session 2: specifying the conceptual and operational models and the research questions that follow
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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, 2008. Workshop on randomized controlled trials. - PowerPoint PPT PresentationTRANSCRIPT
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, 2008
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, 2008
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)