Exploring Relational Communication Patterns in Prereferral Intervention Teams

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Exploring Relational CommunicationPatterns in Prereferral InterventionTeamsMegan S. Bennett a , William P. Erchul a , Hannah L. Young b &Chelsea M. Bartel aa North Carolina State Universityb Alfred UniversityPublished online: 20 Aug 2012.

To cite this article: Megan S. Bennett , William P. Erchul , Hannah L. Young & Chelsea M. Bartel(2012) Exploring Relational Communication Patterns in Prereferral Intervention Teams, Journal ofEducational and Psychological Consultation, 22:3, 187-207, DOI: 10.1080/10474412.2012.706128

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Journal of Educational and Psychological Consultation, 22:187–207, 2012

Copyright © Taylor & Francis Group, LLCISSN: 1047-4412 print/1532-768X online

DOI: 10.1080/10474412.2012.706128

Exploring Relational Communication Patternsin Prereferral Intervention Teams

MEGAN S. BENNETT and WILLIAM P. ERCHULNorth Carolina State University

HANNAH L. YOUNGAlfred University

CHELSEA M. BARTELNorth Carolina State University

The purpose of this research was to understand the relational

communication patterns that characterize school-based prerefer-

ral intervention teams (PITs). Fifteen PIT meetings were used as

the basis for analyses, with each meeting audiotaped, transcribed,

and coded using the Family Relational Communication Control

Coding System (Heatherington & Friedlander, 1987). Addition-

ally, the PIT Meeting Evaluation Coding Form was used to assess

each meeting’s adherence to a traditional problem-solving frame-

work. Important results included (a) relatively consistent domi-

neeringness (i.e., attempted influence) scores across participants,

with the exception of the referring teacher; (b) relatively consistent

dominance (i.e., successful influence) scores across participants;

(c) significantly greater (p < .05) domineeringness displayed by

the school psychologist compared with the referring teacher; and

(d) no significant differences in dominance scores between the

school psychologist and teacher. In sum, this study represents an

important first step in understanding communication patterns

found in school-based problem-solving groups.

Prereferral interventions are initial strategies or supports that are given to stu-dents experiencing academic and/or behavioral difficulties (Graden, Casey, &Christenson, 1985). During the 1980s, prereferral interventions were concep-tualized as a way to deliver important resources to a child before a possiblereferral for special education services. Today, prereferral interventions are

Correspondence should be sent to William P. Erchul, Department of Psychology, NorthCarolina State University, CB 7650, Raleigh, NC 27695–7650. E-mail: [email protected]

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188 M. S. Bennett et al.

most often delivered through prereferral intervention teams (PITs) and arerequired or recommended by most states (Truscott, Cohen, Sams, Sanborn,& Frank, 2005).

PITs are generally composed of multidisciplinary professionals (e.g., re-ferring teacher, school psychologist, administrator, regular education teacher,special education teacher, specialist) who come together to assist generaleducation teachers to develop interventions for students who are experienc-ing difficulties. Following PIT-based intervention development, teachers aretypically responsible for delivering the interventions. After a period of time,the PIT meets again to evaluate student progress and the intervention impact(Slonski-Fowler & Truscott, 2004).

Although a comprehensive review is beyond the scope of this article, ef-fectiveness/efficacy investigations have indicated that prereferral interventionprocesses are useful in improving both student (e.g., academic achievement)and systemic (e.g., referral rates) outcomes (e.g., Burns & Symington, 2002;Nelson, Smith, Taylor, Dodd, & Reavis, 1991). Given the current response-to-intervention (RTI) era, it easily can be seen that PITs and RTI problem-solvingteams (PSTs) have similar purposes, activities, and group membership (Er-chul & Martens, 2010).

There are multiple reasons for wanting to understand processes thatcharacterize PITs. First, research has noted inconsistencies in PIT imple-mentation both within and between states, including variations in membercomposition, overall goals, and interventions created (Truscott et al., 2005).Second, research in social psychology has clearly suggested that group pro-cess variables (e.g., power of majority, value of dissent, shared norms) areimportant to understanding the quality of group decision making; however,this is an underresearched area in the PIT literature (Gutkin & Nemeth, 1997).Finally, because it is likely that group-based decision making is and willcontinue to be used extensively within RTI frameworks (Gutkin & Curtis,2009), it is important to explore the various processes that occur within PITs.

To better understand the processes by which PITs make decisions andaccomplish goals, it would seem important to study the nature of inter-personal interactions between and among team members. One option isto apply a relational communication perspective, which considers the com-munication episodes that unfold as a way of understanding interpersonalrelationships (Rogers & Escudero, 2004). Within this perspective, as messagesare exchanged, ideas about how people regard one another and the natureof their relationship are also transmitted. Because of the changing natureof messages throughout the course of a discussion, it is assumed that therelationship between individuals is constantly being redefined, altered, andrenegotiated.

Relational communication researchers emphasize that the form of themessage and the process of message exchange (as opposed to the messagecontent) dictate the nature of relationships. Therefore, it is ‘‘not what we

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Relational Communication in PITs 189

say, but how we say it’’ that influences our perceptions of others and theirperceptions of us. The relational communication perspective also focuses ondyadic or triadic exchanges as opposed to individual messages. In turn, whenanalyzing messages, a central focus is placed on paired message sequencesinstead of single, isolated messages (Rogers & Escudero, 2004).

