Abstract
Unidimensional item response theory (IRT) models assume that a single model applies to all people in the population. Mixture IRT models can be useful when subpopulations are suspected. The usual mixture IRT model is typically estimated assuming normally distributed latent ability. Research on normal finite mixture models suggests that latent classes potentially can be extracted even in the absence of population heterogeneity if the distribution of the data is nonnormal. Empirical evidence suggests, in fact, that test data may not always be normal. In this study, we examined the sensitivity of mixture IRT models to latent nonnormality. Single-class IRT data sets were generated using different ability distributions and then analyzed with mixture IRT models to determine the impact of these distributions on the extraction of latent classes. Preliminary results suggest that estimation of mixed Rasch models resulted in spurious latent class problems in the data when distributions were bimodal and uniform. Mixture 2PL and mixture 3PL IRT models were found to be more robust to nonnormal latent ability distributions. Two popular information criterion indices, Akaike’s information criterion (AIC) and the Bayesian information criterion (BIC), were used to inform model selection. For most conditions, the performance of BIC index was better than the AIC for selection of the correct model.
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References
Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19:716–723. doi:10.1109/TAC.1974.1100705
Alexeev N, Templin J, Cohen AS (2011) Spurious latent classes in the mixture Rasch model. J Educ Meas 48:313–332. doi:10.1111/j.1745-3984.2011.00146.x
Arminger G, Stein P, Wittenberg J (1999) Mixtures of conditional mean- and covariance-structure models. Psychometrika 64:475–494. doi:10.1007/BF02294568
Bauer DJ (2007) Observations on the use of growth mixture models in psychological research. Multivar Behav Res 42:757–786. doi:10.1080/00273170701710338
Bauer DJ, Curran PJ (2003) Distributional assumptions of growth mixture models: implications for over-extraction of latent trajectory classes. Psychol Methods 8:338–363. doi:10.1037/1082-989X.8.3.338
Bock RD, Aitkin M (1981) Marginal maximum likelihood estimation of item parameters: application of an EM algorithm. Psychometrika 46:443–459. doi:10.1007/BF02293801
Bock RD, Zimowski MF (1997) Multiple group IRT. In: van der Linden WJ, Hambleton RK (eds) Handbook of modern item response theory. Springer, New York, pp 433–448
Bolt DM, Cohen AS, Wollack JA (2002) Item parameter estimation under conditions of test speededness: application of a mixture Rasch model with ordinal constraints. J Educ Meas 39:331–348. doi:10.1111/j.1745-3984.2002.tb01146.x
Bozdogan H (1987) Model selection and Akaike’s information criterion (AIC): the general theory and its analytical extensions. Psychometrika 52:345–370
Clogg CC (1995) Latent class models. In: Arminger G, Clogg CC, Sobel ME (eds) Handbook of statistical modeling for the social and behavioral sciences. Plenum Press, New York, pp. 311–359
Cohen AS, Bolt DM (2005) A mixture model analysis of differential item functioning. J Educ Meas 42:133–148. doi:10.1111/j.1745-3984.2005.00007
Cohen AS, Gregg N, Deng M (2005) The role of extended time and item content on a high-stakes mathematics test. Learn Disabil Res Pract 20:225–233. doi:10.1111/j.1540-5826.2005.00138.x
Congdon P (2003) Applied Bayesian modelling. Wiley, New York
Cowles MK, Carlin BP (1996) Markov chain Monte Carlo convergence diagnostics: a comparative review. J Am Stat Assoc 91:883–904. doi:10.1080/01621459.1996.10476956
Embretson SE, Reise SP (2000) Item response theory for psychologists. Erlbaum, Mahwah
