NEPSscaling: plausible value estimation for competence tests administered in the German National Educational Panel Study

Abstract

Educational large-scale assessments (LSAs) often provide plausible values for the administered competence tests to facilitate the estimation of population effects. This requires the specification of a background model that is appropriate for the specific research question. Because the German National Educational Panel Study (NEPS) is an ongoing longitudinal LSA, the range of potential research questions and, thus, the number of potential background variables for the plausible value estimation grow with each new assessment wave. To facilitate the estimation of plausible values for data users of the NEPS, the R package NEPSscaling allows their estimation following the scaling standards in the NEPS without requiring in-depth psychometric expertise in item response theory. The package requires the user to prepare the data for the background model only. Then, the appropriate item response model including the linking approach adopted for the NEPS is selected automatically, while a nested multiple imputation scheme based on the chained equation approach handles missing values in the background data. For novice users, a graphical interface is provided that only requires minimal knowledge of the R language. Thus, NEPSscaling can be used to estimate cross-sectional and longitudinally linked plausible values for all major competence assessments in the NEPS.

Expanding NAEP and TIMSS Analysis to Include Additional Variables or a New Scoring Model Using the R Package Dire

“…In the R psychometrics view [10], a few packages have capabilities for drawing plausible values (the focus of the Dire package): TAM [5], mirt [11], and Dire. Additionally, the NEPSscaling package may be used to draw plausible values focusing on data from the German National Educational Panel Study (NEPS) [12]. The models shown in this paper may be fit with TAM, mirt, or Dire.…”