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A Procedure for Assessing Fidelity of Implementation in Experiments Testing Educational Interventions Michael C. Nelson 1, David S. Cordray 1, Chris S. Hulleman 2, Catherine L. Darrow 1, & Evan C. Sommer 1 1 Vanderbilt University, 2 James Madison University 1

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Purposes of Paper: 2 To argue for a model-based approach for assessing implementation fidelity To provide a template for assessing implementation fidelity that can be used by intervention developers, researchers, and implementers as a standard approach.

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Presentation Outline 3 I. What is implementation fidelity? II. Why assess implementation fidelity? III. A five-step process for assessing implementation fidelity IV. Concluding points

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A Note on Examples: 4 Examples are drawn from our review of (mainly) elementary math intervention studies, which we are currently deepening and expanding to other subject areas Examples for many areas are imperfect or lacking As our argument depends on having good examples of the most complicated cases, we appreciate any valuable examples to which you can refer us (michael.nelson@vanderbilt.edu.)

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What Is Implementation Fidelity? 5

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What is implementation fidelity? 6 Implementation fidelity is the extent to which the intervention has been implemented as expected Assessing fidelity raises the question: Fidelity to what? Our answer: Fidelity to the intervention model. Background in “theory-based evaluations” (e.g., Chen, 1990; Donaldson & Lipsey, 2006)

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Why Assess Implementation Fidelity? 7

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Fidelity vs. the Black Box 8 The intent-to-treat (ITT) experiment identifies the effects of causes: Assignment to Condition Treatment “Black Box” Intervention’s Causal Processes Outcomes Outcome Measures Control “Black Box” Business As Usual Causal Processes Outcomes Outcome Measures

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Fidelity vs. the Black Box 9 …While fidelity assessment “opens up” the black box to explain the effects of causes: Intervention “Black Box” Intervention Component MediatorOutcome Assignment to Condition Fidelity Measure 1 Fidelity Measure 2 Outcome Measure

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Fidelity assessment allows us to: 10 Determine the extent of construct validity and external validity, contributing to generalizability of results

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Fidelity assessment allows us to: 11 Determine the extent of construct validity and external validity, contributing to generalizability of results For significant results, describe what exactly did work (actual difference between Tx and C)

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Fidelity assessment allows us to: 12 Determine the extent of construct validity and external validity, contributing to generalizability of results For significant results, describe what exactly did work (actual difference between Tx and C) For non-significant results, it may explain why beyond simply “the intervention doesn’t work”

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Fidelity assessment allows us to: 13 Determine the extent of construct validity and external validity, contributing to generalizability of results For significant results, describe what exactly did work (actual difference between Tx and C) For non-significant results, it may explain why beyond simply “the intervention doesn’t work” Potentially improve understanding of results and future implementation

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Limitations of Fidelity Assessment: 14 Not a causal analysis, but it does provide evidence for answering important questions

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Limitations of Fidelity Assessment: 15 Not a causal analysis, but it does provide evidence for answering important questions Involves secondary questions

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Limitations of Fidelity Assessment: 16 Not a causal analysis, but it does provide evidence for answering important questions Involves secondary questions Field is still developing and validating methods and tools for measurement and analysis

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Limitations of Fidelity Assessment: 17 Not a causal analysis, but it does provide evidence for answering important questions Involves secondary questions Field is still developing and validating methods and tools for measurement and analysis Cannot be a specific, one-size-fits-all approach

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A Five Step Process for Assessing Fidelity of Implementation 18 1. Specify the intervention model 2. Identify fidelity indices 3. Determine index reliability and validity 4. Combine fidelity indices* 5. Link fidelity measures to outcomes* *Not always possible or necessary

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Step 1: Specify the Intervention Model 19

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The Change Model 20 A hypothetical set of constructs and relationships among constructs representing the core components of the intervention and the causal processes that result in outcomes

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The Change Model 21 A hypothetical set of constructs and relationships among constructs representing the core components of the intervention and the causal processes that result in outcomes Should be based on theory, empirical findings, discussion with developer, actual implementation

