right) clients

This figure illustrates examples of five participants whose perceptions of engagement were highly variable across sessions (left) and five whose perceptions of engagement were highly stable (right). The ten participants hand-picked for illustration in this figure were selected to demonstrate between-person differences in lability of group process. However, in these data, one could also easily observe differences across persons in average levels, overall growth or decline (i.e., linear slope), the extent to which trajectories reflect acceleration or deceleration in the growth of engagement (e.g., a quadratic slope). Each of these between-person differences can be “extracted” from the data, and examined in relation to participant outcomes.

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Figure 2.3: Sample sociograms depicting between-person differences in centrality (a),

and between-group differences in (b) density, (c) reciprocity, (d) clustering, and (e)

centralization

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Figure 2.4: Sample networks derived from participant responses to network items assessing caring, engagement, and negativity for one of the therapy groups at one measurement occasion In this figure, the diagram on the left (2.4a) represents the network of caring relationships within this group at this assessment occasion, captured with the network item: “who do you like and care about?”, in which light gray “nodes” represent clients and dark gray nodes represent the two group leaders, and the density of ties was .68 (i.e., 68% of all possible ties were present). Similarly, 2.4b represents the network of “engagement” relationships at this occasion, captured with the item: “___ and I engaged in a productive discussion during today’s session”, showing a density of .23. Lastly, 2.4c represents the network of “negative” relationships among participants, in which ties represent nominations in response to any of the negatively-valenced network items (e.g., anger, distrust, avoidance). In this sociograms, dark gray nodes represent clients, light gray represent group leaders, and the density of relations was .25.

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Figure 2.5: Between- and within-group variability in the density of caring relationships across a subsample of groups

Top: Density of caring relationships plotted across sessions for seven of the therapy groups in our sample. Bottom: One of the sample groups for which density was plotted across weeks is visualized, with sociograms representing the network of caring relationships across the ten sessions of this group’s treatment. Both the density plots and the sociograms reveal substantial variability in relationships and group structures across groups and across sessions.

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CHAPTER 3:

MATCHING PSYCHOTHERAPY GROUP PROCESSES TO METHODS ABLE

TO ARTICULATE THOSE PROCESSES: CONTRIBUTIONS OF SOCIAL

NETWORK ANALYSIS AND INTRAINDIVIDUAL VARIABILITY

METHODOLOGIES

Group therapy is extremely common in treating a range of mental health issues,

such as drug abuse, depression, and anxiety disorders, with group therapists commonly

viewing group cohesion as the primary factor underlying therapeutic benefits (e.g.,

Ogrodniczuk & Piper, 2003; Yalom, 2005). While a sense of belonging, opportunities for

participants to relate to one another, learn from and help one another, and practice

healthy, corrective relational skills are generally considered to facilitate positive

outcomes (e.g., Kivlighan & Holmes, 2004; MacKenzie, 1987; Yalom, 2005), empirical

evidence regarding the role of group process (i.e., the nature and quality of participant

interactions) in facilitating participant outcomes remains limited. As several authors have

noted, the most common analytic approaches in studies of group process are limited in

their ability to articulate these processes (Morgan-Lopez & Fals-Stewart, 2006).

Importance of Group Process

Numerous studies have substantiated the value of group-based therapy, several of

which have linked specific features of the group process to client outcomes. For instance,

early ratings of high engagement and low avoidance have been related to therapeutic gain

on a target complaint measure (Braaten, 1989). Similarly, participant reports of the

extent to which they experienced a sense of universality related to their feelings of

acceptance and belonging in the group (MacKenzie & Tschuschke, 1993) and have been

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found to predict improvement of depression, anxiety, interpersonal distress, and overall

symptomology (Joyce, MacNair-Semands, Tasca, & Ogrodniczuk, 2011). Participants�

comfort with and attachment to therapy groups (MacKenzie & Tschuschke, 1993),

personal disclosure and honest communication (MacNair-Semands, Ogrodniczuk, &

Joyce, 2010), insight gained through interpersonal experiences (Kivlighan, Multon, &

Brossart, 1996), and acquisition of interpersonal skills (e.g. communicating about

feelings) have all been found to change over the course of therapy, relate to participant

progress, and predict post-treatment reduction in symptom severity (Joyce et al., 2011;

MacKenzie & Tschuschke, 1993). In complement, participants who experience a sense

of exclusion in their group have reported thwarted needs, decreased mood, reduced

competence, and less liking of group members (E. E. Jones, Carter-Sowell, & Kelly,

2011). There is evidence to suggest that group therapy can be equally or more effective

than individual therapy (Weiss, Jaffe, de Menil, & Cogley, 2004). Given this evidence

and the substantially greater cost efficiency of group delivery, it is critical that we

understand the types of intra-group interactions and relationships that best facilitate client

improvement.

Limitations of the Existing Group Process Literature

The assessment and analytic approaches most commonly employed to the study

of group process are a mismatch to the inherently dynamic and complex nature of intra-

group relations. For instance, a meta-analysis of studies seeking to identify the most

valued therapeutic factors in psychotherapy groups identified twenty-four studies on this

topic (e.g., Kivlighan & Holmes, 2004); as Kivlighan (2011) notes, every one of these

studies aggregated scores across participants to form group-level ratings, thus masking

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the variation across members. Moreover, measures that assess “overall group climate”

would be unable to capture any structural features of the group (e.g., sub-grouping of

members, or the formation of a hierarchical structure). Lastly, it is common for studies to

assess group process at one or two occasions over the course of treatment, which stands

in contrast to theories that groups progress through a series of developmental stages, each

of which is characterized by different degrees of cohesion, engagement, and conflict

(Tuckman, 1965; Wheelan, 1997). Thus, there is a call in the group psychotherapy

literature for new analytic approaches to studying group process that overcome these

limitations (Kivlighan, 2011; Koehly & Shivy, 1998; Morgan-Lopez & Fals-Stewart,

2006). See Molloy, 2012a for further discussion of these limitations.

Addressing the Limitations of Past Research: Social Network Analysis

In the 1930’s, Jacob Moreno – often referred to as the “father” of group

psychotherapy – developed a procedure called sociometry, or “the inquiry into the

evolution and organization of groups and the position of individuals within them”

(Moreno & Moreno, 1934). As originally developed, sociometry was both a quantitative

method and a therapeutic technique for use in group therapy. Sociometry the method was

used to collect data about networks of relationships. These data were then used to

develop sociograms, or graphical representations of individuals within a group and the

relationships connecting them (see Figure 2.4 for sample sociograms). The

psychotherapist would then use this visual aid to identify “hidden structures” within a

group, such as “alliances” that have formed among certain members, subgroups or

“cliques” within the larger group, and “stars” (i.e., high status or highly central

members). Sociometry the therapeutic technique then applied this information to drive

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group therapeutic activities and discussions, with the goal of improving intra-group

relationships.

Despite the initial popularity of sociometry in the field of group psychotherapy,

the compilation of sociometric data and the computation of network statistics by hand

was extremely cumbersome, and severely limited its potential for growth. Once

computers were available to aid in the task, sociometry and the broader field of social

network analysis came back into the spotlight, but its re-emergence did not extend to

group psychotherapy: despite its origins within this field, sociometry or the broader field

of social network analysis (SNA) as a measurement approach in modern studies of group

psychotherapy are scarce or non-existent. The present study re-introduces ideas from

SNA into the field of group psychotherapy, demonstrating its value within a sample of

therapy groups.

The few studies that do exist from SNA’s earlier years of investigation highlight

the potential utility of this approach within group therapy. For instance, using

sociometric measures, a 1953 experimental study of institutionalized patients found

increased cohesion, a breakdown of “cliqueishness”, and decreased isolation and

rejection among patients who participated in group psychotherapy upon entry into the

institution. Moreover, members who developed more positive connections to group-

mates showed greater improvements in pathology and were more likely to end up with

decreased sentences (Newburger & Schauer, 1953).

As currently defined, SNA is a set of measurement and statistical tools designed

to operationalize long-standing theories of group dynamics (Moody & White, 2003;

Wasserman & Faust, 1994). Participants are asked to report on their dyadic relationships

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within the group: for instance, members of a therapy group could be asked: “who do you

like and care about?” Using SNA tools, responses can then be aggregated in various

ways to quantify features of a group’s structure and individuals’ positions, much like in

the sample studies noted above. Molloy (2012a) provides more thorough descriptions of

SNA and its value for studying group process; here, I will only briefly introduce the

terminology of most relevance to the present study.

Group cohesion has been referred to as the primary “curative factor” in group

psychotherapy. SNA provides multiple ways to match precise conceptual definitions of

“cohesion” to statistical operationalization. For instance, group-level density provides us

with a group’s “average connectedness” by dividing the total number of reported

connections among members by the total number of possible connections in that group

(i.e., if every member was connected to ever other member, density would be 1.0, or

100%), and group-level reciprocity is the proportion of ties within a group that are

mutual or bidirectional. Beyond aggregate cohesion, SNA allows us to detect “hidden

structure”, such as the formation of a hierarchy. An ethological study applying concepts

of primate dominance hierarchies to therapy group dynamics suggested that hierarchies

may be detrimental to the group climate and participant progress by promoting

scapegoating and an inhibitive influence of the dominant member(s) (Kennedy &

MacKenzie, 1986). Within an SNA framework, this process is operationalized as

centralization, or the extent to which connections across members are unequally

distributed.

Also with SNA, we can calculate each individual’s centrality score, representing

their level of “embeddedness” within a group. Centrality is operationalized as the number

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of connections an individual has to his or her group-mates. Among youth within inpatient

treatment settings, those with lower centrality (i.e., relatively isolated or rejected) among

their peers have been found to be less well-adjusted, display more problem behaviors,

experience longer hospital stays, and be at greater risk of longer-term disorders (Blitz &

Glenwick, 1990; Connolly, 1987). Lastly, several studies have found that individuals

benefit more from social and emotional support if they are able to both give and receive

support (Mitchell & Trickett, 1980; Westermeyer & Pattison, 1981). Reciprocal support

has been found to offer important psychological benefits such as reduced stress and

depressive symptoms, and enhanced social satisfaction, self-esteem, and overall well-

being (Jung, 1990; Takizawa et al., 2006), while imbalanced relationships (either over- or

under-benefitting) have been associated with a more negative affect, lower self-efficacy,

and poorer mental and physical health (Jaeckel, Seiger, Orth, & Wiese, 2011; Jou &

Fukada, 1996). Once again, SNA provides a useful tool for quantifying this phenomenon

that we did not have with traditional group process measures: reciprocity can be

quantified as the number of an individual’s connections that are mutual.

In sum, SNA provides a rigorous extension of previous methodological

approaches, and there is substantial evidence to suggest that the unique features of group

process it allows us to capture are psychologically meaningful and play important roles in

group settings.

Addressing the Limitations of Past Research: Methods Sensitive to Change and

Variability

Several group psychotherapy researchers have highlighted the dynamic nature of

group process: for instance, it is commonly theorized that there are several distinct

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�developmental stages� of group process, each characterized by different intra-group

relationships (Tuckman, 1965; Wheelan, 1997). More broadly, the intimate connections

assumed to facilitate positive outcomes in psychotherapy groups (e.g., caring, trust) take

time to develop and may fluctuate over time. Yet “change” in group process has typically

been assessed with a limited number of measurement occasions. The present study

applies methods sensitive to intra-individual or intra-group variability (what I will refer

to broadly as intra-entity variability, or “IEV”): a set of data collection and analytic

techniques developed to operationalize change and variability. Thus, logic and tools

from an ecological momentary assessment (EMA) approach to data collection are used to

collect data on intra-group relationships a) within the group delivery setting (i.e., the

therapy room), b) immediately following the sessions, while the intra-group interactions

and feelings are current and fresh in the participants’ minds, and c) at every session (i.e.,

representing each time intra-group relationships have an opportunity to change)

(Shiffman, Stone, & Hufford, 2008). In turn, analytic tools sensitive to IEV across time

allow for characterization of changes observed (e.g., the amount or rate of change) and

relations of these changes to participant progress and outcomes (Ram, Conroy, Pincus,

Hyde, & Molloy, 2012). See Molloy (2012a) for a more thorough discussion of IEV tools

in the study of group process.

Consistent with these ideas, one study of therapy group dynamics demonstrated

that a U-shaped trajectory of change in engagement across sessions (high, low, high) in

combination with an inverted U-shape trajectory of change in conflict was associated

with the best therapeutic gains (Kivlighan & Lilly, 1997). Similarly, it has been referred

to as a sign of “therapeutic failure” when relationships do not change over the course of

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treatment (e.g., Kuypers, Davies, & Van der Vegt, 1987). Given expectations of (and

evidence for) complex changes within therapy groups, studies examining change in group

climate with a simple pre- to post-test difference score have been criticized for failing to

capture the process of change (Kivlighan & Lilly, 1997; Willett, 1988). Overall, it is

clear that there is a push for greater empirical attention to change processes and

variability in the study of group therapy. The use of EMA and analytic tools sensitive to

change and variability over time will be a useful empirical contribution to the literature

on the importance of change processes for group psychotherapy outcomes.

The present study integrates tools from SNA and IEV methodologies to overcome

the limitations of past research. This study will be among the first to integrate SNA and

IEV methods, and will be the first to apply this integration to the study of process in

group therapy. The innovative integration of these methods has the potential to

substantially advance our understanding of how group process operates to improve

behavioral and mental health.

The Present Study

In the present study, data are collected from clients receiving treatment for mental

health issues through group psychotherapy at a university counseling center, in order to

address two aims. The first aim is to examine concurrent relations between group

dynamics and participants’ weekly progress. Specifically, I will examine: (a) Between-

group differences in group structure: Are particular group characteristics (e.g., density)

associated with greater/ fewer treatment benefits to the members of those groups?; and

(b) Between-person differences in position: Do the ways in which an individual relates to

his or her group-mates help to facilitate or hinder weekly progress? The second aim is to

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examine whether average levels and trajectories of change in group- and individual-

connectedness are associated with participants’ post-treatment outcomes. In other words,

are the “average” level of connectedness and trajectories of change across sessions in

social dynamics associated with between-person and between-group differences in

treatment benefits accrued to members? To address these aims, logic and tools from

SNA and IEV methods were integrated and applied to the collection and analysis of

group process data from students enrolled in therapy groups at a university counseling

center. Social network indices of intra-group relationships and indices of participant

progress are collected from participants immediately following each session. Social

network statistics (e.g., group density, individual centrality) are computed from these

relational items, and IEV methods are used to obtain individual- and group-specific

average levels, slopes, and residual scores. These network-based indices are then

employed to address the aims discussed above: in other words, to examine group process

in relation to weekly participant progress and post-therapy outcomes.

Methods

Overview of Group Therapy Operating Procedures at CAPS. Following an

individual intake evaluation, a client seeking mental health services at the University’s

Center for Counseling and Psychological Services (CAPS) is presented with several

treatment options; group therapy is presented as one of these options, and may be

recommended by the intake therapist. Clients interested in pursuing group therapy can

then be considered for inclusion in one of several ongoing therapy groups, depending on

factors such as presenting symptoms and demographics (e.g., gender). Groups begin each

semester as soon as at least four members are admitted, and meet weekly until the end of

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the semester. All groups are run by two therapists and consist of four to eight clients, with

fairly stable membership over the course of a semester.

Participants & Procedures

Prospective participants included all therapists at CAPS who lead therapy groups,

as well as all clients enrolled in therapy groups whose leaders agreed to allow recruitment

in their group(s). Group leaders were initially recruited verbally during a regularly

scheduled staff meeting in January of 2011 for participation during the Spring 2011

semester; recruitment of group leaders for the Fall of 2011 and Spring 2012 semesters

was via email to CAPS staff. For groups in which the leaders agreed to allow

recruitment, clients were recruited via verbal invitation and a recruitment handout (briefly

describing the rationale and procedures of the study) during their first or second session.

Clients indicated anonymously whether they were interested in participating; if and only

if all members of a group (the two leaders and all clients) indicated interest, we

proceeded with formal consent forms.

Of the 32 scheduled groups from which leader-pairs were recruited, 25 agreed to

allow client recruitment in their groups, of which five never obtained enough clients to

run. Thus, client recruitment was conducted in 20 groups over the course of the study

(Spring and Fall of 2011, Spring of 2012), of which two did not have universal interest

among the members. The final sample to date therefore includes 18 groups (six per

semester), consisting of 36 group leaders and 119 clients (total sample size = 155; mean

group size = 6.61). Of the 18 participating groups, 17 were classified as “general

process” groups (i.e., no specific disorder or topic), and one was a substance abuse group.

