Assessing model data fit of unidimensional item response theory models in simulated data

Köse, İbrahim Alper

doi:10.5897/err2014.1729

Cited by 5 publications

(6 citation statements)

References 16 publications

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“…The last test assumption was the parameter invariance assumption test that aims to prove the parameter invariance of items and participants' ability (Köse, 2014). The items parameter invariance test was conducted to determine the items characteristics consistency that answered by the different group of the students.…”

Section: Discussionmentioning

confidence: 99%

A Test of Analytical Thinking and Chemical Representation Ability on 'Rate of Reaction' Topic

Wiyarsi

Fachriyah

Supriadi

et al. 2019

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Assessments play an important role in chemistry learning and for specific uses. The construction of a test based on multiple representation approaches is needed for measuring the 21st century thinking skills. This study aims to construct and validate a standardized test to measure students’ analytical thinking and chemical representation ability in rate of reaction topic. The test captures four aspects on analytical thinking and four levels of multiple representations (macroscopic, sub-microscopic, symbolic and mathematic). A group of experts confirmed the construct and face validity of the Test of Analytical Thinking based on Multiple Representation (TAT-MR) with 32 items. The TAT-MR was then validated by participating 449 high school students. The test characteristics were analyzed usingRasch model with Partial Credit Model-1 Parameter Logistic (PCM-1PL) approach. The results of theRaschmodeling show that there are 22 TAT-MR items with excellent reliability. Hence, the TAT-MR is acceptable as a good instrument to collect the data. This study suggests that TAT-MR will prove to be a useful instrument for measuring the students’ ability on analytical thinking and chemical representation for rate of reaction topic in chemistry learning.

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Section: Discussionmentioning

confidence: 99%

A Test of Analytical Thinking and Chemical Representation Ability on 'Rate of Reaction' Topic

Wiyarsi

Fachriyah

Supriadi

et al. 2019

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“…Therefore, the 3PL model was used for the analysis. Kose(2014) found that in a 1-, 2-and 3-paramter for assessing model data fit, 2-PL model fitted significantly better than the 3-PL model when -2Log likelihood ratio X 2 was used. However, when Orlando and Thissen (2000) evaluated model-data fit from fixed format tests, the results indicated that the three parameter logistic models combined with the generalized partial credit model among various IRT model combinations led to the best fit to the given data sets.…”

Section: Resultsmentioning

confidence: 99%

“…They held that model misfit has dire consequences leading to violation of invariance property. Thus, Kose (2014) emphasized that the property of invariance of item and ability parameters is the main stay of IRT that distinguishes it from CTT. The invariance property of item and ability is not dependent on the examinees distribution and characteristics of set of test items.…”

Section: Introductionmentioning

confidence: 99%

Item level diagnostics and model - data fit in item response theory (IRT) using BILOG - MG v3.0 and IRTPRO v3.0 programmes

Essen¹,

Idaka²,

Metibemu³

2017

Gl Jnl Educ Res

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Item response theory (IRT) is a framework for modeling and analyzing item response data. Item-level modeling gives IRT advantages over classical test theory. The fit of an item score pattern to an item response theory (IRT) models is a necessary condition that must be assessed for further use of item and models that best fit the data. The study investigated item level diagnostic statistics and model-data fit with one-and two-parameter models using IRTPROV3.0 and BILOG-MG V3.0. Ex-post facto design was adopted. The population for the study consisted of 11,538 candidates' responses who took Type L 2014 Unified Tertiary Matriculation Examination (UTME) Mathematics paper in Akwa Ibom State, Nigeria. The sample of 5,192(45%) responses was randomly selected through stratified sampling technique. BILOG-MG V3.0 and IRTPROV3.0 computer software was used to calibrate the candidates' responses. Two research questions were raised to guide the study. Pearson's χ 2 and S -χ 2 statistics as an item fit index for dichotomous item response theory models were used. The outputs from the two computer software were used to answer the questions. The findings revealed that only 1 item fitted 1-parameter model in BILOG-MG V3.0 and IRTPRO V3.0. Furthermore, the findings revealed that 26 items fitted 2-parameter models when using BILOG-MG V3.0. Five items fitted 2-parameter models in IRTPRO. It was recommended that the use of more than one IRT software programme offers more useful information for the choice of model that fit the data.

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“…When the data are fit to the incorrect IRT model, Type I error increased and became inflated as impact increased for both sample sizes. Therefore, choosing an appropriate IRT model for existing data is an important consideration (e.g., Bolt et al, 2014;Köse, 2014;Maydeu-Olivares, 2013) and, done well, can be arduous. In their chapter on the assessment of modeldata fit, Hambleton et al (1991) recommend a comprehensive set of procedures for assessing IRT model fit including checking model assumptions, parameter invariance, and model predictions.…”

Section: Recommendationsmentioning

confidence: 99%

Detecting Differential Item Functioning: Item Response Theory Methods Versus the Mantel-Haenszel Procedure

Diaz

Brooks

Johanson

2021

International Journal of Assessment Tools in Education

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This Monte Carlo study assessed Type I error in differential item functioning analyses using Lord's chi-square (LC), Likelihood Ratio Test (LRT), and Mantel-Haenszel (MH) procedure. Two research interests were investigated: item response theory (IRT) model specification in LC and the LRT and continuity correction in the MH procedure. This study enhances the literature by investigating LC and the LRT using correct and incorrect model-data fit and comparing those results to the MH procedure. There were three fixed factors (number of test items, IRT parameter estimation method, and item parameter equating) and four varied factors (IRT model used to generate data and fit the data, sample size, and impact). The findings suggested the MH procedure without the continuity correction is best based on Type I error rate.

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Assessing model data fit of unidimensional item response theory models in simulated data

Cited by 5 publications

References 16 publications

A Test of Analytical Thinking and Chemical Representation Ability on 'Rate of Reaction' Topic

A Test of Analytical Thinking and Chemical Representation Ability on 'Rate of Reaction' Topic

Item level diagnostics and model - data fit in item response theory (IRT) using BILOG - MG v3.0 and IRTPRO v3.0 programmes

Detecting Differential Item Functioning: Item Response Theory Methods Versus the Mantel-Haenszel Procedure

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