• Baghaei, P., & Ravand, H. (2015). A cognitive processing model of reading comprehension in English as a foreign language using the linear logistic test model. Learning and Individual Differences, 43, 100-105. https://doi.org/10.1016/j.lindif.2015.09.001
• Birenbaum, M., Tatsuoka, C., & Yamada, T. (2004). Diagnostic assessment in TIMSS-R: Between-countries and within-country comparisons of eighth graders’ mathematics performance. Studies in Educational Evaluation, 30(2), 151-173.
• Chen, J., & de la Torre, J. (2013). A general cognitive diagnosis model for expert-defined polytomousattributes. Applied Psychological Measurement, 37(6), 419-437. https://doi.org/10.1177/0146621613479818
• Chung, M. (2014). Estimating the Q-matrix for cognitive diagnosis models in a Bayesian framework. Teachers College.
• Corter, J. E., & Tatsuoka, K. K. (2002). Cognitive and measurement foundations of diagnostic assessments in mathematics. The College Board: Technical Report.
• Corter, J. E., & Tatsuoka, K. K. (2002). Diagnostic Assessments for Mathematics Tests Grades 6-12.
• De La Torre, J. (2008). An empirically based method of Q-matrix validation for the DINA model: Development and applications. Journal of Educational Measurement, 45(4), 343-362. https://doi.org/10.1111/j.1745-3984.2008.00069.x
• De La Torre, J. (2009). A cognitive diagnosis model for cognitively based multiple-choice options. Applied Psychological Measurement, 33(3), 163-183. https://doi.org/10.1177/0146621608320523
• De La Torre, J. (2009). DINA model and parameter estimation: A didactic. Journal of Educational and Behavioral Statistics, 34(1), 115-130. https://doi.org/10.3102/1076998607309474
• De La Torre, J. (2011). The generalized DINA model framework. Psychometrika, 76(2), 179-199. https://doi.org/10.1007/s11336-011-9207-7
• De La Torre, J., & Douglas, J. A. (2004). Higher-order latent trait models for cognitive diagnosis. Psychometrika, 69(3), 333-353. https://doi.org/10.1007/BF02295640
• De la Torre, J., & Lee, Y.-S. (2013). Evaluating the Wald test for item-level comparison of saturated and reduced models in cognitive diagnosis. Journal of Educational Measurement, 50(4), 355-373. https://doi.org/10.1111/jedm.12022
• Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B (Methodological), 39(1), 1-22. https://doi.org/10.1111/j.2517-6161.1977.tb01600.x
• George, A. C., Robitzsch, A., Kiefer, T., Groß, J., & Ünlü, A. (2016). The R package CDM for cognitive diagnosis models. Journal of Statistical Software, 74(2), 1-24. https://doi.org/10.18637/jss.v074.i02
• Gierl, M. J., Zhou, J., & Alves, C. (2008). Developing a taxonomy of item model types to promote assessment engineering. The Journal of Technology, Learning and Assessment, 7(2).
• Grabe, W., & Stoller, F. (2002). Teaching and research reading. Harlow, UK: Longman.
• Haertel, E. H. (1989). Using restricted latent class models to map the skill structure of achievement items. Journal of Educational Measurement, 26(4), 301-321.
• Halimi, N. (2013). Mathematics Education in Secondary School in Afghanistan: Teachers’ View and Practices on Teaching Problem Solving.
• Hartz, S. M. (2002). A Bayesian framework for the unified model for assessing cognitive abilities: Blending theory with practicality. ProQuest Information & Learning.
• Henson, R., & Douglas, J. (2005). Test construction for cognitive diagnosis. Applied Psychological Measurement, 29(4), 262-277. https://doi.org/10.1177/0146621604272623
• Huebner, A., & Wang, C. (2011). A note on comparing examinee classification methods for cognitive diagnosis models. Educational and Psychological Measurement, 71(2), 407-419. https://doi.org/10.1177/0013164410388832
• Junker, B. W., & Sijtsma, K. (2001). Cognitive assessment models with few assumptions, and connections with nonparametric item response theory. Applied Psychological Measurement, 25(3), 258-272. https://doi.org/10.1177/01466210122032064
• Kaya, Y., & Leite, W. L. (2017). Assessing change in latent skills across time with longitudinal cognitive diagnosis modeling: An evaluation of model performance. Educational and psychological measurement, 77(3), 369-388. https://doi.org/10.1177/0013164416659314
• Kunina‐Habenicht, O., Rupp, A. A., & Wilhelm, O. (2012). The impact of model misspecification on parameter estimation and item‐fit assessment in log‐linear diagnostic classification models. Journal of Educational Measurement, 49(1), 59-81. https://doi.org/10.1111/j.1745-3984.2011.00160.x
• Lee, Y.-W., & Sawaki, Y. (2009). Application of three cognitive diagnosis models to ESL reading and listening assessments. Language Assessment Quarterly, 6(3), 239-263. https://doi.org/10.1080/15434300903079562
• Lee, Y.-W., & Sawaki, Y. (2009). Cognitive diagnosis and Q-matrices in language assessment. Taylor & Francis. https://doi.org/10.1080/15434300903059598
• Lee, Y.-W., & Sawaki, Y. (2009). Cognitive diagnosis approaches to language assessment: An overview. Language Assessment Quarterly, 6(3), 172-189.
• Leighton, J. P., Gierl, M. J., & Hunka, S. M. (2004). The attribute hierarchy method for cognitive assessment: A variation on Tatsuoka’s rule-space approach. Journal of Educational Measurement, 41(3), 205-237. https://doi.org/10.1111/j.1745-3984.2004.tb01163.x
• Leighton, J., & Gierl, M. (2007). Cognitive diagnostic assessment for education: Theory and applications. Cambridge University Press. https://doi.org/10.1017/CBO9780511611186
• Mansory, A. M. (2010). Do Children Learn in Afghan Schools? Assessment of Math and Language Achievements of Students at the End of Grades 3 and 6 in SCA Supported Schools. Swedish Committee for Afghanistan (SCA).
• Mullis, I. V. S., Martin, M. O., Smith, T. A., & for the Evaluation of Educational Achievement, I. A. (2003). TIMSS: Assessment frameworks and specifications 2003. International Study Center, Lynch School of Education, Boston College~….
• Ravand, H. (2016). Application of a cognitive diagnostic model to a high-stakes reading comprehension test. Journal of Psychoeducational Assessment, 34(8), 782-799. https://doi.org/10.1177/0734282915623053
• Ravand, H., Barati, H., & Widhiarso, W. (2012). Exploring diagnostic capacity of a high stakes reading comprehension test: A pedagogical demonstration. Iranian Journal of Language Testing, 3(1), 12-37.
• Rupp, A. A., & Templin, J. (2008a). The effects of Q-matrix misspecification on parameter estimates and classification accuracy in the DINA model. Educational and Psychological Measurement, 68(1), 78-96. https://doi.org/10.1177/0013164407301545
• Rupp, A. A., & Templin, J. L. (2008b). Unique characteristics of diagnostic classification models: A comprehensive review of the current state-of-the-art. Measurement, 6(4), 219-262. https://doi.org/10.1080/15366360802490866
• Rupp, A. A., Templin, J., & Henson, R. A. (2010). Diagnostic assessment: Theory, methods, and applications. New York: Guilford.
• Tatsuoka, K. K. (1983). Rule space: An approach for dealing with misconceptions based on item response theory. Journal of Educational Measurement, 20(4), 345-354. https://doi.org/10.1111/j.1745-3984.1983.tb00212.x
• Tatsuoka, K. K. (1995). Architecture of knowledge structures and cognitive diagnosis: A statistical pattern recognition and classification approach. Cognitively Diagnostic Assessment, 327-359.
• Templin, J., & Henson, R. A. (2010). Diagnostic measurement: Theory, methods, and applications. Guilford Press.
• Xu, X., & von Davier, M. (2008). Fitting the structured general diagnostic model to NAEP data. ETS Research Report Series, 2008(1), i-18. https://doi.org/10.1002/j.2333-8504.2008.tb02113.x

