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Hierarchical Models for Educational Data: An Overview

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  • Carl N. Morris

Abstract

The use of hierarchical models in statistical applications, and for educational data, is a promising but still underutilized approach. However, because these models are more complicated than many standard methods, it is important that we, as users and developers, not rush to use them before we understand them. We emphasize here, in support of the views on hierarchical models expressed in the 3 preceding papers by Draper, by Rogosa and Saner, and by de Leeuw and Kreft, the need to not diminish hard thinking about data and iterative model checking when fitting hierarchical models, the need for more and better software, the need to test methods to assure their proper calibration, and the need to produce supporting materials to aid analysts and users of hierarchical modeling methods.

Suggested Citation

  • Carl N. Morris, 1995. "Hierarchical Models for Educational Data: An Overview," Journal of Educational and Behavioral Statistics, , vol. 20(2), pages 190-200, June.
    • Handle: RePEc:sae:jedbes:v:20:y:1995:i:2:p:190-200
    DOI: 10.3102/10769986020002190
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    Cited by:

    1. Yamen, Ahmed & Allam, Amir & Bani-Mustafa, Ahmed & Uyar, Ali, 2018. "Impact of institutional environment quality on tax evasion: A comparative investigation of old versus new EU members," Journal of International Accounting, Auditing and Taxation, Elsevier, vol. 32(C), pages 17-29.
    2. Atozou, Baoubadi & Tamini, Lota D. & Bergeronm, Stephane & Doyon, Maurice, 2020. "Factors Explaining the Hypothetical Bias: How to Improve Models for Meta-Analyses," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 45(2), March.

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