On the unnecessary ubiquity of hierarchical linear modeling

Daniel McNeish, Laura M. Stapleton, Rebecca D. Silverman

Research output: Contribution to journalArticle

90 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)114-140
Number of pages27
JournalPsychological Methods
Volume22
Issue number1
DOIs
StatePublished - Mar 1 2017
Externally publishedYes

Fingerprint

Linear Models
Psychology
Behavioral Sciences
Cluster Analysis
Research

Keywords

  • Cluster robust errors
  • Clustered data
  • GEE
  • HLM
  • Multilevel model

ASJC Scopus subject areas

  • Psychology (miscellaneous)

Cite this

On the unnecessary ubiquity of hierarchical linear modeling. / McNeish, Daniel; Stapleton, Laura M.; Silverman, Rebecca D.

In: Psychological Methods, Vol. 22, No. 1, 01.03.2017, p. 114-140.

Research output: Contribution to journalArticle

McNeish, Daniel ; Stapleton, Laura M. ; Silverman, Rebecca D. / On the unnecessary ubiquity of hierarchical linear modeling. In: Psychological Methods. 2017 ; Vol. 22, No. 1. pp. 114-140.
@article{3037935e483d4391bd341a38c6f58cc3,
title = "On the unnecessary ubiquity of hierarchical linear modeling",
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.",
keywords = "Cluster robust errors, Clustered data, GEE, HLM, Multilevel model",
author = "Daniel McNeish and Stapleton, {Laura M.} and Silverman, {Rebecca D.}",
year = "2017",
month = "3",
day = "1",
doi = "10.1037/met0000078",
language = "English (US)",
volume = "22",
pages = "114--140",
journal = "Psychological Methods",
issn = "1082-989X",
publisher = "American Psychological Association Inc.",
number = "1",

}

TY - JOUR

T1 - On the unnecessary ubiquity of hierarchical linear modeling

AU - McNeish, Daniel

AU - Stapleton, Laura M.

AU - Silverman, Rebecca D.

PY - 2017/3/1

Y1 - 2017/3/1

N2 - 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.

AB - 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.

KW - Cluster robust errors

KW - Clustered data

KW - GEE

KW - HLM

KW - Multilevel model

UR - http://www.scopus.com/inward/record.url?scp=85000936579&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85000936579&partnerID=8YFLogxK

U2 - 10.1037/met0000078

DO - 10.1037/met0000078

M3 - Article

VL - 22

SP - 114

EP - 140

JO - Psychological Methods

JF - Psychological Methods

SN - 1082-989X

IS - 1

ER -