References
Abad, F. J., Colom, R., Rebollo, I., & Escorial, S. (2004). Sex differential item functioning in the Raven's Advanced Progressive Matrices: Evidence for bias. Personality and Individual Differences, 36(6), 1459–1470. https://doi.org/10.1016/S0191-8869(03)00241-1
Alloway, T. P. (2009). Working memory, but not IQ, predicts subsequent learning in children with learning difficulties. European Journal of Psychological Assessment, 25(2), 92–98
Baddeley, A. (2012). Working memory: Theories, models, and controversies. Annual Review of Psychology, 63, 1-29.
Beaujean, A. A., & Osterlind, S. J. (2008). Using Item Response Theory to assess the Flynn Effect in the National Longitudinal Study of Youth 79 Children and Young Adults data. Intelligence, 36(5), 455–463. https://doi.org/10.1016/j.intell.2007.10.004
Brockmole, J. R. & Logie, R. H. (2013). Age-related change in visual working memory: A study of 55,753 participants aged 8–75, Frontiers in Psychology, 4.
Colom, R., Escorial, S., & Rebollo, I. (2004). Sex differences on the Progressive Matrices are influenced by sex differences on spatial ability. Personality and Individual Differences, 37(6), 1289–1293. https://doi.org/10.1016/j.paid.2003.12.014
Cohen, A. S., & Bolt, D. M. (2005). A mixture model analysis of differential item functioning. Journal of Educational Measurement, 42, 133–148.
Cowan, N., Morey, C. C., AuBuchon, A. M., Zwilling, C.E. & Gilchrist, A. L. (2010). Seven-year-olds allocate attention like adults unless working memory is overloaded. Developmental Science, 13, 120–133.
Cowan, N., AuBuchon, A. M., Gilchrist, A. L., Ricker, T. J. & Saults, J. S. (2011). Age differences in visual working memory capacity: Not based on encoding limitations. Developmental Science, 14(5): 1066–1074. doi:10.1111/j.1467-7687.2011.01060.x.
Cubaynes, S., Lavergne, C., Marboutin, E. & Gimenez, O. (2012). Assessing individual heterogeneity using model selection criteria: How many mixture components in capture–recapture models? Methods in Ecology and Evolution, 3, 564–573.
Dai, Y. (2013). A mixture Rasch model with a covariate: A simulation study via Bayesian Markov chain Monte Carlo estimation. Applied Psychological Measurement, 37, 375- 396.
Dehn, M. J. (2014). Essentials of Processing Assessment. 2. Edition. NJ: Wiley.
Embretson, S. E., & Reise, S. P. (2000). Item Response Theory for Psychologists. Mahwah, NJ: Erlbaum.
Ferreira, A. I., Almeida, L. S. & Prieto, G. (2012). Construction of a memory battery for computerized administration, using Item-Response Theory. Psychological Reports, 111(2), 585-609.
Fischer G. H. (1995). Derivations of the Rasch Model. In Fischer & Molenaar (Ed.), Rasch Models Foundations, Recent Developments, and Applications, (pp. 15-38).
Frick, H., Strobl, C. & Zeiles, A. (2015). Rasch mixture models for DIF detection: A comparison of old and new score specifications. Educational and Psychological Measurement, 75(2), 208–234.
Geary D. C. (2011). Cognitive predictors of achievement growth in mathematics: a 5-year longitudinal study. Developmental Psychology, 47(6), 1539–1552. https://doi.org/10.1037/a0025510
Hambleton, R. K. & Swaminathan, H. (1985). Item response theory: Principles and applications. Boston, MA: Kluwer Academic Publishers.
Heyes, S. B., Zokaei, N. & Husain, M. (2016). Longitudinal development of visual working memory precision in childhood and early adolescence, Cognitive Development, 39, 36-44.
Jiao, H., Lissitz, R. W., Macready, G., Wang, S. & Liang, S. Exploring levels of performance using the mixture Rasch model for standard setting. Psychological Test and Assessment Modeling, 53(4), 499-522.
Karadavut, T., Cohen, A. S., Kim, S. H. (2019). Mixture Rasch model with main and interaction effects of covariates on latent class membership. International Journal of Assessment Tools in Education, 6(3), 362–377.
Li, T., Jiao, H. & Macready, G. B. (2016). Different approaches to covariate inclusion in the mixture Rasch model. Educational and Psychological Measurement, 76(5), 848–872.
Muthén, B., & Asparouhov, T. (2006). Item response mixture modeling: Application to tobacco dependence criteria. Addictive Behaviors, 31, 1050–1066. doi:10.1016/j.addbeh.2006.03.026
R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/
Rasch, G. (1960). Probabilistic Models for Some Intelligence and Attainment Tests. Chicago: The University of Chicago Press.
Rost J. (1990). Rasch Models in latent classes: An integration of two approaches to item analysis. Applied Psychological Measurement, 14(3), 271-282.
Rost J., von Davier M. (1995). Mixture Distribution Rasch Models. In Fischer & Molenaar (Ed.), Rasch Models Foundations, Recent Developments, and Applications, (pp. 257-268).
Sak, U., Bal-Sezerel, B., Ayas, B., Tokmak, F., Özdemir, N.N., Demirel-Gürbüz, Ş. & Öpengin, E. (2016). Anadolu Sak Intelligence Scale: ASIS Practitioner's Book.
ASIS MAnual. (2016). Anadolu Sak Zeka Ölçeği (ASİS) Uygulayıcı Kitabı. Anadolu Üniversitesi ÜYEP Merkezi, Eskişehir.
Samuelsen, K. M. (2005). Examining Differential Item Functioning From a Latent Class Perspective. Unpublished doctoral dissertation. University of Maryland, College Park.
Schleicher-Dilks, S. (2015). Exploring the item difficulty and other psychometric properties of the core perceptual, verbal and working memory subtests of the WAIS-IV using Item Response Theory. Unpublished Doctoral Dissertation. Florida: NSU.
Şen, S. & Cohen, A. S. (2019) Applications of mixture IRT models: A literature review. Measurement: Interdisciplinary Research and Perspectives. 17(4), 177-191, doi: 10.1080/15366367.2019.1583506
van der Sluis, S., Posthuma, D., Dolan, C. V., de Geus, E. J. C., Colom, R., & Boomsma, D. I. (2006). Sex differences on the Dutch WAIS-III. Intelligence, 34, 273–289.
von Davier M. & Rost, J. (2017). Logistic mixture-distribution response models. In W. J. van der Linden (Ed.), Handbook of item response theory, volume one: Models (p. 393-406). Chapman and Hall.