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Educational Psychology
An International Journal of Experimental Educational Psychology
Volume 38, 2018 - Issue 10
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Original Articles

Cognitive diagnostic model of best choice: a study of reading comprehension

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Pages 1255-1277 | Received 07 Nov 2016, Accepted 12 Jun 2018, Published online: 25 Aug 2018

References

  • Akaike, H. (1974). A new look at the statistical identification model. IEEE Transactions on Automated Control, 19, 716–723. doi:10.1109/TAC.1974.1100705
  • Alderson, J. (2000). Assessing reading. New York: Cambridge University Press.
  • Bernhardt, E. (2005). Progress and procrastination in second language reading. Annual Review of Applied Linguistics, 25, 133−150.
  • Buck, G. (1994). The appropriacy of psychometric measurement models for testing second language listening comprehension. Language Testing, 11, 145−170. doi:10.1177/026553229401100204
  • Buck, G., & Tatsuoka, K. (1998). Application of the rule-space procedure to language testing: Examining attributes of a free response listening test. Language Testing, 15, 119−157. doi:10.1177/026553229801500201
  • Buck, G., Tatsuoka, K., & Kostin, I. (1997). The sub-skills of reading: Rule‐space analysis of a multiple‐choice test of second language reading comprehension. Language Learning, 47, 423−466. doi:10.1111/0023-8333.00016
  • Buck, G., Tatsuoka, K., Kostin, I., & Phelps, M. (1997). The sub-skills of listening: Rule-space analysis of a multiple-choice test of second language listening comprehension. In V. Kohonen, A. Huhta, L. Kurki-Suonio, and S. Luoma (Eds.), Current developments and alternatives in language assessment: Proceedings of LTRC96 (pp. 589–624). Jyväskylä, Finland: University of Jyväskylä and University of Tampere.
  • Chen, J., de la Torre, J., & Zhang, Z. (2013). Relative and absolute fit evaluation in cognitive diagnosis modeling. Journal of Educational Measurement, 50, 123−140. doi:10.1111/j.1745-3984.2012.00185.x
  • Chen, Y., Li, X., Liu, J., & Ying, Z. (2017). Regularized latent class analysis with application in cognitive diagnosis. Psychometrika, 82, 660−692. doi:10.1007/s11336-016-9545-6
  • Chen, Y., Liu, J., Xu, G., & Ying, Z. (2015). Statistical analysis of Q-matrix based diagnostic classification models. Journal of the American Statistical Association, 110, 850–866. doi:10.1080/01621459.2014.934827
  • Chen, W. H., & Thissen, D. (1997). Local dependence indexes for item pairs using item response theory. Journal of Educational and Behavioral Statistics, 22, 265−289. doi:10.3102/10769986022003265
  • Cohen, J. (1960) A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20, 37−46. doi:10.1177/001316446002000104
  • de la Torre, J. (2011). The generalized DINA model framework. Psychometrika, 76, 179−199. doi:10.1007/s11336-011-9207-7
  • de la Torre, J., & Chiu, C.-Y. (2016). A general method of empirical Q-matrix validation. Psychometrika, 81, 253−273. doi:10.1007/s11336-015-9467-8
  • 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, 355–373. doi:10.1111/jedm.12022
  • de la Torre, J., van der Ark, L. A., & Rossi, G. (2015). Analysis of clinical data from a cognitive diagnosis modeling framework. Measurement and Evaluation in Counseling and Development. doi:10.1177/0748175615569110
  • George, A. C., Robitzsch, A., Kiefer, T., Ünlü, A., & Grosz, J. (2016). The R package CDM for cognitive diagnosis models. Journal of Statistical Software, 74(2), 1–24.
  • Gierl, M. J., Leighton, J. P., & Hunka, S. M. (2007). Using the attribute hierarchy method to make diagnostic inferences about examinees' cognitive skills. In J. P. Leighton & M. J. Gierl (Eds.), Cognitive diagnostic assessment for education: Theory and applications (pp. 242–274). New York, NY: Cambridge University Press.
  • Goldsmith-Phillips, J. (1989). Word and context in reading development: A test of the interactive–compensatory hypothesis. Journal of Educational Psychology, 81, 299–305. doi:10.1037/0022-0663.81.3.299
  • Gough, P. B., & Tunmer, W. E. (1986). Decoding, reading, and reading disability. Remedial and Special Education, 7, 6–10. doi:10.1177/074193258600700104
  • Hartz, S. M. (2002). A Bayesian framework for the unified model for assessing cognitive abilites: Blending theory with practicality. (Doctoral dissertation). University of Illinois at Urbana-Champaign.
