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Ye Ma; Deborah J. Harris – Educational Measurement: Issues and Practice, 2025
Item position effect (IPE) refers to situations where an item performs differently when it is administered in different positions on a test. The majority of previous research studies have focused on investigating IPE under linear testing. There is a lack of IPE research under adaptive testing. In addition, the existence of IPE might violate Item…
Descriptors: Computer Assisted Testing, Adaptive Testing, Item Response Theory, Test Items
Cheng, Yiling – Measurement: Interdisciplinary Research and Perspectives, 2023
Computerized adaptive testing (CAT) offers an efficient and highly accurate method for estimating examinees' abilities. In this article, the free version of Concerto Software for CAT was reviewed, dividing our evaluation into three sections: software implementation, the Item Response Theory (IRT) features of CAT, and user experience. Overall,…
Descriptors: Computer Software, Computer Assisted Testing, Adaptive Testing, Item Response Theory
Falk, Carl F.; Feuerstahler, Leah M. – Educational and Psychological Measurement, 2022
Large-scale assessments often use a computer adaptive test (CAT) for selection of items and for scoring respondents. Such tests often assume a parametric form for the relationship between item responses and the underlying construct. Although semi- and nonparametric response functions could be used, there is scant research on their performance in a…
Descriptors: Item Response Theory, Adaptive Testing, Computer Assisted Testing, Nonparametric Statistics
Lim, Hwanggyu; Choe, Edison M. – Journal of Educational Measurement, 2023
The residual differential item functioning (RDIF) detection framework was developed recently under a linear testing context. To explore the potential application of this framework to computerized adaptive testing (CAT), the present study investigated the utility of the RDIF[subscript R] statistic both as an index for detecting uniform DIF of…
Descriptors: Test Items, Computer Assisted Testing, Item Response Theory, Adaptive Testing
Ince Araci, F. Gul; Tan, Seref – International Journal of Assessment Tools in Education, 2022
Computerized Adaptive Testing (CAT) is a beneficial test technique that decreases the number of items that need to be administered by taking items in accordance with individuals' own ability levels. After the CAT applications were constructed based on the unidimensional Item Response Theory (IRT), Multidimensional CAT (MCAT) applications have…
Descriptors: Adaptive Testing, Computer Assisted Testing, Simulation, Item Response Theory
Stefanie A. Wind; Beyza Aksu-Dunya – Applied Measurement in Education, 2024
Careless responding is a pervasive concern in research using affective surveys. Although researchers have considered various methods for identifying careless responses, studies are limited that consider the utility of these methods in the context of computer adaptive testing (CAT) for affective scales. Using a simulation study informed by recent…
Descriptors: Response Style (Tests), Computer Assisted Testing, Adaptive Testing, Affective Measures
Gorgun, Guher; Bulut, Okan – Large-scale Assessments in Education, 2023
In low-stakes assessment settings, students' performance is not only influenced by students' ability level but also their test-taking engagement. In computerized adaptive tests (CATs), disengaged responses (e.g., rapid guesses) that fail to reflect students' true ability levels may lead to the selection of less informative items and thereby…
Descriptors: Computer Assisted Testing, Adaptive Testing, Test Items, Algorithms
Ozge Ersan Cinar – ProQuest LLC, 2022
In educational tests, a group of questions related to a shared stimulus is called a testlet (e.g., a reading passage with multiple related questions). Use of testlets is very common in educational tests. Additionally, computerized adaptive testing (CAT) is a mode of testing where the test forms are created in real time tailoring to the test…
Descriptors: Test Items, Computer Assisted Testing, Adaptive Testing, Educational Testing
Kreitchmann, Rodrigo S.; Sorrel, Miguel A.; Abad, Francisco J. – Educational and Psychological Measurement, 2023
Multidimensional forced-choice (FC) questionnaires have been consistently found to reduce the effects of socially desirable responding and faking in noncognitive assessments. Although FC has been considered problematic for providing ipsative scores under the classical test theory, item response theory (IRT) models enable the estimation of…
Descriptors: Measurement Techniques, Questionnaires, Social Desirability, Adaptive Testing
Xu, Lingling; Wang, Shiyu; Cai, Yan; Tu, Dongbo – Journal of Educational Measurement, 2021
Designing a multidimensional adaptive test (M-MST) based on a multidimensional item response theory (MIRT) model is critical to make full use of the advantages of both MST and MIRT in implementing multidimensional assessments. This study proposed two types of automated test assembly (ATA) algorithms and one set of routing rules that can facilitate…
Descriptors: Item Response Theory, Adaptive Testing, Automation, Test Construction
Lin, Yin; Brown, Anna; Williams, Paul – Educational and Psychological Measurement, 2023
Several forced-choice (FC) computerized adaptive tests (CATs) have emerged in the field of organizational psychology, all of them employing ideal-point items. However, despite most items developed historically follow dominance response models, research on FC CAT using dominance items is limited. Existing research is heavily dominated by…
Descriptors: Measurement Techniques, Computer Assisted Testing, Adaptive Testing, Industrial Psychology
Hanif Akhtar – International Society for Technology, Education, and Science, 2023
For efficiency, Computerized Adaptive Test (CAT) algorithm selects items with the maximum information, typically with a 50% probability of being answered correctly. However, examinees may not be satisfied if they only correctly answer 50% of the items. Researchers discovered that changing the item selection algorithms to choose easier items (i.e.,…
Descriptors: Success, Probability, Computer Assisted Testing, Adaptive Testing
Wang, Shiyu; Xiao, Houping; Cohen, Allan – Journal of Educational and Behavioral Statistics, 2021
An adaptive weight estimation approach is proposed to provide robust latent ability estimation in computerized adaptive testing (CAT) with response revision. This approach assigns different weights to each distinct response to the same item when response revision is allowed in CAT. Two types of weight estimation procedures, nonfunctional and…
Descriptors: Computer Assisted Testing, Adaptive Testing, Computation, Robustness (Statistics)
Carol Eckerly; Yue Jia; Paul Jewsbury – ETS Research Report Series, 2022
Testing programs have explored the use of technology-enhanced items alongside traditional item types (e.g., multiple-choice and constructed-response items) as measurement evidence of latent constructs modeled with item response theory (IRT). In this report, we discuss considerations in applying IRT models to a particular type of adaptive testlet…
Descriptors: Computer Assisted Testing, Test Items, Item Response Theory, Scoring
Wyse, Adam E.; McBride, James R. – Journal of Educational Measurement, 2021
A key consideration when giving any computerized adaptive test (CAT) is how much adaptation is present when the test is used in practice. This study introduces a new framework to measure the amount of adaptation of Rasch-based CATs based on looking at the differences between the selected item locations (Rasch item difficulty parameters) of the…
Descriptors: Item Response Theory, Computer Assisted Testing, Adaptive Testing, Test Items