Applying the Rasch sampler to identify aberrant responding through person fit statistics under fixed nominal α-level.

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Applying the Rasch sampler to identify aberrant responding through person fit statistics under fixed nominal α-level. / Spoden, Christian; Fleischer, Jens; Leutner, Detlev.
in: Journal of applied measurement, Jahrgang 15, Nr. 3, 2014, S. 276-291.

Publikation: Beiträge in ZeitschriftenZeitschriftenaufsätzeForschungbegutachtet

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@article{b13fe5c4539f412eaebffeee4d1f0b07,
title = "Applying the Rasch sampler to identify aberrant responding through person fit statistics under fixed nominal α-level.",
abstract = "Testing hypotheses on a respondent's individual fit under the Rasch model requires knowledge of the distributional properties of a person fit statistic. We argue that the Rasch Sampler (Verhelst, 2008), a Markov chain Monte Carlo algorithm for sampling binary data matrices from a uniform distribution, can be applied for simulating the distribution of person fit statistics with the Rasch model in the same way as it used to test for other forms of misfit. Results from two simulation studies are presented which compare the approach to the original person fit statistics based on normalization formulas. Simulation 1 shows the new approach to hold the expected Type I error rates while the normalized statistics deviate from the nominal alpha-level. In Simulation 2 the power of the new approach was found to be approximately the same or higher than for the normalized statistics under most conditions.",
keywords = "Psychology",
author = "Christian Spoden and Jens Fleischer and Detlev Leutner",
year = "2014",
language = "English",
volume = "15",
pages = "276--291",
journal = "Journal of applied measurement",
issn = "1529-7713",
publisher = "JAM Press",
number = "3",

}

RIS

TY - JOUR

T1 - Applying the Rasch sampler to identify aberrant responding through person fit statistics under fixed nominal α-level.

AU - Spoden, Christian

AU - Fleischer, Jens

AU - Leutner, Detlev

PY - 2014

Y1 - 2014

N2 - Testing hypotheses on a respondent's individual fit under the Rasch model requires knowledge of the distributional properties of a person fit statistic. We argue that the Rasch Sampler (Verhelst, 2008), a Markov chain Monte Carlo algorithm for sampling binary data matrices from a uniform distribution, can be applied for simulating the distribution of person fit statistics with the Rasch model in the same way as it used to test for other forms of misfit. Results from two simulation studies are presented which compare the approach to the original person fit statistics based on normalization formulas. Simulation 1 shows the new approach to hold the expected Type I error rates while the normalized statistics deviate from the nominal alpha-level. In Simulation 2 the power of the new approach was found to be approximately the same or higher than for the normalized statistics under most conditions.

AB - Testing hypotheses on a respondent's individual fit under the Rasch model requires knowledge of the distributional properties of a person fit statistic. We argue that the Rasch Sampler (Verhelst, 2008), a Markov chain Monte Carlo algorithm for sampling binary data matrices from a uniform distribution, can be applied for simulating the distribution of person fit statistics with the Rasch model in the same way as it used to test for other forms of misfit. Results from two simulation studies are presented which compare the approach to the original person fit statistics based on normalization formulas. Simulation 1 shows the new approach to hold the expected Type I error rates while the normalized statistics deviate from the nominal alpha-level. In Simulation 2 the power of the new approach was found to be approximately the same or higher than for the normalized statistics under most conditions.

KW - Psychology

UR - http://www.scopus.com/inward/record.url?scp=84907249556&partnerID=8YFLogxK

M3 - Journal articles

C2 - 24992251

AN - SCOPUS:84907249556

VL - 15

SP - 276

EP - 291

JO - Journal of applied measurement

JF - Journal of applied measurement

SN - 1529-7713

IS - 3

ER -