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Jin, Kuan-Yu; Siu, Wai-Lok; Huang, Xiaoting – Journal of Educational Measurement, 2022
Multiple-choice (MC) items are widely used in educational tests. Distractor analysis, an important procedure for checking the utility of response options within an MC item, can be readily implemented in the framework of item response theory (IRT). Although random guessing is a popular behavior of test-takers when answering MC items, none of the…
Descriptors: Guessing (Tests), Multiple Choice Tests, Item Response Theory, Attention
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DeCarlo, Lawrence T. – Journal of Educational Measurement, 2023
A conceptualization of multiple-choice exams in terms of signal detection theory (SDT) leads to simple measures of item difficulty and item discrimination that are closely related to, but also distinct from, those used in classical item analysis (CIA). The theory defines a "true split," depending on whether or not examinees know an item,…
Descriptors: Multiple Choice Tests, Test Items, Item Analysis, Test Wiseness
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Andrich, David; Marais, Ida – Journal of Educational Measurement, 2018
Even though guessing biases difficulty estimates as a function of item difficulty in the dichotomous Rasch model, assessment programs with tests which include multiple-choice items often construct scales using this model. Research has shown that when all items are multiple-choice, this bias can largely be eliminated. However, many assessments have…
Descriptors: Multiple Choice Tests, Test Items, Guessing (Tests), Test Bias
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Donlon, Thomas F. – Journal of Educational Measurement, 1981
Scores within the chance range are differentiated, "uninterpretable" scores being those that demonstrate randomness (broadly defined) by failing to achieve typical levels of correlation with group-determined difficulty. The relevant literature is reviewed. Finally, randomness and uninterpretability are examined in light of the…
Descriptors: Difficulty Level, Guessing (Tests), Multiple Choice Tests, Scores
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Frary, Robert B. – Journal of Educational Measurement, 1989
Responses to a 50-item, 4-choice test were simulated for 1,000 examinees under conventional formula-scoring instructions. Based on 192 simulation runs, formula scores and expected formula scores were determined for each examinee allowing and not allowing for inappropriate omissions. (TJH)
Descriptors: Computer Simulation, Difficulty Level, Guessing (Tests), Multiple Choice Tests
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Huck, Schuyler W.; Bowers, Norman D. – Journal of Educational Measurement, 1972
Study investigated whether the proportion of examinees who answer an item correctly may be influenced by the difficulty of the immediately preceding item. (Authors/MB)
Descriptors: Achievement Tests, Difficulty Level, Hypothesis Testing, Item Analysis
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Gorin, Joanna S. – Journal of Educational Measurement, 2005
Based on a previously validated cognitive processing model of reading comprehension, this study experimentally examines potential generative components of text-based multiple-choice reading comprehension test questions. Previous research (Embretson & Wetzel, 1987; Gorin & Embretson, 2005; Sheehan & Ginther, 2001) shows text encoding and decision…
Descriptors: Reaction Time, Reading Comprehension, Difficulty Level, Test Items
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Bielinski, John; Davison, Mark L. – Journal of Educational Measurement, 2001
Used mathematics achievement data from the 1992 National Assessment of Educational Progress, the Third International Mathematics and Science Study, and the National Education Longitudinal Study of 1988 to examine the sex difference by item difficulty interaction. The predicted negative correlation was found for all eight populations and was…
Descriptors: Correlation, Difficulty Level, Interaction, Mathematics Tests
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Lord, Frederic M. – Journal of Educational Measurement, 1971
Modifications of administration and item arrangement of a conventional test can force a match between item difficulty levels and the ability level of the examinee. Although different examinees take different sets of items, the scoring method provides comparable scores for all. Furthermore, the test is self-scoring. These advantages are obtained…
Descriptors: Academic Ability, Difficulty Level, Measurement Techniques, Models
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Forsyth, Robert A.; Spratt, Kevin F. – Journal of Educational Measurement, 1980
The effects of two item formats on item difficulty and item discrimination indices for mathematics problem solving multiple-choice tests were investigated. One format required identifying the proper "set-up" for the item; the other format required complete solving of the item. (Author/JKS)
Descriptors: Difficulty Level, Junior High Schools, Multiple Choice Tests, Problem Solving
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Katz, Irvin R.; Bennett, Randy Elliot; Berger, Aliza E. – Journal of Educational Measurement, 2000
Studied the solution strategies of 55 high school students who solved parallel constructed response and multiple-choice items that differed only in the presence of response options. Differences in difficulty between response formats did not correspond to differences in strategy choice. Interprets results in light of the relative comprehension…
Descriptors: College Entrance Examinations, Constructed Response, Difficulty Level, High School Students
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Frisbie, David A.; Sweeney, Daryl C. – Journal of Educational Measurement, 1982
A 100-item five-choice multiple choice (MC) biology final exam was converted to multiple choice true-false (MTF) form to yield two content-parallel test forms comprised of the two item types. Students found the MTF items easier and preferred MTF over MC; the MTF subtests were more reliable. (Author/GK)
Descriptors: Biology, College Science, Comparative Analysis, Difficulty Level
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Smith, Malbert, III; And Others – Journal of Educational Measurement, 1979
Results of multiple-choice tests in educational psychology were examined to discover the effects on students' scores of changing their original answer choices after reconsideration. Eighty-six percent of the students changed one or more answers, and six out of seven students who made changes improved their scores by doing so. (Author/CTM)
Descriptors: Academic Ability, Difficulty Level, Error Patterns, Guessing (Tests)
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Wise, Steven L.; And Others – Journal of Educational Measurement, 1992
Performance of 156 undergraduate and 48 graduate students on a self-adapted test (SFAT)--students choose the difficulty level of their test items--was compared with performance on a computer-adapted test (CAT). Those taking the SFAT obtained higher ability scores and reported lower posttest state anxiety than did CAT takers. (SLD)
Descriptors: Adaptive Testing, Comparative Testing, Computer Assisted Testing, Difficulty Level