scholarly journals Sensitivity Of Differential Item Functioning Detection Methods On National Mathematics Examination In North Sumatera Province, Indonesia

2019 ◽  
Vol 8 (5C) ◽  
pp. 1538-1549
Keyword(s):  
Differential Item Functioning ◽  
Likelihood Ratio ◽  
Classical Test Theory ◽  
Test Theory ◽  
Detection Methods ◽  
Ratio Method ◽  
Suitable Model ◽  
Data Set ◽  
Item Functioning ◽  
Mathematics Examination

The purpose of this study was to examine the differences in sensitivity of three methods: IRT-Likelihood Ratio (IRT-LR), Mantel-Haenszel (MH) and Logistics Regression (LR), in detecting gender differential item functioning (DIF) on National Mathematics Examination (Ujian Nasional: UN) for 2014/2015 academic year in North Sumatera Province of Indonesia. DIF item shows the unfairness. It advantages the test takers of certain groups and disadvantages other group test takers, in the case they have the same ability. The presence of DIF was reviewed in grouping by gender: men as reference groups (R) and women as focus groups (F). This study used the experimental method, 3x1 design, with one factor (i.e. method) with three treatments, in the form of 3 different DIF detection methods. There are 5 types of UN Mathematics Year 2015 packages (codes: 1107, 2207, 3307, 4407 and 5507). The 2207 package code was taken as the sample data, consisting of 5000 participants (3067 women, 1933 men; for 40 UN items). Item selection was carried out based on the classical test theory (CTT) on 40 UN items, producing 32 items that fulfilled, and item response theory selection (IRT) produced 18 items that fulfilled. With program R 3.333 and IRTLRDIF 2.0, it was found 5 items were detected as DIF by the IRT-Likelihood Ratio-method (IRTLR), 4 items were detected as DIF by the Logistic Regression method (LR), and 3 items were detected as DIF by the MantelHaenszel method (MH). To test the sensitivity of the three methods, it is not enough with just one time DIF detection, but formed six groups of data analysis: (4400,40),(4400,32), (4400,18), (3000,40), (3000,32), (3000,18), and generate 40 random data sets (without repetitions) in each group, and conduct detecting DIF on the items in each data set. Although the data lacks model fit, the 3 parameter logistic model (3PL) is chosen as the most suitable model. With the Tukey's HSD post hoc test, the IRT-LR method is known to be more sensitive than the MH and LR methods in the group (4400,40) and (3000,40). The IRT-LR method is not longer more sensitive than LR in the group (4400,32) and (3000,32), but still more sensitive than MH. In the groups (4400,18) and (3000,18) the IRT-LR method is more sensitive than LR, but not significantly more sensitive than MH. The LR method is consistently tested to be more sensitive than the MH method in the entire analysis groups.

scholarly journals Using Differential Item Functioning to Analyze the Domain Generality of a Common Scientific Reasoning Test

2021 ◽  
Author(s):  
Ansgar Opitz ◽  
Moritz Heene ◽  
Frank Fischer
Keyword(s):  
Differential Item Functioning ◽  
Scientific Reasoning ◽  
Classical Test Theory ◽  
Test Theory ◽  
Added Value ◽  
Biology Students ◽  
Classical Test ◽  
Reasoning Test ◽  
Domain Generality ◽  
Item Functioning

Abstract. A significant problem that assessments of scientific reasoning face at the level of higher education is the question of domain generality, that is, whether a test will produce biased results for students from different domains. This study applied three recently developed methods of analyzing differential item functioning (DIF) to evaluate the domain generality assumption of a common scientific reasoning test. Additionally, we evaluated the usefulness of these new, tree- and lasso-based, methods to analyze DIF and compared them with methods based on classical test theory. We gave the scientific reasoning test to 507 university students majoring in physics, biology, or medicine. All three DIF analysis methods indicated a domain bias present in about one-third of the items, mostly benefiting biology students. We did not find this bias by using methods based on classical test theory. Those methods indicated instead that all items were easier for physics students compared to biology students. Thus, the tree- and lasso-based methods provide a clear added value to test evaluation. Taken together, our analyses indicate that the scientific reasoning test is neither entirely domain-general, nor entirely domain-specific. We advise against using it in high-stakes situations involving domain comparisons.

scholarly journals The comparison of the scores obtained by Bayesian nonparametric model and classical test theory methods

2021 ◽  
Vol 104 (3) ◽  
pp. 003685042110283
Author(s):  
Meltem Yurtcu ◽  
Hülya Kelecioglu ◽  
Edward L Boone
Keyword(s):  
Classical Test Theory ◽  
Small Sample ◽  
Test Theory ◽  
Nonparametric Model ◽  
Bayesian Nonparametric ◽  
Test Equating ◽  
Classical Test ◽  
A Value ◽  
Item Functioning ◽  
The Individual

Bayesian Nonparametric (BNP) modelling can be used to obtain more detailed information in test equating studies and to increase the accuracy of equating by accounting for covariates. In this study, two covariates are included in the equating under the Bayes nonparametric model, one is continuous, and the other is discrete. Scores equated with this model were obtained for a single group design for a small group in the study. The equated scores obtained with the model were compared with the mean and linear equating methods in the Classical Test Theory. Considering the equated scores obtained from three different methods, it was found that the equated scores obtained with the BNP model produced a distribution closer to the target test. Even the classical methods will give a good result with the smallest error when using a small sample, making equating studies valuable. The inclusion of the covariates in the model in the classical test equating process is based on some assumptions and cannot be achieved especially using small groups. The BNP model will be more beneficial than using frequentist methods, regardless of this limitation. Information about booklets and variables can be obtained from the distributors and equated scores that obtained with the BNP model. In this case, it makes it possible to compare sub-categories. This can be expressed as indicating the presence of differential item functioning (DIF). Therefore, the BNP model can be used actively in test equating studies, and it provides an opportunity to examine the characteristics of the individual participants at the same time. Thus, it allows test equating even in a small sample and offers the opportunity to reach a value closer to the scores in the target test.

