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This article offers an applied review of key issues and methods for the analysis of longitudinal panel data in the presence of missing values. The authors consider the unique challenges associated with attrition (survey dropout), incomplete repeated measures, and unknown observations of time. Using simulated data based on 4 waves of the Marital Instability Over the Life Course Study (n = 2,034), they applied a fixed effect regression model and an event-history analysis with time-varying covariates. They then compared results for analyses with nonimputed missing data and with imputed data both in long and in wide structures. Imputation produced improved estimates in the event-history analysis but only modest improvements in the estimates and standard errors of the fixed effects analysis. Factors responsible for differences in the value of imputation are examined, and recommendations for handling missing values in panel data are presented.
Key Words: event history analysis, fixed effects, longitudinal data, missing data, multiple imputation, panel data.
The use of longitudinal panel (prospective) survey data is common in the area of family research. From 2010 to 2014, approximately 287 quantitative and qualitative research articles (excluding theory development, research reviews, comments, rejoinders, and methodological innovation articles) were published in the Journal of Marriage and Family (JMF). Of these, 176 (61%) analyzed longitudinal data. Data on the same individuals or families at multiple points in time provide for stronger inferences about change processes and allow for more control of unmeasured differences between individuals that can bias study findings (Johnson, 1995, 2005). What tempers these advantages is the large amount of missing data found in many longitudinal studies. Nearly all of the JMF articles explicitly mentioned the presence of missing values and study dropout-suggestive of the widespread concern with missing data in panel studies.
Few guidelines for the analysis of longitudinal panel data in the presence of missing values are accessible to family researchers. Moreover, no clear appraisals of the consequences of different ways of handling missing data are readily offered. Existing guidelines tend to be directed toward statisticians or focus on types of longitudinal data rarely found in the family literature, such as randomized clinical trials (e.g., Daniels & Hogan, 2008; Enders, 2011; Hedeker & Gibbons, 2006; National Research Council, 2010) or data sets with few...