On the Unnecessary Ubiquity of Hierarchical Linear Modeling

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Multilevel and Single-Level Models for Measured and Latent Variables When Data Are Clustered (2016, background): “As argued by McNeish, Stapleton, and Silverman (2016), these alternative methods to multilevel models can be more accessible to researchers because the dependency is accounted for with statistical corrections rather than requiring the researcher to model the dependency with random effects.”
Analyzing Cross-Sectionally Clustered Data Using Generalized Estimating Equations (2021, methodology): “GEEs, however, are not often used in psychology and education (Norton et al., 1996) even though researchers have highlighted its use as a valid approach for dealing with clustered data (Hardin & Hilbe, 2013; McNeish, 2019; McNeish et al., 2017; Zorn, 2001).”
Performance management and teacher performance: the role of affective organizational commitment and exhaustion (2020, methodology): “Moreover, while the results can be interpreted identically to singlelevel methods, the estimates are adjusted to reflect the clustering of observations (e.g. McNeish et al., 2017).”