Structured coding schemes are often used to measure relational com-munication, and more specifically, relational control or influence conveyedwithin verbal interactions. For example, the Relational Communication Con-trol Coding System (RCCCS; Rogers & Farace, 1975) and Family RelationalCommunication Control Coding System (FRCCCS; Heatherington & Fried-lander, 1987) have been utilized to better understand control within dyadicand group contexts, respectively. In these coding schemes, each message isassigned a three-digit code, where the first digit identifies the speaker, thesecond digit identifies the grammatical format of the message (e.g., assertion,question, talk-over, noncomplete), and the third digit identifies the responsemode or the function of the message in relation to the prior message (e.g.,support, nonsupport, extension, answer, instruction, order, topic change).

Based on the three-digit code that is assigned to each message, a mes-sage is then assigned a control code. A message is considered one-up (") if itrepresents an attempt to control the conversation and define the relationshipbetween the speakers. In contrast, a message is considered one-down (#)if the speaker submits to the other’s attempts to control the conversation.Finally, a message is considered one-across (!) if it represents no controllingmaneuver or an attempt to balance the relationship (Rogers & Farace, 1975).

Relational communication research typically has focused on understand-ing the control differences between individuals (Rogers & Escudero, 2004).Two indices may be derived from the RCCCS and FRCCCS to better under-stand these differences. The first, domineeringness, is the number of one-upmessages spoken by one person without regard to the second speaker’sresponses to them. It is an index of attempted influence or directiveness andis calculated by dividing the number of the speaker’s one-up messages by hisor her total number of messages. Because relational communication researchis frequently interested in paired message sequences, however, there is asecond index that takes into account the second person’s responses to thefirst person’s one-up messages. Dominance is the proportion that a speaker’sone-up messages are responded to by one-down messages by the otherspeaker. In other words, dominance is defined as the proportion of instancesthat one speaker’s attempts to control the conversation are accepted by theother person. Dominance is considered a measure of successful influence(Erchul et al., 2009; Erchul, Grissom, & Getty, 2008).

Relevant to the present study, two previous investigations have used theFRCCCS to better understand the nature of communication in consultationmeetings involving three or more participants. Both studies were conductedon conjoint behavioral consultation (CBC; Sheridan & Kratochwill, 2008).

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Based on both behavioral consultation and ecological-systems theory, CBCrefers to consultation situations in which parents, teachers, and consultantsjoin together to address academic and/or behavioral difficulties that a childis experiencing within the classroom and/or home setting. In the first study,Erchul et al. (1999) examined the relational patterns present in four CBCcases. Based on prior research, they hypothesized that (a) consultants wouldexhibit higher levels of domineeringness than consultees (i.e., teachers andparents) and (b) consultants would exhibit higher levels of dominance thanconsultees. For each case, the FRCCCS was used to code messages acrossall three CBC interviews (i.e., Problem Identification [PII], Problem Analysis,Treatment Evaluation).

Results indicated that consultants and consultees were similar in theiraverage domineeringness scores across interviews (i.e., attempts to influencethe process were fairly equal among all participants). Other analyses revealedthat consultants were somewhat higher in domineeringness toward teachersand parents than parents and teachers were toward consultants. In contrastto domineeringness, consultants tended to be slightly less dominant thanconsultees, though dominance scores themselves were restricted in rangeand suggested that no one person was highly in control of the interviewdirection. Analyses of dyadic interactions showed that both parents andteachers tended to exhibit greater dominance with consultants than con-sultants did with parents and teachers. Overall, domineeringness patternstended to be stable across the three interviews, whereas dominance scoreswere somewhat more variable, particularly as they related to teacher-to-consultant interactions (Erchul et al., 1999).

In a second study, Grissom, Erchul, and Sheridan (2003) examined therelationship between relational communication variables (i.e., dominanceand domineeringness) and outcomes of CBC. Twenty CBC PIIs were codedusing the FRCCCS, and three outcome measures were utilized: (a) consulteeperceptions of the acceptability/effectiveness of CBC, (b) consultee percep-tions of the effectiveness of CBC consultants, and (c) consultee perceptionsof client goal attainment. Analyses failed to produce any significant cor-relations between consultant domineeringness/dominance and any of theseoutcomes. Likewise, no significant relationships were found between teacherdomineeringness/dominance and any outcome measure. There were, how-ever, significant correlations between parent dominance and two outcomes.First, parent-to-consultant dominance was negatively related to teachers’ per-ceptions of acceptability/effectiveness of CBC as an intervention (r D �.49).In other words, as parents were more successful at influencing consultants,teachers tended to view CBC as less acceptable and effective. Second, parentdominance toward both consultants and teachers was negatively related toparents’ perceptions of goal attainment (r D �.61 for parent-to-consultantdominance and r D �.58 for parent-to-teacher dominance; Grissom et al.,2003).

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The goal of this exploratory study was to extend the relational commu-nication perspective to PITs, and the more specific objective was to examineand document the relational patterns that characterize PIT meetings. Twohypotheses were posed:

1. Compared with referring teachers, school psychologists will display higherdomineeringness across PIT initial meetings; and

2. Compared with referring teachers, school psychologists will display higherdominance across PIT initial meetings.

An open-ended research question was also advanced: What relational pat-terns (i.e., domineeringness and dominance) will characterize PIT initialmeetings for specialists (e.g., reading teachers, speech pathologists, Englishas a Second Language teachers), regular education teachers, and specialeducation teachers?

METHOD

The data for this study were drawn from a research project at Alfred Univer-sity that was conducted from fall of 2000 through spring of 2003 (see Young& Gaughan, 2010). Consequently, the methodology section is divided intotwo segments. A brief description of the data collection procedures andsample characteristics from the Alfred University study are presented first,followed by the methodology for the current study, which was conductedat North Carolina State University.

