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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, 2007. Workshop on randomized controlled trials.

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session 2 specifying the conceptual and operational models and the research questions that follow

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
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
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
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 continued5
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
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
Specifying the theory of change embodied in the intervention
  • 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
Specifying the theory of change
  • 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
Logic models as theory schematics

Target

Population

Intervention

Proximal Outcomes

Distal Outcomes

Positive attitudes to school

4 year old pre-K children

Improved pre-literacy skills

Increased school readiness

Greater cognitive gains in K

Exposed to intervention

Learn appropriate school behavior

mapping variables onto the intervention theory sample characteristics
Mapping variables onto the intervention theory: Sample characteristics

Positive attitudes to school

4 year old pre-K children

Improved pre-literacy skills

Increased school readiness

Greater cognitive gains in K

Exposed to intervention

Learn appropriate school behavior

Sample descriptors:

basic demographics

diagnostic, need/eligibility

identification

nuisance factors (for

variance control)

Potential moderators:

setting, context

personal and family

characteristics

prior experience

mapping variables onto the intervention theory intervention characteristics
Mapping variables onto the intervention theory: Intervention characteristics

Positive attitudes to school

4 year old pre-K children

Improved pre-literacy skills

Increased school readiness

Greater cognitive gains in K

Exposed to intervention

Learn appropriate school behavior

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

mapping variables onto the intervention theory intervention outcomes
Mapping variables onto the intervention theory: Intervention outcomes

Positive attitudes to school

4 year old pre-K children

Improved pre-literacy skills

Increased school readiness

Greater cognitive gains in K

Exposed to intervention

Learn appropriate school behavior

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

main relationships of possible interest
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
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

Session 3:Describing and Quantifying Outcomes

Mark W. Lipsey

Vanderbilt University

IES/NCER Summer Research Training Institute, 2007

outcome constructs to measure
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
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
Alignment of instructional tasks with the assessment tasks

Identical

Instructional tasks,

activities, content

Analogous

(near transfer)

Generalized

(far transfer)

basic psychometric issues
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
data from which measurement sensitivity can be inferred
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 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

correlated measures overlap and efficiency
Correlated measures: overlap and efficiency

Factor Analysis of Preschool Outcome Variables

correlated change may be even more relevant
Correlated change may be even more relevant

Factor Analysis of Gain Scores for Pre-K Outcomes

handling multiple correlated outcome measures
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
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)
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