The substance abuse group included both undergraduate and graduate students; of the 17

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general process groups, nine were for undergraduate students and eight were for graduate

students. To maintain client confidentiality, we obtained only first names and did not

collect any demographic data (e.g., gender or ethnicity).

Data collection began the week following consent, at which time participants

completed a pre-therapy “outcome questionnaire” (less than five minutes) assessing a

range of mental health symptoms; the same outcome questionnaire was completed again

at the final session of the semester. The group process survey (approximately five to ten

minutes, administered on pre-programmed smart phones) began the same week as the

pre-therapy questionnaire, and was completed immediately following each weekly

session throughout the course of the semester-long treatment. Smart phones containing

the group process surveys were distributed and collected at the end of each session by a

member of the research team. At the conclusion of data collection (i.e., at the end of the

semester), participants were thanked for their participation and awarded a small

compensation ($10 or $25 gift card, depending on amount of participation).

Measures

Measures for the present study were drawn from the American Group

Psychotherapy Association’s (AGPA) CORE-R Battery (Clinical Outcome Results

Standardized Measures, Revised; see Burlingame et al., 2006; Strauss, Burlingame, &

Bormann, 2008): a manual of evidence-based instruments that serve as a standardized

“toolbox” for clinicians to systematically monitor and evaluate groups and their

members, and for making findings comparable across research studies. The CORE-R

Battery includes measures to aid in the selection of group members, assessment of

member progress and outcomes, and instruments for tracking group process (e.g.,

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climate, therapeutic factors). Given that more than one instrument per category (selection,

process, outcome) is often not feasible, the CORE-R Battery makes a primary

recommendation for an assessment tool in each category, but provides information about

several other well-established and psychometrically sound instruments. The rationale for

the instruments selected and adapted for the present study is described below.

Although instruments recommended by the CORE-R are generally designed to be

sensitive to changes over time, it is rare that items have been administered on a weekly

(session by session) basis. Thus, in choosing among the instruments on the CORE-R

Battery, we considered the likelihood that the constructs being assessed by each

instrument theoretically could or were likely to change on a weekly basis. In addition,

many of the items were re-worded slightly so as to be specifically present-focused: for

instance, the phrases “today”, “during today’s session”, or “in the past week” were added

to many of the items. In some cases, this replaced phrases from the original items (e.g.,

“over the past several weeks”), while in other cases these phrases assigned a time frame

to items that did not originally reference one.

In the weekly measures described below, traditional Likert-type response scales

(e.g., scales of 1 to 7 ranging from “strongly disagree” to “strongly agree”) were

converted to “touch-point continuum” response scales (a horizontal bar along which

participants can click anywhere from one end to the other) for administration on smart

phones. The phones were programmed to log these responses on a scale from 0 to 100.

Continuous response scales such as this have become increasingly common and

encouraged in EMA studies employing digital technology to collect data (see Ram et al.,

2012; Shiffman et al., 2008).

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Process Measures. To create indices of group structure and individual positions

that will overcome limitations of past research, we need a “map” of each group’s network

of relationships (i.e., who is connected to whom). Thus, consistent with standard social

network assessment procedures, process items in the weekly surveys prompt participants

to report on their current relations to group-mates. The goal of the process items was to

tap into social processes that are expected to arise naturally within psychotherapy groups;

in other words, the items selected are intended to reflect what participants are already

naturally thinking about during sessions.

Although the CORE-R Battery includes several process measures, only two of

these assess relationships among members (rather than therapist and client), of which

only one – the Group Climate Questionnaire (GCQ; see MacKenzie, 1983) – assesses

both positive and negative aspects of group process. Thus, the present study adapted

items and themes from the GCQ, the most commonly employed process measure in

groups (Burlingame et al., 2006). As originally designed, the GCQ is a self-report

measure for assessing members’ perceptions of a group’s therapeutic environment.

Participants indicate their agreement with 12 items on a 7-point Likert scale ranging from

0 (not at all) to 6 (extremely), which comprise three subscales: Engagement, Conflict, and

Avoidance. Several studies have established the internal consistency and validity of the

GCQ (e.g., Kivlighan & Goldfine, 1991; MacKenzie, 1983).

The original GCQ asks participants to report on their impressions of the group as

a whole. Where plausible, each item of the GCQ was adapted into a network format:

specifically, items were reworded to prompt respondents to report on their own social

ties, consistent with standard “sociometric” or social network data collection procedures

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(Cairns, 1983; Hale, 2009). Thus, the GCQ item “The members liked and cared about

each other” was reworded as “I like and care about ___”, with the roster of group

members as the response options. Participants may select as many or as few members as

are relevant to each item. Network statistics are computed from participant responses to

these items (described in the analytic plan).

Participants completed two sets of process items. First, weekly surveys consisted

of “network” adaptations of ten of the twelve items comprising the GCQ, as well as two

additional items representing themes identified across several well-validated rating

scales. In some cases, single items from the GCQ were adapted into two separate network

items to capture two different aspects of the original item. Thus, the first portion of the

client survey included a total of 13 network items. The GCQ and our adapted items

capture both positive (e.g., understanding) and negative (e.g., anger) social dynamics

operating within the group. Surveys allowed participants to select both clients and leaders

in response to these items. Network statistics computed from participant responses to the

13 network items were factor-analyzed (see Molloy, 2012a) to create a smaller set of

latent variables for analyses: felt connected, source of connectedness, felt discomfort, and

source of discomfort (described further in the analytic plan).

Weekly client surveys also included the twelve items comprising the original

GCQ. Participant responses were used to compute scores on the three GCQ subscales

(engagement, conflict, and avoidance), computed as the mean of the items comprising

each subscale (Kivlighan & Goldfine, 1991; MacKenzie, 1983). See Appendix C for

client weekly survey.

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Weekly Participant Progress Measures. Participants also reported weekly on the

perceived value of each session and their own progress. Once again, assessment of

progress drew upon the expertise of AGPA’s CORE-R Battery. Because the participant

progress instrument was only one of three sets of measures employed in the weekly

surveys, brevity and ease of administration were especially critical factors for the

selection of items for the present study. Of the options recommended by the CORE-R

Battery, two of the scales were too long for weekly administration, and two of the

instruments were too narrowly focused (e.g., one was specifically a measure of self-

esteem). Thus, the best measure for our purposes (i.e., sufficiently brief but also broad in

scope) was the Group Evaluation Scale (GES; Hess, 1996), a seven-item scale assessing

the overall benefit that a client experienced during a given session. Examples of item

stems include: “Within the group today, I was”, with response options ranging from

“very uncomfortable” to “very comfortable”). Other items assessed participants’ ease of

self-disclosure, feelings of stability, self-perceived progress, and feelings of being

understood, autonomous, and responsible. Consistent with its original design, the seven

GES items are aggregated into a single global score, which will be referred to as the

“global progress” score. Several studies have established the validity and internal

consistency of the GES (e.g., Burlingame et al., 2006; Hess, 1996). One additional item,

not from the GES, was added to assess perceived session value: “This week’s session was

helpful to me”.

Group Leader Measures. Group leaders (i.e., the two therapists leading each

group) also completed surveys on a weekly basis. Group leader surveys were also

completed on smart phone, consisting of the majority of the same network-adapted

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process items that clients completed. (All items considered by the researchers to be

potentially relevant to leaders as well as clients were included on the leader survey). This

excluded only two of the 13 network items on the client survey: “I felt like __ understood

my experiences and feelings today” and “who did you feel supported by today”. It is

conceptually important for group leaders to contribute “network” information too, as

leaders function in many ways as a part of the group and can be nominated by clients:

thus, their responses are required for “complete” network data from all members of the

group (e.g., they allow for reciprocal ties to leaders).

Post-intervention outcome measures. To assess the effectiveness of treatment, we

employed the assessment tool most strongly recommended by the CORE-R Battery: the

Outcome Questionnaire (OQ; Lambert, Hatfield, Vermeersch, & Burlingame, 2001), a

self-report instrument designed for repeated measurement of adult patient symptoms. OQ

surveys were completed at the first or second session of group therapy each semester, and

again at the last session of the semester.

The OQ prompts clients to think about the past week, and indicate the extent to

which they experienced a set of symptoms, with response options ranging from 0 to 4

(“Never” to “Almost always”). The complete set of items comprising the OQ can be

aggregated into one global assessment of patient functioning, but can also be broken

down into three subscale scores: symptom distress, interpersonal functioning, and social

role performance. The symptom distress subscale assesses symptoms of the most

commonly diagnosed mental health disorders (i.e., anxiety, depression, and substance

abuse), and includes items such as: “I have trouble falling or staying asleep”. The

interpersonal functioning subscale assesses problems with friendships, family, and

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romantic relationships, and includes items such as: “I am satisfied with my relationships

with others”. The social role performance subscale assesses client’s level of

dissatisfaction, conflict, or distress in his or her employment, family roles, and leisure

life, and includes items such as: “I am not working/ studying as well as I used to”. For

the present study, we chose to use the 30-item version of the OQ (rather than its original

45-item version) to minimize burden on participants. The OQ-30 takes less than five

minutes to administer, and is a subset of items from the OQ-45 that are found to be most

sensitive to change (Ellsworth, Lambert, & Johnson, 2006). Both the OQ-30 and the OQ-

45 have been widely used, have demonstrated sensitivity to change and ability to

differentiate between normal, outpatient, and inpatient clients, and have well-established

reliability and validity (e.g., M. D. Jones, 2004).

Preliminary data analytic steps

Step 1. First, social network statistics (e.g., centrality, density) were computed

from participant responses to each of the network-based survey items in order to quantify

salient features of the therapy group process. Group-level social network statistics

computed for each item on each occasion include the density, reciprocity, and

centralization. Individual-level social network statistics computed include indices of how

well-embedded individuals were within their groups: indegree centrality (i.e., number of

nominations received), and outdegree centrality (number of nominations made or “sent”).

Individual-level reciprocity was computed as the number of reciprocal relationships (of

each type; e.g., caring reciprocity, understanding reciprocity). The individual-level scores

were standardized by the dividing the raw number of nominations sent or received by the

total possible number of nominations (i.e., number of participants present – 1).

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Step 2. Next, exploratory and confirmatory factor analyses were applied to the

network indices in order to obtain a smaller set of factor scores for use in further

analyses. See Molloy (2012a) for more about the factor analysis and psychometric

properties. The final solution suggested that four factors provided the best, parsimonious

representation of the 13 indegree and 13 outdegree variables. These factors captured the

following constructs: felt connected (i.e., positive outdegree, or the number of times an

individual nominated others in response to a positive-valenced relational item), source of

connectedness (i.e., positive indegree, or the number of times an individual was

nominated by others in response to a positively valenced network item), felt discomfort

(i.e., negative outdegree), and source of discomfort (i.e., negative indegree). The other

four network indices, in which the direction of ties is not distinguished in the resulting

index (individual-level reciprocity and group-level density, reciprocity, and

centralization) were parsimoniously represented by two sets of factor scores (i.e., positive

connections and negative connections).

Step 3. Once all of the network factors had been computed, growth curve models

were fit to each individual’s and each group’s longitudinal data to obtain a time-centered

intercept (used to represent individual/ group-specific average levels), linear slope,

quadratic slope, and occasion-specific residuals around those curves for each individual/

group (see Molloy, 2012a). As well, a within-entity standard deviation of the residuals

was computed for each individual/ group and used as an indicator of the extent of that

individual/ group’s session-to-session fluctuation or “lability”.

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Results

Analytic plan. To address aim 1, occasion-specific residuals were entered into

multilevel models, along with individual- and group-specific average levels, to examine

within- and between-person/ group covariation between group process and weekly

reports of progress. In addition, the individually-fitted linear and quadratic slopes for

centrality and reciprocity were examined in correlations with individually-fitted slopes

for each of the progress items, to test whether gains in progress may be associated with

gains in connectedness. To address aim 2, we used standard regression analyses to assess

if and how the person- and group-specific means, slopes, and lability scores were

associated with pre- to post-treatment change in OQ scores

Aim 1: Covariation between group process and weekly progress

The residuals (representing within-person and within-group variation across time)

and intercepts (representing between-person and between-group differences in average

level) were used to test the covariation between group dynamics and weekly progress. To

account for the nested structure of the data (i.e occasions nested within persons nested

within groups), these scores were tested as independent variables predicting weekly

progress scores in a series of three-level multilevel models (MLM). Week number (i.e.,

time, centered at the “middle” session) and session-specific predictors (i.e., residuals)

represent Level 1, testing whether week-to-week variation in individual connectedness

and group structure coincide with weekly reports of progress. Between-person predictors

(i.e., individuals’ average level of connectedness) represent level 2, to test whether stable

between-person differences in connectedness help to account for between-person

differences in progress. Finally, between-group predictors (i.e., group-level average

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structure scores) represent level 3, testing the role of between-group differences in

structure. Group size and school level (undergraduate or graduate student) were also

added as control variables, and school level was tested as a potential moderator of

session-specific, person-level, and group-level network statistics. In these models,

intercept and time were allowed to vary across individuals and across groups (under the

multivariate normal assumptions). Unfortunately, likely due to the relatively small

number of groups (Ngroups = 18), none of the models converged; thus, random effects at

the third level (group-level variance) were removed. The resulting two-level models

converged without problems.

Given the large number of associations being examined, models were built up in a

series of steps. In addition to the control variables (e.g., session number, school-level),

between-group differences in density and between-person differences in centrality were

entered first. Within-group variation in density and within-person variation in centrality

were entered second. Between-person reciprocity and between-group reciprocity and

centralization were entered third; and within-person reciprocity and within-group

reciprocity and centralization were entered fourth. At each of these steps, interactions of

the corresponding terms with school level were tested. Non-significant interactions and

then main effects were trimmed one by one before moving on to the next step. However,

group-level centralization is a “higher-order” term that is largely dependent on a group’s

density (e.g., in very dense groups, there is less “room” for unequal distribution), and

individual-level reciprocity is a higher-order term dependent on centrality scores (e.g., the

number of nominations an individual makes limits the number of ties that can be

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reciprocated). Thus, the “first-order” terms were not trimmed from the model until and

unless the corresponding higher-order terms had already been trimmed.

Parameter estimates for models predicting global progress are reported in Table

3.1, and results reported here are from the final model (Model 1d). As expected, in the

model predicting global progress, higher average group-level density of negative

connections was associated with less progress (γ = -121.07, p < .05), and at the between-

person level, average level of feeling discomfort (i.e., negative outdegree) was a

marginally significant negative predictor (γ = -60.13, p < .10). In other words,

participants who, on average, felt discomfort with a larger proportion of the group, and

who belonged to groups with a greater frequency of negative connections, tended to

report less global progress. Also at the between-person level, a marginally significant

interaction of average source of connectedness (i.e., positive indegree) with school level

(i.e., undergraduate versus graduate student) suggests that within undergraduate groups,

those who provided a greater source of connectedness for their group-mates reported

greater global progress (γ = -81.90, p < .10; see Figure 3.1 for a plot of the interaction).

In addition, a significant interaction of school level with average felt connected scores

(i.e., positive outdegree) suggests that lower levels of feeling connected were more

detrimental for graduate students’ reports of global progress than for undergraduate

students’ reports, whereas a higher average level of feeling connected predicted

approximately equivalent global progress scores among the two school levels (γ = 57.10,

p < .05; see Figure 3.1). Similar findings were observed at the within-group and within-

person levels. Specifically, on weeks where there was a higher density of negative

connections than usual, members reported lower global progress (γ = -53.15, p < .05); on

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weeks when individuals felt more connected (i.e., positive outdegree; γ = 77.40, p < .001)

or provided a greater source of connectedness to group-mates (i.e., positive indegree; γ =

45.02, p < .01) than usual, they reported greater global progress; and on weeks when

individuals felt more discomfort, they reported lower progress (i.e., negative outdegree; γ

= -37.67, p < .05). Contrary to expectations, participants also reported greater global

progress on weeks when the group was more centralized (γ = 18.41, p < .05).

