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Autor/inn/en | Grund, Simon; Lüdtke, Oliver; Robitzsch, Alexander |
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Titel | Handling Missing Data in Cross-Classified Multilevel Analyses: An Evaluation of Different Multiple Imputation Approaches |
Quelle | In: Journal of Educational and Behavioral Statistics, 48 (2023) 4, S.454-489 (36 Seiten)Infoseite zur Zeitschrift
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Zusatzinformation | ORCID (Grund, Simon) |
Sprache | englisch |
Dokumenttyp | gedruckt; online; Zeitschriftenaufsatz |
ISSN | 1076-9986 |
DOI | 10.3102/10769986231151224 |
Schlagwörter | Educational Research; Data Analysis; Error of Measurement; Computation; Multivariate Analysis; Research Problems; Statistical Analysis; Program Effectiveness; Classification; Models; Simulation |
Abstract | Multiple imputation (MI) is a popular method for handling missing data. In education research, it can be challenging to use MI because the data often have a clustered structure that need to be accommodated during MI. Although much research has considered applications of MI in hierarchical data, little is known about its use in cross-classified data, in which observations are clustered in multiple higher-level units simultaneously (e.g., schools and neighborhoods, transitions from primary to secondary schools). In this article, we consider several approaches to MI for cross-classified data (CC-MI), including a novel fully conditional specification approach, a joint modeling approach, and other approaches that are based on single- and two-level MI. In this context, we clarify the conditions that CC-MI methods need to fulfill to provide a suitable treatment of missing data, and we compare the approaches both from a theoretical perspective and in a simulation study. Finally, we illustrate the use of CC-MI in real data and discuss the implications of our findings for research practice. (As Provided). |
Anmerkungen | SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com |
Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
Update | 2024/1/01 |