Impact of Missing Data on Person-Model Fit and Person Trait Estimation

Autor/inn/en	Zhang, Bo; Walker, Cindy M.
Titel	Impact of Missing Data on Person-Model Fit and Person Trait Estimation
Quelle	In: Applied Psychological Measurement, 32 (2008) 6, S.466-479 (14 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0146-6216
DOI	10.1177/0146621607307692
Schlagwörter	Item Response Theory; Computation; Goodness of Fit; Test Items; Data Analysis; Models; Evaluation Methods; Measurement Techniques; Psychological Studies + Suchen Sie Ihr Suchwort? Item-Response-Theorie; Test content; Testaufgabe; Auswertung; Analogiemodell; Messtechnik
Abstract	The purpose of this research was to examine the effects of missing data on person-model fit and person trait estimation in tests with dichotomous items. Under the missing-completely-at-random framework, four missing data treatment techniques were investigated including pairwise deletion, coding missing responses as incorrect, hotdeck imputation, and model-based imputation. Person traits were estimated using the two-parameter item response model. Overall, missing data increased the difficulty in assessing person-model fit for both model-fitting and model-misfitting persons. The higher the proportion of missing data, the larger the number of persons incorrectly diagnosed. Among the four techniques, the pairwise deletion method performed best in recovering person-model fit and person trait level. Treating missing responses as incorrect caused the examinees with missing data to not fit the measurement model, thus invalidating the person trait estimates. (Contains 6 figures.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10