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Yanan Feng – ProQuest LLC, 2021
This dissertation aims to investigate the effect size measures of differential item functioning (DIF) detection in the context of cognitive diagnostic models (CDMs). A variety of DIF detection techniques have been developed in the context of CDMs. However, most of the DIF detection procedures focus on the null hypothesis significance test. Few…
Descriptors: Effect Size, Item Response Theory, Cognitive Measurement, Models
Wang, Jianjun; Ma, Xin – Athens Journal of Education, 2019
This rejoinder keeps the original focus on statistical computing pertaining to the correlation of student achievement between mathematics and science from the Trend in Mathematics and Science Study (TIMSS). Albeit the availability of student performance data in TIMSS and the emphasis of the inter-subject connection in the Next Generation Science…
Descriptors: Scores, Correlation, Achievement Tests, Elementary Secondary Education
Yalcin, Seher – Eurasian Journal of Educational Research, 2018
Purpose: Studies in the literature have generally demonstrated that the causes of differential item functioning (DIF) are complex and not directly related to defined groups. The purpose of this study is to determine the DIF according to the mixture item response theory (MixIRT) model, based on the latent group approach, as well as the…
Descriptors: Item Response Theory, Test Items, Test Bias, Error of Measurement
Svetina, Dubravka; Rutkowski, Leslie – Large-scale Assessments in Education, 2014
Background: When studying student performance across different countries or cultures, an important aspect for comparisons is that of score comparability. In other words, it is imperative that the latent variable (i.e., construct of interest) is understood and measured equivalently across all participating groups or countries, if our inferences…
Descriptors: Test Items, Item Response Theory, Item Analysis, Regression (Statistics)
Bouhlila, Donia Smaali; Sellaouti, Fethi – Large-scale Assessments in Education, 2013
In this paper, we document a study that involved applying a multiple imputation technique with chained equations to data drawn from the 2007 iteration of the TIMSS database. More precisely, we imputed missing variables contained in the student background datafile for Tunisia (one of the TIMSS 2007 participating countries), by using Van Buuren,…
Descriptors: Databases, Student Characteristics, Error of Measurement, Intervals