ERIC Number: EJ1214846
Record Type: Journal
Publication Date: 2019
Pages: 16
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-0022-0973
EISSN: N/A
The Performance of Multilevel Models When Outcome Data Are Incomplete
Chang, Wanchen; Pituch, Keenan A.
Journal of Experimental Education, v87 n1 p1-16 2019
When data for multiple outcomes are collected in a multilevel design, researchers can select a univariate or multivariate analysis to examine group-mean differences. When correlated outcomes are incomplete, a multivariate multilevel model (MVMM) may provide greater power than univariate multilevel models (MLMs). For a two-group multilevel design with two correlated outcomes, a simulation study was conducted to compare the performance of MVMM to MLMs. The results showed that MVMM and MLM performed similarly when data were complete or missing completely at random. However, when outcome data were missing at random, MVMM continued to provide unbiased estimates, whereas MLM produced grossly biased estimates and severely inflated Type I error rates. As such, this study provides further support for using MVMM rather than univariate analyses, particularly when outcome data are incomplete.
Descriptors: Hierarchical Linear Modeling, Multivariate Analysis, Research Problems, Error of Measurement, Data, Monte Carlo Methods, Maximum Likelihood Statistics
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Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A