Does the Package Matter? A Comparison of Five Common Multilevel Modeling Software Packages

D. Betsy McCoach, Graham G. Rifenbark, Sarah D. Newton, Xiaoran Li, Janice Kooken, Dani Yomtov, Anthony J. Gambino and Aarti Bellara
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Xiaoran Li: University of Connecticut
Janice Kooken: Kooken Research and Consulting, LLC
Aarti Bellara: University of Connecticut

Journal of Educational and Behavioral Statistics, 2018, vol. 43, issue 5, 594-627

Abstract: This study compared five common multilevel software packages via Monte Carlo simulation: HLM 7, M plus 7.4, R (lme4 V1.1-12), Stata 14.1, and SAS 9.4 to determine how the programs differ in estimation accuracy and speed, as well as convergence, when modeling multiple randomly varying slopes of different magnitudes. Simulated data included population variance estimates, which were zero or near zero for two of the five random slopes. Generally, when yielding admissible solutions, all five software packages produced comparable and reasonably unbiased parameter estimates. However, noticeable differences among the five packages arose in terms of speed, convergence rates, and the production of standard errors for random effects, especially when the variances of these effects were zero in the population. The results of this study suggest that applied researchers should carefully consider which random effects they wish to include in their models. In addition, nonconvergence rates vary across packages, and models that fail to converge in one package may converge in another.

Keywords: multilevel modeling; statistical software; Monte Carlo simulation (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:43:y:2018:i:5:p:594-627

DOI: 10.3102/1076998618776348

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