This article documents the number of target persons participating in the panel surveys of the National Educational Panel Study (NEPS) as well as the number of respondents who temporarily dropout and of those leaving the panel (attrition). NEPS comprises panel surveys with six mutually exclusive starting cohorts covering the complete life span. Sample sizes, numbers of participants and temporary as well as final dropouts and participation rates are reported in detail for each wave and for subsamples, if applicable. Sample particularities, such as the conversion of temporary dropouts into final ones, are elaborated on. All figures presented are derived from the corresponding Scientific Use Files (SUFs) published by February 1, 2018. Selectivity due to attrition (i.e., final dropouts) is studied. For this purpose, we examine how attrition distorts the NEPS samples with respect to relevant design variables (such as stratification criteria) and panel member characteristics (like sex and birth year). In detail, we study the panel status of each panel member, that is being part of the panel or having dropped out finally, along all of the panel waves with respect to starting cohort and population specific characteristics. We conclude this article with some recommendations for dealing with the detected selection bias in statistical analyses.

We introduce a selection model-based multilevel imputation approach to be used within the fully conditional specification framework for multiple imputation. Concretely, we apply a censored bivariate probit model to describe binary variables assumed to be missing not at random. The first equation of the model defines the regression model for the missing data mechanism. The second equation specifies the regression model of the variable to be imputed. The non-random selection of the binary data is mapped by correlations between the error terms of the two regression models. Hierarchical data structures are modelled by random intercepts in both equations. To fit the novel imputation model we use maximum likelihood and adaptive Gauss-Hermite quadrature. A comprehensive simulation study shows the overall performance of the approach.We test its usefulness for empirical research by applying it to a common problem in social scientific research: the emergence of educational aspirations. Our software is designed to be used in the R package mice.

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