2017
DOI: 10.1037/met0000078
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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 … Show more

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Cited by 585 publications
(418 citation statements)
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References 126 publications
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“…This allowed us to inspect the relationship between four variables of interest and an outcome while controlling for the level of the other dependent variables. This model included random intercepts at both school and country level, separating all observation dependencies and allowing us to draw cluster-specific inferences for school learning environments (McNeish et al 2017). With the appropriate centering, this model supports the estimation of the overall mean of our covariate of interest across all samples (Brincks et al 2017).…”
Section: Analytical Strategymentioning
confidence: 88%
“…This allowed us to inspect the relationship between four variables of interest and an outcome while controlling for the level of the other dependent variables. This model included random intercepts at both school and country level, separating all observation dependencies and allowing us to draw cluster-specific inferences for school learning environments (McNeish et al 2017). With the appropriate centering, this model supports the estimation of the overall mean of our covariate of interest across all samples (Brincks et al 2017).…”
Section: Analytical Strategymentioning
confidence: 88%
“…Clustered standard errors correct for non-independence of observations (see McNeish et al, 2017 for a general description of the technique, and; Cheung, 2014 for a somewhat similar meta-analytic approach). Effects were weighted by the inverse of the sampling variance multiplied by the inverse of the number of effect sizes derived per sample.…”
Section: Methodsmentioning
confidence: 99%
“…Our data did not meet these thresholds (i.e., design effects ranged from 1.48 to 1.53), indicating that a general analysis was more appropriate. We chose TYPE=COMPLEX to take into account the nesting in our data (i.e., individuals who had experienced multiple events; McNeish et al, 2017). 4,5 We thank an anonymous reviewer for this suggestion.…”
Section: Footnotesmentioning
confidence: 99%