  • 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, 191–210. doi:10.1007/s11336-008-9089-5
  • Hoover, W. A., & Gough, P. B. (1990). The simple view of reading. Reading and Writing, 2, 127–160. doi:10.1007/BF00401799
  • Jang, E. E. (2005). A validity narrative: Effects of reading skills diagnosis on teaching and learning in the context of NG TOEFL. (Doctoral dissertation). University of Illinois at Urbana-Champaign.
  • Jang, E. E. (2009). Demystifying a Q-Matrix for making diagnostic inferences about L2 reading skills. Language Assessment Quarterly, 6, 210–238. doi:10.1080/15434300903071817
  • 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. doi:10.1177/01466210122032064
  • Kasai, M., & Saito, H. (1996). The rule-space model applied to the reading comprehension section of the Test of English as a Foreign Language. Talk presented at National Council on Measurement in Education, New York, NY.
  • Kim, A. Y. A. (2015). Exploring ways to provide diagnostic feedback with an ESL placement test: Cognitive diagnostic assessment of L2 reading ability. Language Testing, 32, 227–258. doi:10.1177/0265532214558457
  • Kim, Y. H. (2011). Diagnosing EAP writing ability using the reduced reparameterized unified model. Language Testing, 28, 509–541. doi:10.1177/0265532211400860
  • 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, 59−81. doi:10.1111/j.1745-3984.2011.00160.x
  • Lahuerta Martínez, A. C. (2011). Clarification of L2 reading theories through the analysis of empirical studies. Aula Abierta, 39, 149–158.
  • Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33, 159–174. doi:10.2307/2529310
  • Lee, Y.-W., & Sawaki, Y. (2009a). Application of three cognitive diagnosis models to ESL reading and listening assessments. Language Assessment Quarterly, 6, 239−263. doi:10.1080/15434300903079562
  • Lee, Y.-W., & Sawaki, Y. (2009b). Cognitive diagnosis approaches to language assessment: An overview. Language Assessment Quarterly, 6, 172−189 doi:10.1080/15434300902985108
  • Lei, P.-W., & Li, H. (2016). Fit indices’ performance in choosing cognitive diagnostic models and Q-matrices. Paper presented at the annual meeting of the National Council on Measurement in Education (NCME), Philadelphia, PA.
  • Li, H. (2011). Evaluating language group differences in the subskills of reading using a cognitive diagnostic modeling and differential skill functioning approach. (Doctoral dissertation). Penn State University, State College, PA.
  • Li, H., Hunter, C. V., & Lei, P. W. (2015). The selection of cognitive diagnostic models for a reading comprehension test. Language Testing, 33, 391–409.
  • Li, H., & Suen, H. K. (2013). Detecting native language group differences at the subskills level of reading: A differential skill functioning approach. Language Testing, 30, 273–298. doi:10.1177/0265532212459031
  • Ma, W. & de la Torre, J. (2018). GDINA: The generalized DINA model framework. R package version 2.1. Retrieved from https://CRAN.R-project.org/package=GDINA
  • Ma, W., Iaconangelo, C., & de la Torre, J. (2016). Model similarity, model selection, and attribute classification. Applied Psychological Measurement, 40, 200−217. doi:10.1177/0146621615621717
  • Maydeu-Olivares, A. (2013). Goodness-of-fit assessment of item response theory models. Measurement: Interdisciplinary Research and Perspectives, 11, 71–101. doi:10.1080/15366367.2013.831680
  • McDonald, R. P., & Mok, M. M. C. (1995). Goodness of fit in item in response models. Multivariate Behavioral Research, 30, 23−40. doi:10.1207/s15327906mbr3001_2
  • R Core Team (2013). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Retrieved from http://www.R-project.org/
  • Ravand, H. (2016). Application of a cognitive diagnostic model to a high-stakes reading comprehension test. Journal of Psychoeducational Assessment, 34, 782−799. doi:10.1177/0734282915623053
  • Ravand, H., & Robitzsch, A. (2015). Cognitive diagnostic modeling using R. Practical Assessment, Research & Evaluation, 20, 1−12.
  • Robitzsch, A., & George, A. C. (in press). The R package CDM. In M. von Davier & Y.-S. Lee (Eds.), Handbook of diagnostic classification models. New York: Springer.
  • Robitzsch, A., Kiefer, T., George, A., C., & Uenlue, A. (2018). CDM: Cognitive diagnosis modeling. R package version 6.3-45. Retrived from https://CRAN.R-project.org/package=CDM