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The Change Model 22 A hypothetical set of constructs and relationships among constructs representing the core components of the intervention and the causal processes that result in outcomes Should be based on theory, empirical findings, discussion with developer, actual implementation Start with Change Model because it is sufficiently abstract to be generalizable, but also specifies important components/processes, thus guiding operationalization, measurement, and analysis

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Change Model: Generic Example 23 Teacher training in use of educational software Teachers assist students in using educational software Improved student learning Intervention Component MediatorOutcome

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Change Model: Project LINCS 24 Adapted from Swafford, Jones, and Thornton, 1997 Instruction in student cognition of geometry Instruction in geometry Increase in teacher knowledge of student cognition Increase in teacher knowledge of geometry Improved teacher instructional practice

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The Logic Model 25 The set of resources and activities that operationalize the change model for a particular implementation

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The Logic Model 26 The set of resources and activities that operationalize the change model for a particular implementation A roadmap for implementation

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The Logic Model 27 The set of resources and activities that operationalize the change model for a particular implementation A roadmap for implementation Derived from the change model with input from developer and other sources (literature, implementers, etc.)

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Logic Model: Project LINCS 28 Adapted from Swafford, Jones, and Thornton, 1997 Research seminar on van Hiele model Geometry content course Increase in teacher knowledge of student cognition Increase in teacher knowledge of geometry Instruction in geometry Instruction in student cognition of geometry Improved teacher instructional practice How it is taught Characteristics teachers display What is taught

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A Note on Models and Analysis: 29 Recall that one can specify models for both the treatment and control conditions. The “true” cause is the difference between conditions, as reflected in the model for each. Using the change model as a guide, one may design equivalent indices for each condition to determine the relative strength of the intervention (Achieved Relative Strength, ARS). This approach will be discussed in the next presentation (Hulleman).

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Steps 2 and 3: Develop Reliable and Valid Fidelity Indices and Apply to the Model 30

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Examples of Fidelity Indices 31 Self-report surveys Interviews Participant logs Observations Examination of permanent products created during the implementation process

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Index Reliability and Validity 32 Both are reported inconsistently Report reliability at a minimum, because unreliable indices cannot be valid Validity is probably best established from pre-existing information or side studies

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Index Reliability and Validity 33 Both are reported inconsistently Report reliability at a minimum, because unreliable indices cannot be valid Validity is probably best established from pre-existing information or side studies We should be as careful in measuring the cause as we are in measuring its effects!

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Selecting Indices 34 Guided foremost by the change model: identify core components as those that differ significantly between conditions and upon which the causal processes are thought to depend

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Selecting Indices 35 Guided foremost by the change model: identify core components as those that differ significantly between conditions and upon which the causal processes are thought to depend Use the logic model to determine fidelity indicator(s) for each change component

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Selecting Indices 36 Guided foremost by the change model: identify core components as those that differ significantly between conditions and upon which the causal processes are thought to depend Use the logic model to determine fidelity indicator(s) for each change component Base the number and type of indices on the nature and importance of each component

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Selecting Indices: Project LINCS 37 Adapted from Swafford, Jones, and Thornton, 1997 Change Model Construct Logic Model Components IndicatorsIndices Instruction in geometryGeometry content course None; Proposed: Teacher attendance, content delivery None; Proposed: Head count, observation Instruction of student cognition of geometry Research seminar van Hiele model None; Proposed: Teacher attendance, content delivery None; Proposed: Head count, observation Increase of teacher knowledge of geometry NoneAbility to apply geometry knowledge Pre/post test of geometry knowledge Increase of teacher knowledge of student cognition NoneAbility to describe student cognition Pre/post test of van Hiele levels Improved teacher instructional practice What is taughtAlignment of lesson content with van Hiele levels Observations Improved teacher instructional practice How it is taughtParticular instructional behaviors of teachers Observations Improved teacher instructional practice Characteristics teachers display Reflecting knowledge of student cognition in planning Lesson plan task

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Step 4: Combining Fidelity Indices* 38

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Why Combine Indices? 39 *May not be possible for the simplest models *Depends on particular questions

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Why Combine Indices? 40 *May not be possible for the simplest models *Depends on particular questions Combine within component to assess fidelity to a construct Combine across components to assess phase of implementation Combine across model to characterize overall fidelity and facilitate comparison of studies

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Some Approaches to Combining Indices: 41 Total percentage of steps implemented Average number of steps implemented

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Some Approaches to Combining Indices: 42 Total percentage of steps implemented Average number of steps implemented HOWEVER: These approaches may underestimate or overestimate the importance of some components!