Alfred University Study Methodology

General description. Beginning in 2000, Alfred University partnered withfour rural elementary schools in a multiyear project aimed at training schoolpsychology graduate students to become leaders in coordinating efforts tointegrate special education and regular education services. As part of thiseffort, each graduate student completed a yearlong internship at one of fourparticipating schools. Interns were responsible for implementing practices toeither improve existing PITs or develop a new team that followed a problem-solving model. As part of this project, interns participated in all PIT meetings.During the 3 years of implementation, interns used feedback from previousinterns, teacher satisfactions surveys, and other available data to identifyareas of need and create action plans to improve each school’s PIT.

Setting and participants. The current study utilized data from only twoof the four schools; consequently, only data on these two schools are de-scribed here. Those data were collected during the 2002–2003 academicyear at School 1 and during the 2001–2002 and 2002–2003 academic years

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at School 2. School 1 had approximately 580 students enrolled in gradesK–6 during the time of this study. School 2, which served students in gradesK–5, had approximately 550 students. The socioeconomic characteristics ofthe student population may be inferred by rates of free or reduced lunch;at School 1, 62.8% of students received either free or reduced lunch, and atSchool 2, approximately 42%. At both schools, 98% of the population wasWhite (not Hispanic; New York State Education Department, 2003).

The PITs at each school served all students within the school’s popu-lation. Teams met approximately once a week at which time one or tworeferred cases were discussed. Both behavioral and academic referrals wereconsidered. The PIT compositions tended to vary both between and withinthe schools but typically included regular education teachers, special educa-tion teachers, and specialists (e.g., counselors, school psychologists, readingteachers). At both schools, parents were intermittently invited but rarelyattended. At School 1, administrators were not present at meetings, whereasat School 2 the vice principal regularly attended. PIT meetings were alsocategorized as either initial (i.e., new referral) or follow-up (i.e., prior referralmeeting held) in nature.

Data collection procedures. Each intern was responsible for oversee-ing PIT data collection. Relevant to the present study, PIT meetings wereaudiotaped and transcribed. Prior to audiotaping meetings, a consent formthat detailed how the researcher would use the information was distributed,signed, and collected from each PIT member. After PIT meetings were au-diotaped, graduate students prepared verbatim transcriptions.

North Carolina State University Study Methodology

Stimulus material. This research utilized 15 PIT initial meetings from thetwo described schools. Meetings from only two of four schools were usedbecause of transcript availability, audiotape quality, and the assurance ofaccurate PIT member identification. Additionally, only data from initial meet-ings were considered because of the likelihood of including more stages ofthe problem-solving process (e.g., problem identification, problem analysis,intervention development). Each initial PIT meeting was approximately 30min in length. More information on PIT participants is presented in Table 1and the Results.

Process variables. The Family Relational Control Coding System (FRC-CCS; Heatherington & Friedlander, 1987) was applied to obtain measures ofrelational control for PIT meetings. Since its development, the FRCCCS hasbeen used in studies of familial interactions during therapy (e.g., Friedlander,Wildman, & Heatherington, 1991) and studies of conjoint behavioral consul-tation (Erchul et al., 1999; Grissom et al., 2003). The reliability and validity ofthe FRCCCS has been documented previously (e.g., see Erchul et al., 1999).

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TABLE 1 Individuals Present at Each PIT Meeting by Role

Meeting number

Speaker 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Special Ed1 Xa Xa Xa Xa X Xa Xa Xa X Xa Xa

Special Ed2 Xa Xa

Special Ed3 XSpecial Ed4 XSP1 X X X X Xa X X X X X XSP2 X X X X X X X X XSP3 X X X Xa

SP4SP5 X X Xa XSpecialist1 X X X X X X X X X XSpecialist2 XSpecialist3 XSpecialist4 X XSpecialist5 XSpecailist6 XReg Ed1 X X X X X XReg Ed2 X X X X X X X X X

Reg Ed3 X X X X X X X XReg Ed4 X X XReg Ed5 X X XReg Ed6 XReg Ed7 XReg Ed8 XRT1 X XRT2 X XRT3 XRT4 X XRT5 XRT6 X XRT7 X XRT8 XRT9 XRT10 XRT11 XRT12 XRT13 X

Parent X XAdmin X X X

N D 6 6 10 8 5 7 7 7 8 7 7 9 8 9 9

Note. PIT D prereferral intervention team; Special Ed D special education teacher; SP D school

psychologist or school psychologist intern; Specialist D speech language teacher, counselor, or readingspecialist; Regular Ed D regular education teacher; RT D referring teacher; Admin D school administrator.

Meetings 1–11 are from School 1; Meetings 12–15 are from School 2.aMeeting facilitator.

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Within the FRCCCS (Heatherington & Friedlander, 1987), a message (i.e.,most often defined as a speaking turn) is used as the unit of analysis andreceives a three-digit code. The first digit code (although it technically may becomposed of multiple digits) identifies both the speaker and the target of thespeaker’s communication. The second digit code specifies the grammatical orstructural format of the message (e.g., assertion, talk-over, question). Finally,the third digit code refers to the response mode or metacommunicationalsignificance of the message (e.g., support, nonsupport, disconfirmation).Further descriptions of coding categories are provided in Table A1.

