Dealing with item nonresponse in large-scale cognitive assessments. The impact of missing data methods on estimated explanatory relationships.

Autor/inn/en	Köhler, Carmen; Pohl, Steffi; Carstensen, Claus H.
Titel	Dealing with item nonresponse in large-scale cognitive assessments. The impact of missing data methods on estimated explanatory relationships.
Quelle	In: Journal of educational measurement, 54 (2017) 4, S. 397-419Infoseite zur Zeitschrift PDF als Volltext (1); PDF als Volltext kostenfreie Datei (2) Link als defekt meldenVerfügbarkeit
Beigaben	Literaturangaben
Sprache	englisch
Dokumenttyp	online; gedruckt; Zeitschriftenaufsatz
ISSN	0022-0655; 1745-3984
DOI	10.1111/jedm.12154
Schlagwörter	Antwortverhalten; Validität; Kompetenzmessung; Itemanalyse; Fehlerbewertung; Antwortvergleich; Strukturmodell; Regressionsanalyse; Daten; Messfehler
Abstract	Competence data from low-stakes educational large-scale assessment studies allow for evaluating relationships between competencies and other variables. The impact of item-level nonresponse has not been investigated with regard to statistics that determine the size of these relationships (e.g., correlations, regression coefficients). Classical approaches such as ignoring missing values or treating them as incorrect are currently applied in many large-scale studies, while recent model-based approaches that can account for nonignorable nonresponse have been developed. Estimates of item and person parameters have been demonstrated to be biased for classical approaches when missing data are missing not at random (MNAR). In our study, we focus on parameter estimates of the structural model (i.e., the true regression coefficient when regressing competence on an explanatory variable), simulating data according to various missing data mechanisms. We found that model-based approaches and ignoring missing values performed well in retrieving regression coefficients even when we induced missing data that were MNAR. Treating missing values as incorrect responses can lead to substantial bias. We demonstrate the validity of our approach empirically and discuss the relevance of our results (Orig.).
Erfasst von	DIPF \| Leibniz-Institut für Bildungsforschung und Bildungsinformation, Frankfurt am Main
Update	2020/2