Evaluating significance in linear mixed-effects models in R

Mixed-effects models are being used ever more frequently in the analysis of experimental data. However, in the lme4 package in R the standards for evaluating significance of fixed effects in these models (i.e., obtaining p -values) are somewhat vague. There are good reasons for this, but as research...

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Bibliographic Details
Published in:Behavior research methods Vol. 49; no. 4; pp. 1494 - 1502
Main Author: Luke, Steven G.
Format: Journal Article
Language:English
Published: New York Springer US 01.08.2017
Subjects:
Behavioral Science and Psychology
Cognitive Psychology
Humans
Linear Models
Psychology
Sample Size
Statistics as Topic
Type 1 error
lme4
Linear mixed-effects models
Statistics
values
p-values
ISSN:1554-3528, 1554-3528
Online Access:Get full text
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Summary:Mixed-effects models are being used ever more frequently in the analysis of experimental data. However, in the lme4 package in R the standards for evaluating significance of fixed effects in these models (i.e., obtaining p -values) are somewhat vague. There are good reasons for this, but as researchers who are using these models are required in many cases to report p -values, some method for evaluating the significance of the model output is needed. This paper reports the results of simulations showing that the two most common methods for evaluating significance, using likelihood ratio tests and applying the z distribution to the Wald t values from the model output ( t -as- z ), are somewhat anti-conservative, especially for smaller sample sizes. Other methods for evaluating significance, including parametric bootstrapping and the Kenward-Roger and Satterthwaite approximations for degrees of freedom, were also evaluated. The results of these simulations suggest that Type 1 error rates are closest to .05 when models are fitted using REML and p -values are derived using the Kenward-Roger or Satterthwaite approximations, as these approximations both produced acceptable Type 1 error rates even for smaller samples.
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ISSN:1554-3528
1554-3528
DOI:10.3758/s13428-016-0809-y