Once three-digit codes have been assigned to individual messages,control codes are given. As noted previously, control codes are designatedone-up, one-down, or one-across based on combinations of second andthird digit codes (see Table A2). Following the assignment of control codes,measures of relational control can be calculated. The two used in this studywere domineeringness and dominance, both operationally defined earlier.As both measures are proportions, their potential range is .00 to 1.00.

PIT Meeting Evaluation Coding Form. A coding sheet was developedto evaluate the fidelity of the problem-solving process used within eachPIT meeting (see Table A3). This form is comparable to one devised byTelzrow, McNamara, and Hollinger (2000) but was developed independentlyand is better suited to the aims of this study. Similar to Telzrow et al.’s(2000) instrument, this instrument includes items that measure the pres-ence of a behavioral definition of a target behavior, systematic step-by-step intervention plan, and data to show the student’s response to inter-vention. These items reflect the basic requirements of problem solving asoutlined by Bergan and Kratochwill (1990) and include ratings of (a) whetherthe problem was identified, (b) whether ideas of intervention were brain-stormed, (c) whether a plan was generated, (d) how many interventionideas were generated, (e) the quality of the plan, and (f ) whether datacollection procedures were discussed. Additionally, information on the typeof problem behavior (i.e., academic or behavioral) is collected for descriptivepurposes.

Procedure. Prior to coding, PIT meeting transcripts were prepared ac-cording to the guidelines specified by Heatherington and Friedlander (1987)so that the coding system could be effectively utilized. To achieve thisgoal, all meeting participants were identified by both name and role (e.g.,school psychologist, administrator, special education teacher). Second, twograduate-level coders participated in approximately 30 hr of training in thedevelopment and application of the RCCCS (Rogers & Farace, 1975) andFRCCCS (Heatherington & Friedlander, 1987). Practice coding occurred untila predetermined acceptable level of reliability for both the second and thirddigit codes was achieved. Based on prior research utilizing the FRCCCS inCBC studies (e.g., Grissom et al., 2003), percentage agreement levels neededto begin actual coding was set at 85% for second digit codes and 75% for

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third digit codes. In total, four practice transcripts were coded and percentageagreement was calculated on the final three independently coded practicetranscripts.

Prior to coding actual PIT meetings, the transcripts were prepared bythe first author, who identified the speakers, targets, and talk-over messagesfollowing procedures specified by Heatherington and Friedlander (1987).Two coders were employed, and each was instructed to independentlycode approximately 60% of the total messages (i.e., 8–10 meetings) andto complete the PIT Meeting Evaluation Coding Form for all 15 meetings.Coding itself proceeded by following four steps. First, each coder listenedto audio recordings of meetings and followed along with transcripts toassign each message a second and third digit code based on the codingmanual and additional decision rules developed during practice coding.Second, following the assignment of message codes, control codes wereassigned by referring to the matrix presented in Table A2. Third, messageswere linked following a series of rules specified by the coding manual andseveral modifications.1 Fourth, domineeringness and dominance scores werecalculated for each professional role represented in PIT meetings.

Intercoder Reliability

FRCCCS. Intercoder reliability was calculated for second digit and thirddigit codes from transcripts using Cohen’s kappa (1960). Kappa was cal-culated on three randomly chosen transcripts comprising 22% of the totalcoded messages. This method has been used in prior research to determineintercoder reliability when using the RCCCS and FRCCCS (Erchul et al., 2007;Heatherington & Friedlander, 1990). Kappa coefficients ranged from .959to .964 (M D .96) for second digit categories and .89 to .95 (M D .92) forthird digit categories. The relatively few coding discrepancies were generallyresolved through discussion, a list of rules generated during practice coding,and decision trees available in the FRCCCS manual.

PIT Meeting Evaluation Coding Form. Each coder independently eval-uated every meeting utilizing the PIT Meeting Evaluation Coding Form.In order to assess intercoder reliability, simple percentage agreement wascalculated for each of the seven questions found on the form. Simple per-centage agreement ranged from 80% to 100% (M D 91%). The relatively fewdiscrepancies were resolved by an alternation procedure where either Coder1’s or Coder 2’s rating was used to resolve a discrepancy and then the othercoder’s rating was selected to resolve the next one. This method was useduntil all discrepancies were addressed.

1FRCCCS decision rules and coding system modifications that were developed and used in this study

are available upon request from the first author.

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RESULTS

General Description of Meetings and Meeting Participants

Fifteen PIT meetings, 11 from School 1 and 4 from School 2, were codedusing the FRCCCS and PIT Meeting Evaluation Coding Form. To facilitateFRCCCS coding and data analysis procedures, PIT participants were cate-gorized into specific roles based on profession. These roles were as fol-lows: school psychologist (including school psychologist interns), referringteacher, special education teacher, specialist (i.e., reading teacher, counselor,speech pathologist), regular education teacher, administrator, and parent.Additionally, a code was designated for ‘‘group’’ to represent messages thatwere sent to the group as a whole.

Table 1 contains a detailed description of the members present at eachPIT meeting. It is of interest to note that the role of meeting facilitatorvaried across and within schools such that a school psychologist and spe-cial education teacher were facilitators at different times at both schools.When relevant to do so, findings are presented both by professional role(i.e., school psychologist or special education teacher) and professional rolewithin each meeting (e.g., school psychologist as meeting facilitator).

School psychologists, referring teachers, special education teachers, spe-cialists, and regular education teachers were all active participants across thevarious meetings. Overall, participants contributed the following percentagesof total messages (i.e., 5,286) that were coded: referring teachers (28.24%),school psychologists (22.59%), special education teachers (19.88%), special-ists (13.77%), and regular education teachers (11.31%). Although parents(3.24%) and administrators (.98%) also intermittently attended, their partic-ipation in meetings was substantially limited. For this reason, parent andadministrator data were not included in analyses.

