Modeled Variance in Two-Level Models

Tom A. B. Snijders and Roel J. Bosker
Additional contact information
Tom A. B. Snijders: University of Groningen, Netherlands
Roel J. Bosker: University of Twente, Netherlands

Sociological Methods & Research, 1994, vol. 22, issue 3, 342-363

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.

Date: 1994
References: View complete reference list from CitEc
Citations: View citations in EconPapers (15)

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/0049124194022003004 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:somere:v:22:y:1994:i:3:p:342-363

DOI: 10.1177/0049124194022003004

Access Statistics for this article

More articles in Sociological Methods & Research
Bibliographic data for series maintained by SAGE Publications ().