Hostname: page-component-745bb68f8f-lrblm Total loading time: 0 Render date: 2025-01-07T17:51:36.775Z Has data issue: false hasContentIssue false

A Race Model for Responses and Response Times in Tests

Published online by Cambridge University Press:  01 January 2025

Jochen Ranger*
Affiliation:
Martin-Luther-University Halle-Wittenberg
Jörg-Tobias Kuhn
Affiliation:
University of Münster
José-Luis Gaviria
Affiliation:
Universidad Complutense De Madrid
*
Correspondence should be made to Jochen Ranger, Martin-Luther-University Halle-Wittenberg, Halle, Germany. Email: Jochen.Ranger@psych.uni-halle.de

Abstract

Latent trait models for responses and response times in tests are often pure statistical models without a close connection to features of the assumed response process. In the present paper, a new model is presented that is more closely related to assumptions about the response process. The model is based on two increasing stochastic processes. Each stochastic process represents the accumulation of knowledge with respect to one of two response options, the correct and incorrect response. Both accumulators compete and the accumulator that first exceeds a critical level determines the response. General assumptions about the accumulators result in a race between two response times that follow a bivariate Birnbaum Saunders distribution. The model can be calibrated with marginal maximum likelihood estimation. Feasibility of the estimation approach is demonstrated in a simulation study. Additionally, a test of model fit is proposed. Finally, the model will be used for the analysis of an empirical data set.

