Specifying the Conceptual and Operational Models and the Research Questions that Follow

Mark W. LipseyVanderbilt University

IES/NCER Summer Research Training Institute, 2010

Focus on randomized controlled trials

Purpose of the Summer Training Institute: 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 intervention1. 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 change2. 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 instructionMost important: What aspects of the above are

different from the counterfactual conditionWhat 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?

Describing and Quantifying Outcomes

Mark W. LipseyVanderbilt University

IES/NCER Summer Research Training Institute, 2010

Outcome constructs to measureIdentifying the relevant outcome constructs follows from the theory development and other considerations covered in the earlier session 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 issuesValidity (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

Sensitivity to change: Achievement effect sizes from 124 randomized education studies

Type of Outcome Measure

Mean EffectSize

Number of Measures

Standardized test, broad .04 103Standardized test, narrow .28 426Focal topic test, mastery test .40 300

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 LoadingsPre 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

IES Guidelines on multiple significance testsSchochet, P.Z. (2008). Technical methods report: Guidelines for

multiple testing in impact evaluations. IES/NCEE 2008-4108.http://ies.ed.gov/pubsearch/pubsinfo.asp?pubid=NCEE20084018

Delineate separate outcome domains in the study protocol. Define confirmatory and exploratory analysis prior to data analysis Specify which subgroups will be part of the confirmatory analysis and

which will be part of the exploratory analysis Design the evaluation to have sufficient statistical power for examining

effects for all prespecified confirmatory analyses For domain-specific confirmatory analysis, conduct hypothesis testing

for domain outcomes as a group Multiplicity adjustments are not required for exploratory analysis Qualify confirmatory and exploratory analysis findings in the study

report

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)