The final model predicting weekly perceptions of session value showed similar

results. Parameter estimates for the final model predicting session are in Table 3.2, Model

2d. Once again, between-group differences in average density of negative connections

significantly negatively predicted member reports of session value (γ = -252.72, p <

.001): members of groups with a higher prevalence of negative connections were

significantly less likely to view the therapy sessions as valuable. And, consistent with the

global progress model, there was a trend toward members of more groups in which

positive connections were more centralized to report a higher session value, on average (γ

= 68.41, p < .10). At the between-person level, individuals who felt more connected on

average (positive outdegree; γ = 52.90, p < .05) tended to perceive sessions as more

valuable. Contrary to expectations, however, individuals who were, on average, a greater

source of discomfort for their group-mates (i.e., negative outdegree) were also marginally

more likely to view sessions as valuable (γ = 83.08, p < .10). At the within-group level,

results were mixed: on weeks with a greater density of negative connections than usual,

participants reported lower session value than usual (γ = -99.45, p < .01), as expected.

However, group members also reported lower session value on weeks when there was a

higher density of positive connections than usual (γ = -25.55, p < .05). Lastly, at the

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within-person level, both feeling connected and being a source of connectedness were

significantly related to perceived session value (γ = 195.41, p < .001; γ = 70.36, p < .01,

respectively), as hypothesized: on weeks when participants were more connected and

contributed more to group-mates’ feelings of connectedness than usual, they were more

likely than usual to perceive sessions as valuable. Like at the between-person level,

however, source of discomfort (i.e., negative indegree) had an effect contrary to what was

expected: a marginally significant interaction (γ = 119.34, p < .10; see Figure 3.1)

suggests that in graduate student groups, members reported higher session value on

weeks when they were a greater source of discomfort.

As a final piece of our investigation into the covariation between group process

and progress, we examined whether participants’ gains in connectedness across sessions

corresponded with their gains in progress. The individually-fitted growth curves for

feeling connected, source of connectedness, feeling discomfort, and source of discomfort

(i.e., positive and negative indegree and outdegree), as well as for individual-level

reciprocal positive relationships, were correlated with individually-fitted growth curves

for global progress and session value. Consistent with our hypotheses, rates of change

and rate of acceleration (linear and quadratic slopes) significantly correlated with linear

and quadratic slopes in our network indices of progress: individuals who became

increasingly prevalent sources of connectedness across sessions also experienced

increases in global progress and perceived session value across session (r = .85, p<.001; r

= .90, p < .001, respectively); and those who showed greater acceleration of growth in the

degree to which they were a source of connectedness for group-mates also showed

greater acceleration of growth in global progress and perceived session value (r = .68, p <

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.001; r = .73, p < .001, respectively). In complement, and consistent with expectations,

those who became an increasingly prevalent source of discomfort across sessions

experienced declines in global progress and session value (r = -.78, p < .001; r = .87, p <

.001, respectively). Correlations of linear and quadratic trajectories of feeling connected,

reciprocal positive relationships, and feeling discomfort with trajectories of global

progress and session value followed similar patterns; see Table 3.3 for the complete set

of correlations.

Aim 2: Predicting post-therapy outcomes from average levels and change in group

process

In this aim, I examined indices of between-person and between-group differences

in connectedness across time in relation to participants’ pre- to post-treatment

improvement in mental health (i.e., total OQ score and each of its three subscales). For

each outcome, I conducted a linear regression analysis3 in which the predictors included

average levels, trajectories of change, and lability in individual connectedness and group

structure. Models controlled for corresponding pre-treatment baseline scores, as well as

group size, number of sessions attended, and school level. School level is also tested as a

moderator of each network predictor. This final analysis provides new insight about how

the dynamic nature of group process contributes to a treatment’s effectiveness, by

simultaneously testing the unique contributions of average levels and indices of change in

connectedness.

Average levels, slopes, and lability scores for group-level density of positive and

negative connections, person-level feelings and sources of connectedness and discomfort

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!3 Aim 2 hypotheses were initially tested with multilevel models using SAS proc mixed, with individuals (level 1) nested within groups (level 2). However, due to the small number of groups, the models could not converge. Thus, we switched to using simpler linear regressions.

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(i.e., outdegree and indegree, respectively) and individual-level reciprocity were initially

entered together in each model predicting post-therapy OQ scores. However, due to the

small sample size of individuals with both pre- and post-treatment scores (N=98), the

large number of terms being tested, and high correlations among the four sets of network

indices to be tested (e.g., r = .88 between the individual-specific intercepts used to

represent average indegree and outdegree on positive connections), results showed beta

coefficients with extremely large standard errors, suggesting unstable models. Thus, each

set of predictors was examined in a separate analysis (e.g., indegree scores were tested in

one model, outdegree scores were tested in another model, and so on). Once again, non-

significant terms were trimmed from the models; however, individual- and group-specific

average levels were not trimmed unless or until all of its corresponding change scores

(i.e., linear slopes, quadratic slopes, and lability scores for the relationships under

investigation) had been trimmed, and linear slopes were not trimmed unless or until

corresponding quadratic slopes had been trimmed.

Across models, results suggest that graduate students tend to report more mental

health symptoms than undergraduate students. However, marginally significant

interactions of school level with average group-level density of positive connections

across the total score (B = -66.06, p < .10), symptom distress subscale (B = -41.94, p <

.10), and interpersonal relations subscale (B = -14.37, p < .10) models consistently

suggest that graduate students benefitted from greater average density of positive

connections in their groups, while undergraduate students demonstrated the opposite

effect (i.e., less improvement when the density of positive connections was high). See ;

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see Figure 3.2 for plots of these interactions, and Table 3.4 for final models predicting

OQ improvement from group-level density summary scores.

In contrast, for the models in which individual-level source of connectedness and

source of discomfort (i.e., indegree scores) were used as predictors, trajectories of change

(i.e., linear and quadratic slopes) were the strongest predictors of mental health

improvement. Specifically, there was a trend toward an accelerated trajectory of

connectedness (i.e., positive quadratic slope) predicting better improvement in total OQ

score (B = -138.18, p < .10); similarly, an accelerated trajectory of connectedness

significantly predicted improvement in the symptom distress subscale (B = -132.24, p <

.05). The interpersonal relations subscale was most strongly predicted by slopes in

source of discomfort, but in the opposite direction from what was anticipated. Participants

who became an increasing source of discomfort across sessions (linear slope B = -33.66,

p < .10) and whose source of discomfort followed an accelerated trajectory across

sessions (quadratic slope B = -47.01, p < .10) were marginally more likely to report

improvement on the interpersonal relationships subscale. See Table 3.5 for final models

predicting OQ improvement from individual-level indegree summary scores. When

individual-level felt connected and felt discomfort summary scores (i.e., outdegree) were

used as predictors, yet a different pattern emerged. Consistent with expectations, there

was a trend toward individuals’ average feelings of discomfort predicting a worsening of

symptoms on the interpersonal subscale (B = 12.05, p < .10) and the social role subscale

(B = 11.22, p < .10). Also consistent with expectations, growth in feeling connected

predicted participant improvement on the social role subscale. See Table 3.6 for final

models predicting OQ scores from outdegree summary scores.

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The final set of models was used to predict mental health improvement from

reciprocal positive relationships. In these models, it was quadratic growth that was most

predictive: an accelerated trajectory of reciprocal positive relationship development

significantly predicted improvement in total OQ score (B = -107.16, p < .01) and on the

symptom distress subscale (B = -86.81). See Table 3.7 for final models predicting OQ

scores from reciprocal relationship summary scores. In sum, results of aim 2 analyses

suggest implications of network process indices for longer-term participant outcomes,

and provide evidence that both average level and change trajectories in these indices may

be associated with longer-term outcomes.

Discussion

In the present study, logic and tools from social network analysis (SNA) and

methods sensitive to intra-entity variability (IEV) were integrated and applied to the

study of group process within 18 psychotherapy groups. In general, results provided

support for our hypotheses that individuals would report better weekly progress and

session value and experience a greater reduction in mental health symptoms when their

groups were more well-connected and when individuals themselves were more embedded

in their groups. In addition, though the significance and strength of relations varied

across post-treatment outcomes, results suggested that linear and quadratic trajectories of

change in connectedness can be predictive of treatment benefits. More broadly, results

provide support for the value of an integrated SNA and IEV approach to group process

research. This innovative approach provides more precise, empirical quantifications of

theoretical processes, and provides useful insight into features of the group process that

could not be examined with more traditional approaches

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Aim 1: Covariation between group process and weekly progress

Session to session covariation. In the first aim, I examined whether participants’

session to session reports of progress and session value were predicted by session to

session network indices of group process. Evidence from the present study at within- and

between-person and group levels suggests that the group processes occurring during

weekly sessions may help to facilitate participant progress. Consistent with expectations,

individuals who were more connected to group-mates reported greater session benefits:

participants who were a more frequent source of connectedness perceived sessions as

more valuable, and undergraduate participants who were a more frequent source of

connectedness tended to report greater global progress. Moreover, those who felt more

connected perceived sessions as more valuable and – among graduate students – reported

greater global progress. These patterns were replicated at the within-person level: on

weeks when participants felt more connected than usual, or were a greater source of

connectedness to their group-mates than usual, they also reported greater progress and

session value. Conversely, greater feelings of discomfort predicted lower global progress

at both the between- and within-person levels, as expected. These findings provide

further support for past research indicating the value of positive intra-group relations

within therapy groups; in addition, it extends past research by demonstrating benefits

both for those who feel connected and those who are a source of connectedness in group

therapy.

Being a source of discomfort, on the other hand, had an unexpected effect on

perceived session value: at both the between- and within-person levels, there was a

tendency for participants to report higher session value when they were a greater source

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of discomfort. It is possible that members receiving multiple “nominations” for being

disruptive are receiving those nominations because they dominated the session. While

others may perceive this as negative – because they may have had less opportunity to

contribute to or benefit from that session themselves – the individual dominating the

session would, conversely, have had ample opportunity to use the session to his or her

own benefit. Another possibility is that a client may receive negative “nominations”

because he or she challenged or confronted individuals in the group, or expressed

disagreement or distaste for what others said or did during session. This type of

contribution may cause discomfort for group-mates for obvious reasons, but may be

experienced as a personal gain for the “challenger” if it helped him or her to feel superior,

or represented a unique opportunity for that member to assert his or her own opinions or

needs.

At the group level, some interesting dynamics were revealed that were not always

in line with expectations. Consistent with expectations, the group-level density of

negative connections appeared to have effects on the group as a whole. Members of

groups with a greater average density of negative connections reported lower global

progress scores and lower perceived session value; and participants reported lower

progress and session value on weeks when the group-level density of negative

connections was higher than usual. Group-level density of positive connections, on the

other hand, did not significantly predict either of the weekly outcome measures; and

session-specific group-level density of positive connections only predicted weekly

reports of session value, but in the opposite direction than expected. Taken together, these

findings may suggest that at the level of group as a whole, it is only the occurrence of

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disruptive dynamics that have group-wide “effects”. When it comes to positive

connections, the present findings would suggest that their biggest role is played at the

between- and within-person levels: it was differences in how well-embedded individuals

were within their groups – rather than how cohesive the groups were overall – that most

consistently predicted weekly reports of progress and session value.

Lastly, contrary to expectations, findings from both weekly covariation models

suggest that participants benefit from more centralized groups (for instance, one or a few

members received the majority of nominations). Past social network theory and literature

led to the hypothesis that more evenly distributed ties (i.e., less centralization) would

facilitate better outcomes for participants (e.g., Kennedy & MacKenzie, 1986). Perhaps

within the context of therapy groups, centralization holds a different meaning than in

most other contexts. Rather than indicating an imbalance of “power” as centralization

often implies (e.g., social status hierarchies in classrooms), centralization in the context

of a psychotherapy group may result from one or a few participants providing particularly

useful contributions to the larger discussion or divulging sensitive personal information

that provided members with a greater sense of universality or hope in a given week.

Given the limited timeframe of each session, it is rare that everybody in the group has the

opportunity to contribute every week. Weeks in which the network indices suggest

higher centralization may represent weeks during which the one or few who did share had

a more profound effect on his or her group-mates, and there was somewhat universal

agreement about the benefits of that participant’s contributions.

Correlated growth curves. The models described above tested whether average

levels of connectedness and session-to-session fluctuations in connectedness correspond

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to levels of participant progress. However, it was also expected that participants might

become increasingly connected across sessions as new opportunities arise for participants

to relate. Similarly, it was to be expected that participants would make gains in

“progress” – and view sessions as increasingly valuable – over the course of treatment.

Thus, another question explored was whether the gains across time in connectedness may

help to facilitate gains in participant functioning. As hypothesized, correlations between

individual-specific slopes suggest that gains in connectedness over the course of the

treatment strongly correspond with gains in global progress and increases in perceived

session value. As participants became increasingly well-regarded, felt a growing sense of

connections, and developed a growing number of reciprocal relationships with their

group-mates, they also tended to experience gains in global progress and perceived

session value. These trajectories of process and progress map onto one another in both

their linear and curvilinear forms: those who make gains in connectedness make gains in

progress, and those who experience an accelerated trajectory of connectedness experience

an accelerated trajectory of progress. Taken together, results of aim 1 provide

compelling evidence for hypotheses that the network indices of group process tap into

meaningful features of the group process at the session-to-session level, with implications

for participants’ progress.

Aim 2: Predicting post-therapy outcomes from average levels and change in group

process

In the second aim, indices of change and variability in group process were tested

as predictors of pre- to post-treatment improvement in mental health symptoms. In

general, results provide support for the idea that both the overall climate as well as the

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dynamic nature of group process have important implications for treatment effectiveness:

across models, there was evidence of the value of average levels and change trajectories

of both individual- and group-level indices of connectedness for predicting participants’

pre- to post-treatment improvement.

When considering group-level connectedness as a predictor of mental health

improvement, findings suggest that between-group differences in average level of

“positive climate” were the strongest predictor of outcomes: higher average density of

positive connections predicted improvement in total mental health, the symptom distress

subscale, and the interpersonal relationships subscale, but only among graduate student

groups. Among undergraduate groups, the opposite effect was observed: members of

more “cohesive” groups reported more symptoms. One possible explanation for the

differing effects may be differing timing and development of undergraduate versus

graduate groups. According to theories of group development, group cohesion –

considered to be the third “stage” of development – must be achieved before the group

can move into the “working” (fourth) stage (Tuckman, 1965; Wheelan, 1997). The

working stage is characterized by mature, productive, and open exchange of expression

and feedback. It is during this stage that the group has the best capacity for making

progress on therapeutic work (e.g., Wheelan, 1997). Given the maturity needed for this

stage to be productively achieved, it is possible that graduate student groups tend to reach

the cohesion stage more quickly than do undergraduate groups, and are thus able to

progress more quickly into therapeutic work. If, in contrast, undergraduate groups that

achieve cohesion tend to do so only shortly before termination (due to the time-limited

nature of the treatment), they will have just begun their hardest therapeutic work at the

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time when they are completing the post-treatment OQ surveys. Though they will be

well-positioned to make gains if they choose to continue in individual or group therapy

afterwards, they may temporarily “feel worse” as they have begun to uncover some of

their toughest issues. Undergraduate groups with a low average connectedness, on the

other hand, likely never reached the cohesion or working stages, and thus may

paradoxically report better mental health post-treatment. This is just one possibility;

more research will be needed to uncover the processes accounting for these findings.

When individual-level sources of connectedness and discomfort (i.e., indegree

scores) were used as predictors of mental health improvement, trajectories of change in

individual connectedness most strongly predicted outcomes. Accelerated trajectories in

participants’ “contributions” of support, understanding, and so on (i.e., positive quadratic

slope in source of connectedness) predicted better improvement in total mental health and

symptom distress. Contrary to expectations, becoming an increasing source of discomfort

across sessions and following a U-shape trajectory in this domain also predicted better

improvements on the interpersonal relationships subscale. Perhaps, as noted earlier, an

increase across sessions in causing others discomfort is representative of an individual

becoming increasingly confident in expressing his or her own opinions or needs, even if

they contradict those of the group. Alternatively, perhaps increasingly judging or

dismissing others – or reaching a peak in these dynamics at the end of treatment – is

indicative of or even provides certain members with a sense of being “better off” than

others in their group. Again, further research will be needed.

