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ERIC Number: EJ1140378
Record Type: Journal
Publication Date: 2017-Apr
Pages: 19
Abstractor: As Provided
ISBN: N/A
ISSN: EISSN-1531-7714
EISSN: N/A
Hierarchical Linear Modeling with Maximum Likelihood, Restricted Maximum Likelihood, and Fully Bayesian Estimation
Boedeker, Peter
Practical Assessment, Research & Evaluation, v22 n2 Apr 2017
Hierarchical linear modeling (HLM) is a useful tool when analyzing data collected from groups. There are many decisions to be made when constructing and estimating a model in HLM including which estimation technique to use. Three of the estimation techniques available when analyzing data with HLM are maximum likelihood, restricted maximum likelihood, and fully Bayesian estimation. Which estimation technique is employed determines how estimates can be interpreted and the models that may be compared. The purpose of this paper is to conceptually introduce and compare these methods of estimation in HLM and interpret the computer output that results from using them. This is done for the intraclass correlation, parameter estimates, and model fit indices using a simulated dataset that is available online. The statistical program R is utilized for all analyses and syntax is provided in Appendix 1. This paper is written to aid applied researchers who wish to better understand the differences between the estimation techniques and how to interpret their HLM results.
Center for Educational Assessment. 813 North Pleasant Street, Amherst, MA 01002. e-mail: pare@umass.edu; Tel: 413-577-2180; Web site: https://scholarworks.umass.edu/pare
Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A