Relational Patterns Within PIT Meetings by Role:

Research Question

Control codes. Because of the type of data collected and the exploratorynature of the study, the analyses for the posed research question (i.e., un-derstanding relational patterns of specialists, regular education teachers, andspecial education teachers) were primarily descriptive. Control codes weretabulated to describe the number of one-up, one-down, and one-acrossmessages that were sent by each role. By way of summary, 36% of allmessages sent by the school psychologist (including when in the facilitatorrole) were one-across; 33%, one-up, and 31%, one-down. For the referringteacher, 45% were one-across, 36% one-down, and 19% one-up. Forty-onepercent of the messages sent by the special education teacher (includingwhen in the facilitator role) were one-up, 35% one-down, and 23% one-

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across. For the specialist, 42% were one-across, 31% one-up, and 27% one-down. The regular education teacher used one-across messages 40% of thetime with 36% of the messages one-up and 24% one-down. One conclusionto draw from these control code data is that the referring teacher sent thelowest percentage of one-up messages (19%) and the special educationteacher sent the highest (41%).

Domineeringness and dominance. Domineeringness reflects an indi-vidual’s attempts to influence or define a relationship and is calculated bydividing Speaker A’s total number of one-up messages by his or her totalmessages. Dominance is defined as an individual’s success in influencinganother and is calculated by dividing the total number of one-up messagesby Speaker A that are directly followed by one-downs from Speaker Bby the total number of one-up messages emitted by Speaker A. Overalldomineeringness and dominance scores by role are displayed in Table 2.

From this table, it may be seen that overall, most domineeringnessscores were fairly consistent with the exception of the referring teacherbeing relatively lower compared with other roles. Also, when consideringthe role of meeting facilitator, domineeringness scores seemed to differ:school psychologist domineeringness while serving as facilitator was .28 andspecial education teacher domineeringness serving in the same capacity was.44. Therefore, though based only on three meetings, school psychologistdomineeringness as facilitator tended to be somewhat lower than the specialeducation teacher’s in the same role; however, the meaningfulness of thisdifference is unknown.

With regard to dominance, Table 2 indicates that overall dominancescores were fairly consistent across the various roles (i.e., range .47–.51).

TABLE 2 Mean Domineeringness and Dominance Scores by PIT Role

Domineeringness Dominance

Role M SD M SD Na

School psychologistb .34 .12 .48 .16 15Referring teacher .18 .07 .46 .11 15Special education teacherb .43 .06 .53 .17 14Specialist .30 .11 .47 .17 13Regular education teacher .39 .08 .51 .19 15School psychologistc .35 .12 .49 .13 12Special education teacherc .37 .09 .74 .19 2School psychologist as

meeting facilitator.28 .12 .47 .29 3

Special education teacheras meeting facilitator

.44 .05 .50 .14 12

Note. PIT D prereferral intervention team.aNumber of meetings when present. bIncludes instances where the school psychologist or specialeducation teacher also was the meeting facilitator. cExcludes instances where the school psychologist

or special education teacher also was the meeting facilitator.

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Meeting facilitators also had dominance scores that were fairly consistentwith other roles: school psychologist dominance as meeting facilitator was.47 and special education teacher dominance in the same role was .50.

Relational Control for School Psychologist Versus

Referring Teacher: Hypotheses 1 and 2

Hypotheses 1 and 2 predicted that, when compared with referring teachers,school psychologists would display higher domineeringness (Hypothesis 1)and dominance (Hypothesis 2) across PIT meetings. To examine the dif-ference between mean domineeringness and dominance scores, two pairedsamples t tests were performed. For these analyses, dominance scores fromthe original data set (that does not exclude instances of the school psy-chologist as meeting facilitator) were used. This decision was based on therelatively small difference (i.e., .01 for both domineeringness and dominance)between school psychologists’ domineeringness and dominance scores whenincluding versus not including the meeting facilitator role. Results demon-strated a significant difference for domineeringness, t (14) D 4.88, p < .001,with school psychologists displaying higher rates of domineeringness (M D

.34) than referring teachers (M D .17). Thus, school psychologists weresignificantly more directive during PITs compared with referring teachers. Incontrast, there was not a significant difference between school psychologist(M D .48) and referring teacher (M D .46) dominance, t (14) D .65, p > .05,suggesting comparable levels of successful influence for these two roles.

Post Hoc Analysis: Process-Outcome Correlations

Because of the exploratory nature of the study and the existence of pre-vious consultation studies that investigated the relationship between dom-ineeringness and dominance and various outcome measures, an additionalanalysis was conducted. Domineeringness and dominance scores of schoolpsychologists, referring teachers, special education teachers, specialists, andregular education teachers were correlated with the total score of the PITMeeting Evaluation Coding Form. Means and standard deviation for the PITMeeting Evaluation Coding Form questions and total score are displayed inTable 3 with the results of the correlational analyses shown in Table 4.The relationships between referring teacher, specialist, special educationteacher, and regular education teacher domineeringness scores and the to-tal outcome score were not significant. However, the relationship betweenschool psychologist domineeringness and the total outcome score was sig-nificant, r (14) D .54, p < .05, though when excluding cases when theschool psychologist was the meeting facilitator, this relationship was nolonger significant, r (11) D .57, p D .15. Thus, instances where the schoolpsychologist served as facilitator may have accounted for the initial signif-

