A Bayesian Approach Towards Missing Covariate Data in Multilevel Latent Regression Models





Faculty/Professorship: Survey Statistics and Data Analysis ; Statistics and Econometrics  
Author(s): Aßmann, Christian  ; Gaasch, Jean-Christoph; Stingl, Doris  
Title of the Journal: Psychometrika : a journal of quantitative psychology
ISSN: 1860-0980
Publisher Information: New York : Springer
Year of publication: 2022
Pages: 34
Language(s): English
Remark: 
This paper uses data from the German National Educational Panel Study (NEPS), see Blossfeld and Roßbach (2019) and https://doi.org/10.5157/NEPS:SC4:10.0.0
DOI: 10.1007/s11336-022-09888-0
Peer Reviewed: Ja
International Distribution: Ja
Type: Article
URI: https://fis.uni-bamberg.de/handle/uniba/56766
Release Date: 28. November 2022
Project: Ein Bayesianischer Modellrahmen für die Auswertung von Daten aus Iängsschnittlichen Large-scale Assessments