Session 2:Specifying the Conceptual and

Operational Models and the Research Questions that Follow

Mark W. Lipsey

Vanderbilt University

IES/NCER Summer Research Training Institute, 2007

Workshop on randomized controlled trials

• Purpose: Increasing capacity to develop and conduct rigorous evaluations of the effectiveness of education interventions

• Caveat: “Rigorous evaluations” are not appropriate for every intervention or every research project involving an intervention– They require special resources (funding, amenable

circumstances, expertise, time)– They can produce misleading or uninformative results

if not done well– The preconditions for making them meaningful may

not be met.

Critical preconditions for rigorous evaluation

• A well-specified, fully developed intervention with useful scope– basis in theory and prior research– identified target population– specification of intended outcomes/effects– “theory of change” explication of what it does and why

it should have the intended effects for the intended population

– operators’ manual: complete instructions for implementing

– ready-to-go materials, training procedures, software, etc.

Critical preconditions for rigorous evaluation (continued)

• A plausible rationale that the intervention is needed; reason to believe it has advantages over what’s currently proven and available

• Clarity about the relevant counterfactual– what it is supposed to be better than

• Demonstrated “implementability”– can be implemented well enough in practice to plausibly have effects

• Some evidence that it can produce the intended effects albeit short of standards for rigorous evaluation

Critical preconditions for rigorous evaluation (continued)

• Amenable research sites and circumstances:– cooperative schools, teachers, parents, and

administrators willing to participate– student sample appropriate in terms of

representativeness and size for showing educationally meaningful effects

– access to students (e.g., for testing), records, classrooms (e.g., for observations)

IES funding categories

• Goal 2 (intervention development) for advancing intervention concepts to the point where rigorous evaluation of its effects may be justified

• Goal 3 (efficacy studies) for determining whether an intervention can produce worthwhile effects; RCT evaluations preferred.

• Goal 4 (effectiveness studies) for investigating the effects of an intervention implemented under realistic conditions at scale; RCT evaluations preferred.

Specifying the theory of change embodied in the intervention

1. Nature of the need addressed– what and for whom (e.g., 2nd grade students

who don’t read well) – why (e.g., poor decoding skills, limited

vocabulary)– where the issues addressed fit in the

developmental progression (e.g., prerequisites to fluency and comprehension, assumes concepts of print)

– rationale/evidence supporting these specific intervention targets at this particular time

Specifying the theory of change

2. How the intervention addresses the need and why it should work

– content: what the student should know or be able to do; why this meets the need

– pedagogy: instructional techniques and methods to be used; why appropriate

– delivery system: how the intervention will arrange to deliver the instruction

Most important: What aspects of the above are different from the counterfactual condition

What are the key factors or core ingredients most essential and distinctive to the intervention

Logic models as theory schematics

4 year old pre-K

children

Exposed to intervention

Positive attitudes to

school

Improved pre-literacy

skills

Learn appropriate

school behavior

Increased school

readiness

Greater cognitive gains in K

TargetPopulation Intervention Proximal Outcomes Distal Outcomes

Mapping variables onto the intervention theory: Sample characteristics

4 year old pre-K

children

Exposed to intervention

Positive attitudes to

school

Improved pre-literacy

skills

Learn appropriate

school behavior

Increased school

readiness

Greater cognitive gains in K

Sample descriptors:basic demographics diagnostic, need/eligibility identificationnuisance factors (for variance control)

Potential moderators:setting, contextpersonal and family characteristicsprior experience

Mapping variables onto the intervention theory: Intervention characteristics

4 year old pre-K

children

Exposed to intervention

Positive attitudes to

school

Improved pre-literacy

skills

Learn appropriate

school behavior

Increased school

readiness

Greater cognitive gains in K

Independent variable:T vs. C experimental condition

Generic fidelity:T and C exposure to the generic aspects of the intervention (type, amount, quality)

Specific fidelity:T and C(?) exposure to distinctive aspects of the intervention (type, amount, quality)

Potential moderators:characteristics of personnelintervention setting, context e.g., class size

Mapping variables onto the intervention theory: Intervention outcomes

4 year old pre-K

children

Exposed to intervention

Positive attitudes to

school

Improved pre-literacy

skills

Learn appropriate

school behavior

Increased school

readiness

Greater cognitive gains in K

Focal dependent variables:pretests (pre-intervention)posttests (at end of intervention)follow-ups (lagged after end of intervention

Other dependent variables:construct controls– related DVs not expected to be affectedside effects– unplanned positive or negative outcomesmediators– DVs on causal pathways from intervention to other DVs

Main relationships of (possible) interest

• Causal relationship between IV and DVs (effects of causes); tested as T-C differences

• Duration of effects post-intervention; growth trajectories

• Moderator relationships; ATIs (aptitude-Tx interactions): differential T effects for different subgroups; tested as T x M interactions or T-C differences between subgroups

• Mediator relationships: stepwise causal relationship with effect on one DV causing effect on another; tested via Baron & Kenny (1986), SEM type techniques.