When felt connected and felt discomfort summary scores (i.e., outdegree on

positive and negative connections) were tested as predictors, yet a different pattern

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emerged. First, those who felt the most discomfort made the fewest gains in their

interpersonal relationships and social roles outside of the therapy group, as expected. In

other words, individuals’ average feelings of discomfort predicted a worsening of

symptoms on the interpersonal and social role subscales. Also consistent with

expectations, growth in feelings of connectedness predicted improvement on the social

role subscale. The group therapy literature would predict that group cohesion develops

over time and that participants experience the greatest gains when they achieve better

group cohesion – the present findings would similarly suggest that an individual’s

personal feelings of “cohesiveness” or connectedness to the group facilitate the best

outcomes.

Lastly, the number of reciprocal positive relationships that participants

accumulated was tested as a predictor of mental health improvement. Similar to being a

source of connectedness (i.e., indegree), participants whose growth in reciprocal positive

relationships accelerated over time (i.e., positive quadratic slope) experienced the most

improvement in total mental health and symptom distress. These findings are consistent

with past research: although giving or receiving social and emotional support each has

benefits, studies have found that the best psychological outcomes result from reciprocal

rather than unbalanced relationships. While those experiencing reciprocal supportive

relationships are found to experience benefits such as higher self-esteem and fewer

depressive symptoms, those in imbalanced relationships are found to experience lower

self-efficacy and poorer mental health (e.g., Jaeckel et al., 2011; Jou & Fukada, 1996).

While more research would be needed to substantiate these hypotheses, mutual positive

regard and reciprocity in other types of interactions, such as those assessed here (e.g.,

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providing insight and understanding to one another), might have similar psychological

benefits.

Taken together, the results of aim 2 suggest that, when considering longer-term

impact of the group process, the group climate may have more of an “overall” (i.e.,

average-level) association with participant outcomes, while the role of theorized change

processes may be most relevant at the level of individuals’ positions within their group.

In other words, perhaps it is the unique developmental trajectories of change in one’s

position within the group – rather than in the group as a whole – that have the most

important implications for participant outcomes. Future research should examine

whether these findings replicate across other samples.

Strengths, Limitations, & Future Directions

The often-cited developmental stages of group psychotherapy highlight expected

peaks in conflict and a tendency for cohesion to wax and wane over the course of

treatment. These theories simply cannot be tested with widely spaced intervals of

measurement that are typical in much of the existing literature, which are unable to detect

un-patterned variability or non-linear trajectories of growth across sessions. Yet these

change processes appear have to psychological significance: several of the “quadratic”

effects tested in the present study were significant predictors of pre- to post-treatment

improvement. Thus, the present study contributes to a growing literature on the

importance of capturing change in group dynamics across sessions.

Moreover, findings from the present study contribute to a growing body of

literature on the importance of group process for participant progress and outcomes, but

also suggest that SNA and IEV indices provide some unique insight into features of the

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group process that could not be detected with traditional approaches (e.g., reciprocity and

group centralization). In addition, the majority of indices – e.g., indegree, centralization

– are not based solely on participant self-reports, suggesting that the observed relations

are not simply the product of same-reporter biases. The SNA statistics applied in the

present study are just a few examples of the individual and structural dynamics that these

techniques allow us to capture. The wide range of options serves as both a limitation and

strength of the present study. Because this study was largely exploratory, it is possible

that other indices not employed here would have mapped more closely onto the processes

operating in therapy groups; more research will be needed to identify the “best” indices

for this context. Yet the plethora of options also highlights the large “toolbox” that these

approaches have to offer for precisely operationalizing and studying the complex and

nuanced social processes involved in group psychotherapy.

The analytic approach demonstrated in the present study opens the door to a

number of future studies with great potential value to the field. For instance, future

research could examine the degree to which participants’ network indices of

embeddedness correspond to group leader perceptions of the roles that different members

are playing in the group: in other words, do groups function more effectively when the

group leaders’ perceptions line up with group-mates’ perceptions of members’ roles (i.e.,

indegree)? This is just one example of the important research questions that can be

addressed with the analytic approach demonstrated in this paper. See Molloy, 2012a for a

more in-depth discussion of the limitations and strengths of this approach, and

suggestions for future directions.

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Concluding Remarks

The greater our understanding of the social dynamics in group psychotherapy, the

more group facilitators will be able to actively target specific process dynamics shown to

facilitate or hinder treatment effectiveness. Yet in order to expand our understanding of

group processes, we need to continue to expand the analytic “toolbox” we use to

operationalize those processes. In the present study, it was demonstrated how two such

statistical tools can be used to open new windows into the “black box” of group

psychotherapy. By matching known group process phenomena to assessment and

analytic methods that precisely operationalize those phenomena, we can continue to test

approaches to and improve the treatment of mental health disorders.

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Table 3.1: Multilevel models predicting weekly progress from between- and within-group/ person variation in network indices

Model 1a: Between-Grp/Ind Diff in Connectedness (i.e., Density & Centrality)

Model 1b: Within- Grp/Ind Variability in Connectedness (i.e. Density & Centrality)

Model 1c: Between- Grp/Ind Higher-Order Diff (i.e., Reciprocity & Centralization)

Model 1d: Final Model: Within- Grp/Ind Variability (Recip & Centralization)

Fixed Effects B SE B SE B SE B SE Intercept 66.18*** .96 66.15*** .96 66.13*** .96 66.14*** .96 Session Number .19 .16 .19 .14 .25 .17 .25 .17 School Level (UG=0, G=1) -6.76** 2.07 -6.70** 2.07 -6.44** 2.02 -6.38** 2.02 Group-Level Average Density Positive Connections -14.50 18.27 -12.71 18.30 Negative Connections -135.15* 59.45 -137.26* 58.65 -124.10* 54.92 -121.07* 54.86 Ind-Level Average Connectedness Source of Connectedness 43.70 27.54 44.76 27.37 39.99 26.24 40.09 26.20 Felt Connected -13.14 14.50 -13.07 14.36 -12.76 14.35 -12.99 14.33 Source of Discomfort 70.46 46.31 68.52 45.74 62.93 44.81 62.18 44.74 Felt Discomfort -60.67+ 32.17 -57.25+ 31.66 -60.39+ 31.54 -60.13+ 31.49 SchLvl X Srce Connectedness -80.63+ 47.40 -82.82+ 47.19 -81.07+ 46.94 -81.90+ 46.87 School Level X Felt Connected 55.42* 27.77 55.26* 27.63 56.98* 27.58 57.10* 27.53 Within-Grp Variability in Density Positive Connections 0.40 7.56 0.33 7.48 7.78 8.30 Negative Connections -59.37* 24.00 -57.60* 23.70 -53.15* 23.75 Within-Person Variability in Connectedness Source of Connectedness 45.68** 16.15 45.79** 15.91 45.02** 15.88 Felt Connected 78.31*** 14.37 78.54*** 14.14 77.40*** 14.12 Felt Discomfort -36.48* 17.28 -36.99* 17.02 -37.67* 16.98 School Level X Felt Discomfort -46.20 29.86 -46.27 29.38 -47.73 29.33 Higher-Order Effects: Within-Grp Group Centralization 18.41* 8.99 Random Effects Between Person Intercept 74.45*** 13.88 80.54*** 13.97 79.76*** 13.80 79.55*** 13.75 Session Number 0.44+ 0.32 0.44+ 0.32 Residual Variance 175.41*** 9.58 135.38*** 7.51 131.08*** 7.63 130.50*** 7.6

Note. ***p < .001, **p < .01, *p < .05, +p < .10

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Table 3.2: Multilevel models predicting weekly session value from between- and within-entity variation in network indices

Model 2a: Between- Grp/Ind Diff in Connectedness (i.e., Density & Centrality)

Model 2b: Within- Grp/Ind Variability in Connectedness (i.e. Density & Centrality)

Model 2c: Between- Grp/Ind Higher-Order Diff (i.e., Reciprocity & Centralization)

Model 2d: Final Model: Within- Grp/Ind Variability (Recip & Centralization)

Fixed Effects B SE B SE B SE B SE Intercept 70.12*** 1.07 69.75*** 1.07 70.33*** 1.15 70.33*** 1.15 Session Number 0.46+ 0.24 0.48* 0.20 0.49* 0.20 0.49* .20 School Level (UG=0, G=1) -4.27+ 2.26 -4.72* 2.26 -4.44+ 2.28 -4.44+ 2.28 Grp-Level Average Density Positive Connections 17.94 20.15 17.94 20.15 Negative Connections -223.00*** 62.32 -235.99*** 61.11 -252.72*** 63.39 -252.72*** 63.39 Ind-Level Average Connectedness Source of Connectedness 7.80 29.49 14.71 29.33 Felt Connected 25.86+ 14.55 24.86+ 14.38 52.90* 20.44 52.90* 20.44 Source of Discomfort 89.8167+ 45.90 101.00* 45.06 83.08+ 42.31 83.08+ 42.31 Sch Lvl X Srce of Connectedness -70.28 53.20 -82.80 52.90 School Level X Felt Connected 51.31 31.03 53.15+ 30.79 27.64 19.35 27.64 19.35 Within-Grp Variability in Density Positive Connections -25.72* 10.68 -25.55* 10.70 -25.55* 10.70 Negative Connections -102.27** 35.53 -99.45** 35.59 -99.45** 35.59 Within-Ind Var in Connectedness Source of Connectedness 69.97** 23.12 70.36** 23.15 70.36** 23.15 Felt Connected 195.72*** 19.99 195.41*** 20.01 195.41*** 20.01 Source of Discomfort 48.33 38.56 46.58 38.61 46.58 38.61 School Lvl X Source of Discomfort 119.19+ 64.21 119.34+ 64.29 119.34+ 64.29 Average Level Higher-Order Effects

Average Grp-Level Centralization

of Positive Connections 68.41+ 37.76 68.41+ 37.76

Average Individual Number of

Reciprocal Positive Connections -18.90 14.66 -18.90 14.66 Random Effects Between Person Intercept 63.75*** 16.96 82.43*** 17.35 78.85*** 17.16 78.85*** 17.16 Residual Variance 390.06*** 21.28 268.53*** 14.92 269.18*** 14.98 269.18*** 14.98

Note. ***p < .001, **p < .01, *p < .05, +p < .10

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Table 3.3: Correlations of linear and quadratic slopes in progress indices with linear and quadratic slopes in SNA indices of individual-level connectedness Individual-Level SNA Indices of Connectedness

Linear growth

Positive Composite Indegree

Positive Composite Outdegree

Positive Composite Reciprocity

Distaste Composite Indegree

Distaste Composite Outdegree

GES Composite .85*** .92*** .84*** -.78*** -.85*** Session Value .90*** .96*** .89*** -.87*** -.86*** Self-Efficacy .42*** .42*** .41*** -.57*** -.39*** Quadratic growth

GES Composite .68*** .73*** .72*** -.39*** -.30***

Session Value .73*** .81*** .78*** -.53*** -.37***

Self-Efficacy .03 .07 .20* .49*** .62*** ***p < .001, **p < .01, *p < .05, +p < .10

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Table 3.4: Predicting pre- to post-therapy improvement from between-group differences in average level, slope, and lability in group-level density of positive and negative connections

Model 1: Total Score

Model 2: Symptom Distress Subscale

Model 3: Interpersonal Relations Subscale

Model 4: Social Roles Subscale

Predictors B SE B SE B SE B SE

Intercept 6.99 8.19 1.43 5.47 4.74* 2.02 1.66 1.84 Control Variables

Baseline score .62*** .07 .65*** .07 .49*** .08 .58*** .08 Num Sessions

Attended -.80* .39 -.43 .26 -.24* .10 -.14 .10 School Level (UG or

Grad) (age) 4.76* 2.06 2.83* 1.40 .88+ .52 1.20* .47 Group size

Density of Positive Connections

Average Level 9.07 16.69 9.48 11.34 -3.49 4.25 .45 3.70 School Level X Average Level -66.06+ 33.34 -41.94+ 22.59 -14.37+ 8.37

Linear Slope Quadratic Slope

Within-Group Variability 12.26 7.58 ***p < .001, **p < .01, *p < .05, +p < .10 Note. In the above analyses, positive values represent a worsening of the number or severity of psychological symptoms, and negative values indicate improvement in symptoms.

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Table 3.5: Predicting pre- to post-therapy improvement from between-person differences in average level, slope, and lability in individual-level sources of connectedness and discomfort

Model 1: Total Score

Model 2: Symptom Distress Subscale

Model 3: Interpersonal Relations Subscale

Model 4: Social Roles Subscale

Predictors Coef. SE Coef. SE Coef. SE Coef. SE

Intercept 13.10*** 3.24 5.43* 2.50 3.22*** .64 2.09* .90

Control Variables Baseline score .59*** .07 .60*** .07 .48*** .08 .58*** .08

Num Sessions Attended -.95* .40 -.65* .31 -.21* .10 -.19+ .11 School Level (UG or Grad) 4.73* 2.07 3.85* 1.55 .96+ .52 1.29** .48

Group size .09 1.04 Source of Connectedness (i.e., Indegree)

Average Level 2.28 23.16 2.45 18.10 .48 4.66 School Level X Average Level

Linear Slope 58.72 44.97 Quadratic Slope -138.18+ 80.90 -132.24* 60.91

Within-Person Variability 20.93 14.08 Source of Discomfort (i.e., Indegree)

Average Level -14.22 39.34 -6.24 10.57 School Level X Average Level -78.77 56.68

Linear Slope -33.66+ 18.57 Quadratic Slope -47.01+ 28.25

Within-Person Variability 95.29 72.91 ***p < .001, **p < .01, *p < .05, +p < .10 Note. In the above analyses, positive values represent a worsening of the number or severity of psychological symptoms, and negative values indicate improvement in symptoms.

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Table 3.6: Predicting pre- to post-therapy improvement from between-person differences in average level, slope, and lability in individual-level feelings of connectedness and discomfort

Model 1: Total Score

Model 2: Symptom Distress Subscale

Model 3: Interpersonal Relations Subscale

Model 4: Social Roles Subscale

Predictors Coef. SE Coef. SE Coef. SE Coef. SE

Intercept 15.37*** 3.90 6.35** 2.04 3.86*** .76 3.00*** .72 Control Variables

Baseline score .61*** .07 .62*** .07 .53*** .08 .62*** .08 Num Sessions Attended -.54 .49 -.50+ .26 -.09 .13 -.08 .10

School Level (UG or Grad) 5.76** 2.19 2.88* 1.41 1.28* .55 1.20* .48

Group size -.51 .32 Felt Connected (i.e., Outdegree)

Average Level -2.35 16.44 1.82 3.86 .47 3.45 School Level X Average

Level Linear Slope -16.54** 5.88

Quadratic Slope Within-Person Variability -91.50 62.13 -25.17 15.99

Felt Discomfort (i.e., Outdegree)

Average Level 12.05+ 6.47 11.22+ 6.43 School Level X Average

Level Linear Slope

Quadratic Slope Within-Person Variability -18.86 11.47

***p < .001, **p < .01, *p < .05, +p < .10 Note. In the above analyses, positive values represent a worsening of the number or severity of psychological symptoms, and negative values indicate improvement in symptoms.

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Table 3.7: Predicting pre- to post-therapy improvement from between-person differences in average level, slope, and lability in individual-level number of reciprocal positive relationships

Model 1: Total Score

Model 2: Symptom Distress Subscale

Model 3: Interpersonal Relations Subscale

Model 4: Social Roles Subscale

Predictors Coef. SE Coef. SE Coef. SE Coef. SE

Intercept 14.50*** 3.80 7.38** 2.41 3.33*** .62 2.87*** .71

Control Variables Baseline score .60*** .07 .62*** .07 .48*** .08 .57*** .08

Num Sessions Attended -1.06** .40 -.65* .26 -.23* .10 -.11 .09 School Level (UG or Grad) 4.70* 2.07 2.60+ 1.37 .91+ .52 1.37** .46

Group size Reciprocity of Positive Connections

Average Level -5.36 10.16 -.50 6.75 School Level X Average

Level Linear Slope

Quadratic Slope -107.16** 40.07 -86.81*** 26.47 Within-Person Variability

***p < .001, **p < .01, *p < .05, +p < .10 Note. In the above analyses, positive values represent a worsening of the number or severity of psychological symptoms, and negative values indicate improvement in symptoms.

