NDLI: Multilevel Model Prediction

Content Provider	Springer Nature Link
Author	Frees, Edward W. Kim, Jee Seon
Copyright Year	2006
Abstract	Multilevel models are proven tools in social research for modeling complex, hierarchical systems. In multilevel modeling, statistical inference is based largely on quantification of random variables. This paper distinguishes among three types of random variables in multilevel modeling—model disturbances, random coefficients, and future response outcomes—and provides a unified procedure for predicting them. These predictors are best linear unbiased and are commonly known via the acronym BLUP; they are optimal in the sense of minimizing mean square error and are Bayesian under a diffuse prior.For parameter estimation purposes, a multilevel model can be written as a linear mixed-effects model. In this way, parameters of the many equations can be estimated simultaneously and hence efficiently. For prediction purposes, we show that it is more convenient to retain the multiple equation feature of multilevel models. In this way, the efficient BLUPs are easy to compute and retain their intuitively appealing recursive form. We also derive explicit equations for standard errors of these different types of predictors.Prediction in multilevel modeling is important in a wide range of applications. To demonstrate the applicability of our results, this paper discusses prediction in the context of a study of school effectiveness.
Starting Page	79
Ending Page	104
Page Count	26
File Format	PDF
ISSN	00333123
Journal	Psychometrika
Volume Number	71
Issue Number	1
e-ISSN	18600980
Language	English
Publisher	Kluwer Academic Publishers-Plenum Publishers
Publisher Date	2006-04-07
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	best linear unbiased predictors linear mixed-effects models hierarchical linear models random coefficients school effectiveness Psychometrics Statistical Theory and Methods Assessment, Testing and Evaluation Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Psychology

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