Modeled variance in two-level models

Tom A.B. Snijders, Roel Bosker

    Research output: Contribution to journalArticleAcademicpeer-review

    458 Citations (Scopus)
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    Abstract

    The concept of explained proportion of variance or modeled proportion of variance is reviewed in the situation of the random effects hierarchical two-level model. It is argued that the proportional reduction in (estimated) variance components is not an attractive parameter to represent the joint importance of the explanatory (independent) variables for modeling the dependent variable. It is preferable instead to work with the proportional reduction in mean squared prediction error for predicting individual values (for the modeled variance at level 1) and the proportional reduction in mean squared prediction error for predicting group averages (for the modeled variance at level 2). It is shown that when predictors are added, the proportion of modeled variance defined in this way cannot go down in the population if the model is correctly specified, but can go down in a sample; the latter situation then points to the possibility of misspecification. This provides a diagnostic means for identifying misspecification.
    Original languageEnglish
    Pages (from-to)342-363
    Number of pages22
    JournalSociological methods and research
    Volume22
    Issue number3
    DOIs
    Publication statusPublished - 1994

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