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Figure 3.1: Predicting weekly self-reports of “global progress” from interactions of school level with individual-level SNA indices

This figure depicts interactions of school level with (a) average level source of connectedness (i.e., indegree), (b) average level felt connected (i.e., outdegree), and (c) occasion-specific source of discomfort (i.e., indegree).

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Figure 3.2: Predicting pre- to post-therapy improvement (on three mental health subscales) from interactions of school level with group-level density

This figure depicts predictions of pre- to post-therapy improvement on (a) total mental health, (b) symptom distress, and (c), interpersonal roles. Note that in these figures, lower scores represent greater improvement (i.e., fewer symptoms).

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Appendix A: Client weekly surveys

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CHAPTER 4:

UNDERSTANDING PROCESS IN GROUP-BASED INTERVENTIONS: SOCIAL

NETWORK ANALYSIS AND INTRAINDIVIDUAL METHODOLOGIES AS

WINDOWS INTO THE “BLACK BOX”

Group delivery formats are more cost-efficient than individual or family

interventions, and provide unique opportunities to facilitate positive outcomes in

prevention settings through a supportive environment, and opportunities for participants

to relate to one another, learn from each other, and practice targeted skills (Kivlighan &

Holmes, 2004). Thus, the nature and quality of participant interactions – i.e., the group

process – is likely to influence whether and how much participants benefit from a

program (Yalom, 2005). Consistent with this idea, program developers typically devote a

portion of their training workshops or manuals to “group management” skills (e.g.,

Project KEEP, DeGarmo, Chamberlain, Leve, & Price, 2009; Incredible Years, Webster-

Stratton, 2001). Yet group process is rarely included in program logic models or

explicitly measured in studies of implementation quality. Thus, the foundational

assumption that group process relates to program outcomes remains largely untested.!

Importance of Understanding Process

As a growing number of evidence-based programs are disseminated into real

world settings, seminal papers in the field have begun calling for a move beyond an

exclusive focus on outcomes, and greater attention to the “black box” (i.e., process) that

leads from program to outcomes (Durlak & DuPre, 2008). Without systematic

documentation of process, it is impossible to know whether a program’s effects could

have been stronger or extended to more individuals. For instance, program delivery

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under imperfect conditions (e.g. without administrative support, or delivery over fewer

sessions than intended) has been found to reduce program effect sizes to half or a third of

what they would otherwise be (Domitrovich & Greenberg, 2000). In order to maximize

program effectiveness, we must understand the circumstances under which programs are

most likely to thrive. Within group settings, a key piece of identifying “ideal”

circumstances is identifying the types of intra-group interactions that best facilitate

intended program effects.

Though empirical attention to group process has been scarce, the literature that

does exist demonstrates its importance. The majority of empirical work on group process

thus far has been focused on psychotherapy groups, also referred to as “process groups”.

In contrast, within non-process groups, such as psycho-educational or training groups

(i.e., most evidence-based prevention groups) the purpose of the group is to teach specific

content, and structured lessons and activities are focus of group sessions. Although

completeness of the curriculum is important, “group processes will not unobtrusively step

aside” (Ettin, Vaughan, & Fiedler, 1987, p. 178). Instead, group process can be used to a

facilitator’s advantage in order to support the content being delivered, highlight its

applicability in participants’ lives, and engage participants in an active learning process.

For instance, facilitators can assess when it may be useful to elicit participant reactions or

spend extra time on a particular topic (Ettin et al., 1987).

Some of the key beneficial processes of psychotherapy groups are expected to

have similar value within psycho-educational groups (Ettin et al., 1987). For instance, in

both settings, altruism (e.g., providing others with emotional support, or offering a

suggestion) is believed to provide a boost in self-efficacy (Yalom, 2005), and a cohesive

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group provides members with a sense of belonging, acceptance, and validation (Joyce,

Piper, & Ogrodniczuk, 2007), as well as a support network that may extend beyond the

life of the group (Borden, Schultz, Herman, & Brooks, 2010; Iwaniec, 1997). This is

particularly valuable for participants who began the group feeling socially isolated – an

experience that is common among participants in parent training groups (Sharry, 1999).

Similarly, it is common for parents to experience self-blame when struggling with child-

rearing; thus, the opportunity to realize that other parents are experiencing the same

difficulties (i.e., achieving a sense of universality) can be a major relief, allowing parents

to move past their guilt and toward finding solutions (Sharry, 1999).

In the domain of psycho-educational or other content-based groups, the majority

of the literature on group process has been theoretical or descriptive, and largely based on

qualitative data (e.g., Hornillos & Crespo, 2012). For instance, several programs are

described as being heavily based on group processes in their development and

implementation, and provide qualitative descriptions of the process in a sample group

(e.g., Down, Willner, Watts, & Griffiths, 2011; McWhirter & McWhirter, 2010). These

qualitative studies provide useful preliminary support for a link between group process

and participant outcomes. For instance, during interviews following an after-school

program, participating youth described the opportunity to �talk about feelings� as a

primary reason that they valued and enjoyed the program (Bazyk & Bazyk, 2009).

Similarly, in a program targeted at reducing relational aggression, participant interviews

and leader observations led to the author�s conclusion that group discussions helped

participants to develop pro-social skills, empathy, and effective conflict resolution skills,

in turn resulting in greater emotional literacy and a sense of empowerment (Chessor,

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2008). In yet another study, researchers reviewed group leaders� logs to identify major

group process �themes�, observing increases in group cohesion and feelings of trust

across sessions (Barrera, Chung, Greenberg, & Fleming, 2002).

Quantitative analyses of group process are especially rare, but lend support to the

qualitative findings discussed above. In an intervention with foster parents, group leaders

completed ratings of participant �engagement� after each session (e.g., participation,

openness to ideas). Ratings across 16 sessions were averaged, and results indicated that

participant engagement moderated the relation between prior foster child placements and

child problem behaviors (DeGarmo et al., 2009). Similarly, college student reports of

open communication and group �atmosphere� in a training program were predictive of

greater self-efficacy (Choi, Price, & Vinokur, 2003), and aspects of youths� engagement

(e.g., time on task) in interpersonal cognitive problem-solving groups predicted

improvement in skills (Erwin, Purves, & Johannes, 2005). Taken together, the

descriptive, qualitative, and initial quantitative work on group process in intervention

settings provides an important foundation for further investigation.

Limitations of the Existing Group Process Literature

When considering quantitative studies across both the process and non-process

group literature, it is apparent that the most common analytic approaches are limited in

their ability to articulate group process (e.g., see Molloy, 2012a; Morgan-Lopez & Fals-

Stewart, 2006 for more in depth reviews of the limitations). Broadly, current measures

tend to over-simplify group process by overlooking potentially important differences

between groups (e.g., some groups may be more tightly knit than others), between

persons (e.g., some individuals may be more well-embedded or central within their group

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than others), and across time (e.g., groups’ or individuals’ connectedness may vary from

session to session). Overall, it is clear that the field of prevention science is in need of a

new analytic approach to studying group process. The present study draws upon and

integrates current best practices for studying social relationships and change over time to

overcome previous limitations.

Addressing the Limitations of Past Research: Social Network Analysis

Social network analysis (SNA) is a set of measurement and statistical tools

designed to operationalize long-standing theories of group dynamics (Moody & White,

2003; Wasserman & Faust, 1994). Participant self-reports of their relations to group-

mates (e.g., “who do you like?”) are used to quantify the structure of a group and

individuals’ positions within that group. See Molloy (2012a) for an in-depth discussion of

the value of SNA as a tool for examining group process. Here I will simply provide a

brief definition of the social network statistics of relevance to the present study. At the

group level, density is an operationalization of group cohesion that gives us the “average”

connectedness of a group, and is computed as the proportion of all possible ties (e.g.,

social relationships) that are present within a group. Centralization, on the other hand,

quantifies the extent to which social ties within a group are unequally distributed (i.e., the

extent to which the group has a “hierarchical structure”). At the individual level,

centrality quantifies each individual’s level of “embeddedness” or involvement within a

group (Borgatti, 2005). With SNA, we can distinguish those who feel connected (i.e.,

outdegree centrality, or the number of group-mates to whom a member reports a

connection), from those who are a source of positive connections (i.e., indegree

centrality, or the number of times others indicated a connection with a given member).

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Depending on what relational constructs are assessed, we can similarly distinguish those

who feel uncomfortable from those who are the source of discomfort). Lastly, reciprocity

quantifies the number or proportion of a member’s connections that are mutual (i.e.,

reported by both members). Numerous studies have linked each of these network indices

to participant outcomes, in general indicating that “networks” or groups are most

beneficial when they are cohesive (i.e., high density) and ties are relatively equally

distributed (i.e., low centralization), and when individuals are more well-embedded

within their groups and experience high levels of reciprocity (see Molloy, 2012a). In sum,

a social network analytic approach provides a toolbox with the potential to substantially

expand our knowledge of prevention group process (e.g., Gest, Osgood, Feinberg,

Bierman, & Moody, 2011).

Many school-based interventions focus on social skills training, and others focus

on integration of students with behavioral problems or special needs (e.g., autistic

spectrum disorders) into classrooms with their normally-developing peers. In these

cases, where social integration is the specific end goal of the intervention, SNA is

frequently used to compute outcome measures for evaluating program impact. Moreover,

SNA is increasingly being used as a tool for informing and developing interventions:

Valente and colleagues have employed sociometric measures to identify natural “peer

leaders” that may be more effective than adults at spreading intervention messages

among adolescents in a school-based substance use prevention program, and social

network analytic tools are commonly used to assess networks of relationships among

professionals (e.g., teachers and administrators in a school, or medical professionals in a

hospital) in order to identify key points of intervention. However, SNA has never been

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used as a measure of process during the implementation of group-based interventions.

This study will be the first to use SNA to examine the link from process to outcomes in

group-based interventions.

Addressing the Limitations of Past Research: Methods Sensitive to Change and

Variability

The social connections assumed to facilitate positive outcomes in intervention

groups (e.g., caring, understanding) take time to develop, but past studies have yet to

systematically track changes in intervention group processes or their role in participant

outcomes. To overcome these limitations, the present study applies logic and tools from

an ecological momentary assessment (EMA) approach to data collection: in other words,

data are collected within their natural environment, coincide with the expected timescale

of change, and assess current feelings or thoughts (see Molloy, 2012a for a more in-depth

discussion). Analytic tools are selected that are sensitive to intraindividual or intra-group

variability (i.e., intra-entity variability, or IEV), allowing for characterization of the

changes observed (e.g., between-person differences in amount of change) and relations of

these changes to participant progress and outcomes (Ram, Conroy, Pincus, Hyde, &

Molloy, 2012; Ram & Gerstorf, 2009).

The existing qualitative research on “non-process” groups highlight the dynamic

nature of participant interactions and the overall group climate. In their study of

“Siblings Coping Together”, Barrera and colleagues (Barrera et al., 2002) described an

increase in cohesion across sessions as participants slowly gained trust and comfort

within the group. Similarly, Ettin and colleagues described their session by session

observations of group process in a stress management group, noting numerous

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differences in participant engagement across persons and sessions. Out of eleven

sessions, Ettin describes some being marked by hesitation and anxiety, others by conflict,

and still others by active engagement; by the final few sessions, it was clear that a group

cohesiveness had developed and participant interactions were primarily warm and

supportive (Ettin et al., 1987). Given expectations (and preliminary evidence) of

variability over time in group dynamics, it is clear that greater empirical attention to

session-to-session variability in group process is needed. This study will be the first to

apply IEV tools to SNA measures in order to investigate how change across sessions in

the structure of intra-group relationships and the positions of individual members

contribute to intervention outcomes. Based on past research, it is expected that growth in

positive connections will be predictive of the best participant outcomes, at both the

between-person and between-group level.

Integration of SNA and IEV. The innovative integration of these methods applied

to the study of intervention group process will provide much clearer answers than could

past studies to questions about how group process relates to intervention effectiveness.

Specifically, an integration of these tools will allow us to precisely test whether the

evolution of individual- and group-level social dynamics over the course of an

intervention is associated with between-person and between-group differences in

program benefits accrued to members.

Other Moderating Factors

Due to differences in maturity and purpose of parent versus youth groups, it is

likely that group process operates quite differently in each setting. A review of the

literature on parent training programs and intervention programs for youth reveals some

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noteworthy differences in the features of group process that have been theorized and

observed. For instance, the hypothesized benefits of a group format for youth typically

center around the opportunities it provides for youth to build and practice pro-social skills

(e.g., assertiveness, social problem solving), gain experience with identifying and

expressing feelings, motivations, and goals, and observe the effects of their behaviors on

others. Fun activities and games are frequently used to reinforce program concepts, and

role-playing and group discussions provide valuable opportunities for youth to observe

and model positive behaviors and brainstorm ideas together (e.g., strategies for coping

with anger; Barrera et al., 2002; Down et al., 2011). A common theme in the literature on

parent training programs, on the other hand, is the idea that parents often feel a sense of

isolation and self-blame when they are experiencing difficulties with child-rearing (e.g.,

Sharry, 1999). Thus, benefits of the group format for parents are based heavily on the

opportunity for parents to experience a sense of universality – i.e., the realization that

others share similar feelings and experiences – as well as an important source of social,

emotional, and instrumental support. The opportunity to offer solutions, tips, and

resources to one another provides parents with a reduced sense of isolation as well as the

experience of altruism. The primary purposes of group discussions and activities in

parent groups include modeling and testing out healthy interpersonal skills in the safe,

non-threatening context of the group that can be generalized to family relationships. For

instance, the experiences of active listening, sharing, collaborating, and reinforcing one

another’s ideas and behaviors serve as valuable practice for forms of interaction that will

be critical for improving the parent-child relationship.

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Given these examples of the different mechanisms by which individuals’

connectedness is expected to matter for participant outcomes, age group will be tested as

a moderator in analyses relating group process to intervention outcomes. For instance,

with universality and a support network as primary factors in the parent group, we may

find that “outdegree” – or the extent to which participants relate to or feel understood by

their group-mates – will be especially strong predictors of outcomes among parents.

In addition, several studies have examined the role of group size in shaping group

outcomes, with mixed results depending on the outcome under consideration. For

instance, group productivity and creativity both demonstrate a curvilinear relationship

with group size, such that results are best from moderately sized groups. Conformity to

group norms and member satisfaction, on the other hand, show linear relationships with

group size: the larger the group, the more conformity and the less member satisfaction is

observed. The relation each of these processes (e.g. group productivity, participant

satisfaction) to group size may be relevant in the context of psycho-education groups.

More direct discussions of group size within intervention and treatment groups has also

been mixed: it has been noted that small groups often leave participants feeling

“awkward”, while larger groups allow fewer attention to and contributions from each

individual member of the group (Peterson, 1979). In terms of the network statistics

discussed in the present study, group size risks playing a confounding role: for instance,

group size enters directly into calculations of density and standardized indices of

centrality. At this point, it remains unclear whether use of group sizes in the

“denominator” and the values that result map on directly to the actual experiential

differences between being a member of a small versus a large group. With a wide range

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of group sizes in the present study, there is a concern that group size differences would be

too strong of a factor driving observed effects. Thus, in the present study, a dichotomous

indicator was created to distinguish “small groups” (i.e., those below the mean group size

of 8) from “large groups” (those at or above the mean group size), and was used as a

moderator in some of our analyses.

The Present Study

The proposed research will address two aims. The first aim is to examine

concurrent relations between group dynamics and participants’ weekly progress.

Specifically, I will examine: (a) Between-group differences in group structure: Are

particular group characteristics associated with greater/ fewer program benefits to the

members of those groups?; and (b) Between-person differences in position: Are the ways

in which an individual relates to his or her group-mates associated with greater/ fewer

program benefits to that individual? The second aim is to examine whether individual

and group levels of connectedness and their trajectories of change are associated with

pre- to post-intervention improvement in targeted outcomes (e.g., parent-child relations

and youths’ self-beliefs). In other words, are the average level of connectedness and the

amount of change across sessions in social dynamics associated with between-person and

between-group differences in program benefits accrued to members?

To address these aims, logic and tools from SNA and IEV are integrated and

applied to the collection and analysis of group process data within a group-based

prevention setting. Data are collected from groups of early adolescents and their parents

enrolled in the Strengthening Families Program (SFP). Social network indices of intra-

group relationships and indices of participant progress are collected from participants

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immediately following each session. Social network statistics (e.g., group density,

individual centrality) are computed, and IEV methods are used to obtain individual- and

group-specific average levels, slopes, and residual scores. These network-based indices

are then used to examine group process in relation to weekly participant progress and

post-intervention outcomes specifically targeted by the intervention (e.g., quality of the

parent-youth relationship, youths’ self-beliefs).

