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On the Unnecessary Ubiquity of Hierarchical Linear Modeling

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Title: On the Unnecessary Ubiquity of Hierarchical Linear Modeling
Language: English
Authors: Daniel McNeish (ORCID 0000-0003-1643-9408), Laura M. Stapleton (ORCID 0000-0003-2383-772X), Rebecca D. Silverman (ORCID 0000-0002-9785-0313)
Source: Grantee Submission. 2017 22(1).
Peer Reviewed: Y
Page Count: 28
Publication Date: 2017
Sponsoring Agency: Institute of Education Sciences (ED)
Contract Number: R305A110142
Document Type: Journal Articles
Reports - Research
Descriptors: Hierarchical Linear Modeling, Social Science Research, Multivariate Analysis, Error Patterns, Alternative Assessment, Robustness (Statistics), Regression (Statistics), Computation, Equations (Mathematics)
DOI: 10.1037/met0000078
ISSN: 1082-989X
Abstract: In psychology and the behavioral sciences generally, the use of the hierarchical linear model (HLM) and its extensions for discrete outcomes are popular methods for modeling clustered data. HLM and its discrete outcome extensions, however, are certainly not the only methods available to model clustered data. Although other methods exist and are widely implemented in other disciplines, it seems that psychologists have yet to consider these methods in substantive studies. This article compares and contrasts HLM with alternative methods including generalized estimating equations and cluster-robust standard errors. These alternative methods do not model random effects and thus make a smaller number of assumptions and are interpreted identically to single-level methods with the benefit that estimates are adjusted to reflect clustering of observations. Situations where these alternative methods may be advantageous are discussed including research questions where random effects are and are not required, when random effects can change the interpretation of regression coefficients, challenges of modeling with random effects with discrete outcomes, and examples of published psychology articles that use HLM that may have benefitted from using alternative methods. Illustrative examples are provided and discussed to demonstrate the advantages of the alternative methods and also when HLM would be the preferred method.
Abstractor: As Provided
IES Funded: Yes
Entry Date: 2024
Accession Number: ED646272
Database: ERIC
FullText Text:
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  Data: In psychology and the behavioral sciences generally, the use of the hierarchical linear model (HLM) and its extensions for discrete outcomes are popular methods for modeling clustered data. HLM and its discrete outcome extensions, however, are certainly not the only methods available to model clustered data. Although other methods exist and are widely implemented in other disciplines, it seems that psychologists have yet to consider these methods in substantive studies. This article compares and contrasts HLM with alternative methods including generalized estimating equations and cluster-robust standard errors. These alternative methods do not model random effects and thus make a smaller number of assumptions and are interpreted identically to single-level methods with the benefit that estimates are adjusted to reflect clustering of observations. Situations where these alternative methods may be advantageous are discussed including research questions where random effects are and are not required, when random effects can change the interpretation of regression coefficients, challenges of modeling with random effects with discrete outcomes, and examples of published psychology articles that use HLM that may have benefitted from using alternative methods. Illustrative examples are provided and discussed to demonstrate the advantages of the alternative methods and also when HLM would be the preferred method.
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      – SubjectFull: Social Science Research
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