Explained Variance Measures for Multilevel Models

Autor:	David M. LaHuis, Shotaro Hakoyama, Patrick C. Clark, Michael J. Hartman
Rok vydání:	2014
Předmět:	One-way analysis of variance Management of Technology and Innovation Strategy and Management Multilevel model Statistics Econometrics Mode (statistics) General Decision Sciences Analysis of variance Explained variation Variance-based sensitivity analysis Control variates Mathematics
Zdroj:	Organizational Research Methods. 17:433-451
ISSN:	1552-7425 1094-4281
Popis:	One challenge in using multilevel models is determining how to report the amount of explained variance. In multilevel models, explained variance can be reported for each level or for the total model. Existing measures have been based primarily on the reduction of variance components across models. However, these measures have not been reported consistently because they have some undesirable properties. The present study is one of the first to evaluate the accuracy of these measures using Monte Carlo simulations. In addition, a measure based on the full partitioning of variance in multilevel models was examined. With the exception of the Level 2 explained variance measure, all other measures performed well across our simulated conditions.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::1649609fad01c6fb13d0e23ab8f7bfd0 https://doi.org/10.1177/1094428114541701 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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