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Modelling response error in school effectiveness research

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  • Jean‐Paul Fox

Abstract

Statistical modelling of school effectiveness in educational research is considered. Variance component models are generally accepted for the analysis of such studies. A shortcoming is that outcome variables are still treated as measured without an error. Unreliable variables produce biases in the estimates of the other model parameters. The variability of the relationships across schools and the effects of schools on students’ outcomes differ substantially when taking the measurement error in the dependent variables of the variance component models into account. The random effects model can be extended to handle measurement error using a response model, leading to a random effects item response theory model. This extended random effects model is in particular suitable when subjects are measured repeatedly on the same outcome at several points in time.

Suggested Citation

  • Jean‐Paul Fox, 2004. "Modelling response error in school effectiveness research," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 58(2), pages 138-160, May.
    • Handle: RePEc:bla:stanee:v:58:y:2004:i:2:p:138-160
    DOI: 10.1046/j.0039-0402.2003.00253.x
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    1. repec:jss:jstsof:20:i05 is not listed on IDEAS
    2. Kyle Cox & Benjamin Kelcey, 2019. "Optimal Design of Cluster- and Multisite-Randomized Studies Using Fallible Outcome Measures," Evaluation Review, , vol. 43(3-4), pages 189-225, June.
    3. Fox, Jean-Paul, 2007. "Multilevel IRT Modeling in Practice with the Package mlirt," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 20(i05).

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