Estimation of Random Coefficient Multilevel Models in the Context of Small Numbers of Level 2 Clusters

Autor/inn/en	Bolin, Jocelyn H.; Finch, W. Holmes; Stenger, Rachel
Titel	Estimation of Random Coefficient Multilevel Models in the Context of Small Numbers of Level 2 Clusters
Quelle	In: Educational and Psychological Measurement, 79 (2019) 2, S.217-248 (32 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/0013164418773494
Schlagwörter	Hierarchical Linear Modeling; Computation; Statistical Analysis; Maximum Likelihood Statistics; Bayesian Statistics; Markov Processes; Monte Carlo Methods; Statistical Bias; Error of Measurement; Sample Size + Suchen Sie Ihr Suchwort? Statistische Analyse; Markowscher Prozess; Monte-Carlo-Methode; Messfehler
Abstract	Multilevel data are a reality for many disciplines. Currently, although multiple options exist for the treatment of multilevel data, most disciplines strictly adhere to one method for multilevel data regardless of the specific research design circumstances. The purpose of this Monte Carlo simulation study is to compare several methods for the treatment of multilevel data specifically when there is random coefficient variation in small samples. The methods being compared are fixed effects modeling (the industry standard in business and managerial sciences), multilevel modeling using restricted maximum likelihood (REML) estimation (the industry standard in the social and behavioral sciences), multilevel modeling using the Kenward-Rogers correction, and Bayesian estimation using Markov Chain Monte Carlo. Results indicate that multilevel modeling does have an advantage over fixed effects modeling when Level 2 slope parameter variance exists. Bayesian estimation of multilevel effects can be advantageous over traditional multilevel modeling using REML, but only when prior probabilities are correctly specified. Results are presented in terms of Type I error, power, parameter estimation bias, empirical parameter estimate standard error, and parameter 95% coverage rates, and recommendations are presented. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01