Longitudinal and multi-group modeling with missing data

Missing data are almost always a problem in longitudinal research. Item non-response, differential attrition, failure to obtain measurements at equal time intervals, and unbalanced panel designs used to be difficult to analyze at best and remain a threat to the validity of a study. A related technical problem, customarily given little importance but nevertheless strongly related to validity threats, is that most multivariate methods require complete data.

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