Why Missing Data Matter in the Longitudinal Study of Adolescent Development: Using the 4-H Study to Understand the Uses of Different Missing Data Methods

Abstract

The study of adolescent development rests on methodologically appropriate collection and interpretation of longitudinal data. While all longitudinal studies of adolescent development involve missing data, the methods to treat missingness that have been recommended most often focus on missing data from cross-sectional studies. The problems of missing data in longitudinal studies are not described well, there are not many statistical software programs developed for researchers to use, and there are no longitudinal empirical examples involving adolescent development that show the extent to which different missing data procedures can yield different results. Data from the first three waves of the 4-H Study of Positive Youth Development were used to provide such an illustration. The sample contains 2,265 participants (56.7% females) who were in Grade 5 at Wave 1, in Grade 6 at Wave 2, and in Grade 7 at Wave 3, and varied in race, ethnicity, socioeconomic status, family structure, rural-urban location, and geographic region. The results showed that three missing data techniques, i.e., listwise deletion, direct maximum likelihood (DirML), and multiple imputation (MI), did not yield comparable results for research questions assessing different aspects of development (i.e., change over time or prediction effects). The results indicated also that listwise deletion should not be used. Instead, both DirML and MI methods should be used to determine if and how results change when these procedures are employed.