Small-sample degrees of freedom for multi-component significance tests with multiple imputation for missing data

Journal Article (Journal Article)

When performing multi-component significance tests with multiply-imputed datasets, analysts can use a Wald-like test statistic and a reference F-distribution. The currently employed degrees of freedom in the denominator of this F-distribution are derived assuming an infinite sample size. For modest complete-data sample sizes, this degrees of freedom can be unrealistic; for example, it may exceed the complete-data degrees of freedom. This paper presents an alternative denominator degrees of freedom that is always less than or equal to the complete-data denominator degrees of freedom, and equals the currently employed denominator degrees of freedom for infinite sample sizes. Its advantages over the currently employed degrees of freedom are illustrated with a simulation. ©2007 Biometrika Trust.

Full Text

Duke Authors

Cited Authors

  • Reiter, JP

Published Date

  • June 1, 2007

Published In

Volume / Issue

  • 94 / 2

Start / End Page

  • 502 - 508

Electronic International Standard Serial Number (EISSN)

  • 1464-3510

International Standard Serial Number (ISSN)

  • 0006-3444

Digital Object Identifier (DOI)

  • 10.1093/biomet/asm028

Citation Source

  • Scopus