On the Treatment of Missing Data in Background Questionnaires in Educational Large-Scale Assessments: An Evaluation of Different Procedures

Autor/inn/en	Grund, Simon; Lüdtke, Oliver; Robitzsch, Alexander
Titel	On the Treatment of Missing Data in Background Questionnaires in Educational Large-Scale Assessments: An Evaluation of Different Procedures
Quelle	In: Journal of Educational and Behavioral Statistics, 46 (2021) 4, S.430-465 (36 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Grund, Simon)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1076-9986
DOI	10.3102/1076998620959058
Schlagwörter	Data Analysis; Error of Measurement; Research Problems; Statistical Inference; Foreign Countries; International Assessment; Secondary School Students; Achievement Tests; Elementary Secondary Education; Mathematics Achievement; Mathematics Tests; Science Tests; Science Achievement; Evaluation Methods; Questionnaires; Statistical Bias; Simulation; Program for International Student Assessment; Trends in International Mathematics and Science Study + Suchen Sie Ihr Suchwort? Auswertung; Messfehler; Forschungskritik; Inferential statistics; Schließende Statistik; Ausland; Sekundarschüler; Achievement test; Achievement; Testing; Test; Tests; Leistungsbeurteilung; Leistungsüberprüfung; Leistung; Testdurchführung; Testen; Mathmatics sikills; Mathmatics achievement; Mathematical ability; Mathematische Kompetenz; Fragebogen; Simulation program; Simulationsprogramm
Abstract	Large-scale assessments (LSAs) use Mislevy's "plausible value" (PV) approach to relate student proficiency to noncognitive variables administered in a background questionnaire. This method requires background variables to be completely observed, a requirement that is seldom fulfilled. In this article, we evaluate and compare the properties of methods used in current practice for dealing with missing data in background variables in educational LSAs, which rely on the missing indicator method (MIM), with other methods based on multiple imputation. In this context, we present a fully conditional specification (FCS) approach that allows for a joint treatment of PVs and missing data. Using theoretical arguments and two simulation studies, we illustrate under what conditions the MIM provides biased or unbiased estimates of population parameters and provide evidence that methods such as FCS can provide an effective alternative to the MIM. We discuss the strengths and weaknesses of the approaches and outline potential consequences for operational practice in educational LSAs. An illustration is provided using data from the PISA 2015 study. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01