Improved degrees of freedom for multivariate significance tests obtained from multiply imputed, small-sample data
Abstract. We propose improvements to existing degrees of freedom used for
significance testing of multivariate hypotheses in small samples when missing data
are handled using multiple imputation. The improvements are for 1) tests based on
unrestricted fractions of missing information and 2) tests based on equal fractions
of missing information with M(p−1) ≤ 4, where M is
the number of imputations
and p is the number of tested parameters. Using the mi command available as of
Stata 11, we demonstrate via simulation that using these adjustments can result
in a more sensible degrees of freedom (and hence closer-to-nominal rejection rates)
than existing degrees of freedom.
View all articles by these authors:
Yulia V. Marchenko, Jerome P. Reiter
View all articles with these keywords:
multiple imputation, degrees of freedom, sample, missing, testing, multivariate
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