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TABLE 3 Means and Standard Deviations for PIT Meeting Evaluation Coding Form byQuestion

Question M SD N

1. Was one target problem behavior identified and describedin specific, objective terms? (1 D No, 2 D Yes)

1.67 0.49 15

2. What was the type of problem behavior identified? NA NA 153. Were ideas about possible interventions brainstormed?

(1 D No, 2 D Yes)2.00 0.00 15

4. Was a plan (i.e., at least one intervention idea) generated?(1 D No, 2 D Yes)

2.00 0.00 14

5. How many intervention ideas were brainstormed?(1 D less than 5, 2 D 6–7, 3 D 8–9, 4 D 10C)

2.21 1.12 14

6. Rate the quality of the plan. (1 D very general, 2 D general,3 D specific, 4 D very specific)

2.64 0.84 14

7. Did the team discuss data collection procedures?(1 D No, 2 D Yes)

1.50 0.52 14

Total outcome score 12.00 1.80 14

Note. PIT D prereferral intervention team; NA D not applicable because it is a nominal variable.

icant relationship between problem-solving process adherence and schoolpsychologist domineeringness.

DISCUSSION

The intent of this study was to examine and describe relational communica-tion patterns that occur within prereferral intervention teams. This researchis a logical extension of prior work specifying relational communication pat-terns in dyadic (i.e., behavioral consultation; BC) and small-group (i.e., CBC)

TABLE 4 Correlations Between Domineeringness and Dominance by Role and MeetingEvaluation Total Outcome Score

Domineeringness Dominance

Roles r r N a

School psychologist .54* �.02 14Referring teacher .10 .39 14Special education teacher �.07 �.13 13Specialist .23 .07 13Regular education �.27 .31 14Meeting facilitatorb .36 �.07 14

aNumber of prereferral intervention team (PIT) Meeting Evaluation Coding Form total outcome scoresincluded in analysis. bTotal meeting facilitator; includes overlapping data from instances where the school

psychologist or special education teacher also functioned as the meeting facilitator.*p < .05.

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school-based problem-solving contexts. In this section, results are discussedwith respect to previous research and theory, implications, limitations, andfuture research directions.

Hypotheses 1 and 2

Hypothesis 1 proposed that, compared with the referring teacher, the schoolpsychologist would display higher domineeringness across initial PIT meet-ings. Hypothesis 2 predicted that, compared with the referring teacher, theschool psychologist would display higher dominance across initial PIT meet-ings. Summarizing results pertaining to these predictions is relatively simple:support was generated for the first hypothesis but there was little or nosupport for the second.

It is useful to look at these findings relative to past studies, specifi-cally those focusing on school psychologists as consultants and teachers asconsultees. This comparison allows for an analysis of teacher and schoolpsychologist domineeringness and dominance scores in PITs versus teacherand school psychologist domineeringness and dominance scores during BCand/or CBC. To date, there have been three published studies that haveexclusively looked at relational patterns within BC (i.e., Erchul, 1987; Erchulet al., 2009; Erchul et al., 2007) utilizing the RCCCS and two studies thathave looked at relational patterns within CBC using the FRCCCS (Erchul et al.,1999; Grissom et al., 2003). Before beginning this discussion, it is important toacknowledge that any ‘‘true’’ differences/similarities between studies cannotbe determined because statistical procedures have not been used to comparethe various studies. Instead, results are looked at descriptively. Additionally,the types of interviews were variable across each study such that somestudies were focused on one specific portion of the problem-solving process,such as problem identification (i.e., Erchul et al., 2007; Grissom et al., 2003)or problem analysis (i.e., Erchul et al., 2009), whereas others included amixture of various interview types (e.g., Erchul, 1987; Erchul et al., 1999;current study). The intent of this discussion is simply to describe relationalpatterns observed across studies that have used similar methodologies andposed similar questions over the past 25 years.

First, when looking at overall domineeringness scores, referring teacherstend to exhibit similar levels of domineeringness (i.e., attempts to influence).For example, domineeringness patterns of teachers reported in the literaturehave been .18 (Erchul, 1987), .27 (Erchul et al., 1999), .29 (Grissom et al.,2003), .11 (Erchul et al., 2007), .15 (Erchul et al., 2009) and .18 (presentstudy). Thus, teachers tend to have consistent, and relatively low, levelsof domineeringness regardless of whether they are involved in dyadic BCor group CBC/PIT meetings. Similar domineeringness patterns are seen forschool psychologists: mean domineeringness scores of school psychologistshave been .39 (Erchul, 1987), .33 (Erchul et al., 1999), .40 (Grissom et al.,

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2003), .24 (Erchul et al., 2007), .31 (Erchul et al., 2009), and .34 (presentstudy). Thus, school psychologist domineeringness tends to be similar indyadic BC and group CBC/PIT interactions. In this respect, it appears thatattempts to influence others are rather consistent across various types ofmeetings. Thus, levels of directiveness may depend more on professionalrole than the type of interaction or group size with school psychologists mostoften taking charge in dyadic and group-based interactions as compared withteachers.

