Avoiding Bias When Estimating the Consistency and Stability of Value-Added School Effects

Autor/in	Leckie, George
Titel	Avoiding Bias When Estimating the Consistency and Stability of Value-Added School Effects
Quelle	In: Journal of Educational and Behavioral Statistics, 43 (2018) 4, S.440-468 (29 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Leckie, George)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1076-9986
DOI	10.3102/1076998618755351
Schlagwörter	Value Added Models; Reliability; Statistical Bias; Computation; Hierarchical Linear Modeling; Correlation; Bayesian Statistics; Elementary Schools; Secondary Schools; Foreign Countries; Maximum Likelihood Statistics; Statistical Analysis; United Kingdom (England) + Suchen Sie Ihr Suchwort? Reliabilität; Korrelation; Elementary school; Grundschule; Volksschule; Sekundarschule; Ausland; Statistische Analyse
Abstract	The traditional approach to estimating the consistency of school effects across subject areas and the stability of school effects across time is to fit separate value-added multilevel models to each subject or cohort and to correlate the resulting empirical Bayes predictions. We show that this gives biased correlations and these biases cannot be avoided by simply correlating "unshruken" or "reflated" versions of these predicted random effects. In contrast, we show that fitting a joint value-added multilevel multivariate response model simultaneously to all subjects or cohorts directly gives unbiased estimates of the correlations of interest. There is no need to correlate the resulting empirical Bayes predictions and indeed we show that this should again be avoided as the resulting correlations are also biased. We illustrate our arguments with separate applications to measuring the consistency and stability of school effects in primary and secondary school settings. However, our arguments apply more generally to other areas of application where researchers routinely interpret correlations between predicted random effects rather than estimating and interpreting these correlation directly. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01