Methods

Overview of the Strengthening Families Program and the Ongoing MSFP

Efficacy Trial. SFP is a universal, evidence-based prevention program for parents and

youth ages 10-14. Seven weekly sessions are delivered to groups of up to 10 families.

Each session begins with a one-hour parenting skills course and an adolescence life skills

course (taught separately to parents and teens), and ends with a one-hour family skills

training course for parents and youth together. The ongoing efficacy trial from which data

were collected is testing a mindfulness-based adaptation of SFP (MSFP; see Coatsworth,

Duncan, Greenberg, & Nix, 2010). Because the group process survey was only included

in the MSFP efficacy trial during the Spring of 2011, analyses for the present study are

restricted to families from the Spring 2011 cohort.

Study Design Overview for the Present Study. Participants complete pre- and post-

intervention surveys that assess specific outcomes targeted by the intervention (in the

present study, we focus on parent-youth relationships and communication, and youths’

mental health, self-beliefs, and attitudes toward substance use). In addition, weekly

measures – administered to participants immediately following each session – target the

network of ties among group members and two indices of participant “progress”.

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Participants & Procedures

Data were collected from all participants (early adolescents and their parents) in

either of the two experimental conditions of the MSFP efficacy trial during Spring 2011.

When participants consented to participate in the efficacy trial, they consented to

completing surveys administered at the weekly sessions. Thus, data were collected from

participants of all 20 groups in the Spring 2011 cohort (10 parent groups, 10 youth

groups), for a total of 105 parents and 86 youth (total N = 191) from three counties in

Central Pennsylvania. Parent groups included one or both parents from each family, and

the number of parents present in a given week ranged in size from three to 18 (M = 7.94);

youth groups included a target child and some siblings in the appropriate age range (10-

14), ranging in size from two to 19 (M = 7.05). Representative of the population served

by the targeted school districts, the majority of the sample is white (80%).

Data collection on the group process survey began during either the first or

second of the seven weekly sessions. Group facilitators administered the brief paper and

pencil surveys (less than two minutes) each week immediately following the separate

parent and child sessions. Pre- and post-intervention surveys were completed as a part of

the original MSFP efficacy trial, via paper and pencil surveys mailed to participants’

homes.

Measures

Process Measures. To create indices of group structure and individual positions,

we need a “map” of each group’s network of relationships (i.e., who is connected to

whom). Thus, consistent with standard social network assessment procedures, process

items in the weekly surveys prompt participants to report on their current relations to

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group-mates. Survey items selected for the present study drew upon and adapted themes

from well-validated rating scales commonly used to assess group climate in prevention

and treatment settings (e.g., Macgowan, 2000; MacKenzie, 1983). Original scales from

which items were adapted ask participants or facilitators to report on their impressions of

the group as a whole. To adapt the items into a network format, items were reworded to

prompt respondents to report on their own social ties, consistent with standard

“sociometric” or social network data collection procedures (e.g., Bukowski & Cillessen,

1998; Coie, Dodge, & Coppotelli, 1982). Thus, the item “The members liked and cared

about each other” (MacKenzie, 1983) was reworded as “I like and care about ___”, with

the roster of group members as the response option set. Participants may select as many

or as few members as they see fit.

Item content was selected to tap into two broad domains of intra-group relations:

1) positive emotional connections in the form of caring, acceptance, and understanding,

and 2) negative relationships or interactions in the form of dismissiveness, distaste, and

rejection. Both parent and youth surveys included the caring item noted above: “I like

and care about__”. In addition, parent surveys included the following two network items:

“I felt like ___ understood my experiences and feelings today”, representing positive

emotional connections, or acceptance; and “___ judged or disapproved of my

experiences and feelings today”, representing negative relationships, or distaste. In place

of the latter two parent survey items, the youth questionnaire included the following two

network items: “I got along well with ___ today” and “I did not get along so well with

___ today” (in order to draw upon similar themes of acceptance and distaste, but in

language thought to be more consistent with both the reading level and experiences of

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youth in their groups). Network statistics computed from the caring, acceptance, and

distaste items are the “process” variables to be examined in the present study. See

Appendices A and B for samples of the youth and parent weekly surveys.

Weekly Participant Progress Measures. Participants also reported weekly on two

“progress” indices. The SFP preventive intervention is primarily aimed at empowering

parents and children with the skills needed to handle the challenges of adolescence (e.g.,

problem-solving and coping skills). Thus, self-efficacy was selected as a broad indicator

of “progress”. Parenting self-efficacy was measured among parents each week as: “Right

now, I feel confident that my parenting will help my child through adolescence” (Gibaud-

Wallston & Wandersman, 1978), and general self-efficacy was measured among youth as:

“I feel like I can handle whatever comes my way” (Schwarzer & Jerusalem, 1995). In

addition, broader perceptions of session value were measured among parents as: “This

week’s session was helpful to me” and among youth with two items (the mean of which

will be employed in analyses): “What we did and talked about tonight will be useful to

me”, and “I enjoyed being at tonight’s session”. Participants rated their agreement with

each statement on a scale of 1 to 7, ranging from “strongly disagree” to “strongly agree”

(see Appendices A and B).

Pre and post-intervention outcome measures. Post-intervention outcomes

examined in the present study include several of the subscales from the larger assessment

battery used in evaluations of SFP efficacy (Redmond, Spoth, Shin, & Lepper, 1999).

Items on the affective quality of the parent-youth relationship (Redmond et al., 1999;

Spoth, Redmond, & Shin, 1998) assess both parent and youth perceptions. Parents

indicated on a scale of 1 to 5 (ranging from “Almost always” to “Almost never”, or

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“Strongly agree” to “Strongly disagree”) their agreement with 12 items tapping into their

behavioral expression of affect in their relationship to their child (e.g., “During the past

month, when you and your child have spent time talking or doing things together, how

often did you act loving and affectionate toward him/ her?”), and youth surveys included

7 items prompting youth to report on parents’ expressions of positive affect and negative

affect (e.g., “How often did your mom act lovingly and affectionate toward you?”).

Parents and youth also indicated their agreement on a scale of 1 to 5 (“Strongly Agree” to

“Strongly Disagree”) with items about parents’ communication of substance-related

rules and consequences, such as: “I have clear and specific rules about my child’s

association with peers who use alcohol” (Spoth et al., 1998). Youth surveys include a

series of items prompting participants to self-report on their psychological health and

well-being, such as: “I feel lonely” (with response options ranging from “Not true” to

“Very true or often true”), as well as (Huebner, 1991), as well as their self-esteem,

confidence in refusal skills, and beliefs about the future with items such as: “I think that

my plans for the future will progress in the best possible way” (rated on a scale of 1 to 5,

ranging from “completely disagree” to “completely agree”) (Sharp, Coatsworth, Darling,

Cumsille, & Ranieri, 2007). Lastly, youths’ substance use attitudes (Spoth et al., 1998)

were assessed with a series of items asking youth to indicate how wrong it is for

somebody their age to engage in each of several forms of substance use, on a scale from 1

to 4 (“Not at all wrong” to “Very wrong”), such as: “Drink beer, wine, wine coolers, or

liquor” (Spoth et al., 1998). The scales discussed here have been examined in several

studies evaluating the efficacy of SFP, which have established the measures’ sound

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psychometric properties (Coatsworth et al., 2010; Redmond et al., 1999; Spoth et al.,

1998).

Preliminary data analytic steps

Step 1. First, social network statistics (e.g., centrality, density) were computed

from participant responses to each of the network-based survey items (i.e., caring,

acceptance, and distaste) in order to quantify salient features of the SFP group process.

Group-level social network statistics computed from the data include the density and

centralization of intra-group relations for each group at each occasion. Individual-level

social network statistics computed include indices of how well-embedded individuals

were within their groups: the degree to which individuals were a source of connectedness

for their group-mates was represented by their indegree centrality (i.e., number of

nominations received), and the degree to which individuals felt connected was

represented by their outdegree centrality (number of nominations made or “sent”).

Outdegree and indegree centrality scores on the distaste item represented the extent to

which individuals felt discomfort and were a source of discomfort. Individual-level

reciprocity was computed as the number of reciprocal positive relationships individuals

experienced (reciprocity of distaste relationships were too infrequent to be used in the

present analyses). The individual-level scores were standardized by the dividing the raw

number of nominations sent or received by the total possible number of nominations (i.e.,

number of participants present – 1).

Step 2. Next, growth curve models were fit to each individual and group – with

time centered at the fourth or “middle” session – in order to extract individual-specific

and group-specific intercepts (representing each individual’s or group’s “average level”),

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linear slopes, and residuals around those slopes on each of the network indices described

above (see Molloy, 2012a). Group-specific intercepts derived from these growth curves

represent each group’s average level of density and centralization, and person-specific

intercepts represent each individual’s average level of feeling connected or discomfort

(i.e., outdegree centrality), being a source of connectedness or discomfort (i.e., indegree

centrality), and reciprocity. Similarly, group- and individual-specific slopes represent

group- and person-level trajectories of change in group structure and individual

connectedness. Lastly, residuals around the growth curves represent occasion-level

deviation or session-specific scores: in other words, “de-trended” within-group and

within-person variation in group structure and individual connectedness (Hoffman &

Stawski, 2009). In order to prevent collinearity between caring- and acceptance-based

network indices (e.g., r = .81 between average levels of caring and understanding

outdegree) from masking the effects of both in the analyses below, the caring and

understanding values of each index were averaged to test the role of “positive

connections”.

Results

Analytic Plan. To address aim 1, individual- and group-level average levels and

residuals were used in multilevel models to examine covariation between group process

(i.e., the individual-and group-level network indices) and participant weekly reports of

self-efficacy and session value. In addition, individual-specific slopes in network indices

were correlated with individual-specific slopes in session value and self-efficacy in order

to examine corresponding growth across time in process and progress. To address the

second aim, the individual-and group-specific average levels and slopes in group process

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were used in regression analyses to test whether post-intervention outcomes may be

predicted by differences in average levels and trajectories of change in individual

connectedness and group structure.

Aim 1: Covariation between group process and weekly “progress”

The residuals (representing within-person and within-group variation across time)

and intercepts (representing between-person and between-group differences in average

level) were used to test the covariation between group dynamics and weekly progress. To

account for dependencies in the data (i.e., nesting of occasions within persons within

groups), these scores were tested as independent variables predicting weekly progress

scores in a series of three-level multilevel models (MLM). Week number (i.e., time,

centered at the “middle” or 4th session) and session-specific predictors (i.e., residuals)

represent Level 1, testing whether week-to-week variation in individual connectedness

and group structure coincide with weekly reports of progress. Between-person predictors

(i.e., individuals’ average level of connectedness and age group) represent level 2, to test

whether stable between-person differences in connectedness help to account for between-

person differences in progress. Finally, between-group predictors (i.e., group-level

average structure scores) represent level 3, testing the role of between-group differences

in structure. Experimental condition and the dichotomous indicator of group size were

also added as control variables, and group size was tested as a potential moderator of

session-specific, person-level, and group-level network statistics. In these models,

intercept and time were allowed to vary across individuals and across groups (under the

multivariate normal assumptions). However, random effects at the group-level were

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ultimately removed, because models did not converge when they were included, likely

due to the small number of groups.

Given the large number of parameters to be tested, each model was tested in a

series of model-building steps. In addition to the control variables noted above, between-

group differences in density and between-person differences in centrality were entered

first. Within-group variation in density and within-person variation in centrality were

entered second. Between-person reciprocity and between-group centralization were

entered third; and within-person reciprocity and within-group centralization were entered

fourth. At each of these steps, interactions of the corresponding terms with group size

were tested. Non-significant interactions and then main effects were trimmed one by one

before moving on to the next step. However, group-level centralization is a “higher-

order” term that is largely dependent on a group’s density (e.g., in very dense groups,

there is less “room” for unequal distribution), and individual-level reciprocity is a higher-

order term dependent on centrality scores (e.g., the number of nominations an individual

makes limits the number of ties that can be reciprocated). Thus, the “first-order” terms

were not trimmed from the model until and unless the corresponding higher-order terms

had already been trimmed.

Parameter estimates for final models are presented in Table 4.1. As hypothesized,

results of the final model (Table 4.1, Model 1d) suggest that, on average, participants in

groups with a higher average density of positive connections tended to report higher self-

efficacy each week (γ = 2.34, p < .05). Also consistent with hypotheses, individuals who

were, on average, a greater source of connectedness (i.e., indegree of positive

connections) were also marginally more likely to report higher self-efficacy (γ = 1.04, p <

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.10), and a significant interaction of average indegree with group size suggests that this

tendency is strongest among large groups (γ = 1.44, p < .05). See Figure 4.1 for a plot of

this interaction. As expected, results also suggest evidence of these relations at the

within-person level: participants were marginally more likely to report higher self-

efficacy on weeks when their group was more densely connected than usual (γ = 0.98, p <

.10). Contrary to expectations, participants were also significantly more likely to report

higher self-efficacy on weeks when their group was more centralized than usual (γ =

1.43, p < .05).

Results of the multilevel model predicting participants’ reports of “session value”

suggest similar patterns (see Table 4.2, Model 2d). Consistent with hypotheses, there

was a trend toward participants in more densely connected groups perceiving sessions as

more useful (γ = 1.36, p < .10). Moreover, participants who felt more connected (i.e.,

higher average outdegree of positive connections) were marginally more likely to rate

session value more highly (γ = 0.64, p < .10). A significant interaction of group size with

average indegree suggests that within large groups, participants who were greater sources

of connectedness were significantly more likely to view the sessions as helpful (γ = 1.52,

p < .05). See Figure 4.1 for a plot of this interaction. Lastly, participants who experienced

greater reciprocity than usual in a given session experienced that session as more

valuable (γ = 0.88, p < .05). Once again, occasion-specific centralization was in the

opposite direction of expectations: in weeks when groups were more centralized,

members gave the session marginally higher ratings (γ = 0.87, p < .10).

In the next step, I examined whether growth in individuals’ connectedness across

sessions corresponded to the growth they experienced in self-efficacy and perceptions of

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session value. The individually-fitted growth curves for indegree, outdegree, and

reciprocity on each relationship type were correlated with individually-fitted growth

curves for session value and self-efficacy (see Table 4.3). As expected, growth in source

of connectedness (i.e., indegree of positive connections) significantly correlated with

session value (r = .20, p < .01), as did growth in reciprocity (r = .24, p < .01). Although

self-efficacy correlated in the expected direction with these two measures, the

correlations were not significant. In the analyses prior to and following this step, the

caring and acceptance indices were aggregated into “positive connectedness” values to

avoid collinearity. In this section, because each network index is examined in a separate

correlation with each progress measure, we also tested the correlations of caring and

acceptance separately with the two progress indices. Results suggest that the strongest

relations between progress and connectedness were with the caring relations: growth in

the number of group-mates caring about a given individual (i.e., caring indegree)

significantly related to growth in both perceived session value (r = .24, p < .001) and self-

efficacy (r = .16, p < .05), and growth in reciprocal caring relationships significantly

related to growth in perceived session value (r = .25, p < .01), with the correlation to self-

efficacy also showing a trend in the expected direction (r = .13, p < .10). Growth in

reciprocal acceptance relationships significantly related to growth in perceived session

value (r = .16, p < .05), but none of the other acceptance slopes showed significant

relations to the progress slope measures.

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Aim 2: Predicting post-intervention outcomes from average levels and change in group

process

In this aim, I examined indices of between-person and between-group differences

in connectedness across time in relation to participants’ pre- to post-intervention

improvement on two broad categories of outcomes. In the first series of models, several

indices of improvement in the mother-youth relationship (as reported by both mother and

youth) serve as the dependent variables. In the second series of models, improvement in

several youth outcomes targeted by the intervention, including mental health outcomes,

self-beliefs, and attitudes toward substance use, serve as the dependent variables. For

each set of outcomes, I conducted a linear regression analysis4 in which the predictors

included average levels and trajectories of change in individual connectedness and group

structure. Models controlled for corresponding pre-intervention scores, as well as average

group size, slope in group size, number of sessions attended, and age group (parent

versus youth). Age group is also tested as a moderator of each network predictor.