Second, when looking at dominance scores across studies, the patternof results is less clear. It appears that school psychologists tend to be lessdominant (i.e., less influential) during PIT meetings than during BC, andpatterns of dominance within PITs seem to be more similar to what hasbeen observed in studies of CBC. For BC, patterns of dominance appear to behigher (.73 in Erchul, 1987; .80 in Erchul et al., 2007; .78 in Erchul et al., 2009)than what has been seen in CBC (i.e., .35 in Erchul et al., 1999; .49 in Grissomet al., 2003) or within PITs (.48 in the present study), though it is noted againthat the meaningfulness of these differences is unknown. However, schoolpsychologists do tend to be more successful at influencing participants indyadic versus group situations. This result may also be attributed to thesmaller number of interactions that are possible in groups versus dyads,which may afford fewer opportunities to be influential. Dominance patternsfor teachers are less consistent. The type of interaction (i.e., dyadic or group)does not appear to affect dominance scores of teachers (i.e., .51, .74, .61 inBC and .41, .39, .46 in CBC and PITs, respectively). In this light, the success ofteachers in influencing others may depend less on the type of interaction andmore on other factors that have yet to be researched (e.g., type of problem,level of concern for student).

Research Question

The research question looked more broadly at individual professional roles(i.e., special education teachers, specialists, regular education teachers) andtheir patterns of domineeringness and dominance. It is useful to look at thesefindings in conjunction with domineeringness/dominance patterns of schoolpsychologists and teachers as a whole to better understand the nature ofPIT interactions. Taken together, with the exception of the referring teacher,patterns of dominance and domineeringness were somewhat similar acrossroles, suggesting at least some shared directiveness (i.e., domineeringness)and shared influence (i.e., dominance) among group members.

Erchul et al. (2007) wrote that ‘‘school-based dyadic behavioral consul-tation involves a complementary, leader-follower, cooperative relationship’’(p. 124). In contrast, Erchul et al.’s (1999) study of CBC found that relationalpatterns were more likely to be symmetrical in nature. The results of thelatter appear to be similar to the current study. Relating these findings back

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to relational communication theory and Bateson’s (1958) original terminol-ogy (i.e., symmetry, complementary, reciprocity), it appears that interactionswithin PIT groups are more symmetrical and perhaps even reciprocal innature. Thus, in group interactions, it seems that no one professional roledisproportionately influences other group members, even when includingthe meeting facilitator role.

The one exception to this point may be patterns of domineeringnessand dominance observed for referring teachers. Although referring teachers’domineeringness tended to be substantially lower than other group members’domineeringness, it is worth pointing out that referring teachers’ dominance

levels were fairly similar to those of other professionals. This finding suggeststhat there was an attempt on the part of group members to make teachers feel‘‘heard’’ within group meetings. This finding contrasts to Slonski-Fowler andTruscott (2004), who found that many teachers felt as if their ideas, opinions,and input were devalued within PIT meetings. However, the methodologyof current study differed from that used by Slonski-Fowler and Truscott inthat it was a university-supported, federally funded research project, whichperhaps offered greater overall teacher support. This feature may explainwhy, when teachers asserted themselves, they tended to influence groupmembers at rates similar to other professional roles.

Limitations

The many limitations in the design, methodology, and sample are apparentin this research and thus deserve attention. First, a major drawback to thedesign was the use of only 15 cases that were drawn from only two schools.Thus, the verbal behavior that was coded was limited in that the interactionsoccurred within a small sample of school-based professionals. Althoughsome variability was evident among professional roles (which allowed foran initial examination of relational patterns in school-based groups), thenumber of people who occupied each role was rather small and thus it isdifficult to generalize these results with confidence to other school-basedproblem-solving teams. Additionally, the small sample size contributed tolow statistical power, which may have contributed to the failure to detectsignificant findings in several instances.

A second, related limitation was nesting within the research design.Some persons repeatedly occupied various roles (e.g., there were only 4special education teachers across both schools), whereas other roles tendedto be less constrained (e.g., referring teachers usually differed by PIT meet-ing for a total of 13 different referring teachers). Scores were analyzed byindividual and did not account for instances when the same person occupiedvarious roles. Although this limitation may have led to some difficultiesin interpreting data, it was deemed necessary to analyze the PIT meetingsconsidered here.

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A third limitation, which tends to be pervasive in school consultationrelational communication process research, was the use of audio recordingsas opposed to video recordings. Some inherent meanings in commentsmay be missed when relying solely on verbal behavior (Ketrow, 1999).Additionally, the use of video may help to provide more insight into therelational meanings that are conveyed in various interactions.

A fourth important limitation was the lack of outcome data. Althoughadherence to problem-solving steps was coded, this study did not incorpo-rate PIT outcome variables that would be more important to understandingthe usefulness of PITs as well as variables that have been used in priorconsultation research. For example, intervention effectiveness, interventionintegrity, and intervention acceptability are all outcome variables that havebeen studied in past BC studies and CBC studies. Additionally, studies onPIT outcomes have frequently included measures such as referral to specialeducation, PIT member satisfaction, behavior rating scales, teacher ratings ofstudent progress made toward goals, and various student outcomes (Burns& Symington, 2002). Although the current study did provide some limitedinformation on outcomes through its use of the Meeting Evaluation Cod-ing Form, other important outcome measures were not utilized and thusstatements about domineeringness/dominance being related to better/worseoutcomes cannot be made.

Future Directions

Several future research directions follow directly from the described lim-itations. First, the use of video recordings or direct observations of PITmeetings would greatly increase the reliability of participant identificationand perhaps then lead to a better understanding of what is being conveyedin each message. A second direction would be to use different types ofPIT teams to see whether findings could be generalized to a larger, morediverse sample. Third, and perhaps most important, future research shouldincorporate meaningful outcome variables in order to link process (e.g.,communication behavior) with important results. Some important variablesto include are systemic outcomes (e.g., referral rates), student outcomes (e.g.,teacher ratings of student progress toward goals), and treatment plan integrity(cf. Burns, Peters, & Noell, 2008; Duhon, Mesmer, Gregerson, & Witt, 2009).

