Evaluating the Effects of Missing Data Handling Methods on Scale Linking Accuracy

Autor/inn/en	Wu, Tong; Kim, Stella Y.; Westine, Carl
Titel	Evaluating the Effects of Missing Data Handling Methods on Scale Linking Accuracy
Quelle	In: Educational and Psychological Measurement, 83 (2023) 6, S.1202-1228 (27 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Wu, Tong) ORCID (Kim, Stella Y.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/00131644221140941
Schlagwörter	Data Analysis; Responses; Statistical Analysis; Measurement; Item Response Theory; Accuracy; Test Items + Suchen Sie Ihr Suchwort? Auswertung; Statistische Analyse; Messverfahren; Item-Response-Theorie; Test content; Testaufgabe
Abstract	For large-scale assessments, data are often collected with missing responses. Despite the wide use of item response theory (IRT) in many testing programs, however, the existing literature offers little insight into the effectiveness of various approaches to handling missing responses in the context of scale linking. Scale linking is commonly used in large-scale assessments to maintain scale comparability over multiple forms of a test. Under a common-item nonequivalent group design (CINEG), missing data that occur to common items potentially influence the linking coefficients and, consequently, may affect scale comparability, test validity, and reliability. The objective of this study was to evaluate the effect of six missing data handling approaches, including listwise deletion (LWD), treating missing data as incorrect responses (IN), corrected item mean imputation (CM), imputing with a response function (RF), multiple imputation (MI), and full information likelihood information (FIML), on IRT scale linking accuracy when missing data occur to common items. Under a set of simulation conditions, the relative performance of the six missing data treatment methods under two missing mechanisms was explored. Results showed that RF, MI, and FIML produced less errors for conducting scale linking whereas LWD was associated with the most errors regardless of various testing conditions. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01