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Belzak, William C. M. – Educational Measurement: Issues and Practice, 2023
Test developers and psychometricians have historically examined measurement bias and differential item functioning (DIF) across a single categorical variable (e.g., gender), independently of other variables (e.g., race, age, etc.). This is problematic when more complex forms of measurement bias may adversely affect test responses and, ultimately,…
Descriptors: Test Bias, High Stakes Tests, Artificial Intelligence, Test Items
Weese, James D.; Turner, Ronna C.; Ames, Allison; Crawford, Brandon; Liang, Xinya – Educational and Psychological Measurement, 2022
A simulation study was conducted to investigate the heuristics of the SIBTEST procedure and how it compares with ETS classification guidelines used with the Mantel-Haenszel procedure. Prior heuristics have been used for nearly 25 years, but they are based on a simulation study that was restricted due to computer limitations and that modeled item…
Descriptors: Test Bias, Heuristics, Classification, Statistical Analysis
Dimitrov, Dimiter M.; Atanasov, Dimitar V. – Educational and Psychological Measurement, 2022
This study offers an approach to testing for differential item functioning (DIF) in a recently developed measurement framework, referred to as "D"-scoring method (DSM). Under the proposed approach, called "P-Z" method of testing for DIF, the item response functions of two groups (reference and focal) are compared by…
Descriptors: Test Bias, Methods, Test Items, Scoring
Randall, Jennifer – Educational Assessment, 2023
In a justice-oriented antiracist assessment process, attention to the disruption of white supremacy must occur at every stage--from construct articulation to score reporting. An important step in the assessment development process is the item review stage often referred to as Bias/Fairness and Sensitivity Review. I argue that typical approaches to…
Descriptors: Social Justice, Racism, Test Bias, Test Items
James D. Weese; Ronna C. Turner; Allison Ames; Xinya Liang; Brandon Crawford – Journal of Experimental Education, 2024
In this study a standardized effect size was created for use with the SIBTEST procedure. Using this standardized effect size, a single set of heuristics was developed that are appropriate for data fitting different item response models (e.g., 2-parameter logistic, 3-parameter logistic). The standardized effect size rescales the raw beta-uni value…
Descriptors: Test Bias, Test Items, Item Response Theory, Effect Size
Finch, W. Holmes – Educational and Psychological Measurement, 2023
Psychometricians have devoted much research and attention to categorical item responses, leading to the development and widespread use of item response theory for the estimation of model parameters and identification of items that do not perform in the same way for examinees from different population subgroups (e.g., differential item functioning…
Descriptors: Test Bias, Item Response Theory, Computation, Methods
Martijn Schoenmakers; Jesper Tijmstra; Jeroen Vermunt; Maria Bolsinova – Educational and Psychological Measurement, 2024
Extreme response style (ERS), the tendency of participants to select extreme item categories regardless of the item content, has frequently been found to decrease the validity of Likert-type questionnaire results. For this reason, various item response theory (IRT) models have been proposed to model ERS and correct for it. Comparisons of these…
Descriptors: Item Response Theory, Response Style (Tests), Models, Likert Scales
Andrew D. Ho – Journal of Educational and Behavioral Statistics, 2024
I review opportunities and threats that widely accessible Artificial Intelligence (AI)-powered services present for educational statistics and measurement. Algorithmic and computational advances continue to improve approaches to item generation, scale maintenance, test security, test scoring, and score reporting. Predictable misuses of AI for…
Descriptors: Artificial Intelligence, Measurement, Educational Assessment, Technology Uses in Education
Paula Elosua – Language Assessment Quarterly, 2024
In sociolinguistic contexts where standardized languages coexist with regional dialects, the study of differential item functioning is a valuable tool for examining certain linguistic uses or varieties as threats to score validity. From an ecological perspective, this paper describes three stages in the study of differential item functioning…
Descriptors: Reading Tests, Reading Comprehension, Scores, Test Validity
Kim, Sooyeon; Walker, Michael E. – Educational Measurement: Issues and Practice, 2022
Test equating requires collecting data to link the scores from different forms of a test. Problems arise when equating samples are not equivalent and the test forms to be linked share no common items by which to measure or adjust for the group nonequivalence. Using data from five operational test forms, we created five pairs of research forms for…
Descriptors: Ability, Tests, Equated Scores, Testing Problems
Bolt, Daniel M.; Liao, Xiangyi – Journal of Educational Measurement, 2021
We revisit the empirically observed positive correlation between DIF and difficulty studied by Freedle and commonly seen in tests of verbal proficiency when comparing populations of different mean latent proficiency levels. It is shown that a positive correlation between DIF and difficulty estimates is actually an expected result (absent any true…
Descriptors: Test Bias, Difficulty Level, Correlation, Verbal Tests
Marjolein Muskens; Willem E. Frankenhuis; Lex Borghans – npj Science of Learning, 2024
In many countries, standardized math tests are important for achieving academic success. Here, we examine whether content of items, the story that explains a mathematical question, biases performance of low-SES students. In a large-scale cohort study of Trends in International Mathematics and Science Studies (TIMSS)--including data from 58…
Descriptors: Mathematics Tests, Standardized Tests, Test Items, Low Income Students
Chalmers, R. Philip; Zheng, Guoguo – Applied Measurement in Education, 2023
This article presents generalizations of SIBTEST and crossing-SIBTEST statistics for differential item functioning (DIF) investigations involving more than two groups. After reviewing the original two-group setup for these statistics, a set of multigroup generalizations that support contrast matrices for joint tests of DIF are presented. To…
Descriptors: Test Bias, Test Items, Item Response Theory, Error of Measurement
Wang, Weimeng; Liu, Yang; Liu, Hongyun – Journal of Educational and Behavioral Statistics, 2022
Differential item functioning (DIF) occurs when the probability of endorsing an item differs across groups for individuals with the same latent trait level. The presence of DIF items may jeopardize the validity of an instrument; therefore, it is crucial to identify DIF items in routine operations of educational assessment. While DIF detection…
Descriptors: Test Bias, Test Items, Equated Scores, Regression (Statistics)
Lanrong Li – ProQuest LLC, 2021
When developing a test, it is essential to ensure that the test is free of items with differential item functioning (DIF). DIF occurs when examinees of equal ability, but from different examinee subgroups, have different chances of getting the item correct. According to the multidimensional perspective, DIF occurs because the test measures more…
Descriptors: Test Bias, Test Items, Meta Analysis, Effect Size