Taking the Missing Propensity into Account When Estimating Competence Scores: Evaluation of Item Response Theory Models for Nonignorable Omissions

Autor/inn/en	Köhler, Carmen; Pohl, Steffi; Carstensen, Claus H.
Titel	Taking the Missing Propensity into Account When Estimating Competence Scores: Evaluation of Item Response Theory Models for Nonignorable Omissions
Quelle	In: Educational and Psychological Measurement, 75 (2015) 5, S.850-874 (25 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/0013164414561785
Schlagwörter	Competence; Tests; Evaluation Methods; Adults; Responses; Item Response Theory; Performance; Ability; Measurement Techniques; Data Analysis; Error of Measurement; Statistical Inference; Computation; Probability; Scaling; Longitudinal Studies; Grade 9; Foreign Countries; Germany + Suchen Sie Ihr Suchwort? Kompetenz; Examination; Prüfung; Examen; Item-Response-Theorie; Achievement; Leistung; Fähigkeit; Fertigkeit; Messtechnik; Auswertung; Messfehler; Inferential statistics; Schließende Statistik; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Scale construction; Skalenkonstruktion; Longitudinal study; Longitudinal method; Longitudinal methods; Längsschnittuntersuchung; School year 09; 9. Schuljahr; Schuljahr 09; Ausland; Deutschland
Abstract	When competence tests are administered, subjects frequently omit items. These missing responses pose a threat to correctly estimating the proficiency level. Newer model-based approaches aim to take nonignorable missing data processes into account by incorporating a latent missing propensity into the measurement model. Two assumptions are typically made when using these models: (1) The missing propensity is unidimensional and (2) the missing propensity and the ability are bivariate normally distributed. These assumptions may, however, be violated in real data sets and could, thus, pose a threat to the validity of this approach. The present study focuses on modeling competencies in various domains, using data from a school sample (N = 15,396) and an adult sample (N = 7,256) from the National Educational Panel Study. Our interest was to investigate whether violations of unidimensionality and the normal distribution assumption severely affect the performance of the model-based approach in terms of differences in ability estimates. We propose a model with a competence dimension, a unidimensional missing propensity and a distributional assumption more flexible than a multivariate normal. Using this model for ability estimation results in different ability estimates compared with a model ignoring missing responses. Implications for ability estimation in large-scale assessments are discussed. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01