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Small-sample inference for linear mixed-effects models

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  • Xiao Yang

    (StataCorp LP)

Abstract

Researchers are often interested in making inferences about fixed effects in a linear mixed-effects model. For a large sample, the null sampling distributions of the test statistics can be approximated by a normal distribution for a one-hypothesis test and a chi-squared distribution for a multiple-hypotheses test. For a small sample, these large-sample approximations may not be appropriate, and t and F distributions may provide better approximations. In this presentation, I will describe five denominator-degrees-of-freedom (DDF) methods available with mixed in Stata 14, including the Satterthwaite and Kenward–Roger methods, and I will demonstrate examples of when and how to use these methods.

Suggested Citation

  • Xiao Yang, 2015. "Small-sample inference for linear mixed-effects models," 2015 Stata Conference 25, Stata Users Group.
    • Handle: RePEc:boc:scon15:25
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    File URL: http://repec.org/col2015/columbus15_yang.pdf
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    Cited by:

    1. Mira Bierbaum & Eleonora E M Nillesen, 2021. "Sustaining the integrity of the threatened self: A cluster-randomised trial among social assistance applicants in the Netherlands," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-21, June.

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