TR
EN
Examination of the Extreme Response Style of Students using IRTree: The Case of TIMSS 2015
Abstract
In
the literature, response style is one of the factors causing an
achievement-attitude paradox and threatens the validity of the results obtained
from studies. In this regard, the aim of this study is two-fold. Firstly, it
attempts to determine which item response tree (IRTree) models based on the
generalized linear mixed model (GLMM) approach (random intercept, random
intercept with the fixed effect of extreme response and random intercept-slope
model) best fit the Trends in International Mathematics and Science
Study (TIMSS) 2015 data. Secondly, it purports to explore how the extreme
response style affects students’ attitudes toward mathematics of students. This
study is both basic research and descriptive research in terms of seeking for
answers for two different research questions. For the sample of this research,
15 countries were randomly selected among countries participated in TIMSS 2015.
The students’ responses to items measuring attitude in the student
questionnaire were analyzed with the packages “lme4” and “irtrees” in R
software. When the model fit indices were evaluated, the random intercept-slope
model was found to be the best fit to the data. According to this model, the
extreme response style explains a significant amount of variances in the students’
attitude toward mathematics. Additionally, students with a negative attitude
toward mathematics were found to have an extreme response style. It was
concluded that an extreme response style had an effect on students’ attitude.
Keywords
References
- Andrich, D. (1978). A rating formulation for ordered response categories. Psychometrika, 43, 561–573.
- Atkinson, J. W. (1957). Motivational determinants of risk-taking behavior. Psychological Review, 64, 359-373.
- Bachman, J. G., O’Malley, P. M., & Freedman-Doan, P. (2010). Response styles revisited: Racial/ethnic and gender differences in extreme responding (Monitoring the Future Occasional Paper No. 72). Ann Arbor, MI: Institute for Social Research.
- Bates, D., Maechler, M., Bolker, B. & Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1), 1-48. doi:10.18637/jss.v067.i01
- Bofah, E. A. and Hannula, M. S. (2015). TIMSS data in an African comparative perspective: Investigating the factors influencing achievement in mathematics and their psychometric properties. Large-Scale Assessments in Education, 3(1), doi:1.1186/s40536-015-0014-y
- Bolt, D. M., & Newton, J. (2011). Multiscale measurement of extreme response style. Educational and Psychological Measurement, 71, 814-833.
- Bolt, D., Wollack, J., & Suh, Y. (2012). Application of a multidimensional nested logit model to multiple-choice test items. Psychometrika, 77, 339–357.
- Böckenholt, U. (2012). Modeling multiple response processes in judgment and choice. Psychological Methods, 17(4), 665-678.
Details
Primary Language
English
Subjects
Studies on Education
Journal Section
Research Article
Authors
Publication Date
July 15, 2019
Submission Date
March 1, 2019
Acceptance Date
May 18, 2019
Published in Issue
Year 2019 Volume: 6 Number: 2
APA
İlgün Dibek, M. (2019). Examination of the Extreme Response Style of Students using IRTree: The Case of TIMSS 2015. International Journal of Assessment Tools in Education, 6(2), 300-313. https://doi.org/10.21449/ijate.534118
Cited By
A Monte Carlo study of IRTree models’ ability to recover item parameters
Frontiers in Psychology
https://doi.org/10.3389/fpsyg.2023.1003756The Efficacy of the IRTree Framework for Detecting Missing Data Mechanisms in Educational Assessments
Eğitimde ve Psikolojide Ölçme ve Değerlendirme Dergisi
https://doi.org/10.21031/epod.1514741The Effect of Modeling Missing Data With IRTree Approach on Parameter Estimates Under Different Simulation Conditions
Educational and Psychological Measurement
https://doi.org/10.1177/00131644241306024