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Bayesian Logistic Regression: A New Method to Calibrate Pretest Items in Multistage Adaptive Testing
TsungHan Ho – Applied Measurement in Education, 2023
An operational multistage adaptive test (MST) requires the development of a large item bank and the effort to continuously replenish the item bank due to concerns about test security and validity over the long term. New items should be pretested and linked to the item bank before being used operationally. The linking item volume fluctuations in…
Descriptors: Bayesian Statistics, Regression (Statistics), Test Items, Pretesting
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
Yi-Hsuan Lee; Yue Jia – Applied Measurement in Education, 2024
Test-taking experience is a consequence of the interaction between students and assessment properties. We define a new notion, rapid-pacing behavior, to reflect two types of test-taking experience -- disengagement and speededness. To identify rapid-pacing behavior, we extend existing methods to develop response-time thresholds for individual items…
Descriptors: Adaptive Testing, Reaction Time, Item Response Theory, Test Format
Soland, James – Applied Measurement in Education, 2018
This study estimated male-female and Black-White achievement gaps without accounting for low test motivation, then compared those estimates to ones that used several approaches to addressing rapid guessing. Researchers investigated two issues: (1) The differences in rates of rapid guessing across subgroups and (2) How much achievement gap…
Descriptors: Guessing (Tests), Achievement Gap, Student Motivation, Learner Engagement