IDEAS home Printed from https://ideas.repec.org/a/sae/jedbes/v38y2013i3p219-238.html
   My bibliography  Save this article

Estimators for Clustered Education RCTs Using the Neyman Model for Causal Inference

Author

Listed:
  • Peter Z. Schochet

Abstract

This article examines the estimation of two-stage clustered designs for education randomized control trials (RCTs) using the nonparametric Neyman causal inference framework that underlies experiments. The key distinction between the considered causal models is whether potential treatment and control group outcomes are considered to be fixed for the study population (the finite-population model) or randomly selected from a vaguely defined universe (the super-population model). Both approaches allow for heterogeneity of treatment effects. Appropriate estimation methods and asymptotic moments are discussed for each model using simple differences-in-means estimators and those that include baseline covariates. An empirical application using a large-scale education RCT shows that the choice of the finite- or super-population approach can matter. Thus, the choice of framework and sensitivity analyses should be specified and justified in the analysis protocols.

Suggested Citation

  • Peter Z. Schochet, 2013. "Estimators for Clustered Education RCTs Using the Neyman Model for Causal Inference," Journal of Educational and Behavioral Statistics, , vol. 38(3), pages 219-238, June.
    • Handle: RePEc:sae:jedbes:v:38:y:2013:i:3:p:219-238
    DOI: 10.3102/1076998611432176
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.3102/1076998611432176
    Download Restriction: no
    File URL: https://libkey.io/10.3102/1076998611432176?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. repec:mpr:mprres:6192 is not listed on IDEAS
    2. Yang L. & Tsiatis A. A., 2001. "Efficiency Study of Estimators for a Treatment Effect in a Pretest-Posttest Trial," The American Statistician, American Statistical Association, vol. 55, pages 314-321, November.
    3. repec:mpr:mprres:7723 is not listed on IDEAS
    4. repec:mpr:mprres:6573 is not listed on IDEAS
    5. repec:mpr:mprres:5863 is not listed on IDEAS
    6. Baltagi, Badi H. & Chang, Young-Jae, 1994. "Incomplete panels : A comparative study of alternative estimators for the unbalanced one-way error component regression model," Journal of Econometrics, Elsevier, vol. 62(2), pages 67-89, June.
    7. Small, Dylan S. & Ten Have, Thomas R. & Rosenbaum, Paul R., 2008. "Randomization Inference in a GroupRandomized Trial of Treatments for Depression: Covariate Adjustment, Noncompliance, and Quantile Effects," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 271-279, March.
    8. D. Pfeffermann & C. J. Skinner & D. J. Holmes & H. Goldstein & J. Rasbash, 1998. "Weighting for unequal selection probabilities in multilevel models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(1), pages 23-40.
    9. Peter Z. Schochet, "undated". "Is Regression Adjustment Supported by the Neyman Model for Causal Inference? (Presentation)," Mathematica Policy Research Reports abfc39d59c714499b2fe42f68, Mathematica Policy Research.
    10. Peter Z. Schochet, "undated". "Is Regression Adjustment Supported By the Neyman Model for Causal Inference?," Mathematica Policy Research Reports 782da2242fba458eb61752f96, Mathematica Policy Research.
    11. Swamy, P A V B & Arora, S S, 1972. "The Exact Finite Sample Properties of the Estimators of Coefficients in the Error Components Regression Models," Econometrica, Econometric Society, vol. 40(2), pages 261-275, March.
    12. Roberto Agodini, 2009. "Achievement Effects of Four Early Elementary School Math Curricula: Findings from First Graders in 39 Schools (Conference Paper)," Mathematica Policy Research Reports a08d661306a843fa89aff4c30, Mathematica Policy Research.
    13. Peter Z. Schochet, "undated". "Statistical Power for Random Assignment Evaluations of Education Programs," Mathematica Policy Research Reports 6749d31ad72d4acf988f7dce5, Mathematica Policy Research.
    14. repec:mpr:mprres:7337 is not listed on IDEAS
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Peter Z. Schochet, "undated". "Statistical Theory for the RCT-YES Software: Design-Based Causal Inference for RCTs," Mathematica Policy Research Reports a0c005c003c242308a92c02dc, Mathematica Policy Research.
    2. repec:mpr:mprres:8128 is not listed on IDEAS
    3. Tim Kautz & Peter Z. Schochet & Charles Tilley, "undated". "Comparing Impact Findings from Design-Based and Model-Based Methods: An Empirical Investigation," Mathematica Policy Research Reports b7656ddce20f4007b71836e99, Mathematica Policy Research.
    4. Peter Z. Schochet, "undated". "What is Design-Based Causal Inference and Why Should I Use It?," Mathematica Policy Research Reports 82a207630f374ef6a7dfd4a60, Mathematica Policy Research.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Peter Z. Schochet, 2018. "Design-Based Estimators for Average Treatment Effects for Multi-Armed RCTs," Journal of Educational and Behavioral Statistics, , vol. 43(5), pages 568-593, October.
