Comparing Methods for Addressing Missingness in Longitudinal Modeling of Panel Data

Abstract

Respondent attrition is a common problem in national longitudinal panel surveys. To make full use of the data, weights are provided to account for attrition. Weight adjustments are based on sampling design information and data from the base year; information from subsequent waves is typically not utilized. Alternative methods to address bias from nonresponse are full information maximum likelihood (FIML) or multiple imputation (MI). The effects on bias of growth parameter estimates from using these methods are compared via a simulation study. The results indicate that caution needs to be taken when utilizing panel weights when there is missing data, and to consider methods like FIML and MI, which are not as susceptible to the omission of important auxiliary variables.

Keywords:

• Panel weights
• full information maximum likelihood
• multiple imputation
• missing data
• longitudinal modeling
• surveys
• ECLS-K

Note

Notes

1 A subsample (30%) of individuals assessed in the fall of first grade (Wave 3) was not included (Stapleton, Harring, & Lee, 2015).