Standard

A bias-corrected RMSD item fit statistic : An evaluation and comparison to alternatives. / Köhler, Carmen; Robitzsch, Alexander; Hartig, Johannes.

in: Journal of Educational and Behavioral Statistics, Jahrgang 45, Nr. 3, 06.2020, S. 251-273.

Publikationen: Beitrag in FachzeitschriftArtikel in FachzeitschriftForschungBegutachtung

Harvard

Köhler, C, Robitzsch, A & Hartig, J 2020, 'A bias-corrected RMSD item fit statistic: An evaluation and comparison to alternatives', Journal of Educational and Behavioral Statistics, Jg. 45, Nr. 3, S. 251-273. https://doi.org/10.3102/1076998619890566

APA

Köhler, C., Robitzsch, A., & Hartig, J. (2020). A bias-corrected RMSD item fit statistic: An evaluation and comparison to alternatives. Journal of Educational and Behavioral Statistics, 45(3), 251-273. https://doi.org/10.3102/1076998619890566

Vancouver

Köhler C, Robitzsch A, Hartig J. A bias-corrected RMSD item fit statistic: An evaluation and comparison to alternatives. Journal of Educational and Behavioral Statistics. 2020 Jun;45(3):251-273. https://doi.org/10.3102/1076998619890566

Author

Köhler, Carmen ; Robitzsch, Alexander ; Hartig, Johannes. / A bias-corrected RMSD item fit statistic : An evaluation and comparison to alternatives. in: Journal of Educational and Behavioral Statistics. 2020 ; Jahrgang 45, Nr. 3. S. 251-273.

BibTeX

@article{174fc46528444a1dbc3aa304aa946afa,
title = "A bias-corrected RMSD item fit statistic: An evaluation and comparison to alternatives",
abstract = "Testing whether items fit the assumptions of an item response theory model is an important step in evaluating a test. In the literature, numerous item fit statistics exist, many of which show severe limitations. The current study investigates the root mean squared deviation (RMSD) item fit statistic, which is used for evaluating item fit in various large-scale assessment studies. The three research questions of this study are (1) whether the empirical RMSD is an unbiased estimator of the population RMSD; (2) if this is not the case, whether this bias can be corrected; and (3) whether the test statistic provides an adequate significance test to detect misfitting items. Using simulation studies, it was found that the empirical RMSD is not an unbiased estimator of the population RMSD, and nonparametric bootstrapping falls short of entirely eliminating this bias. Using parametric bootstrapping, however, the RMSD can be used as a test statistic that outperforms the other approaches—infit and outfit, S − X 2—with respect to both Type I error rate and power. The empirical application showed that parametric bootstrapping of the RMSD results in rather conservative item fit decisions, which suggests more lenient cut-off criteria.",
keywords = "bootstrap, educational measurement, item fit, item response theory",
author = "Carmen K{\"o}hler and Alexander Robitzsch and Johannes Hartig",
year = "2020",
month = jun,
doi = "10.3102/1076998619890566",
language = "English",
volume = "45",
pages = "251--273",
journal = "Journal of Educational and Behavioral Statistics",
issn = "1076-9986",
publisher = "Sage",
number = "3",

}

RIS

TY - JOUR

T1 - A bias-corrected RMSD item fit statistic

T2 - An evaluation and comparison to alternatives

AU - Köhler, Carmen

AU - Robitzsch, Alexander

AU - Hartig, Johannes

PY - 2020/6

Y1 - 2020/6

N2 - Testing whether items fit the assumptions of an item response theory model is an important step in evaluating a test. In the literature, numerous item fit statistics exist, many of which show severe limitations. The current study investigates the root mean squared deviation (RMSD) item fit statistic, which is used for evaluating item fit in various large-scale assessment studies. The three research questions of this study are (1) whether the empirical RMSD is an unbiased estimator of the population RMSD; (2) if this is not the case, whether this bias can be corrected; and (3) whether the test statistic provides an adequate significance test to detect misfitting items. Using simulation studies, it was found that the empirical RMSD is not an unbiased estimator of the population RMSD, and nonparametric bootstrapping falls short of entirely eliminating this bias. Using parametric bootstrapping, however, the RMSD can be used as a test statistic that outperforms the other approaches—infit and outfit, S − X 2—with respect to both Type I error rate and power. The empirical application showed that parametric bootstrapping of the RMSD results in rather conservative item fit decisions, which suggests more lenient cut-off criteria.

AB - Testing whether items fit the assumptions of an item response theory model is an important step in evaluating a test. In the literature, numerous item fit statistics exist, many of which show severe limitations. The current study investigates the root mean squared deviation (RMSD) item fit statistic, which is used for evaluating item fit in various large-scale assessment studies. The three research questions of this study are (1) whether the empirical RMSD is an unbiased estimator of the population RMSD; (2) if this is not the case, whether this bias can be corrected; and (3) whether the test statistic provides an adequate significance test to detect misfitting items. Using simulation studies, it was found that the empirical RMSD is not an unbiased estimator of the population RMSD, and nonparametric bootstrapping falls short of entirely eliminating this bias. Using parametric bootstrapping, however, the RMSD can be used as a test statistic that outperforms the other approaches—infit and outfit, S − X 2—with respect to both Type I error rate and power. The empirical application showed that parametric bootstrapping of the RMSD results in rather conservative item fit decisions, which suggests more lenient cut-off criteria.

KW - bootstrap

KW - educational measurement

KW - item fit

KW - item response theory

U2 - 10.3102/1076998619890566

DO - 10.3102/1076998619890566

M3 - Journal article

VL - 45

SP - 251

EP - 273

JO - Journal of Educational and Behavioral Statistics

JF - Journal of Educational and Behavioral Statistics

SN - 1076-9986

IS - 3

ER -

ID: 1037971