ERIC Number: ED609283
Record Type: Non-Journal
Publication Date: 2018-Jan-17
Pages: 25
Abstractor: As Provided
ISBN: N/A
ISSN: EISSN-
EISSN: N/A
Available Date: N/A
Review of Software Packages for Bayesian Multilevel Modeling
Mai, Yujiao; Zhang, Zhiyong
Grantee Submission
Multilevel modeling is a statistical approach to analyze hierarchical data, which consist of individual observations nested within clusters. Bayesian methods is a well-known, sometimes better, alternative of Maximum likelihood methods for fitting multilevel models. Lack of user-friendly and computationally efficient software packages or programs was a main obstacle in applying Bayesian multilevel modeling. In recent years, the development of software packages for multilevel modeling with improved Bayesian algorithms and faster speed has been growing. This article aims to update the knowledge of available software packages for Bayesian multilevel modeling and therefore to promote the use of these packages. Three categories of software packages capable of Bayesian multilevel modeling including brms, MCMCglmm, glmmBUGS, Bambi, R2BayesX, BayesReg, R2MLwiN and others are introduced and compared in terms of computational efficiency, modeling capability and flexibility, as well as user-friendliness. Recommendations to practical users and suggestions for future development are also discussed. [This paper was published in "Structural Equation Modeling" v25 n4 p650-658 2018.]
Descriptors: Hierarchical Linear Modeling, Computer Software, Bayesian Statistics, Efficiency, Usability
Publication Type: Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: Institute of Education Sciences (ED)
Authoring Institution: N/A
IES Funded: Yes
Grant or Contract Numbers: R305D140037
Author Affiliations: N/A