Pattern Mixture Models for Quantifying Missing Data Uncertainty in Longitudinal Invariance Testing

• Published in: Structural equation modeling Vol. 24; no. 2; pp. 283 - 300
• Main Author: Sterba, Sonya K.
• Format: Journal Article
• Language: English
• Published: Hove Routledge 04.03.2017; Psychology Press
• Subjects: Dropouts; Inferences; longitudinal factor model; longitudinal invariance; Missing data; nonignorable missing data; pattern mixture model; Psychology; Social sciences; Statistical analysis

Many psychology applications assess measurement invariance of a construct (e.g., depression) over time. These applications are often characterized by few time points (e.g., 3), but high rates of dropout. Although such applications routinely assume that the dropout mechanism is ignorable, this assump...