Type
Original Paper
Copyright
Copyright © 2014 The Psychometric Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Baker, F., & Seock-Ho, K. (2004). Item response theory: Parameter estimation techniques. New York, NY: Marcel Dekker.CrossRefGoogle Scholar
Birnbaum, Z., & Saunders, S. (1968). A new family of life distributions. Journal of Applied Probability, 6, 319327.CrossRefGoogle Scholar
Brown, S., & Heathcote, A. (2005). A ballistic model for choice response times. Psychological Review, 112, 117128.CrossRefGoogle Scholar
Busemeyer, J., & Townsend, J. (1992). Fundamental derivations from decision field theory. Mathematical Social Sciences, 23, 255282.CrossRefGoogle Scholar
Caro-Lopera, F., Leiva, V., & Balakrishnan, N. (2001). Connection between the Hadamard and matrix products with an application to matrix-variate Birnbaum–Saunders distributions. Journal of Multivariate Analysis, 104, 126139.CrossRefGoogle Scholar
Casey, M., & Tyron, W. (2001). Validating a double-press method for computer administration of personality inventory items. Psychological Assessment, 13, 521530.CrossRefGoogle ScholarPubMed
Chernoff, H., & Lehmann, E., (1954). The use of the maximum likelihood estimates in χ2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\chi ^2$$\end{document}-tests for goodness of fit. Annals of Mathematical Statistics. 25, 579586.CrossRefGoogle Scholar
Cosineau, D. (2004). Merging race models and adaptive networks: A parallel race network. Psychonomic Bulletin and Review, 11, 807825.CrossRefGoogle Scholar
Dufau, S., Grainger, J., & Ziegler, J., (2012). How to say no to a nonword: A leaky competing accumulator model of lexical decision. Journal of Experimental Psychology Online First.CrossRefGoogle Scholar
Ferrando, P., & Lorenzo-Seva, U. (2007). An item response theory model for incorporating response time data in binary personality items. Applied Psychological Measurement, 31, 525543.CrossRefGoogle Scholar
Hunter, J. (1974). Renewal theory in two dimensions: Asymptotic results. Advances in Applied Probability, 6, 546562.CrossRefGoogle Scholar
Kallsen, J., & Tankov, P. (2006). Characterization of dependence of multidimensional Levy processes using Levy copulas. Journal of Multivariate Analysis, 97, 11511572.CrossRefGoogle Scholar
Kundu, D., Balakrishnan, N., & Jamalizadeh, A. (2010). Bivariate Birnbaum–Saunders distribution and associated inference. Journal of Multivariate Analysis, 101, 113125.CrossRefGoogle Scholar
Kundu, D., Balakrishnan, N., & Jamalizadeh, A. (2013). Generalized multivariate Birnbaum–Saunders distributions and related inferential issues. Journal of Multivariate Analysis, 116, 230244.CrossRefGoogle Scholar
Leiva, V., Riquelme, M., Balakrishnan, N., & Sanhueza, A. (2008). Lifetime analysis based on the generalized Birnbaum–Saunders distribution. Computational Statistics and Data Analysis, 52, 20792097.CrossRefGoogle Scholar
Lemonte, A., Martinez-Florez, G., & Moreno-Arenas, G., (2013). Multivariate Birnbaum–Saunders distribution: Properties and associated inference. Computational Statistics and Data Analysis Online First,. doi:10.1080/00949655.2013.823964.CrossRefGoogle Scholar
Mislevy, R., & Stocking, M. (1989). A consumer’s guide to LOGIST and BILOG. Applied Psychological Measurement, 13, 5775.CrossRefGoogle Scholar
Nash, J. (1990). Compact numerical methods for computers. Linear algebra and function minimisation. Bristol: Adam Hilger.Google Scholar
Otter, T., Allenby, G., & Van Zandt, T. (2008). An integrated model of discrete choice and response time. Journal of Marketing Research, 45, 593607.CrossRefGoogle Scholar
Pan, Z., & Balakrishnan, N. (2011). Reliability modeling of degradation of products with multiple performance characteristics based on gamma processes. Reliability Engineering and System Safety, 96, 949957.CrossRefGoogle Scholar
Park, C., & Padgett, W. (2005). Accelerated degradation models for failure based on geometric Brownian motion and gamma processes. Lifetime Data Analysis, 11, 511527.CrossRefGoogle ScholarPubMed
Pike, R. (1973). Response latency models for signal detection. Psychological Review, 80, 5368.CrossRefGoogle ScholarPubMed
R Development Core Team. (2009). R: A language and environment for statistical computing. [Computer Software Manual] Vienna, Austria. http://www.R-project.org ISBN 3-900051-07-0.Google Scholar
Ratcliff, R. (1978). A theory of memory retrieval. Psychological Review, 85, 59108.CrossRefGoogle Scholar
Ratcliff, R., & Rouder, J. (1998). Modeling response times for two-choice decisions. Psychological Science, 9, 347356.CrossRefGoogle Scholar
Ratcliff, R., & Smith, P. (2004). A comparison of sequential sampling models for two-choice reaction time. Psychological Review, 111, 333367.CrossRefGoogle ScholarPubMed
Ratcliff, R., & Tuerlinckx, F. (2002). Estimating parameters of the diffusion model: Approaches to dealing with contaminant reaction times and parameter variability. Psychonomic Bulletin and Review, 9, 438481.CrossRefGoogle ScholarPubMed
Rouder, J. (2005). Are unshifted distributional models appropriate for response time. Psychometrika, 70, 377381.CrossRefGoogle Scholar
Rouder, J., Province, J., Morey, R., Gomez, P., & Heathcote, A., (2014). The lognormal race: A cognitive-process model of choice and latency with desirable psychometric properties. Psychometrika Online First.Google Scholar
Ruan, S., MacEachern, S., Otter, T., & Dean, A. (2008). The dependent Poisson race model and modeling dependence in conjoint choice experiments. Psychometrika, 73, 261288.CrossRefGoogle Scholar
Schilling, S., & Bock, R. (2005). High-dimensional maximum marginal likelihood item factor analysis by adaptive quadrature. Psychometrika, 70, 533555.Google Scholar
Schnipke, D., & Scrams, D. (2002). Exploring issues of examinee behavior: Insights gaines from response-time analyses. In Mills, C., Potenza, M., Fremer, J., & Ward, W. (Eds.), Computer-based testing: Building the foundation for future assessments (pp. 237266). Mahwah, NJ: Lawrence Erlbaum.Google Scholar
Smith, P., & Van Zandt, T. (2000). Time-dependent Poisson counter models of response latency in simple judgment. British Journal of Mathematical and Statistical Psychology, 53, 293315.CrossRefGoogle ScholarPubMed
Smith, P., & Vickers, D. (1988). The accumulator model of two-choice discrimination. Journal of Mathematical Psychology, 32, 135168.CrossRefGoogle Scholar
Townsend, J., & Ashby, F. (1983). Stochastic modeling of elementary psychological processes. Cambridge: Cambridge University Press.Google Scholar
Tuerlinckx, F. (2004). A multivariate counting process with Weibull-distributed first-arrival times. Journal of Mathematical Psychology, 48, 6579.CrossRefGoogle Scholar
Tuerlinckx, F., & De Boeck, P. (2005). Two interpretations of the discrimination parameter. Psychometrika, 70, 629650.CrossRefGoogle Scholar
Usher, M., & McClelland, J. (2001). On the time course of perceptual choice: The leaky competing accumulator model. Psychological Review, 108, 550592.CrossRefGoogle ScholarPubMed
van der Linden, W. (2009). Conceptual issues in response-time modeling. Journal of Educational Measurement, 46, 247272.CrossRefGoogle Scholar
van der Linden, W., Breithaupt, K., Chuah, S., & Zhang, Y., (2007). Detecting differential speededness in multistage testing. Journal of Educational Measurement, 44, 117130.CrossRefGoogle Scholar
van der Maas, H., Molenaar, D., Maris, G., Kievit, R., & Boorsboom, D. (2011). Cognitive psychology meets psychometric theory: On the relation between process models for decision making and latent variable models for individual differences. Psychological Review, 118, 339356.CrossRefGoogle ScholarPubMed
van der Maas, H., & Wagenmakers, E. (2005). A psychometric analysis of chess expertise. American Journal of Psychology, 118, 2960.CrossRefGoogle ScholarPubMed
Vandekerckhove, J., Tuerlinckx, F., & Lee, M. (2011). Hierarchical diffusion models for two-choice response times. Psychological Methods, 16, 4462.CrossRefGoogle ScholarPubMed
Vickers, D. (1970). Evidence for an accumulator model of psychophysical discrimination. Ergonomics, 13, 3758.CrossRefGoogle ScholarPubMed
Wagenmakers, E., van der Maas, H., & Grasman, R. (2007). An EZ-diffusion model for response time and accuracy. Psychonomic Bulletin and Review, 14, 322.CrossRefGoogle ScholarPubMed
Wild, B. (1989). Neue Erkenntnisse zur Effizienz des tailored-adaptiven Testens [New insights into the efficiency of tailored-adaptive testing]. In Kubinger, K. (Eds.), Moderne Testtheorie [Modern test theory] (2nd ed., pp. 179187). Weinheim: Beltz.Google Scholar
Wirth, R., & Edwards, M. (2007). Item factor analysis: Current approaches and future directions. Psychological Methods, 12, 5879.CrossRefGoogle ScholarPubMed