    2. repec:mpr:mprres:6094 is not listed on IDEAS
    3. Peter Z. Schochet, 2020. "Analyzing Grouped Administrative Data for RCTs Using Design-Based Methods," Journal of Educational and Behavioral Statistics, , vol. 45(1), pages 32-57, February.
    4. repec:mpr:mprres:6372 is not listed on IDEAS
    5. Peter Z. Schochet, "undated". "Statistical Theory for the RCT-YES Software: Design-Based Causal Inference for RCTs," Mathematica Policy Research Reports a0c005c003c242308a92c02dc, Mathematica Policy Research.
    6. Peter Z. Schochet, 2010. "The Late Pretest Problem in Randomized Control Trials of Education Interventions," Journal of Educational and Behavioral Statistics, , vol. 35(4), pages 379-406, August.
    7. Peter Z. Schochet, "undated". "The Late Pretest Problem in Randomized Control Trials of Education Interventions," Mathematica Policy Research Reports fb514df5dbb84a5dbea79865c, Mathematica Policy Research.
    8. Peter Z. Schochet, "undated". "Technical Methods Report: Statistical Power for Regression Discontinuity Designs in Education Evaluations," Mathematica Policy Research Reports 61fb6c057561451a8a6074508, Mathematica Policy Research.
    9. repec:mpr:mprres:6286 is not listed on IDEAS
    10. Kenneth Fortson & Natalya Verbitsky-Savitz & Emma Kopa & Philip Gleason, 2012. "Using an Experimental Evaluation of Charter Schools to Test Whether Nonexperimental Comparison Group Methods Can Replicate Experimental Impact Estimates," Mathematica Policy Research Reports 27f871b5b7b94f3a80278a593, Mathematica Policy Research.
    11. John Deke, 2016. "Design and Analysis Considerations for Cluster Randomized Controlled Trials That Have a Small Number of Clusters," Evaluation Review, , vol. 40(5), pages 444-486, October.
    12. Peter Z. Schochet, 2021. "Statistical Power for Estimating Treatment Effects Using Difference-in-Differences and Comparative Interrupted Time Series Designs with Variation in Treatment Timing," Papers 2102.06770, arXiv.org, revised Oct 2021.
    13. Peter Z. Schochet & Hanley Chiang, "undated". "Technical Methods Report: Estimation and Identification of the Complier Average Causal Effect Parameter in Education RCTs," Mathematica Policy Research Reports 947d1823e3ff42208532a763d, Mathematica Policy Research.
    14. repec:mpr:mprres:7443 is not listed on IDEAS
    15. repec:mpr:mprres:7638 is not listed on IDEAS
    16. repec:mpr:mprres:7273 is not listed on IDEAS
    17. Lu, Jiannan, 2016. "On randomization-based and regression-based inferences for 2K factorial designs," Statistics & Probability Letters, Elsevier, vol. 112(C), pages 72-78.
    18. Joel A. Middleton, 2021. "Unifying Design-based Inference: On Bounding and Estimating the Variance of any Linear Estimator in any Experimental Design," Papers 2109.09220, arXiv.org.
    19. Melissa A. Clark & Philip Gleason & Christina Clark Tuttle & Marsha K. Silverberg, 2011. "Do Charter Schools Improve Student Achievement? Evidence from a National Randomized Study," Mathematica Policy Research Reports af41392138504f369930e6f2b, Mathematica Policy Research.
    20. Pandey, Ashish & Guhathakurta, Kousik, 2022. "Value relevance of loan loss provision components and the choice of model specification," Advances in accounting, Elsevier, vol. 58(C).
    21. Fadhuile, A., 2018. "Can we explain pesticide price trend by the regulation changes ?," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277112, International Association of Agricultural Economists.
    22. Yang, Juan & Mitchell, Paul D. & Gray, Michael E. & Steffey, Kevin L., 2007. "Unbalanced Nested Component Error Model and the Value of Soil Insecticide and Bt Corn for Controlling Western Corn Rootworm," Staff Papers 92127, University of Wisconsin-Madison, Department of Agricultural and Applied Economics.
    23. Dědeček Radek & Dudzich Viktar, 2022. "Exploring the limitations of GDP per capita as an indicator of economic development: a cross-country perspective," Review of Economic Perspectives, Sciendo, vol. 22(3), pages 193-217, September.
    24. Susan Athey & Guido Imbens, 2016. "The Econometrics of Randomized Experiments," Papers 1607.00698, arXiv.org.
    25. Yang, Juan & Mitchell, Paul D. & Gray, Michael & Steffey, Kevin, 2007. "Unbalanced Nested Component Error Model and the Value of Soil Insecticide and Bt Corn for Controlling Western Corn Rootworm," Staff Paper Series 510, University of Wisconsin, Agricultural and Applied Economics.
    26. Christos A. Grambovas & Begoña Giner & Demetris Christodoulou, 2006. "Earnings conservatism: panel data evidence from the European Union and the United States," Abacus, Accounting Foundation, University of Sydney, vol. 42(3‐4), pages 354-378, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:jedbes:v:38:y:2013:i:3:p:219-238. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.