A Bifactor Multidimensional Item Response Theory Model for Differential Item Functioning Analysis on Testlet-Based Items

2011 ◽  
Vol 35 (8) ◽  
pp. 604-622 ◽  
Author(s):  
Hirotaka Fukuhara ◽  
Akihito Kamata
Keyword(s):  
Item Response Theory ◽  
Differential Item Functioning ◽  
Item Response ◽  
Estimation Method ◽  
Multidimensional Item Response Theory ◽  
Multidimensional Item Response ◽  
Response Theory ◽  
Data Set ◽  
Detection Rates ◽  
Item Functioning

A differential item functioning (DIF) detection method for testlet-based data was proposed and evaluated in this study. The proposed DIF model is an extension of a bifactor multidimensional item response theory (MIRT) model for testlets. Unlike traditional item response theory (IRT) DIF models, the proposed model takes testlet effects into account, thus estimating DIF magnitude appropriately when a test is composed of testlets. A fully Bayesian estimation method was adopted for parameter estimation. The recovery of parameters was evaluated for the proposed DIF model. Simulation results revealed that the proposed bifactor MIRT DIF model produced better estimates of DIF magnitude and higher DIF detection rates than the traditional IRT DIF model for all simulation conditions. A real data analysis was also conducted by applying the proposed DIF model to a statewide reading assessment data set.

scholarly journals Analysis of the difficulty index of item bank according to cognitive aspects during the Covid-19 pandemic

2021 ◽  
Vol 25 (2) ◽  
Author(s):  
Maizura Fauzie ◽  
Andi Ulfa Tenri Pada ◽  
Supriatno Supriatno
Keyword(s):  
Classical Test Theory ◽  
Item Bank ◽  
Test Theory ◽  
Data Set ◽  
Face To Face ◽  
Aceh Province ◽  
The Face ◽  
Computer Based ◽  
Face To Face Learning ◽  
Difficulty Index

The Covid-19 pandemic is a major challenge for the education system. The face-to-face learning process shifted to online learning, including the school exams. In Aceh province, the school exams have changed from paper-based and computer-based. This research aims to analyze the difficulty index of an item bank based on cognitive aspects of Bloom’s Taxonomy. The study samples included 850 students. The data were the item bank of a final semester exam consisting of 200 multiple-choice items, answer keys, and students’ answer sheets. The empirical analysis of the item bank using classical test theory (CTT) found that 141 out of 200 items are valid based on content validity and computing data set using the Aiken’s V formula. Item tests have reliability of 0.983. The reliability is calculated using the Kuder-Richardson 21 formula. If the reliability coefficient is r11 ≥ 0.70, then the item is declared reliable. In addition, 62 out of 141 (43.97%) items from the item bank are classified with a moderate difficulty index, and 79 items (56.03%) are categorized with a high difficulty index. The cognitive aspects found in the items are remembering, understanding, applying, and analyzing. Students mostly found items with the cognitive aspects of remembering and understanding are difficult to solve.

scholarly journals A primer on standardized testing: History, measurement, classical test theory, item response theory, and equating

2019 ◽  
Vol 33 (2) ◽  
pp. 151-163 ◽  
Author(s):  
Igor Himelfarb
Keyword(s):  
Standardized Testing ◽  
Classical Test Theory ◽  
Health Science ◽  
Test Theory ◽  
Data Set ◽  
Classical Test ◽  
Irt Models ◽  
Graded Response ◽  
Item Responses ◽  
Science Educators

Objective:This article presents health science educators and researchers with an overview of standardized testing in educational measurement. The history, theoretical frameworks of classical test theory, item response theory (IRT), and the most common IRT models used in modern testing are presented.Methods:A narrative overview of the history, theoretical concepts, test theory, and IRT is provided to familiarize the reader with these concepts of modern testing. Examples of data analyses using different models are shown using 2 simulated data sets. One set consisted of a sample of 2000 item responses to 40 multiple-choice, dichotomously scored items. This set was used to fit 1-parameter logistic (PL) model, 2PL, and 3PL IRT models. Another data set was a sample of 1500 item responses to 10 polytomously scored items. The second data set was used to fit a graded response model.Results:Model-based item parameter estimates for 1PL, 2PL, 3PL, and graded response are presented, evaluated, and explained.Conclusion:This study provides health science educators and education researchers with an introduction to educational measurement. The history of standardized testing, the frameworks of classical test theory and IRT, and the logic of scaling and equating are presented. This introductory article will aid readers in understanding these concepts.

A Classical Test Theory Analysis of the Light and Spectroscopy Concept Inventory National Study Data Set

2012 ◽  
Vol 11 (1) ◽  
Author(s):  
Wayne M. Schlingman ◽  
Edward E. Prather ◽  
Colin S. Wallace ◽  
Alexander L. Rudolph ◽  
Gina Brissenden
Keyword(s):  
Classical Test Theory ◽  
Study Data ◽  
Test Theory ◽  
Concept Inventory ◽  
National Study ◽  
Theory Analysis ◽  
Data Set ◽  
Classical Test
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