Fleishman AI (1978) A method for simulating non-normal distributions. Psychometrika 43:521–532. doi:10.1007/BF02293811
Florida Department of Education (2002) Florida Comprehensive Assessment Test. Tallahassee, FL: Author
Frick H, Strobl C, Leisch F, Zeileis A (2012) Flexible Rasch mixture models with package psychomix. J Stat Softw 48(7):1–25. Retrieved from http://www.jstatsoft.org/v48/i07/
Gelman A, Rubin DB (1992) Inference from iterative simulation using multiple sequences. Stat Sci 7:457–472. Retrieved from http://www.jstor.org/stable/2246093
Jedidi K, Jagpal HS, DeSarbo WS (1997) Finite mixture structural equation models for response-based segmentation and unobserved heterogeneity. Mark Sci 16:39–59. doi:10.1287/mksc.16.1.39
Kolen MJ, Brennan RL (2004) Test equating: methods and practices, 2nd edn. Springer, New York
Li F, Duncan TE, Duncan SC (2001) Latent growth modeling of longitudinal data: a finite growth mixture modeling approach. Struct Equ Model 8:493–530. doi:10.1207/S15328 007SEM0804_01
Li F, Cohen AS, Kim S-H, Cho S-J (2009) Model selection methods for mixture dichotomous IRT models. Appl Psychol Meas 33:353–373. doi:10.1177/0146621608326422
Lo Y, Mendell NR, Rubin DB (2001) Testing the number of components in a normal mixture. Biometrika 88:767–778. doi:10.1093/biomet/88.3.767
Lubke GH, Muthén BO (2005) Investigating population heterogeneity with factor mixture models. Psychol Methods 10:21–39. doi:10.1037/1082-989X.10.1.21
McLachlan G, Peel D (2000) Finite mixture models. Wiley, New York
Mislevy RJ, Verhelst N (1990) Modeling item responses when different subjects employ different solution strategies. Psychometrika 55:195–215. doi:10.1007/BF02295283
Muthén LK, Muthén BO (2011) Mplus user’s guide, 6th edn. Author, Los Angeles
Nylund KL, Asparouhov T, Muthén BO (2007) Deciding on the number of classes in latent class analysis and growth mixture modeling: a Monte Carlo simulation study. Struct Equ Model 14:535–569. doi:10.1080/10705510701575396
Pearson ES, Please NW (1975) Relation between the shape of population distribution and the robustness of four simple test statistics. Biometrika 62:223–241. doi:10.1093/biomet/62.2.223
Plummer M, Best N, Cowles K, Vines K (2006) CODA: convergence diagnosis and output analysis for MCMC. R News 6:7–11. Retrieved from http://cran.r-project.org/doc/Rnews/Rnews_2006-1.pdf#page=7
Preinerstorfer D, Formann AK (2011) Parameter recovery and model selection in mixed Rasch models. Br J Math Stat Psychol 65:251–262. doi:10.1111/j.2044-8317.2011.02020.x
R Development Core Team (2011) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. Retrieved from http://www.R-project.org/
Raftery AE, Lewis S (1992) How many iterations in the Gibbs sampler. Bayesian Stat 4:763–773
Reckase MD (2009) Multidimensional item response theory. Springer, New York
Rost J (1990) Rasch models in latent classes: an integration of two approaches to item analysis. Appl Psychol Meas 14:271–282. doi:10.1177/014662169001400305
Rost J, von Davier M (1993) Measuring different traits in different populations with the same items. In: Steyer R, Wender KF, Widaman KF (eds) Psychometric methodology. Proceedings of the 7th European meeting of the psychometric society in Trier. Gustav Fischer, Stuttgart, pp 446–450
Rost J, Carstensen CH, von Davier M (1997) Applying the mixed-Rasch model to personality questionaires. In: Rost R, Langeheine R (eds) Applications of latent trait and latent class models in the social sciences. Waxmann, New York, pp 324–332
Samuelsen KM (2005) Examining differential item functioning from a latent class perspective. Doctoral dissertation, University of Maryland
SAS Institute (2008) SAS/STAT 9.2 user’s guide. SAS Institute, Cary
Schwarz G (1978) Estimating the dimension of a model. Ann Stat 6:461–464. doi:10.1214/aos/1176344136
