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A flexible latent trait model for response times in tests

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Abstract

Latent trait models for response times in tests have become popular recently. One challenge for response time modeling is the fact that the distribution of response times can differ considerably even in similar tests. In order to reduce the need for tailor-made models, a model is proposed that unifies two popular approaches to response time modeling: Proportional hazard models and the accelerated failure time model with log–normally distributed response times. This is accomplished by resorting to discrete time. The categorization of response time allows the formulation of a response time model within the framework of generalized linear models by using a flexible link function. Item parameters of the proposed model can be estimated with marginal maximum likelihood estimation. Applicability of the proposed approach is demonstrated with a simulation study and an empirical application. Additionally, means for the evaluation of model fit are suggested.

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References

  • Aranda-Ordaz, F.J. (1981). On two families of transformations to additivity for binary response data. Biometrika, 68, 357–363.

    Article  Google Scholar 

  • Bartholomew, D., & Knott, M. (1999). Latent variable models and factor analysis. London: Arnold.

    Google Scholar 

  • Berger, M. (1997). Optimal designs for latent variable models: a review. In J. Rost & R. Langeheine (Eds.), Applications of latent trait and latent class models in the social sciences (pp. 71–79). Münster: Waxmann.

    Google Scholar 

  • Berger, M. (1998). Optimal design of tests with dichotomous and polytomous items. Applied Psychological Measurement, 22, 248–258.

    Article  Google Scholar 

  • Borkenau, P., & Ostendorf, F. (1993). NEO-Fünf-Faktoren Inventar (NEO-FFI) nach Costa und McCrae. Göttingen: Hogrefe.

    Google Scholar 

  • Bos, C. (2002). A comparison of marginal likelihood computation methods (Tinbergen Institute Discussion Paper No. TI2002-084/4). Amsterdam: Vrije Universiteit.

  • Bradburn, M., Clark, T., Love, S., & Altman, D. (2003). Survival analysis Part II: Multivariate data analysis: an introduction to concepts and methods. British Journal of Cancer, 89, 431–436.

    Article  PubMed  Google Scholar 

  • Cowan, N., Elliott, E.M., Saults, J.S., Morey, C.C., Mattox, S., Hismjatullina, A., et al. (2005). On the capacity of attention: its estimation and its role in working memory and cognitive aptitudes. Cognitive Psychology, 51, 42–100.

    Article  PubMed  Google Scholar 

  • Cox, D. (1972). Regression models and life-tables. Journal of the Royal Statistical Society, B, 34, 187–220.

    Google Scholar 

  • Czado, C. (1994). Parametric link modification of both tails in binary regression. Statistical Papers, 35, 189–201.

    Article  Google Scholar 

  • DeMars, C. (2005). Type I error rates for Parscale’s fit index. Educational and Psychological Measurement, 65, 42–50.

    Article  Google Scholar 

  • Dempster, A., Laird, N., & Rubin, D. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, B, 39, 1–38.

    Google Scholar 

  • Doksum, K. (1987). An extension of partial likelihood methods for proportional hazard models to general transformation models. The Annals of Statistics, 15, 325–345.

    Article  Google Scholar 

  • Doksum, K., & Gasko, M. (1990). On a correspondence between models in binary regression analysis and in survival analysis. International Statistical Review, 58, 243–252.

    Article  Google Scholar 

  • Douglas, J., Kosorok, M., & Chewing, B. (1999). A latent variable model for discrete multivariate psychometric waiting times. Psychometrika, 64, 69–82.

    Article  Google Scholar 

  • Evans, M., & Swartz, T. (1995). Methods for approximating integrals in statistics with special emphasis on Bayesian integration problems. Statistical Science, 10, 254–272.

    Article  Google Scholar 

  • Eysenck, H., Wilson, C., & Jackson, C. (1998). Eysenck Personality Profiler (EPP-D). Frankfurt: Swets.

    Google Scholar 

  • Fleming, T., & Lin, D. (2000). Survival analysis in clinical trials: past developments and future directions. Biometrics, 56, 971–983.

    Article  PubMed  Google Scholar 

  • Furneaux, W. (1952). Some speed, error and difficulty relationships within a problem-solving situation. Nature, 170, 37–38.

    Article  Google Scholar 

  • Heath, J.W., Fu, M.C., & Jank, W. (2009). New global optimization algorithms for model-based clustering. Computational Statistics & Data Analysis, 53, 3999–4017.

    Article  Google Scholar 

  • Heinzmann, D. (2008). A filtered polynomial approach to density estimation. Computational Statistics, 23, 343–360.

    Article  Google Scholar 

  • Kang, T., & Chen, T. (2008). Performance of the generalized SX 2 item fit index for polytomous IRT models. Journal of Educational Measurement, 45, 391–406.

    Article  Google Scholar 

  • Klein Entink, R., van der Linden, W., & Fox, J. (2009). A Box–Cox normal model for response times. British Journal of Mathematical and Statistical Psychology, 62, 621–640.

    Article  PubMed  Google Scholar 

  • Luck, S.J., & Vogel, E.K. (1997). The capacity of visual working memory for features and conjunctions. Nature, 390, 279–281.

    Article  PubMed  Google Scholar 

  • Maris, E. (1993). Additive and multiplicative models for gamma distributed random variables, and their application as psychometric models for response times. Psychometrika, 58, 445–469.

    Article  Google Scholar 

  • Marubini, E., & Valsecchi, M. (1995). Analysing survival data from clinical trials and observational studies. Chichester: Wiley.

    Google Scholar 

  • Maydeu-Olivares, A., & Joe, H. (2006). Limited information goodness-of-fit testing in multidimensional contingency tables. Psychometrika, 71, 713–732.

