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

Abstract

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.

Measurement of the problematic usage of the Internet unidimensional quasitrait continuum with item response theory.

“…Item-level performance, functional form, and local independence are evaluated prior to overall model fit in IRT analysis ( Essen et al, 2017 ; Toland, 2014 ). The monotonicity assumption was assessed by inspecting the ORFs and ensuring that the probability of endorsement of each successive response category on IAT items increased monotonically as a function of increasing severity in PUI.…”

An analysis of item response theory using program R

“…Several studies have been conducted related to item response theory analysis, such as item response theory analysis, especially the Rasch model using the QUEST program (Rizbudiani et al, 2021), item response theory analysis using IRTPROV3.0 and BILOG-MG V3.0 programs to investigate item level diagnostic statistics and models -data fit (Essen et al, 2017), and item response theory analysis by comparing the fit of the 2-PL and 3-PL models (Reise & Waller, 2003). Research on the R program, such as using the R program, in particular, developing the R PLmixed package into the existing R package lme4 (Jeon & Rockwood, 2017), and using the R program to see the level of difficulty and suitability of the item model as well as the item characteristic curve (ICC) and item information curve (IIC) on the Rasch model (Muchlisin et al, 2019).…”

Analysis and Evaluation of Multiple-Choice Test Items and Test Design: A Study on Application of Item Response Theory

“…In term of level diagnostic statistics and model-data fit with 1 and 2-parameter models using IRTPRO V3.0 and BILOG-MG Version 3.0, Essen recommended that the use of more than one IRT softwares offers more useful information for the choice of a model that fits the data [7]. Similarly, Foster identified and coded 63 articles that used IRT on empirical data published in industrial-organizational and organizational behavior journals since 2000.…”