Francis Tuerlinckx

Contact information

Francis Tuerlinckx
Tiensestraat 102
3000 Leuven


Phone: (+32) 16 325999
Fax: (+32) 16 325993

 

Research interests

• Psychometrics
• Bayesian statistics
• Diffusion models
• Models for reaction time data

Publications

Group by: Type / Year & Type

International journal articles

• Braeken, J., & Tuerlinckx, F. (2009). A mixed model framework for teratology studies. Biostatistics, 10, 744-755. doi:10.1093/biostatistics/kxp028
• Braeken, J., & Tuerlinckx, F. (2009). Investigating latent constructs with item response models: A MATLAB IRTm toolbox. Behavior Research Methods, 41, 1127-1137. doi:10.3758/BRM.41.4.1127
• Braeken, J., Tuerlinckx, F., & De Boeck, P. (2007). Copula functions for residual dependency. Psychometrika, 72, 393-411. doi:10.1007/s11336-007-9005-4
• De Boeck, P., Bakker, M., Zwitser, R., Nivard, M., Hofman, A., Tuerlinckx, F., & Partchev, I. (2011). The estimation of item response models with the lmer function from the lme4 package in R. Journal of Statistical Software, 39, 1-28.
• Dutilh, G., Vandekerckhove, J., Tuerlinckx, F., & Wagenmakers, E.-J. (2009). A diffusion model decomposition of the practice effect. Psychonomic Bulletin & Review, 16, 1026-1036. doi:10.3758/16.6.1026
• Frederickx, S., Kuppens, P., Tuerlinckx, F., & Van Mechelen, I. (2009). Desequens: An R-package for the variance decomposition of sequential processes. Behavior Research Methods, 41, 524-530. doi:10.3758/BRM.41.2.524
• Frederickx, S., Tuerlinckx, F., De Boeck, P., & Magis, D. (2010). RIM: A random item mixture model to detect Differential Item Functioning. Journal of Educational Measurement, 47, 432-457. doi:10.1111/j.1745-3984.2010.00122.x
• Gelman, A., Goegebeur, Y., Tuerlinckx, F., & Van Mechelen, I. (2000). Diagnostic checks for discrete-data regression models using posterior predictive simulations. Applied Statistics, 49, 247-268. doi:10.1111/1467-9876.00190
• Gelman, A., Katz, J. N., & Tuerlinckx, F. (2002). The mathematics and statistics of voting power. Statistical Science, 17, 420-435. doi:10.1214/ss/1049993201
• Gelman, A., & Tuerlinckx, F. (2000). Type S error rates for classical and Bayesian single and multiple comparison procedures. Computational Statistics, 15, 373-390. doi:10.1007/s001800000040
• González, J., De Boeck, P., & Tuerlinckx, F. (2008). A double-structure structural equation model for three-mode data. Psychological Methods, 13, 337-353. doi:10.1037/a0013269
• González, J., Tuerlinckx, F., & De Boeck, P. (2009). Analyzing structural relations in multivariate dyadic binary data. Applied Multivariate Research, 13, 77-92.
• González, J., Tuerlinckx, F., De Boeck, P., & Cools, R. (2006). Numerical integration in logistic-normal models. Computational Statistics & Data Analysis, 51, 1535-1548. doi:10.1016/j.csda.2006.05.003
• Janssen, R., Tuerlinckx, F., Meulders, M., & De Boeck, P. (2000). A hierarchical IRT model for criterion-referenced measurement. Journal of Educational and Behavioral Statistics, 25, 285-306. doi:10.3102/10769986025003285
• Kuppens, P., Oravecz, Z., & Tuerlinckx, F. (2010). Feelings change: Accounting for individual differences in the temporal dynamics of affect. Journal of Personality and Social Psychology, 99, 1042-1060. doi:10.1037/a0020962
• Kuppens, P., & Tuerlinckx, F. (2007). Personality traits predicting anger in self-, ambiguous-, and other-caused unpleasant situations. Personality and Individual Differences, 42, 1105-1115. doi:10.1016/j.paid.2006.09.011
• Lodewyckx, T., Kim, W., Lee, M. D., Tuerlinckx, F., Kuppens, P., & Wagenmakers, E.-J. (2011). A tutorial on Bayes factor estimation with the product space method. Journal of Mathematical Psychology, 55, 331-347. doi:10.1016/j.jmp.2011.06.001
• Lodewyckx, T., Tuerlinckx, F., Kuppens, P., Allen, N. B., & Sheeber, L. (2011). A hierarchical state space approach to affective dynamics. Journal of Mathematical Psychology, 55, 68-83. doi:10.1016/j.jmp.2010.08.004
• Luyten, L., Lowyck, J., & Tuerlinckx, F. (2001). Task perception as a mediating variable: A contribution to the validation of instructional knowledge. British Journal of Educational Psychology, 71, 203-223. doi:10.1348/000709901158488
• Magis, D., Béland, S., Tuerlinckx, F., & De Boeck, P. (2010). A general framework and an R package for the detection of dichotomous differential item functioning. Behavior Research Methods, 42, 847-862. doi:10.3758/BRM.42.3.847
• Oravecz, Z., & Tuerlinckx, F. (2011). The linear mixed model and the hierarchical Ornstein-Uhlenbeck model: Some Equivalences and differences. British Journal of Mathematical and Statistical Psychology, 64, 134-160. doi:10.1348/000711010X498621
• Oravecz, Z., Tuerlinckx, F., & Vandekerckhove, J. (2009). A hierarchical Ornstein-Uhlenbeck model for continuous repeated measurement data. Psychometrika, 74, 395-418. doi:10.1007/s11336-008-9106-8
• Oravecz, Z., Tuerlinckx, F., & Vandekerckhove, J. (2011). A hierarchical latent stochastic differential equation model for affective dynamics. Psychological Methods, 16, 468-490. doi:10.1037/a0024375