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Some Approaches to Combining Indices: 43 Total percentage of steps implemented Average number of steps implemented HOWEVER: These approaches may underestimate or overestimate the importance of some components! Weighting components based on the intervention model Sensitivity analysis

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MAP Example 44 Weighting of training sessions for the MAP intervention Cordray, et al (Unpublished) Training Session MonthContentInitial WeightAdjusted Weight Session 1SeptemberAdministration.25.10 Session 2OctoberData use.25.30 Session 3NovemberDifferentiated Instruction.25.50 Session 4MayGrowth and planning.25.10

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Step 5: Linking Fidelity Measures to Outcomes* 45

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Linking Fidelity and Outcomes 46 *Not possible in (rare) cases of perfect fidelity (no covariation without variation) *Depends on particular questions Provide evidence supporting the model (or not) Identify “weak links” in implementation Point to opportunities for “boosting” strength Identify incorrectly-specified components of the model

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Assessment to Instruction (A2i) 47 Teacher use of web-based software for differentiation of reading instruction Professional development Students use A2i Teachers use A2i recommendations for grouping and lesson planning Students improve learning Measures: Time teachers logged in, observation of instruction, pre/post reading (Connor, Morrison, Fishman, Schatschneider, and Underwood, 1997)

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Assessment to Instruction (A2i) 48 Used Hierarchical Linear Modeling to analyze Overall effect size of.25 Tx vs. C Pooling Tx+C, teacher time using A2i accounted for 15% of student performance Since gains were greatest among teachers who both attended PD and were logged in more, concluded both components were necessary for outcome (Connor, Morrison, Fishman, Schatschneider, and Underwood, 1997)

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Some Other Approaches to Linking from the Literature 49 Compare results of hypothesis testing (e.g., ANOVA) when “low fidelity” classrooms are included or excluded Correlate overall fidelity index with each student outcome Correlate each fidelity indicator with the single outcome Calculate Achieved Relative Strength (ARS) and use HLM to link to outcomes

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Concluding points 50

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In Summary: 51 If we do not know what we are testing, we cannot know what the results of our tests mean.

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In Summary: 52 If we do not know what we are testing, we cannot know what the results of our tests mean. Model-based (change and logic) assessment answers the question “Fidelity to what?”

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In Summary: 53 If we do not know what we are testing, we cannot know what the results of our tests mean. Model-based (change and logic) assessment answers the question “Fidelity to what?” There is a need for a systematic approach to fidelity assessment, which we describe

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In Summary: 54 If we do not know what we are testing, we cannot know what the results of our tests mean. Model-based (change and logic) assessment answers the question “Fidelity to what?” There is a need for a systematic approach to fidelity assessment, which we describe Most useful when research designs are able to incorporate this process from early stages

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In Summary: 55 If we do not know what we are testing, we cannot know what the results of our tests mean. Model-based (change and logic) assessment answers the question “Fidelity to what?” There is a need for a systematic approach to fidelity assessment, which we describe Most useful when research designs are able to incorporate this process from early stages Additional examples and refinement of measurement and analytical tools are needed

