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Missing data are inevitable in longitudinal studies. Traditional methods,such as the full information maximum likelihood (FIML), are commonly used tohandle ignorable missing data. However, they may lead to biased modelestimation due to missing not at random data that often appear in longitudinalstudies. Recently, machine learning methods, such as random forests (RF) andK-nearest neighbors (KNN) imputation methods, have been proposed to cope withmissing values. Although machine learning imputation methods have been gainingpopularity, few studies have investigated the tenability and utility of thesemethods in longitudinal research. Through Monte Carlo simulations, this studyevaluates and compares the performance of traditional and machine learningapproaches (FIML, RF, and KNN) in growth curve modeling. The effects of samplesize, the rate of missingness, and the missing data mechanism on modelestimation are investigated. Results indicate that FIML is a better choice thanthe two machine learning imputation methods in terms of model estimationaccuracy and efficiency.

A Comparison of Full Information Maximum Likelihood and Machine Learning Missing Data Analytical Methods in Growth Curve Modeling