  • Roussos, L. A., DiBello, L. V., Henson, R. A., Jang, E. E., & Templin, J. L. (2006). Skills diagnosis for education and psychology with IRT-based parametric latent class models. In S. Embretson & J. Roberts (Eds.), New directions in psychological measurement with model-based approaches (pp. 35–69). Washington, DC: American Psychological Association.
  • Roussos, L. A., DiBello, L. V., & Stout, W. (2006). Diagnostic skills-based testing using the Fusion-Model Based Arpeggio system. In J. Leighton & M. Gierl (Eds.), Cognitively diagnostic assessment in education: Theory and practice (pp. 275–318). New York, NY: Cambridge University Press.
  • Roussos, L., diBello, L. V., Stout, W., Hartz, S., Henson, R. A., & Templin, J. H. (2007). The fusion model skills diagnosis system. In Leighton J. P., & M. J. Gierl. (Eds.), Cognitively diagnostic assessment for education: Theory and practice (pp. 275–318). Thousand Oaks, CA: SAGE.
  • Rupp, A. A. (2007). The answer is in the question: A guide for describing and investigating the conceptual foundations and statistical properties of cognitive psychometric models. International Journal of Testing, 7, 95−125. doi:10.1080/15305050701193454
  • Rupp, A. A., & Templin, J. L. (2008). Unique characteristics of diagnostic classification models: A comprehensive review of the current state-of-the-art. Measurement: Interdisciplinary Research and Perspectives, 6, 219–262. doi:10.1080/15366360802490866
  • Sawaki, Y., Kim, H. J., & Gentile, C. (2009). Q-Matrix construction: Defining the link between constructs and test items in large-scale reading and listening comprehension assessments. Language Assessment Quarterly, 6, 190–209. doi:10.1080/15434300902801917
  • Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461–464. doi:10.1214/aos/1176344136
  • Scott, J. C. (1998). Seeing like a state: how certain schemes to improve the human condition have failed. New Haven, CT, USA: Yale University Press.
  • Sinharay, S., & Johnson, M. (in press). Measures of agreement: Reliability, classification accuracy, and classification consistency. In M. von Davier & Y.-S. Lee (Eds.), Handbook of diagnostic classification models. New York: Springer.
  • Stanovich, K. E., & West, R. F. (1979). Mechanisms of sentence context effects in reading: Automatic activation and conscious attention. Memory and Cognition, 7, 77–85. doi:10.3758/BF03197588
  • Stanovich, K. E., & West, R. F. (1981). The effect of sentence context on ongoing word recognition: Tests of a two-process theory. Journal of Experimental Psychology: Human Perception and Performance, 7, 658–672. doi:10.1037/0096-1523.7.3.658
  • Tatsuoka, K. K. (1983). Rule space: an approach for dealing with misconception based on item response theory. Journal of Education Measurement, 20, 345–354. doi:10.1111/j.1745-3984.1983.tb00212.x
  • Templin, J. (2009). On the origin of species: The evolution of diagnostic modeling within the 163 psychometric taxonomy. International Meeting of Psychometric Society. Pre-conference workshop, Cambridge University, England.
  • Templin, J., & Bradshaw, L. P. (2014). Hierarchical diagnostic classification models: A family of models for estimating and testing attribute hierarchies. Psychometrika, 79, 317–339. doi:10.1007/s11336-013-9362-0
  • Templin, J. L., & Henson, R. A. (2006). Measurement of psychological disorders using cognitive diagnosis models. Psychological Methods, 11, 287–305. doi:10.1037/1082-989X.11.3.287
  • Usó‐Juan, E. (2006). The compensatory nature of discipline‐related knowledge and English‐language proficiency in reading English for academic purposes. The Modern Language Journal, 90, 210–227. doi:10.1111/j.1540-4781.2006.00393.x
  • von Davier, M. (2005). mdltm: Software for the general diagnostic model and for estimating mixtures of multidimensional discrete latent traits models [Computer software]. Princeton, NJ: ETS.
  • Yamamoto, K. (1989). Hybrid model of IRT and latent class models. Research Report (RR 89–41). Princeton, NJ: Educational Testing Service.
  • Yamamoto, K. (1990). HYBILm: A computer program to estimate the HYBRID model. Princeton, NJ: Educational Testing Service.
  • Yi, Y. (2012). Implementing a cognitive diagnostic assessment in an institutional test: a new networking model in language testing and experiment with a new psychometric model and task type. (Unpublished doctoral dissertation). University of Illinois at Urbana Champaign, Urbana-Champaign, IL.

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