Estimating statistical power with incomplete data

Adam Davey, Jyoti Savla

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


Software developments increasingly facilitate inclusion of incomplete data, but relatively little research has examined the effects of incomplete data on statistical power. Seven steps needed to conduct power analyses with incomplete data for a variety of commonly tested hypotheses are illustrated, focusing on significance tests of individual parameters. The example extends a growth curve model simulation presented by Curran and Muthen (1999) to the incomplete data situation. How to estimate statistical power for a range of sample sizes from a single model, as well as how to calculate the sample size required to obtain a desired value of statistical power, is demonstrated. Effects of data being missing completely at random (MCAR) or missing at random (MAR) across a range from 0% (complete data) to 95% missing data are considered. SAS and LISREL syntax are provided in this paper with syntax for other software available from the authors.

Original languageEnglish (US)
Pages (from-to)320-346
Number of pages27
JournalOrganizational Research Methods
Issue number2
StatePublished - Apr 2009


  • Experiments with repeated measures
  • Latent growth curves
  • Longitudinal data analysis
  • Missing data
  • Statistical power

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Strategy and Management
  • Management of Technology and Innovation


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