APA

Wafa, M. N., Hussaini, S. A. M., & Pazhman, J. (2020). Evaluation of Students’ Mathematical Ability in Afghanistan’s Schools Using Cognitive Diagnosis Models. Eurasia Journal of Mathematics, Science and Technology Education, 16(6), em1849. https://doi.org/10.29333/ejmste/7834

Vancouver

Wafa MN, Hussaini SAM, Pazhman J. Evaluation of Students’ Mathematical Ability in Afghanistan’s Schools Using Cognitive Diagnosis Models. EURASIA J Math Sci Tech Ed. 2020;16(6):em1849. https://doi.org/10.29333/ejmste/7834

AMA

Wafa MN, Hussaini SAM, Pazhman J. Evaluation of Students’ Mathematical Ability in Afghanistan’s Schools Using Cognitive Diagnosis Models. EURASIA J Math Sci Tech Ed. 2020;16(6), em1849. https://doi.org/10.29333/ejmste/7834

Chicago

Wafa, Mohammad Nasim, Sayed Abdul Moqim Hussaini, and Jumakhan Pazhman. "Evaluation of Students’ Mathematical Ability in Afghanistan’s Schools Using Cognitive Diagnosis Models". Eurasia Journal of Mathematics, Science and Technology Education 2020 16 no. 6 (2020): em1849. https://doi.org/10.29333/ejmste/7834

Harvard

Wafa, M. N., Hussaini, S. A. M., and Pazhman, J. (2020). Evaluation of Students’ Mathematical Ability in Afghanistan’s Schools Using Cognitive Diagnosis Models. Eurasia Journal of Mathematics, Science and Technology Education, 16(6), em1849. https://doi.org/10.29333/ejmste/7834

MLA

Wafa, Mohammad Nasim et al. "Evaluation of Students’ Mathematical Ability in Afghanistan’s Schools Using Cognitive Diagnosis Models". Eurasia Journal of Mathematics, Science and Technology Education, vol. 16, no. 6, 2020, em1849. https://doi.org/10.29333/ejmste/7834