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Liu, Yan; Zumbo, Bruno D.; Gustafson, Paul; Huang, Yi; Kroc, Edward; Wu, Amery D. – Practical Assessment, Research & Evaluation, 2016
A variety of differential item functioning (DIF) methods have been proposed and used for ensuring that a test is fair to all test takers in a target population in the situations of, for example, a test being translated to other languages. However, once a method flags an item as DIF, it is difficult to conclude that the grouping variable (e.g.,…
Descriptors: Test Items, Test Bias, Probability, Scores
Rahman, Nazia – ProQuest LLC, 2013
Samejima hypothesized that non-monotonically increasing item response functions (IRFs) of ability might occur for multiple-choice items (referred to here as "Samejima items") if low ability test takers with some, though incomplete, knowledge or skill are drawn to a particularly attractive distractor, while very low ability test takers…
Descriptors: Multiple Choice Tests, Test Items, Item Response Theory, Probability
Ong, Yoke Mooi; Williams, Julian; Lamprianou, Iasonas – International Journal of Testing, 2015
The purpose of this article is to explore crossing differential item functioning (DIF) in a test drawn from a national examination of mathematics for 11-year-old pupils in England. An empirical dataset was analyzed to explore DIF by gender in a mathematics assessment. A two-step process involving the logistic regression (LR) procedure for…
Descriptors: Mathematics Tests, Gender Differences, Test Bias, Test Items
Hauser, Carl; Thum, Yeow Meng; He, Wei; Ma, Lingling – Educational and Psychological Measurement, 2015
When conducting item reviews, analysts evaluate an array of statistical and graphical information to assess the fit of a field test (FT) item to an item response theory model. The process can be tedious, particularly when the number of human reviews (HR) to be completed is large. Furthermore, such a process leads to decisions that are susceptible…
Descriptors: Test Items, Item Response Theory, Research Methodology, Decision Making
Smithson, Michael; Merkle, Edgar C.; Verkuilen, Jay – Journal of Educational and Behavioral Statistics, 2011
This paper describes the application of finite-mixture general linear models based on the beta distribution to modeling response styles, polarization, anchoring, and priming effects in probability judgments. These models, in turn, enhance our capacity for explicitly testing models and theories regarding the aforementioned phenomena. The mixture…
Descriptors: Priming, Research Methodology, Probability, Item Response Theory
Atar, Burcu; Kamata, Akihito – Hacettepe University Journal of Education, 2011
The Type I error rates and the power of IRT likelihood ratio test and cumulative logit ordinal logistic regression procedures in detecting differential item functioning (DIF) for polytomously scored items were investigated in this Monte Carlo simulation study. For this purpose, 54 simulation conditions (combinations of 3 sample sizes, 2 sample…
Descriptors: Test Bias, Sample Size, Monte Carlo Methods, Item Response Theory
Yildirim, Huseyin H.; Yildirim, Selda – Hacettepe University Journal of Education, 2011
Multivariate matching in Differential Item Functioning (DIF) analyses may contribute to understand the sources of DIF. In this context, detecting appropriate additional matching variables is a crucial issue. This present article argues that the variables which are correlated with communalities in item difficulties can be used as an additional…
Descriptors: Test Bias, Multivariate Analysis, Probability, Regression (Statistics)
Sheehan, Kathleen M.; Kostin, Irene; Futagi, Yoko; Hemat, Ramin; Zuckerman, Daniel – ETS Research Report Series, 2006
This paper describes the development, implementation, and evaluation of an automated system for predicting the acceptability status of candidate reading-comprehension stimuli extracted from a database of journal and magazine articles. The system uses a combination of classification and regression techniques to predict the probability that a given…
Descriptors: Automation, Prediction, Reading Comprehension, Classification