Minimum Distance Estimation of Multidimensional Diffusion-Based Item Response Theory Models

References

• Alexandrowicz, R. (2018). The diffusion model visualizer: An interactive tool to understand the diffusion model parameters. Psychological Research, Online First, 1–9. doi:10.1007/s00426-018-1112-6
   Web of Science ®Google Scholar
• Basu, A., Harris, I., & Basu, S. (1997). Minimum distance estimation: The approach using density-based distances. In G. Maddala & C. Rao (Eds.), Handbook of statistics, Vol 15, Robust Estimation (pp. 21–48). Amsterdam: Elsevier. doi:10.1016/S0169-7161(97)15004-0
   Google Scholar
• Bock, R., & Aitkin, M. (1981). Marginal maximum likelihood estimation of item parameters: Application of an EM algorithm. Psychometrika, 46(4), 443–459. doi:10.1007/BF02293801
   Web of Science ®Google Scholar
• Cai, L. (2010). High-dimensional exploratory item factor analysis by a Metropolis-Hastings Robbins-Monro algorithm. Psychometrika, 75(1), 33–57. doi:10.1007/s11336-009-9136-x
   Web of Science ®Google Scholar
• Chun, S., Browne, M., & Shapiro, A. (2018). Modified distribution-free goodness-of-fit test statistic. Psychometrika, 83(1), 48–66. doi:10.1007/s11336-017-9574-9
   PubMed Web of Science ®Google Scholar
• Dominicy, Y., & Veredas, D. (2013). The method of simulated quantiles. Journal of Econometrics, 172(2), 235–247. doi:10.2139/ssrn.1561185.
   Web of Science ®Google 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(6), 525–543. doi:10.1177/0146621606295197
   Web of Science ®Google Scholar
• Gondan, M., Blurton, S., & Kesselmeier, M. (2014). Even faster and even more accurate first-passage time densities and distributions for the Wiener diffusion model. Journal of Mathematical Psychology, 60, 20–22. doi:10.1016/j.jmp.2014.05.002
   Web of Science ®Google Scholar
• Heathcote, A., Brown, S., & Cousineau, D. (2004). QMPE: Estimating lognormal, Wald, and Weibull RT distributions with a parameter-dependent lower bound. Behavior Research Methods, Instruments, & Computers, 36, 277–290. doi:10.3758/BF03195574
   PubMedGoogle Scholar
• Hendrix, E., & Toth, B. (2010). Introduction to nonlinear and global optimization. Berlin: Springer. doi:10.1007/978-0-387-88670-1
   Google Scholar
• Maydeu-Olivares, A., & Joe, H. (2005). Limited- and full-information estimation and goodness-of-fit testing in 2n contingency tables. Journal of the American Statistical Association, 100, 1009–1020. doi:10.1198/016214504000002069
   Web of Science ®Google Scholar
• Meade, A., Pappalardo, G., Braddy, P., & Fleenor, J. (2018). Rapid response measurement: Development of a faking-resistant assessment method for personality. Organizational Research Methods. doi:10.1177/1094428118795295
   Web of Science ®Google Scholar
• Molenaar, D., Tuerlinckx, F., & van der Maas, H. (2015a). A generalized linear factor model approach to the hierarchical framework for responses and response times. British Journal of Mathematical and Statistical Psychology, 68(2), 197–219. doi:10.1111/bmsp.12042
   PubMed Web of Science ®Google Scholar
• Molenaar, D., Tuerlinckx, F., & van der Maas, H. (2015b). Fitting diffusion item response theory models for responses and response times using the R package diffIRT. Journal of Statistical Software, 66(4), 1–34. doi:10.18637/jss.v066.i04
   Web of Science ®Google Scholar
• Newey, W., & McFadden, D. (1994). Large sample estimation and hypothesis testing. In R. Engle & D. McFadden (Eds.), Handbook of econometrics (Vol. 4, pp. 2113–2245). New York: Elsevier. doi:10.1016/S1573-4412(05)80005-4
   Google Scholar
• Peeters, C. (2012). Rotational uniqueness conditions under oblique factor correlation metric. Psychometrika, 77(2), 288–292. doi:10.1007/s11336-012-9259-3
   Web of Science ®Google Scholar
• R Development Core Team. (2009). R: A language and environment for statistical computing [Computer software manual]. Vienna, Austria. Retrieved from http://www.R-project.org. (ISBN 3-900051-07-0)
   Google Scholar
• Ranger, J., & Kuhn, J. (2018). Estimating diffusion-based item response theory models: Exploring the robustness of three old and two new estimators. Journal of Educational and Behavioral Statistics, 43(6), 635–662. doi:10.3102/1076998618787791
   Web of Science ®Google Scholar
• Ranger, J., Kuhn, J., & Szardenings, C. (2016). Limited information estimation of the diffusion based item response theory model for responses and response times. British Journal of Mathematical and Statistical Psychology, 69(2), 122–138. doi:10.1111/bmsp.12064
   PubMed Web of Science ®Google Scholar
• Ranger, J., Kuhn, J., & Szardenings, C. (2017). Analysing model fit of psychometric process models: An overview, a new test and an application to the diffusion model. British Journal of Mathematical and Statistical Psychology, 70(2), 209–224. doi:10.1111/bmsp.12064
   PubMed Web of Science ®Google Scholar
• Ratcliff, R. (1978). A theory of memory retrieval. Psychological Review, 85(2), 59–108. doi:10.1037/0033-295X.85.2.59
   Web of Science ®Google Scholar
• Ratcliff, R., & Childers, R. (2015). Individual differences and fitting methods for the two-choice diffusion model of decision making. Decision, 2(4), 237–279. doi:10.1037/dec0000030
   Google Scholar
• Ratcliff, R., & McKoon, G. (2008). The diffusion decision model: Theory and data for two-choice decision tasks. Neural Computation, 20(4), 873–922. doi:10.1162/neco.2008.12-06-420
   PubMed Web of Science ®Google Scholar
• Ratcliff, R., & Tuerlinckx, F. (2002). Estimating parameters of the diffusion model: Approaches to dealing with contaminant reaction times and parameter variability. Psychonomic Bulletin & Review, 9, 438–481. doi:10.3758/BF03196302
   PubMed Web of Science ®Google Scholar
• Schubert, A., Hagemann, D., Voss, A., & Bergmann, K. (2017). Evaluating the model fit of diffusion models with the root mean square error of approximation. Journal of Mathematical Psychology, 77, 29–45. doi:10.1016/j.jmp.2016.08.004
   Web of Science ®Google Scholar
• Thurstone, L. (1959). The measurement of values. Chicago: University of Chicago Press.