In general, results across models supported hypotheses. The first set of models

predict quality of the mother-youth relationship, with outcomes tested at the individual

level; in other words, youth connectedness scores predict youth reports of the mother-

youth relationship, and mother connectedness scores predict mother reports of the

relationship. All models in this set control for age group and also test moderation of each

relation by age group. In the second set of models, youth outcomes targeted by the

intervention serve as the dependent variables; thus, only youths’ connectedness scores are

entered into the models; no interactions with age are needed.

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!4 Aim 2 hypotheses were initially tested with multilevel models using SAS proc mixed, with individuals (level 1) nested within groups (level 2). However, due to the small number of groups, the models could not converge. Thus, we switched to using simpler linear regressions.

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I will first present patterns of results across models predicting pre- to post-

intervention change in parent-youth relationships (see Table 4.4). At the group-level, the

only significant effect was for density of distaste relationships: consistent with

expectations, groups with a greater average density of distaste relationships experienced

less improvement in conflict within the mother-youth relationship (B = 5.38, p < .05; see

Model 5). At the individual level, several significant relations emerged. As expected,

mothers and youth who were a greater average source of connectedness (i.e., indegree)

reported greater reductions in the negative affective quality of the mother-youth

relationship (B = -1.07, p < .05; Table 4.4, Model 1). A significant interaction of average

source of connectedness (i.e., indegree) with age group (B = -1.18, p < .05) suggests that

mothers who were a greater source of connectedness experienced greater gains in their

communication of substance use rules with their child, while a marginally significant

interaction of slope with age group suggests that growth in source of connectedness (i.e.,

indegree slope) was marginally more associated with youths’ reports of gains in

communication (B = 4.60, p < .10; see Model 4; see Figure 4.2 for plots of these

interactions). In contrast, mothers and youth who became an increasing source of

discomfort (i.e., disapproval indegree) for group-mates reported increases in negative

affective quality (B = 10.74, p < .01) and conflict (B = 10.86, p < .01) within the mother-

youth relationship. Contrary to expectations, individuals who were a greater average

source of discomfort for group members reported greater reductions in conflict within the

mother-youth relationship (B = -7.56, p < .01), and those who became an increasing

source of discomfort were marginally more likely to report an increase in mother-youth

shared activities (B = 4.08, p < .10; see Model 3). Lastly, individuals who felt

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increasingly connected across sessions were marginally more likely to experience gains

in mother-youth shared activities (B = .97, p < .10), and a marginally significant

interaction (B = 3.93, p < .10; see Figure 4.2 for a plot of the interaction) suggests that

mothers who felt increasingly connected reported greater reductions in the negative

affective quality of relationships with their children.

Next, I move on to present models predicting youth outcomes targeted by the

intervention from youths’ connectedness scores (see Table 4.5). Findings at the group-

level were consistent with expectations: average density of caring and acceptance

relationships significantly predicted decreases in internalizing problem behaviors (B = -

9.17, p < .05; Model 6) and increases in youths’ self-esteem (B = 1.18, p < .05; Model 8).

At the individual-level, findings were somewhat mixed. Consistent with expectations,

individuals who felt increasingly connected (i.e., positive indegree slope) experienced a

greater boost in confidence in refusal skills (B = 4.76, p < .10; Model 10) and reported

less favorable attitudes toward substance use (B = -2.04, p < .10; Model 11). Also

consistent with expectations, youth who were a greater average source of discomfort

were less likely to experience gains in self-esteem (B = -2.45, p < .01). Contrary to

expectations, however, being a greater average source of connectedness (i.e., indegree)

was a marginally significant predictor of increases in externalizing problem behaviors (B

= 9.62, p < .10; see Model 7); and feeling more connected, on average, predicted more

favorable attitudes toward substance use (B = .43, p < .10).

On the whole, findings of aim 2 provide support for hypotheses that both group-

level and individual-level indices of connectedness are predictive of longer-term

outcomes of the intervention. It appears that average levels and trajectories of change

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were predictive of outcomes. In terms of the mother-youth relationship, findings suggest

that parents’ levels and changes in connectedness were more relevant than those of youth.

Youths’ connectedness did, however, appear to have implications for mental health, self-

beliefs, and substance use attitudes.

Discussion

Despite a large theoretical literature and the inclusion of “group management

skills” in many facilitator-training manuals, group process and its role in facilitating

participant outcomes has received surprisingly little empirical attention. In the present

study, I examine group process within groups of parents and youth participating in a

parent training and substance use prevention program, the Strengthening Families

Program (SFP). An innovative integration of social network analysis and methods

sensitive to intraindividual variability is applied that addresses the limitations of past

research and can begin to fill the gaps in our understanding of group process. Overall,

findings from the present study provide support for hypotheses that groups that are more

cohesive and individuals who are more well-embedded in their group report better

weekly progress. In addition, findings suggest that between-group and between-person

differences in both “average” levels and trajectories of change in connectedness across

sessions have implications for intervention benefits. More broadly, results provide

support for the value of an integrated SNA and IEV approach to group process research.

This innovative approach provides precise, empirical quantifications of theoretical

processes, and provides useful insight into features of the group process that could not be

examined with more traditional approaches.

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Aim 1: Covariation of group process and weekly progress

In the first aim, I explored the co-evolution of group process and participant

progress by examining whether these two develop together on a session by session basis:

in other words, do we see evidence that weekly group process facilitates weekly

participant progress? Findings provide support for this hypothesis at both the within- and

between- person and group levels. Specifically, present results suggest that groups that

are more densely connected on average demonstrate the expected benefits of group

cohesion, with members reporting greater self-efficacy and session value. Moreover,

individuals who felt more connected (i.e., outdegree of positive ties) reported greater

session value, and those that were greater sources of connectedness for their group-mates

(i.e., indegree of positive ties) experienced both greater session value and higher self-

efficacy.

Interestingly, the benefits of being a source of connectedness (i.e., higher

indegree) were more pronounced within large groups than small groups. Research on the

moderating role of group size in intervention effectiveness is somewhat mixed, and will

need to be examined further to clarify these findings. However, the difference in

program benefits between more central members and more peripheral members is

consistent with seminal work by Bales and colleagues (Bales, Strodtbeck, Mills, &

Roseborough, 1951). In their study, Bales and colleagues observed that the difference in

session involvement between the most involved member and other members was most

pronounced in large groups, whereas in smaller groups, the drop-off from the “most

involved” member to other members was subtler. In addition, the most central member

was found to provide the most advice to group members and to be the most frequent

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recipient of statements and questions from group-mates, taking on almost a leadership

role in the group, while other members more frequently posed questions or requested

information (Bales et al., 1951). With the limited amount of time allotted for each

session of SFP – especially after accounting for the amount of non-interaction time

during facilitator presentations – only a limited number of group members have the

opportunity to contribute to a given session. If session benefits are facilitated by active

engagement, then some members of large groups may “miss out” on the opportunity to

benefit from engagement, due to time constraints or anxiety about disclosing personal

information in front of more “intimidating” large groups. This is just one potential

explanation as to why we might see a wider range of individual differences within large

groups than within small groups.

On a session-by-session level (i.e., within-group/ person variation), we see some

similar patterns. Members report greater self-efficacy on weeks when their groups are

more densely connected than usual, and individuals perceive sessions as more valuable

on weeks when they experience greater reciprocity in their caring and acceptance

relationships than usual. Contrary to expectations, participants in this study also reported

greater session value and self-efficacy on weeks when their groups were more

centralized. Past social network theory and literature led us to hypothesize that more

evenly distributed ties (i.e., lower centralization) facilitate better outcomes for

participants. Perhaps centralization holds a different meaning within SFP groups than it

does in other contexts. Rather than indicating an imbalance of “power” as centralization

often implies (e.g., social status hierarchies in classrooms), centralization in the context

of the SFP group may result from a few participants providing particularly useful

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contributions to the larger discussions, or receiving positive attention for their

contributions. This may reflect a tendency for the group to benefit more from sessions in

which one or a few participants have the opportunity to be the focus of discussion, rather

than the group facilitators.

Beyond random fluctuations around individuals’ or groups’ average scores, it was

also expected that participants might become increasingly connected across sessions as

they encounter opportunities to interact with and learn about their group-mates. Similarly,

it was to be expected that participants might experience a growing sense of self-efficacy –

and come to value the program and the sessions as increasingly beneficial – as they

progressed through the intervention. Thus, another question we wished to explore was

whether the expected gains across time in connectedness might help to facilitate gains in

participant progress. To address this question, we examined correlations between

individual-specific slopes in connectedness and slopes in the two progress indices. As

hypothesized, the present study suggests that gains in connectedness over the course of

the SFP intervention correspond with gains in perceptions of session value and, to some

extent, gains in self-efficacy. As participants became increasingly well-liked among their

group-mates and experienced an increasing amount of reciprocal relationships, they also

tended to experience gains in perceived session value and self-efficacy.

In sum, the findings of aim 1 provide support for our hypotheses that participants’

connectedness to group-mates and the cohesiveness of the groups help to facilitate greater

week to week “progress” among participants. Groups that were more densely connected,

participants who were personally more well-connected within their groups, and sessions

in which group or participant connectedness was higher than usual, each uniquely

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predicted higher weekly reports of progress. Overall, results of aim 1 provide valuable

evidence that group process – and in particular, the features of group process that an

integrated SNA and IEV approach allowed us to measure – has important implications

for participants’ experiences in the SFP intervention.

Aim 2: Predicting post-intervention outcomes from average levels and change in group

process

In the second aim, I assessed the extent to which “overall” group process indices

had implications for pre- to post-intervention changes in a subset of outcomes targeted by

the program. Specifically, group- and individual-level averages and trajectories of

change were tested as predictors of improvement in the quality of the mother-youth

relationship and in youths’ mental health, self-beliefs, and substance use attitudes. In

addition, age group was tested as a moderator of the relations between group process and

improvement in the mother-youth relationship. In general, findings supported study

hypotheses: groups with more positive connections and less distaste among members

tended to experience greater overall benefits from the SFP intervention. Similarly,

individuals who felt and were greater sources of connectedness, those who were not

sources of discomfort, and who experienced gains in connectedness and a decline in

negative connections across sessions tended to experience pre- to post-intervention

changes in the intended direction. Interactions with age group suggested that, in general,

intra-group relations in parent groups may be more relevant to the quality of the mother-

youth relationship than are relations in youth groups.

Because a large number of models were tested, here I will simply discuss patterns

of findings. At the group level, several important findings emerged that were consistent

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with expectations. A group’s overall level of “cohesion” (i.e., average density of positive

connections) significantly predicted reductions in youths’ internalizing problem

behaviors and improvement in youths’ self-esteem. In addition, the overall prevalence of

negativity among members (i.e., average density of distaste relationships) appeared to

have some detrimental effects: members of groups with a higher prevalence of negativity

were less likely to experience improvements in recurring conflict within the mother-

youth relationship.

At the individual level, findings across models provide evidence that a

participant’s contributions to the group can have important implications for the extent to

which participants benefit from the intervention. Participants who were greater sources of

connectedness for their group-mates experienced greater declines in the negative

affective quality of the mother-youth relationship and greater improvements in their

communication about substance use rules. Moreover, mothers who were a greater source

of connectedness on average (i.e., higher average indegree of positive connections), and

youth who increasingly became a source of connectedness across sessions (i.e., upward

slope in indegree of positive connections), each reported increases in mother-child shared

activities. Conversely, participants who increasingly became a source of discomfort (i.e.,

upward slope in indegree of negative connections) experienced an increase in negative

affective quality and conflict within the mother-youth relationship; and youth who were a

greater average source of discomfort for group-mates experienced a decrease in self-

esteem.

Also at the individual level, findings from the present analyses provide evidence

that feeling connected may be an important predictor of overall intervention benefits. In

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terms of the parent-youth relationship, participants who felt increasingly connected

across sessions (i.e., upward slope in outdegree of positive connections) reported greater

gains in mother-youth shared activities, and parents who felt increasingly connected

reported improvements in the affective quality of their relationships with their children.

Similarly, youth who felt increasingly connected over time experienced greater boosts in

self-esteem and confidence in their ability to refuse substances when offered.

Surprisingly, feelings of discomfort were not significantly predictive of participant

outcomes in the present study. This may be explained by past research suggesting that

the negative behaviors within relationships may only be detrimental when they are not

balanced by positive behaviors (Parker & Gottman, 1989).

In a few cases, age group was a significant moderator of the relations between

group process and participant outcomes, providing some indication that group process –

and its role in facilitating outcomes – may operate differently in parent versus youth

groups. As described earlier, the theoretical and qualitative literature on group process in

parent training groups suggests that the sense of universality that the group provides to

struggling parents, the sense of altruism that parents experience by providing one another

with suggestions and support, and the opportunities that parents have to test out effective

interpersonal skills within the group that are directly applicable to interactions with their

children, are seen as some of the key theoretical benefits of the group format for parent

training. Given these hypothesized processes, and the fact that improved parent-youth

relationships are the primary goal of parent training groups, it is not surprising that

mothers showed a more positive relation between group process and improvement in the

parent-youth relationship than did youth, for three of the four significant interactions.

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Although the evidence in general supported our hypotheses, a few findings

emerged that were contrary to expectations. For instance, participants who became an

increasing source of discomfort among members reported an increase in mother-youth

activities. The explanation for this is unclear, but one possibility is that participants who

became an increasing source of discomfort were members who began to feel and/ or

express a growing sense of superiority or being “better off” than group-mates. This is

consistent with a study by Buunk and colleagues (Buunk, Cohen-Schotanus, & van Nek,

2007) demonstrating a tendency for participants to make social comparisons within social

skills training groups. It was observed that some participants made downward

comparisons (i.e., compared themselves to somebody who was “worse off”), in order to

boost their own sense of achievement or competence. Perhaps in some cases, these

feelings of being “better off” reflected real improvements (e.g., in shared mother-youth

activities), but were expressed to group-mates in ways that came across as judgmental.

Similarly, mothers who were a greater average source of discomfort reported

greater declines in recurring mother-youth conflict. However, considered together with

the positive relation between slope in source of discomfort and change in recurring

conflict, this relation may be quite logical. Not all mothers will have reported recurring

conflict with their child prior to the intervention. A likely possibility is that the “type” of

mothers who had a history of recurring conflict with their children were more likely to be

the “type” of group members to have conflict with or evoke negative feelings from

group-mates in session. Yet it would be those with the greater history of conflict – and

greater negative impact on group-mates – that stand to make the most gains in these

areas. In other words, participants needed to have experienced some negativity in the

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group and at home (i.e., be a higher average source of discomfort) in order to make

improvements in these areas (i.e., see declines in source of discomfort and conflict).

Two other effects in the unexpected direction were increased externalizing

problem behaviors among youth who were a greater source of connectedness, and more

favorable attitudes toward substance use among youth who felt more connected to group-

mates. One possibility is that some of these youth came from schools in which there is a

positive association between “popularity” and some degree of delinquency and/ or

substance use; thus, their greater prominence or “embeddedness” in the SFP groups may

be reflective of their greater popularity in school, where their attitudes and engagement in

these behaviors are being rewarded by their peers (e.g., Ludden, 2012). Another

explanation that should be considered is “deviancy training”; in other words, the

possibility that these unexpected associations are driven by the grouping of several youth

with favorable attitudes toward substance use and delinquent behaviors into the same

groups. In this case, there is a risk that youth are receiving positive attention for their

counter-productive behaviors or attitudes within the group. For instance, antisocial youth

may reinforce or “egg on” each other’s disruptive behaviors in the group, or forge a new

friendship in which they provide each other opportunities for substance use (Dishion &

Piehler, 2009). These findings are by no means conclusive, however, and certainly more

research would be needed on a larger sample of SFP participants to assess whether these

dynamics may be occurring. Given that the SFP intervention is universal, and that

sessions are very structured, the risk of “deviancy training” should be low.

In sum, findings from aim 2 analyses provide valuable support for the hypothesis

that group process – at both the group and individual level – may have important

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implications for an intervention’s effectiveness and should more often be considered in

studies of implementation quality. Moreover, findings provided evidence of both average

level and development of connections (i.e., slope) as predictive of participant outcomes,

highlighting the importance of tracking changes in the group process across sessions.

More broadly, these results again suggest the value of network-based indices for

quantifying features of the group process, and of methods sensitive to IEV for examining

the relations of these indices to participant outcomes.