Formulation of the research questions

• Organized around key variables and relationships

• Specific with regard to the nature of the variables and relationships

• Supported with a rationale for why the question is important to answer

• Connected to real-world education issues

• What works, for whom, under what circumstances, how, and why?

Session 3:Describing and Quantifying

Outcomes

Mark W. Lipsey

Vanderbilt University

IES/NCER Summer Research Training Institute, 2007

Outcome constructs to measure

Identifying the relevant outcome constructs follows from the theory development and other considerations covered earlier in Session 2– What: proximal/mediating and distal outcomes– When: temporal status– baseline, immediate

outcome, longer term outcomes– What else:

• possible positive or negative side effects• construct control outcomes not targeted for change

Aligning the outcome constructs and measures with the intervention and policy objectives

Instruction

Assessment

Policy relevant outcomes(e.g., state achievement standards)

Alignment of instructional tasks with the assessment tasks

Identical

Analogous(near transfer)

Generalized(far transfer)

Instructional tasks,activities, content

Basic psychometric issues

Validity (typically correlation with established measures or subgroup differences)

Reliability (typically internal consistency or test-retest correlation)– standardized measures of established validity

and reliability– researcher developed measures with validity

and reliability demonstrated in prior research– new measures with validity and/or reliability to

be investigated in present study

Special issue for intervention studies: sensitivity to change

Achievement effect sizes from 97 randomized education studies

Type of Outcome Measure

Mean Effect

Size

Number of Measures

Standardized test, broad .09 29

Standardized test, narrow .32 127

Focal topic test, mastery test .50 263

Data from which measurement sensitivity can be inferred

• Observed effects from other intervention studies using the measure

• Mean effect sizes and their standard deviations from meta-analysis

• Longitudinal research and descriptive research showing change over time or differences between relevant criterion groups

• Archival data allowing ad hoc analysis of, e.g., change over time, differences between groups

• Pilot data on change over time or group differences with the measure

Variance control and measurement sensitivity

Variance control via procedural consistency and statistical control usingcovariates for e.g., pre-intervention individual differences and differences in testing procedures or conditions

Issues related to multiple outcome measures

Correlated measures: overlap and efficiency

Subtest

Factor Loadings

Pre-KPretest

Pre-KPosttest

KindergartenFollow-up

Letter Word IdentificationQuantitative ConceptsApplied ProblemsPicture VocabularyOral ComprehensionStory Recall

.60

.82

.82

.75

.82

.53

.69

.82

.80

.76

.79

.55

.73

.78

.75

.67

.74

.61

Factor Analysis of Preschool Outcome Variables

Correlated change may be even more relevant

Subtest

Factor Loadings

Pre toPost

Post toFollow-up

Pre toFollow-up

Basic School Skills Letter Word Identification Quantitative Concepts Applied Problems

Complex Language Picture Vocabulary Oral Comprehension Story Recall

.74 -.19

.66 .14

.54 .08

.09 .77

.16 .75-.08 .37

.73 -.06

.70 .06

.47 .16

.14 .48

.17 .72-.16 .68

.79 -.15

.74 .13

.40 .41

-.04 .74.13 .69-.01 .37

Factor Analysis of Gain Scores for Pre-K Outcomes

Handling multiple correlated outcome measures

• Pruning– try to avoid measures that have high conceptual overlap and are likely to have relatively large intercorrelations

• Procedural– organize assessment and data collection to combine where possible for efficiency

• Analytic– create composite variables to use in the analysis– use multivariate techniques like MANOVA to examine

omnibus effects as context for univariate effects– use latent variable analysis, e.g., in SEM

Practicality and appropriateness to the circumstances

• Feasibility– time and resources required• Respondent burden– minimize demands,

provide incentives/compensation• Developmental appropriateness– consider not

only age but performance level, possible ceiling and floor effect

• For follow-up beyond one school year, may need measures designed for a broad age span to maintain comparability

• May need to tailor measures or assessment procedures for special populations (disabilities, English language learners)