Research article | Open Access
International Journal of Progressive Education 2019, Vol. 15(4) 174-186
Investigating Differential Item Functioning in DINA Model
pp. 174 - 186 | DOI: https://doi.org/10.29329/ijpe.2019.203.13
Publish Date: August 02, 2019 | Single/Total View: 221/848 | Single/Total Download: 326/1.639
Abstract
In this study, it is aimed to investigate the effects of various factors on the performance of the methods used in the determination of differential item functioning (DIF) in the DINA model included in the Cognitive Diagnosis Models. The current study is limited with Logistic Regression and Wald test methods which were used to determine the differential item functioning in DINA model. The Type I error and power rates of these methods in certain conditions were investigated to evaluate their performances. In the simulation study for the Type I error rates, four variables were manipulated: sample sizes, the number of attributes, correlations between attributes and reference group s and g parameter values. In the determination of the power rates of the methods, additionally, the variables that were manipulated in the Type I error study, DIF sizes and percentages of DIF items were manipulated, too. As a result, it was observed that especially in all cases where reference group' s and g parameter values are low, both methods yielded a good control of Type I error rates. In addition, according to the results, it was observed that both DIF size and sample size affect the power rates of both methods.
Keywords: DINA model, differential item functioning, Wald test, logistic regression
APA 7th edition
Sunbul, S.O. (2019). Investigating Differential Item Functioning in DINA Model. International Journal of Progressive Education, 15(4), 174-186. https://doi.org/10.29329/ijpe.2019.203.13
Harvard
Sunbul, S. (2019). Investigating Differential Item Functioning in DINA Model. International Journal of Progressive Education, 15(4), pp. 174-186.
Chicago 16th edition
Sunbul, Secil Omur (2019). "Investigating Differential Item Functioning in DINA Model". International Journal of Progressive Education 15 (4):174-186. https://doi.org/10.29329/ijpe.2019.203.13
- Camilli, G. ve Shepard, L. A. (1994). Methods for identifying biased test items. London: Sage Publications. [Google Scholar]
- de la Torre, J. (2009). DINA model and parameter estimation: A didactic. Journal of Educational and Behavioral Statistics, 34, 115-130. [Google Scholar]
- de la Torre, J. (2011). The generalized DINA model framework. Psychometrika, 76, 179–199 [Google Scholar]
- de la Torre, J., & Douglas, J. (2004). Higher-order latent trait models for cognitive diagnosis. Psychometrika, 69, 333-353. [Google Scholar]
- de la Torre, J., & Lee, Y. S. (2010). A note on the invariance of the DINA model parameters. Journal of Educational Measurement, 47, 115–127. [Google Scholar]
- DiBello, L. V., Stout, W. F., & Roussos, L. A. (1995). Unified cognitive/psychometric diagnostic assessment likelihood-based classification techniques. In P. Nichol, S. Chipman, & R. Brennan (Eds.), Cognitive diagnostic assessment (pp. 361-389). Hillsdale, NJ: Lawrence Erlbaum. [Google Scholar]
- Embretson, S. E. (1984). A general latent trait model for response processes. Psychometrika, 49, 175–186. [Google Scholar]
- Haertel, E. H. (1989). Using restricted latent class models to map the skill structure of achievement items. Journal of Educational Measurement, 26, 333-352. [Google Scholar]
- Hambleton, R K., Swaminathan, H. ve Rogers, H. J. (1991). Fundamentals of item response theory. London: Sage Publication [Google Scholar]
- Hartz, S. (2002). Skills diagnosis: Theory and practice. User Manual for Arpeggio software. Princeton, NJ: ETS. [Google Scholar]
- Henson, R. A., Templin, J. L., & Willse, J. T. (2009). Defining a family of cognitive diagnosis models using log-linear models with latent variables. Psychometrika, 74(2), 191. [Google Scholar]
- Hou, L., de la Torre, J. D., and Nandakumar, R. (2014). Differential item functioning assessment in cognitive diagnostic modeling: application of the Wald test to investigate DIF in the DINA model. Journal of Educational Measurement, 51, 98–125. [Google Scholar]
- Junker, B. W., & Sijtsma, K. (2001). Cognitive assessment models with few assumptions and connections with nonparametric item response theory. Applied Psychological Measurement, 25, 258-272. [Google Scholar]
- Li, F. (2008). A modified higher-order DINA model for detecting differential item functioning and differential attribute functioning (Doctoral dissertation). University of Georgia, Athens. [Google Scholar]
- Li, X., and Wang, W. C. (2015). Assessment of differential item functioning under cognitive diagnosis models: the DINA model example. Journal of Educational Measurement, 52, 28–54. [Google Scholar]
- Osterlind, S. (1983). Test item bias. Newbury Park: Sage Publications. [Google Scholar]
- Raju, N. S. (1988). The area between two item characteristic curves. Psychometrika, 53, 495-502. [Google Scholar]
- Rogers, S. J., & Swaminathan, H. (1993). A comparison of logistic regression and MH procedures for detecting differential item functioning. Applied Psychological Measurement, 17, 105–116. [Google Scholar]
- Tatsuoka, K. (1985). A probabilistic model for diagnosing misconceptions in the pattern classification approach. Journal of Educational Statistics, 12, 55-73. [Google Scholar]
- Templin, J. L., & Henson, R. A. (2006). Measurement of psychological disorders using cognitive diagnosis models. Psychological Methods, 11(3), 287-305. [Google Scholar]
- Templin, J. L., Henson, R. A., & Douglas, J. (2006). General theory and estimation of cognitive diagnosis models: Using Mplus to derive model estimates. Manuscript under review. [Google Scholar]
- Zhang, W. (2006). Detecting Differential Item Functioning Using the DINA Model. Doctoral dissertations, University of North Carolina at Greensboro, Greensboro, NC. [Google Scholar]
- Zumbo, D. B. (1999). A Handbook on the Theory and Methods of Differential Item Functioning (DIF): Logistic regression modeling as a unitary framework for binary and likert-type item scores. Ottowa: Directorate of Human Resources Research and Evaluation, Department of National Defense. [Google Scholar]