Contributions & Implications Moving Forward

Among the limited number of studies that have assessed group process in

intervention settings, the majority examined it at the level of the “group as a whole”.

Moreover, the majority of past studies have only assessed group process at one or two

occasions. The present study extends these findings in several important ways. First, the

assessment approach applied here allows us to more precisely quantify group-level

features but also identify differences between members in their degree of connectedness.

In addition, the analytic approach applied in the present study allowed us to examine the

role of two conceptually distinct individual-level processes. The first could be

considered indices of the role that participants played in the group, or what each

participant contributed to the group: “indegree centrality” quantified the extent to which

participants were sources of connectedness or sources of discomfort for their group-

mates. The second could be considered indices of the gains versus detriments that

participants perceived from their group-mates: “outdegree centrality” quantified the

extent to which participants felt connected or felt discomfort with group-mates. Findings

provide valuable preliminary evidence that both of these individual-level processes, as

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well as features of the group as a whole, may have important implications for the extent

to which participants benefit from an intervention.

Second, measuring group process at each weekly session provided us with a more

accurate picture of groups’ and individuals’ “overall” or average connectedness, but also

allowed us to assess between-group and between-person differences in trajectories of

change. Given evidence that many parents struggling with child-rearing experience a

sense of isolation and lack a support network, the potential for parents to build

connections within the program (i.e., experience an increase in connectedness over the

course of the program) is an important strength of SFP and other parent training programs

(Sharry, 1999). With the unique methodological approach applied in this study, we were

able to directly test whether the experience of building connections across sessions –

after controlling for the individual’s overall level of connectedness – uniquely contributed

to participant outcomes.

The analytic approach demonstrated in the present study opens the door to a

number of future studies with great potential value to the field. For instance, future

research could examine the degree to which participants’ network indices of

embeddedness correspond to group leader perceptions of the roles that different members

are playing in the group: in other words, do groups function more smoothly or effectively

when the group leaders’ perceptions line up closely with how participants view their own

roles (i.e., outdegree) and are perceived by group-mates (i.e., indegree)? In addition, two

logical next steps for future research will be a) to examine whether the group processes

identified in the present study would look similar within other group-based intervention

settings, and b) to examine how specific actions by group facilitators may influence

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session-level group process or its trajectories of change – for instance, are there particular

strategies that group leaders should emphasize to facilitate the social dynamics found to

be most beneficial for participant outcomes? These are just a couple examples of the

important research questions that can and should be addressed with the analytic approach

demonstrated in this paper. The more that we can learn about of the social dynamics in

group-based prevention programs – a process for which the present approach may be of

great use – the more group facilitators will be able to actively target specific process

dynamics in designing, evaluating, and improving upon group-based interventions.

Strengths & Limitations

Findings from the present study provide empirical evidence to support past

theoretical and qualitative work highlighting the importance of group process for

participant progress and outcomes. Moreover, the present findings suggest that SNA and

IEV indices provide unique insight into group process that could not have been achieved

through traditional measurement approaches. Using network indices to quantify features

of the group process, we are able to distinguish participants who are a source of

connectedness or negativity from those perceiving positivity or discomfort, assess the

degree and implications of reciprocity in participant interactions and relationships, and

characterize the extent to which a group is centralized – features of the group process that

could not be detected by traditional approaches. Another important strength of this

approach is method source variance: the majority of indices – e.g., being a source of

connectedness (i.e., indegree), reciprocity of relationships, and group-level centralization

– are not based solely on participant self-reports. In other words, individuals who are

greater sources of connectedness for their group-mates are identified as such by their

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group-mates, suggesting that the observed relations between indegree and participant

outcomes are not simply the product of same reporter biases.

Moreover, few studies have yet considered the implications of changes across

sessions in group process for participant outcomes. Qualitative reports of group process

have noted session-to-session changes in participant interactions, and a tendency for

group cohesion to increase across sessions and participant “negativity” or resistance to

decline across sessions (Barrera et al., 2002; Ettin et al., 1987). To our knowledge, the

present study is the first to systematically examine how these changes – or the between-

group and between-person differences in these changes – may account for variability in

prevention program outcomes. Findings from the present study suggest that change

processes are an important consideration for future work.

The SNA and IEV statistics applied in the present study are just a few examples

of the individual, structural, and change dynamics that these techniques allow us to

capture. For instance, studies with more statistical power could quantify the degree to

which there is “sub-grouping” within larger groups, and assess its implications for

members within and outside of those subgroups (see Molloy, 2012a for further

discussion). The wide range of options for quantifying group structure and individual

position serves as both a limitation and a strength of the present study. Because this

study was largely exploratory, it is possible that other indices not employed in the present

study would have mapped more closely onto the processes operating in SFP groups; more

research will be needed to identify the “best” indices for this context. Yet the range of

options also highlights the large “toolbox” that these approaches have to offer for

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precisely operationalizing and studying the social processes involved in group

interventions.

Concluding Remarks

Yet in order to expand our understanding of group processes, we need to increase

the number of studies that systematically examine the relations between group process

and participant outcomes, and we need to expand the analytic “toolbox” we use to

operationalize those processes. In the present study, it was demonstrated how two such

statistical tools can be used to open new windows into the “black box” of group delivery

formats. By matching hypothesized group process phenomena to assessment and analytic

methods that precisely operationalize those phenomena, we can improve our prevention

strategies and foster positive youth development.

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Table 4.1: Multilevel models predicting weekly self-efficacy from between- and within-group/ person variation in network indices of group process

Model 1a: Between- Group/ Person Differences (i.e., Density & Centrality)

Model 1b: Within- Grp/ Person Variability in Connectedness (i.e., Density & Centrality)

Model 1c: Between-Grp/ Person Higher-Order Differences (i.e., Reciprocity & Centralization)

Model 1d (Final Model): Within-Grp/ Person Variability (i.e., Recip, Centralization)

Fixed Effects B SE B SE B SE B SE Intercept 5.76*** .11 5.75*** 0.12 5.66*** 0.12 5.67*** 0.12 Session Number .10*** .03 0.09*** 0.03 .09*** 0.03 0.10* 0.03 Group Size -.20+ .12 -0.19 0.13 -.12 .14 -0.04 0.15 Average Group-Level Structure Density 1.46* .66 1.46* 0.67 2.15* 1.02 2.34* 1.01 Reciprocity -1.68 1.12 -1.71 1.11 Centralization -0.61 1.83 -0.2 1.81

Group size X

Centralization -3.28+ 1.94 -2.84 1.93

Average Individual-Level Connectedness

Source of

Connectedness .61 .58 0.69 0.59 1.16+ 0.63 1.04+ 0.62

Grp Size X Source of

Connectedness 2.09*** .62 2.14*** 0.62 1.68* .68 1.44* 0.67 Within-Group Variability in Structure Density 0.34 0.57 0.38 .57 0.98+ 0.53 Centralization 1.43* 0.57 Within-Person Variability in Connectedness

Source of Connectedness (i.e.,

indegree) -0.02 0.37 0.02 .37 Random Effects

Between Person Intercept .77*** .11 .80*** .12 .79*** .12 .77*** 0.11 Session Number .04*** .01 .04*** .01 .04*** .01 .04*** 0.01 Residual Variance .88*** .06 .88*** .06 .88*** .06 .88*** 0.06

Note: ***p < .001, **p < .01, *p < .05, +p < .10

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Table 4.2: Multilevel models predicting weekly session value from between- and within-group/ person variation in network indices of group process

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Model 1a: Between- Group/ Person Differences (i.e., Density & Centrality)

Model 1b: Within- Group/ Person Variability in Connectedness (i.e., Density & Centrality)

Model 1c: Between-Group/ Person Higher-Order Differences (i.e., Reciprocity & Centralization)

Model 1d (Final Model): Within-Group/ Person Variability (i.e., Reciprocity, Centralization)

Fixed Effects B SE B SE B SE B SE Intercept 5.59*** .13 5.58*** .13 5.58*** .13 5.56*** 0.13 Control Variables Session Number .07** .02 .06* .02 .06* .02 .06* 0.02 Group Size -.13 .12 -.10 .12 -.10 .12 -0.06 0.12 Average Group-Level Structure of Positive Connections Density 1.33+ .75 1.39+ 0.76 1.39+ 0.76 1.36+ 0.76 Average Individual-Level Connectedness Source of Connectedness .21 .66 0.25 .67 0.25 .67 0.29 0.67 Felt Connected .60+ .36 0.61+ .37 0.61+ .37 0.64+ 0.37

Group size X Source of

Connectedness 1.63** .60 1.72** .61 1.72** .61 1.52* 0.62 Within-Group Variability in Structure of Positive Connections Density 0.73 .52 0.73 .52 0.85 0.57 Centralization 0.87+ 0.53 Within-Person Variability in Connectedness Source of Connectedness -.26 .34 -.26 .34 -0.71+ 0.41 Reciprocity 0.88* 0.44 Random Effects Between Person Intercept 1.11*** .15 1.15*** .15 1.15*** .15 1.16*** 0.16 Session Number .04*** .01 .04*** .01 .04*** .01 .04*** 0.01 Residual Variance .74*** .05 .73*** .05 .73*** .05 .73*** 0.05

***p < .001, **p < .01, *p < .05, +p < .10

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Table 4.3: Correlations of individual-specific growth trajectories of SNA indices to individual-specific growth in session value and self-efficacy

Session Value Self-Efficacy Positive Connections

Source of Connectedness (Indegree of Positive Connections) .20** .11

Felt Connected (Outdegree of Positive Connections) .10 .01

Reciprocity of Positive Connections .24** .12 Liking and Caring Relationships

Liked and Cared About (Caring Indegree) .24*** .16*

Like and Care About Others (Caring Outdegree) .07 -.05

Reciprocity of Caring Relationships .25** .13+ Acceptance Relationships

Source of Acceptance (Acceptance Indegree) .08 .02

Felt Accepted (Acceptance Outdegree) .11 .08

Reciprocity of Acceptance Relationships .16* .07 Distaste Relationships

Source of Discomfort (Indegree of Distaste Relationships) .10 -.03

Felt Discomfort (Outdegree of Distaste Relationships) .02 -.01

***p < .001, **p < .01, *p < .05, +p < .10

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Table 4.4: Regression analyses predicting pre- to post-intervention change in the quality of mother-youth relationship from between-person and between-group differences in average level and slope of network indices of process

Model 1: Predicting Negative Affective Quality of Mother-Youth Relationship

Model 2: Predicting Positive Affective Quality of Mother-Youth Relationship

Model 3: Predicting Mother-Youth Shared Activities

Model 4: Predicting parent communication about SU rules

Model 5: Reccurring Mother-Youth Conflict (Mom-Reported)

Predictors B SE B SE B SE B SE B SE

Intercept .12 .15 1.42*** .33 .54** .16 2.21*** .16 1.15*** .22 Control Variables

Baseline Score .88*** .09 .72*** .07 .73*** .08 .36*** .05 .27* .13 Age Grp (Parent or Youth) .36+ .19 .18 -.76*** -.76

Mean Group Size -.02 .03 -.04 .03 -.01 .02 -.03 .02 -.02 .02 Slope in Group Size .20 .43 -.61 .46 -.02 .25 -.34 .28 -.41 .27

Number of sessions attended .09* .04

Average Individual-Level Connectedness Source of Connectedness -1.07* .53 .17 .35

Age X Source of Connectedness -1.18* .52

Source of Discomfort -1.88 1.36 -.24 .78 -7.56** 2.37 Felt Connected -.10 .31 .01 .17 -.17 .19

Slope in Individual-Level Connectedness Source of Connectedness .43 1.18

Age X Source of Connectedness 4.60+ 2.44 Felt Connected .72 1.02 .97+ .58 -1.06 .72

Age X Felt Connected 3.93+ 2.15 -3.07* 1.51 Source of Discomfort 10.74** 3.93 4.08+ 2.18 10.86** 3.93

Average Group-Level Structure Density of Negative

Connections 5.38* 2.55 ***p < .001, **p < .01, *p < .05, +p < .10

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Table 4.5: Regression analyses predicting pre- to post-intervention change in youths’ mental health and targeted self-beliefs and attitudes from between-person and between-group differences in average level and slope of network indices of process

Model 6: Predicting Youths' Internalizing Behaviors

Model 7: Predicting Youths' Externalizing Behaviors

Model 8: Predicting Youths' Self-Esteem

Model 9: Predicting Youths' Beliefs about the Future

Model 10: Confidence in Refusal Skills

Model 11: Predicting youths' SU attitudes

Predictors B SE B SE B SE B SE B SE B SE

Intercept 1.20 1.07 2.87+ 1.44 .79* .32 1.37** .43 1.76** .60 .97 .68 Control variables

Baseline Score .78*** .12 .69*** .14 .78*** .12 .44** .15 .40* .15 .63** .23 Mean Group Size -.25 .28 .34 .33 <.01 .03 <.01 .03 -.03 .05 .02 .02

Slope in Group Size -12.28 7.51 -1.60+ .84 1.26 1.23 .16 .58 Number of sessions

attended -1.26* .56 .10+ .05 .12+ .07 .23+ .12 Average Individual-Level Connectedness

Source of Connectedness 9.62+ 5.08 Source of Discomfort -2.45** .79

Felt Connected .78* .38 -.39 .50 .45+ .24 Slope in Individual-Level Connectedness

Source of Connectedness

Felt Connected 4.76+ 2.44 -

2.04+ 1.15 Average Group-Level Structure

Density of Positive Connections -9.17* 4.17 1.18* .58

Slope in Group-Level Structure

Density of Positive Connections

***p < .001, **p < .01, *p < .05, +p < .10

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Figure 4.1: Group size moderates the relations between average level source of connectedness and weekly reports of self-efficacy (a) and session value (b)

Both figures suggest that dynamics are more pronounced in larger groups.

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Figure 4.2: Age group moderates the prediction of changes in parent-youth (“P-Y”) relationship quality by average levels and slope in individuals’ connectedness

Panel a predicts reduction in the negative affective quality of the relationship from slopes in felt connected. Panels b, c, and d each predict improvements in parent-youth communication about substance use (“SU”) rules from average level source of connectedness (panel b), slope in source of connectedness (panel c) and slope in feelings of connectedness (panel d). Results are in the expected direction for parents only in panels a, b, and d, and for youth only in panel c.

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Appendix A: Youth weekly surveys

Note. The names shown in sample surveys are fictional.

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Appendix B: Parent weekly survey

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VITA

Lauren E. Molloy

EDUCATION

The Pennsylvania State University, University Park, PA

2012 Ph.D., Human Development & Family Studies, The Pennsylvania State University

2009 M.S., Human Development & Family Studies, The Pennsylvania State University

2006 B.A., Psychology, High Distinction, University of Virginia

FELLOWSHIPS AND AWARDS

2010-2012 Network Science Exploration Research Grant ($4000), Pennsylvania State University

2009-2011 National Institute on Drug Abuse: Institutional Pre-doctoral Research Fellow Prevention and Methodology Training Program

2006–2010 University Graduate Fellowship, Pennsylvania State University

Fall 2006 Hintz Fellowship ($3000), Human Development & Family Studies, Pennsylvania State University

2005-2006 Double Hoo Research Award for Undergraduate-Graduate Student Collaborative Research ($2000), University of Virginia

PUBLICATIONS

Molloy, L. E., Moore, J. E., Trail, J. B., Van Epps, J. J., & Hopfer, S. (In press). Understanding real-world implementation quality of PBIS: Relation to problem behaviors. Prevention Science.

Ram, N., Conroy, D. E., Pincus, A., Hyde, A. L. & Molloy, L. E. (2012). Tethering

theory to method: Using measures of intraindividual variability to operationalize individuals’ dynamic characteristics. In G. Hancock & J. Herring (Eds.), Advances in longitudinal modeling in the social and behavioral sciences. NY, NY: Routledge.

Molloy, L. E., Ram, N., & Gest, S. D. (2011). The storm and stress (or calm) of early

adolescent self-concepts: Within- and between-person variability. Developmental Psychology, 47(6), 1589-1607.

Molloy, L. E., Gest, S. D., & Rulison, K. L. (2011). Peer influences on academic

adjustment: Exploring multiple methods of assessing youths’ most “influential” peer relationships. Journal of Early Adolescence, 31(1), 13-40.