Sclove LS (1987) Application of model-selection criteria to some problems in multivariate analysis. Psychometrika 52:333–343. doi:10.1007/BF02294360
Seong TJ (1990). Sensitivity of marginal maximum likelihood estimation of item and ability parameters to the characteristics of the prior ability distributions. Appl Psychol Meas 14:299–311. doi:10.1177/014662169001400307
Spiegelhalter DJ, Best NG, Carlin BP (1998) Bayesian deviance, the effective number of parameters, and the comparison of arbitrarily complex models. Research Report No. 98-009. MRC Biostatistics Unit, Cambridge
Spiegelhalter D, Thomas A, Best N (2003) WinBUGS (version 1.4) [Computer software]. Biostatistics Unit, Institute of Public Health, Cambridge
Thissen D (2003) MULTILOG: multiple, categorical item analysis and test scoring using item response theory (Version 7.03) [Computer software]. Scientific Software International, Chicago
Titterington DM, Smith AFM, Makov UE (1985) Statical analysis of finite mixture distributions. Wiley, Chichester
Tofighi D, Enders CK (2007) Identifying the correct number of classes in a growth mixture model. In: Hancock GR, Samuelsen KM (eds) Mixture models in latent variable research. Information Age, Greenwich, pp 317–341
Vermunt JK, Magidson J (2005) Latent GOLD (Version 4.0) [Computer software]. Statistical Innovations, Inc., Belmont
von Davier M (2001) WINMIRA 2001 [Computer software]. Assessment Systems Corporation, St. Paul
von Davier M (2005) mdltm: software for the general diagnostic model and for estimating mixtures of multidimensional discrete latent traits models [Computer software]. ETS, Princeton
von Davier M, Rost J (1997) Self monitoring-A class variable? In: Rost J, Langeheime R (eds) Applications of latent trait and latent class models in the social sciences. Waxmann, Muenster, pp 296–305
von Davier M, Rost J (2007) Mixture distribution item response models. In: Rao CR, Sinharay S (eds) Handbook of statistics. Psychometrics, vol 26. Elsevier, Amsterdam, pp 643–661
von Davier M, Rost J, Carstensen CH (2007) Introduction: extending the Rasch model. In: von Davier M, Carstensen CH (eds) Multivariate and mixture distribution Rasch models: extensions and applications. Springer, New York, pp 1–12
Wall MM, Guo J, Amemiya Y (2012) Mixture factor analysis for approximating a nonnormally distributed continuous latent factor with continuous and dichotomous observed variables. Multivar Behav Res 47:276–313. doi:10.1080/00273171.2012.658339
Wollack JA, Cohen AS, Wells CS (2003) A method for maintaining scale stability in the presence of test speededness. J Educ Meas 40:307–330. doi:10.1111/j.1745-3984.2003.tb01149.x
Woods CM (2004) Item response theory with estimation of the latent population distribution using spline-based densities. Unpublished doctoral dissertation, University of North Carolina at Chapel Hill
Yamamoto KY, Everson HT (1997) Modeling the effects of test length and test time on parameter estimation using the HYBRID model. In: Rost J, Langeheine R (eds) Applications of latent trait and latent class models in the social sciences. Waxmann, Munster, pp 89–98
Zwinderman AH, Van den Wollenberg AL (1990) Robustness of marginal maximum likelihood estimation in the Rasch model. Appl Psychol Meas 14:73–81. doi:10.1177/014662 169001400107
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Sen, S., Cohen, A.S., Kim, SH. (2015). Robustness of Mixture IRT Models to Violations of Latent Normality. In: Millsap, R., Bolt, D., van der Ark, L., Wang, WC. (eds) Quantitative Psychology Research. Springer Proceedings in Mathematics & Statistics, vol 89. Springer, Cham. https://doi.org/10.1007/978-3-319-07503-7_3
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