.png){kind=icon}
.png){kind=icon}
Return
Missing Data Analysis
.png){kind=icon}
PRE
AI
DOI：10.1146/annurev-clinpsy-080822-051727.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Methods for handling missing data in clinical psychology studies are reviewed. Missing data are defined, and a taxonomy of main approaches to analysis is presented, including complete-case and available-case analysis, weighting, maximum likelihood, Bayes, single and multiple imputation, and augmented inverse probability weighting. Missingness mechanisms, which play a key role in the performance of alternative methods, are defined. Approaches to robust inference, and to inference when the mechanism is potentially missing not at random, are discussed.
Keywords:
missing at random
ignorable missing data
incomplete data
informative missingness
likelihood inference
missingness mechanism
partially missing at random
Journal
IF:
16.5
Papers:
3.0K
Citations:
1.0W