The Performance of Multivariate Methods for Two-Group Comparisons with Small Samples and Incomplete Data
| Autor: | Tiffany A. Whittaker, Molly E. Cain, Graham J. McDougall, Wanchen Chang, Keenan A. Pituch, Megha Joshi, Ryoungsun Park |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Statistics and Probability
Multivariate statistics Restricted maximum likelihood Experimental and Cognitive Psychology Models Psychological Article Cognition Arts and Humanities (miscellaneous) Multivariate analysis of variance Statistics Humans Mathematics Aged Likelihood Functions Models Statistical Multilevel model Univariate General Medicine Missing data Self Efficacy Sample Size Ordinary least squares Multivariate Analysis Linear Models Multilevel Analysis Female Psychomotor Performance Type I and type II errors |
| Zdroj: | Multivariate Behav Res |
| ISSN: | 1532-7906 |
| Popis: | In intervention studies having multiple outcomes, researchers often use a series of univariate tests (e.g., ANOVAs) to assess group mean differences. Previous research found that this approach properly controls Type I error and generally provides greater power compared to MANOVA, especially under realistic effect size and correlation combinations. However, when group differences are assessed for a specific outcome, these procedures are strictly univariate and do not consider the outcome correlations, which may be problematic with missing outcome data. Linear mixed or multivariate multilevel models (MVMMs), implemented with maximum likelihood estimation, present an alternative analysis option where outcome correlations are taken into account when specific group mean differences are estimated. In this study, we use simulation methods to compare the performance of separate independent samples t tests estimated with ordinary least squares and analogous t tests from MVMMs to assess two-group mean differences with multiple outcomes under small sample and missingness conditions. Study results indicated that a MVMM implemented with restricted maximum likelihood estimation combined with the Kenward–Roger correction had the best performance. Therefore, for intervention studies with small N and normally distributed multivariate outcomes, the Kenward–Roger procedure is recommended over traditional methods and conventional MVMM analyses, particularly with incomplete data. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|