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References 56 Chen, H.T. (1990). Theory-driven evaluation. Thousand Oaks, CA: Sage Publications. Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences, 2nd ed. Hillsdale, NJ: Erlbaum. Connor, C. M., Morrison, F. M., Fishman, B. J., Schatschneider, C., & Underwood, P. (2007). Algorithm-guided individualized reading instruction. Science, 315, 464-465. Cook, T. (1985). Postpositivist critical multiplism. In R. L. Shotland & M. M. Marks (Eds.), Social science and social policy (pp. 21-62). Beverly Hills, CA: Sage. Cordray, D.S. (2007) Assessing Intervention Fidelity in Randomized Field Experiments. Funded Goal 5 proposal to Institute of Education Sciences. Cordray, D.S., Pion, G.M., Dawson, M., and Brandt, C. (2008). The Efficacy of NWEA’s MAP Program. Institute of Education Sciences funded proposal. Donaldson, S.I., & Lipsey, M.W. (2006). Roles for theory in contemporary evaluation practice: Developing practical knowledge. In I. Shaw, J.C. Greene, & M.M. Mark (Eds.), The Handbook of Evaluation: Policies, Programs, and Practices (pp. 56-75). London: Sage. Fuchs, L.S., Fuchs, D., and Karns, K. (2001). Enhancing kindergarteners’ mathematical development: Effects of peer-assisted learning strategies. Elementary School Journal, 101, 495-510. Fuchs, L. S., Fuchs, D., Yazdian, L, & Powell, S. R. (2002). Enhancing First-Grade Children's Mathematical Development with Peer-Assisted Learning Strategies. School Psychology Review, 31, 569-583. Gamse, B.C., Jacob, R.T., Megan, H., Boulay, B., Unlu, Fatih, Bozzi, L., Caswell, L., Rodger, C., Smith, W.C., Brigham, N., and Rosenblum, S. (2008). Reading First Impact Study Final Report. Washington, D.C.: Institute of Education Sciences.

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References 57 Ginsburg-Block, M. & Fantuzzo, J. (1997). Reciprocal peer tutoring: An analysis of teacher and student interactions as a function of training and experience. School Psychology Quarterly, 12, 1-16. Holland, P.W. (1986). Statistics and causal inference. Journal of the American Statistical Association.81(396), 945- 960. Hulleman, C. S., & Cordray, D. (2009). Moving from the lab to the field: The role of fidelity and achieved relative intervention strength. Journal of Research on Intervention Effectiveness, 2(1), 88-110. Hulleman, C.S., Cordray, D.S., Nelson, M.C., Darrow, C.L., & Sommer, E.C. (2009, June). The State of Treatment Fidelity Assessment in Elementary Mathematics Interventions. Poster presented at the annual Institute of Education Sciences Conference, Washington, D.C. Institute of Education Sciences (2004). Pre-doctoral training grant announcement. Washington, DC: US Department of Education. Knowlton, L.W. and Phillips, C.C. (2009). The Logic Model Guidebook: Better Strategies for Great Results. Washington, D.C.: Sage. Kutash, K., Duchnowski, A. J., Sumi, W. C., Rudo, Z. & Harris, K. M. (2002). A school, family, and community collaborative program for children who have emotional disturbances. Journal of Emotional and Behavioral Disorders, 10(2), 99-107. McIntyre, L.L., Gresham, F.M., DiGennaro, F.D., and Reed, D.D. (2007). Treatment integrity of school-based interventions with children in the Journal of Applied Behavior Analysis 1991-2005. Journal of Applied Behavior Analysis. 40, 659-672.

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References 58 Michalopoulos, C. (2005). Precedents and Prospects for Randomized Experiments. In H.S. Bloom (Ed.) Learning More from Social Experiments, (pp. 1-36). New York, NY: Russell Sage Foundation. Noell, G.H., Witt, J.C., Slider, N.J., Connell, J.E., Gatti, S.L., Williams, K.L., Koenig, J.L. & Resetar, J.L. (2005). Treatment Implementation Following Behavioral Consultation in Schools: A Comparison of Three Follow-up Strategies. School Psychology Review, 34(1), 87-106. O'Donnell, C. L. (2008). Defining, Conceptualizing, and Measuring Fidelity of Implementation and Its Relationship to Outcomes in K-12 Curriculum Intervention Research. Review of Educational Research, 78(1), 33-84. Shadish, W.R., Cook, T.D., and Campbell, D.T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. New York, NY: Houghton Mifflin Company. Swafford, J.O., Jones, G.A., and Thornton, C.A. (1997). Increased Knowledge in Geometry and Instructional Practice. Journal for Research in Mathematics Education, 28(4), 467- 483. Trochim, W. and Cook, J. (1992). Pattern matching in theory-driven evaluation: A field example from psychiatric rehabilitation. In H. Chen and P.H. Rossi (Eds.) Using Theory to Improve Program and Policy Evaluations. Greenwood Press, New York, 49-69.

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