   Google Scholar
• Tuerlinckx, F. (2004). The efficient computation of the cumulative distribution and probability density functions in the diffusion model. Behavior Research Methods, Instruments, & Computers, 36, 702–716. doi:10.3758/BF03206552
   PubMedGoogle Scholar
• Tuerlinckx, F., & De Boeck, P. (2005). Two interpretations of the discrimination parameter. Psychometrika, 70(4), 629–650. doi:10.1007/s11336-000-0810-3
   Web of Science ®Google Scholar
• Tuerlinckx, F., Molenaar, D., & van der Maas, H. (2016). Diffusion-based item response theory modeling. In W. van der Linden & R. Hambleton (Eds.), Handbook of modern item response theory. London: Chapman and Hall/CRC Press.
   Google Scholar
• van der Linden, W. (2007). A hierarchical framework for modeling speed and accuracy on test items. Psychometrika, 72(3), 287–308. doi:10.1007/s11336-006-1478-z
   Web of Science ®Google Scholar
• van der Maas, H., Molenaar, D., Maris, G., Kievit, R., & Borsboom, 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(2), 339–356. doi:10.1037/a0022749
   PubMed Web of Science ®Google Scholar
• Vandekerckhove, J. (2014). A cognitive latent variable model for the simultaneous analysis of behavioral and personality data. Journal of Mathematical Psychology, 60, 58–71. doi:10.1016/j.jmp.2014.06.004
   Web of Science ®Google Scholar
• Vandekerckhove, J., Tuerlinckx, F., & Lee, M. (2011). Hierarchical diffusion models for two-choice response times. Psychological Methods, 16(1), 44–62. doi:10.1037/a0021765
   PubMed Web of Science ®Google Scholar
• Varin, C., Reid, N., & Firth, D. (2011). An overview of composite likelihood methods. Statistica Sinica, 21, 5–42. doi:10.2307/24309261
   Web of Science ®Google Scholar
• Voss, A., Nagler, M., & Lerche, V. (2013). Diffusion models in experimental psychology: A practical introduction. Experimental Psychology, 60(6), 385–402. doi:10.1027/1618-3169/a000218
   PubMed Web of Science ®Google Scholar
• Voss, A., & Voss, J. (2008). A fast numerical algorithm for the estimation of diffusion model parameters. Journal of Mathematical Psychology, 52(1), 1–9. doi:10.1016/j.jmp.2007.09.005
   Web of Science ®Google Scholar
• Wabersich, D. (2014). RWiener: Wiener process distribution functions [Computer software manual]. (R package version 1.2-0).
   Google Scholar
• Wagenmakers, E. (2009). Methodological and empirical developments for the Ratcliff diffusion model of response times and accuracy. European Journal of Cognitive Psychology, 21(5), 641–671. doi:10.1080/09541440802205067
   Web of Science ®Google Scholar
• Wagenmakers, E., Grasman, R., & Molenaar, P. (2005). On the relation between the mean and the variance of a diffusion model response time distribution. Journal of Mathematical Psychology, 49(3), 195–204. doi:10.1016/j.jmp.2005.02.003
   Web of Science ®Google Scholar
• Wagenmakers, E., van der Maas, H., & Grasman, R. (2007). An EZ-diffusion model for response time and accuracy. Psychonomic Bulletin & Review, 14, 3–22. doi:10.3758/BF03194023
   PubMed Web of Science ®Google Scholar
• Welch, B. (1938). The significance of the difference between two means when the population variances are unequal. Biometrika, 29(3-4), 350–362. doi:10.1093/biomet/29.3-4.350
   Google Scholar
• Wolfinger, R. (1993). Laplace’s approximation for nonlinear mixed models. Biometrika, 80(4), 791–795. doi:10.2307/2336870
   Web of Science ®Google Scholar
• Yuan, K.-H., & Bentler, P. (2010). Two simple approximations to the distributions of quadratic forms. British Journal of Mathematical and Statistical Psychology, 63(2), 273–291. doi:10.1348/000711009X449771
   PubMed Web of Science ®Google Scholar
• Yuan, K.-H., Marshall, L., & Bentler, P. (2002). A unified approach to exploratory factor analysis with missing data, nonnormal data, and in the presence of outliers. Psychometrika, 67(1), 95–122. doi:10.1007/BF02294711
   Web of Science ®Google Scholar