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Intraclass Correlations and Covariate Outcome Correlations for Planning Two- and Three-Level Cluster-Randomized Experiments in Education

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  • Larry V. Hedges
  • E. C. Hedberg

Abstract

Background: Cluster-randomized experiments that assign intact groups such as schools or school districts to treatment conditions are increasingly common in educational research. Such experiments are inherently multilevel designs whose sensitivity (statistical power and precision of estimates) depends on the variance decomposition across levels. This variance decomposition is usually summarized by the intraclass correlation (ICC) structure and, if covariates are used, the effectiveness of the covariates in explaining variation at each level of the design. Objectives: This article provides a compilation of school- and district-level ICC values of academic achievement and related covariate effectiveness based on state longitudinal data systems. These values are designed to be used for planning group-randomized experiments in education. The use of these values to compute statistical power and plan two- and three-level group-randomized experiments is illustrated. Research Design: We fit several hierarchical linear models to state data by grade and subject to estimate ICCs and covariate effectiveness. The total sample size is over 4.8 million students. We then compare our average of state estimates with the national work by Hedges and Hedberg.

Suggested Citation

  • Larry V. Hedges & E. C. Hedberg, 2013. "Intraclass Correlations and Covariate Outcome Correlations for Planning Two- and Three-Level Cluster-Randomized Experiments in Education," Evaluation Review, , vol. 37(6), pages 445-489, December.
    • Handle: RePEc:sae:evarev:v:37:y:2013:i:6:p:445-489
    DOI: 10.1177/0193841X14529126
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    Citations

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    Cited by:

    1. Ben Kelcey & Zuchao Shen & Jessaca Spybrook, 2016. "Intraclass Correlation Coefficients for Designing Cluster-Randomized Trials in Sub-Saharan Africa Education," Evaluation Review, , vol. 40(6), pages 500-525, December.
    2. Daniel McNeish & Jeffrey R. Harring & Denis Dumas, 2023. "A multilevel structured latent curve model for disaggregating student and school contributions to learning," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(2), pages 545-575, June.
    3. Elizabeth Tipton & Robert B. Olsen, "undated". "Enhancing the Generalizability of Impact Studies in Education," Mathematica Policy Research Reports 35d5625333dc480aba9765b3b, Mathematica Policy Research.
    4. Nianbo Dong & Wendy M. Reinke & Keith C. Herman & Catherine P. Bradshaw & Desiree W. Murray, 2016. "Meaningful Effect Sizes, Intraclass Correlations, and Proportions of Variance Explained by Covariates for Planning Two- and Three-Level Cluster Randomized Trials of Social and Behavioral Outcomes," Evaluation Review, , vol. 40(4), pages 334-377, August.
    5. Blair S Grace & Tess Gregory & Luke Collier & Sally Brinkman, 2022. "Clustering of Wellbeing, Engagement and Academic Outcomes in Australian Primary Schools," Child Indicators Research, Springer;The International Society of Child Indicators (ISCI), vol. 15(6), pages 2171-2195, December.
    6. E. C. Hedberg, 2016. "Academic and Behavioral Design Parameters for Cluster Randomized Trials in Kindergarten," Evaluation Review, , vol. 40(4), pages 279-313, August.
    7. Nianbo Dong & Benjamin Kelcey & Jessaca Spybrook, 2021. "Design Considerations in Multisite Randomized Trials Probing Moderated Treatment Effects," Journal of Educational and Behavioral Statistics, , vol. 46(5), pages 527-559, October.
    8. E. C. Hedberg & Larry V. Hedges, 2014. "Reference Values of Within-District Intraclass Correlations of Academic Achievement by District Characteristics," Evaluation Review, , vol. 38(6), pages 546-582, December.
    9. Larry V. Hedges & Michael Borenstein, 2014. "Conditional Optimal Design in Three- and Four-Level Experiments," Journal of Educational and Behavioral Statistics, , vol. 39(4), pages 257-281, August.
    10. Jessaca Spybrook & Benjamin Kelcey, 2016. "Introduction to Three Special Issues on Design Parameter Values for Planning Cluster Randomized Trials in the Social Sciences," Evaluation Review, , vol. 40(6), pages 491-499, December.

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