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Multilevel models in psychometrics

Steele, Fiona and Goldstein, Harvey (2006) Multilevel models in psychometrics. In: Rao, C. R. and Sinharay, S., (eds.) Handbook of Statistics. Elsevier North-Holland, Amsterdam; London, pp. 401-420. ISBN 9780444521033

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This chapter provides models for repeated measures and multivariate data. It also introduces structural equation models and provides a description of simple multilevel models for data from populations with a two-level hierarchical structure. An analysis of variance (ANOVA) or fixed effects model is a way of allowing for school effects, which involves explanatory variables as a set of dummy variables that indicate the school to which a student belongs. While ANOVA can also be used to compare any number of schools, the random effects approach has a number of advantages over fixed effects models. First, if there are J schools to be compared, then J−1 parameters are required to capture school effects, and therefore, if J is large, a large number of parameters need to be estimated. Second, the origins of ANOVA lie in experimental design where there are typically a small number of groups under comparison and all groups of interest are sampled. In a fixed effects model, the effects of level 2 explanatory variables cannot be separately estimated.

Item Type: Book Section
Official URL:
Additional Information: © 2006 Elsevier B.V.
Divisions: Statistics
Subjects: H Social Sciences > HA Statistics
Sets: Departments > Statistics
Date Deposited: 02 Sep 2013 14:03
Last Modified: 20 Feb 2021 02:40

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