L
Laura M. Stapleton
Researcher at University of Maryland, College Park
Publications - 85
Citations - 4742
Laura M. Stapleton is an academic researcher from University of Maryland, College Park. The author has contributed to research in topics: Multilevel model & Structural equation modeling. The author has an hindex of 28, co-authored 83 publications receiving 3829 citations. Previous affiliations of Laura M. Stapleton include University of Maryland, Baltimore & University of Texas at Austin.
Papers
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Book
The Reviewer’s Guide to Quantitative Methods in the Social Sciences
TL;DR: The Reviewer's Guide to Quantitative Methods in the Social Sciences as mentioned in this paper is a collection of thirty-one uniquely structured chapters covering both traditional and emerging methods of quantitative data analysis, which neither junior nor veteran reviewers can be expected to know in detail.
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On the unnecessary ubiquity of hierarchical linear modeling.
TL;DR: This article compares and contrasts HLM with alternative methods including generalized estimating equations and cluster-robust standard errors and demonstrates the advantages of the alternative methods and also when HLM would be the preferred method.
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The Effect of Small Sample Size on Two-Level Model Estimates: A Review and Illustration
TL;DR: This paper provides an illustrative simulation to demonstrate how a simple model becomes adversely affected by small numbers of clusters and outlines methodological topics that have yet to be addressed in the literature on multilevel models with a small number of clusters.
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Modeling Clustered Data with Very Few Clusters.
TL;DR: A simulation study that simultaneously addresses the extreme small sample and differential performance (estimation bias, Type I error rates, and relative power) of 12 methods to account for clustered data with a model that features a more realistic number of predictors.
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Core self-evaluations and job burnout: the test of alternative models.
TL;DR: Results from structural equations modeling analyses revealed an influence of core self-evaluations and perceived organizational constraints on job burnout and satisfaction, suggesting personal and contextual contributions.