1. Specifying the Conceptual and Operational Models and the Research Questions that Follow Mark W. Lipsey Vanderbilt University IES/NCER Summer Research Training Institute, 2010

2. 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.

3. 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.

4. 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

5. 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)

6. 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.

7. Specifying the theory of change embodied in the intervention 1.Nature of the need addressed what and for whom (e.g., 2 nd 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

8. 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

9. 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 Target Population InterventionProximal OutcomesDistal Outcomes

11. 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 identification nuisance factors (for variance control) Potential moderators: setting, context personal and family characteristics prior experience

12. 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 personnel intervention setting, context e.g., class size

13. 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 affected side effects– unplanned positive or negative outcomes mediators– DVs on causal pathways from intervention to other DVs

14. 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.

15. 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?

16. Describing and Quantifying Outcomes Mark W. Lipsey Vanderbilt University IES/NCER Summer Research Training Institute, 2010

17. Outcome constructs to measure Identifying 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

18. Aligning the outcome constructs and measures with the intervention and policy objectives Instruction Assessment Policy relevant outcomes (e.g., state achievement standards)

19. Alignment of instructional tasks with the assessment tasks Identical Analogous (near transfer) Generalized (far transfer) Instructional tasks, activities, content

20. 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

21. Sensitivity to change: Achievement effect sizes from 124 randomized education studies Type of Outcome Measure Mean Effect Size Number of Measures Standardized test, broad.04103 Standardized test, narrow.28426 Focal topic test, mastery test.40300

22. 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

23. Variance control and measurement sensitivity Variance control via procedural consistency and statistical control using covariates for e.g., pre-intervention individual differences and differences in testing procedures or conditions

24. Issues related to multiple outcome measures

25. Correlated measures: overlap and efficiency Subtest Factor Loadings Pre-K Pretest Pre-K Posttest Kindergarten Follow-up Letter Word Identification Quantitative Concepts Applied Problems Picture Vocabulary Oral Comprehension Story Recall.60.82.75.82.53.69.82.80.76.79.55.73.78.75.67.74.61 Factor Analysis of Preschool Outcome Variables

26. Correlated change may be even more relevant Subtest Factor Loadings Pre to Post Post to Follow-up Pre to Follow-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

27. 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

28. IES Guidelines on multiple significance tests Schochet, 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

29. 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)