In conclusion, this study represents a significant first step in understand-ing group communication in school-based decision-making teams, whichwill continue to be essential within schools, particularly as PITs becomePSTs in the RTI era (Erchul & Martens, 2010). It is the hope that this line ofresearch will encourage others to attend to the importance of communicationin school-based teams to promote the best models of service delivery for allstudents.

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ACKNOWLEDGMENTS

This article is based on a poster presented at the annual convention of theAmerican Psychological Association in San Diego in August 2010. This poster,in turn, was based on a doctoral dissertation conducted by Megan S. Bennett,directed by William P. Erchul, with source data and extensive assistanceprovided by Hannah L. Young.

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APPENDIX

TABLE A1 Description of Coding Categories From Heatherington and Friedlander’s (1987)FRCCCS

Coding category Definition

First digit: SpeakerDirect target Specifies whom the speaker is directly addressingIndirect target Specifies a physically present participant who is referred to within

the messageSecond digit: Message format

Assertion A declarative or imperative statementOpen question A statement in the interrogative formTalk-over Any interruption while another person is talkingNoncomplete A phrase or incomplete statementClosed question A direct question that calls for a specific response (e.g., yes/no)Intercept An interruption of a dyadic conversation by a third personIndistinguishable Messages that are inaudible or unintelligible

Third digit: MetacommunicationalFunction/Response mode

Support A message that offers or seeks agreement, acceptance, or approvalNonsupport A message that conveys disagreement, resistance, or rejectionExtension A message that continues the flow or theme of a prior messageAnswer to an open question A reply to an open question that conveys knowledge, firmness,

opinion, or substanceInstruction A qualified suggestion (i.e., a softened order)Order A statement of command usually in the imperative formDisconfirmation A response that disregards previous requestsTopic change A message that lacks continuity with previous messagesAnswer to a closed question A straightforward answer to a closed questionIndistinguishable Messages that are inaudible or unintelligible

Note. FRCCCS D family relational communication control coding system.

TABLE A2 Message Types and Control Code Assignments of Heatherington and Friedlander(1987) Family Relational Communication Control Coding System

Format

Response mode AssertionClosed

questionOpen

questionTalk-over Incomplete Intercept

Support # # # # # #Nonsupport " " " " " "Extension ! " "a " ! "Answer to open question " " " " " "Answer to closed question # " " " # "Instruction " " " " " "Order " NA NA " " "Disconfirmation " " " " " NATopic change " " " " " "

Note. Table was adapted from Heatherington and Friedlander (1987). NA D not applicable because thiscombination is not possible.aModified from original coding system in current research study.

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TABLE A3 PIT Meeting Evaluation Coding Form

1. Was one target problem behavior identified and described in specific, objective terms? (Note: If more than oneproblem behavior was identified, participants must establish a priority in order to get a ‘‘yes’’ on this question.)a. Nob. Yes

2. What was the type of problem behavior identified?a. Academicb. Behavioral

3. Were ideas about possible interventions brainstormed?a. Nob. Yes

4. Was a plan (i.e., at least one intervention idea) generated?a. Nob. Yes

5. How many intervention ideas were brainstormed?a. Less than 5b. 6–7c. 8–9d. 10C

6. Please rate the quality of the plan: (circle one)a. Very specific—In order to qualify as very specific the plan must include all of the following for at least one of the

interventions mentioned during the meeting: name of a specific program or detailed description of intervention,identified person who is responsible for implementation, determined when the program should be implemented(e.g., time of day, day of week), discussion of materials, and staff training if needed.

b. Specific—at least 3 of the aforementioned.c. General—at least 2 of the aforementioned.d. Very general—1 or none of the aforementioned.

7. Did the team discuss data collection procedures (i.e., is there a plan for measuring progress throughout the interventionphase)? Note that simply asking for additional assessment measures does not qualify.a. Nob. Yes

Note. The potential range of scores is 6–16 with higher ratings indicating better adherence to the problem-solvingprocess. Item 2 is purely descriptive and does not contribute to the numerical score.

Megan S. Bennett, PhD, is a school psychologist within the Wake County Public SchoolSystem. She received her doctorate at North Carolina State University. Her research and profes-sional interests include dyadic and group consultation, academic and behavioral interventions,and response to intervention.

William P. Erchul, PhD, ABPP, is a Professor of Psychology and the Director of the SchoolPsychology Training Program at North Carolina State University. He is a Past President of theAmerican Academy of School Psychology and will serve as President of the Society for theStudy of School Psychology in 2013. His research interests include processes and outcomesassociated with school consultation as well as relational communication and social influence.

Hannah L. Young, PsyD, is an Assistant Professor of Counseling and School Psychology atAlfred University and graduated with a doctorate in school psychology from Alfred University.Her research interests include consultation, family violence, prevention programming, andself-injurious behavior.

Chelsea M. Bartel, MA, is a doctoral student in school psychology at North Carolina StateUniversity who recently completed a predoctoral internship in the Guilford County (NC)Public Schools. Her research interests include group consultation, treatment integrity, responseto intervention, and the interactions between/among these areas.

Note : The authors report that to the best of their knowledge neither they nor their affiliatedinstitutions have financial or personal relationships or affiliations that could influence or biasthe opinions, decisions, or work presented in this manuscript.

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Documents

Exploring Relational Communication Patterns in Prereferral Intervention Teams