    Article  Google Scholar 

  • McCullagh, P. (1980). Regression models for ordinal data. Journal of the Royal Statistical Society, B, 42, 109–142.

    Google Scholar 

  • Meng, X., & Rubin, D. (1993). Maximum likelihood estimation via the ECM algorithm: a general framework. Biometrika, 80, 267–278.

    Article  Google Scholar 

  • Micko, H. (1969). A psychological scale for reaction time measurement. Acta Psychologica, 30, 324–335.

    Article  Google Scholar 

  • Moran, P. (1971). Maximum-likelihood estimation in non-standard conditions. Mathematical Proceedings of the Cambridge Philosophical Society, 70, 441–451.

    Article  Google Scholar 

  • Muraki, E., & Bock, R.D. (1997). Parscale: IRT item analysis and test scoring for rating-scale data. Chicago: Scientific Software. [Computer software]

    Google Scholar 

  • Nelder, J., & Mead, R. (1965). A simplex method for function minimization. The Computer Journal, 7, 308–313.

    Google Scholar 

  • Nettleton, D. (1999). Convergence properties of the EM algorithm in constrained parameter spaces. The Canadian Journal of Statistics, 27, 639–648.

    Article  Google Scholar 

  • Orchard, T., & Woodbury, M. (1972). A missing information principle: theory and applications. Proceedings of the Sixth Berkeley Symposium on Mathematical Statistics and Probability, 1, 697–715.

    Google Scholar 

  • Orlando, M., & Thissen, D. (2000). Likelihood-based item-fit indices for dichotomous item response theory models. Applied Psychological Measurement, 24, 50–64.

    Article  Google Scholar 

  • Parner, E. (1997). Inference in semiparametric frailty models. Unpublished doctoral dissertation, University of Aarhus, Arhus, Denmark.

  • Pregibon, D. (1980). Goodness of link tests for generalized linear models. Journal of the Royal Statistical Society, Series C, 29, 15–24.

    Google Scholar 

  • Ramsay, J. (1991). Kernel smoothing approaches to nonparametric item characteristic curve estimation. Psychometrika, 56, 611–630.

    Article  Google Scholar 

  • Rubin, D. (1976). Inference and missing data. Biometrika, 63, 581–592.

    Article  Google Scholar 

  • Salavei, V. (2006). Logistic approximation to the normal: the KL rational. Psychometrika, 71, 763–767.

    Article  Google Scholar 

  • Samejima, F. (1969). Estimation of latent ability using a response pattern of graded scores. Psychometrika Monograph, 17, 1–100.

    Google Scholar 

  • Scheiblechner, H. (1979). Specifically objective stochastic latency mechanisms. Journal of Mathematical Psychology, 19, 19–38.

    Article  Google Scholar 

  • Schilling, S., & Bock, R. (2005). High-dimensional maximum marginal likelihood item factor analysis by adaptive quadrature. Psychometrika, 70, 533–555.

    Google Scholar 

  • Schnipke, D., & Scrams, D. (2002). Exploring issues of examinee behavior: insights gaines from response-time analyses. In C. Mills, M. Potenza, J. Fremer, & W. Ward (Eds.), Computer-based testing: building the foundation for future assessments (pp. 237–266). Mahwah: Lawrence Erlbaum.

    Google Scholar 

  • Self, S., & Liang, K. (1987). Asymptotic properties of maximum likelihood estimators and likelihood ratio tests under nonstandard conditions. Journal of the American Statistical Association, 82, 605–610.

    Article  Google Scholar 

  • Stroud, A. (1971). Approximate calculation of multiple integrals. Englewood Cliffs: Prentice-Hall.

    Google Scholar 

  • Therneau, T., & Grambsch, P. (2000). Modeling survival data: extending the Cox model. New York: Springer.

    Google Scholar 

  • van Breukelen, G. (1995). Psychometric and information processing properties of selected response time models. Psychometrika, 60, 95–113.

    Article  Google Scholar 

  • van Breukelen, G. (1997). Separability of item and person parameters in response time models. Psychometrika, 62, 525–544.

    Article  Google Scholar 

  • van der Linden, W. (2006). A lognormal model for response times on test items. Journal of Educational and Behavioral Statistics, 31, 181–204.

    Article  Google Scholar 

  • van der Linden, W. (2009). Conceptual issues in response-time modeling. Journal of Educational Measurement, 46, 247–272.

    Article  Google Scholar 

  • van der Linden, W., Klein Entink, R., & Fox, J. (2010). IRT parameter estimation with response times as collateral information. Applied Psychological Measurement, 34, 327–347.

    Article  Google Scholar 

  • van der Maas, H., & Wagenmakers, E. (2005). A psychometric analysis of chess expertise. American Journal of Psychology, 118, 29–60.

    PubMed  Google Scholar 

  • Vorberg, D., & Schwarz, W. (1990). Rasch-representable reaction time distributions. Psychometrika, 55, 617–632.

    Article  Google Scholar 

  • Wenger, M., & Gibson, B. (2004). Using hazard functions to assess changes in processing capacity in an attentional cuing paradigm. Journal of Experimental Psychology, 30, 708–719.

    PubMed  Google Scholar 

  • Woods, C. (2007). Ramsay curve IRT for Likert-type data. Applied Psychological Measurement, 31, 195–212.

    Article  Google Scholar 

  • Wu, C.F.J. (1983). On the convergence properties of the EM algorithm. The Annals of Statistics, 11, 95–103.

    Article  Google Scholar 

Download references

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Correspondence to Jochen Ranger.

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Ranger, J., Kuhn, JT. A flexible latent trait model for response times in tests. Psychometrika 77, 31–47 (2012). https://doi.org/10.1007/s11336-011-9231-7

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  • DOI: https://doi.org/10.1007/s11336-011-9231-7

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