• 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.
• Rijmen, F., Tuerlinckx, F., De Boeck, P., & Kuppens, P. (2003). A nonlinear mixed model framework for item response theory. Psychological Methods, 8, 185-205. doi:10.1037/1082-989X.8.2.185
• Rijmen, F., Tuerlinckx, F., Meulders, M., Smits, D. J. M., & Balázs, K. (2005). Mixed model estimation methods for the Rasch model. Journal of Applied Measurement, 6, 273-288.
• Rouder, J., Tuerlinckx, F., Speckman, P., Lu, J., & Gomez, P. (2008). A hierarchical approach for fitting curves to response time measurements. Psychonomic Bulletin & Review, 15, 1201-1208. doi:10.3758/PBR.15.6.1201
• San Martín, E., González, J., & Tuerlinckx, F. (2009). Identified parameters, parameters of interest, and their relationships. Measurement: Interdisciplinary Research and Perspective, 7, 97-105. doi:10.1080/15366360903117053
• Tuerlinckx, F. (2004). A multivariate counting process with Weibull distributed first-arrival times. Journal of Mathematical Psychology, 48, 65-79. doi:10.1016/j.jmp.2003.12.001
• Tuerlinckx, F. (2004). The efficient computation of the cumulative distribution and probability density functions in the diffusion model. Behavior Research Methods, Instruments, & Computers, 6, 702-716.
• Tuerlinckx, F. (2004). The idiographic approach: Where do we come from and where do we go. Measurement: Interdisciplinary Research and Perspectives, 2, 240-243.
• Tuerlinckx, F., & De Boeck, P. (1998). Modeling local item dependencies in item response theory. Psychologica Belgica, 38, 61-82.
• Tuerlinckx, F., & De Boeck, P. (1999). Distinguishing constant and dimension-dependent interaction: A simulation study. Applied Psychological Measurement, 23, 299-307. doi:10.1177/01466219922031419
• Tuerlinckx, F., & De Boeck, P. (2001). Non-modeled item interactions lead to distorted discrimination parameters: A case study. Methods of Psychological Research - Online, 6, 159-174.
• Tuerlinckx, F., & De Boeck, P. (2001). The effect of ignoring item interactions on the estimated discrimination parameters in item response theory. Psychological Methods, 6, 181-195. doi:10.1037/1082-989X.6.2.181
• Tuerlinckx, F., & De Boeck, P. (2005). Two interpretations of the discrimination parameter. Psychometrika, 70, 629-650. doi:10.1007/s11336-000-0810-3
• Tuerlinckx, F., De Boeck, P., & Lens, W. (2002). Measuring needs with the Thematic Apperception Test: A psychometric study. Journal of Personality and Social Psychology, 82, 448-461. doi:10.1037/0022-3514.82.3.448
• Tuerlinckx, F., Maris, E., Ratcliff, R., & De Boeck, P. (2001). A comparison of four methods for simulating the diffusion process. Behavior Research Methods, Instruments, & Computers, 33, 443-456.
• Tuerlinckx, F., Rijmen, F., Verbeke, G., & De Boeck, P. (2006). Statistical inference in generalized linear mixed models: A review. British Journal of Mathematical & Statistical Psychology, 59, 225-255. doi:10.1348/000711005X79857
• Valdivieso, L., Schoutens, W., & Tuerlinckx, F. (2009). Maximum likelihood estimation in processes of Ornstein-Uhlenbeck type. Statistical Inference for Stochastic Processes, 12, 1-19. doi:10.1007/s11203-008-9021-8
• Vanbrabant, K., Kuppens, P., Braeken, J., Demaerschalk, E., Boeren, A., & Tuerlinckx, F. (in press). A relationship between verbal aggression and personal network size. Social Networks.
• Vandekerckhove, J., & Tuerlinckx, F. (2007). Fitting the Ratcliff diffusion model to experimental data. Psychonomic Bulletin & Review, 14, 1011-1026. doi:10.3758/PBR.15.6.1229
• Vandekerckhove, J., & Tuerlinckx, F. (2008). Diffusion model analysis with MATLAB: A DMAT primer. Behavior Research Methods, 40, 61-72. doi:10.3758/BRM.40.1.61
• Vandekerckhove, J., & Tuerlinckx, F. (in press). MATLAB for behavioral scientists: A novice's guide to MATLAB. Experimental Psychology.
• Vandekerckhove, J., Tuerlinckx, F., & Lee, M. D. (2011). Hierarchical diffusion models for two-choice response times. Psychological Methods, 16, 44-62. doi:10.1037/a0021765
• Vandekerckhove, J., Verheyen, S., & Tuerlinckx, F. (2010). A crossed random effects diffusion model for speeded semantic categorization decisions. Acta Psychologica, 133, 269-282. doi:10.1016/j.actpsy.2009.10.009
• Verduyn, P., Delvaux, E., Van Coillie, H., Tuerlinckx, F., & Van Mechelen, I. (2009). Predicting the duration of emotional experience: Two experience sampling studies. Emotion, 9, 83-91. doi:10.1037/a0014610
• Verduyn, P., Tuerlinckx, F., & Van Gorp, K. (in press). Measuring the duration of emotional experience: The influence of actual duration and response format. Quality & Quantity. doi:10.1007/s11135-012-9671-x
• Verduyn, P., Van Mechelen, I., & Tuerlinckx, F. (2011). The relation between event processing and the duration of emotional experience. Emotion, 11, 20-28. doi:10.1037/a0021239
• Verduyn, P., Van Mechelen, I., Tuerlinckx, F., Meers, K., & Van Coillie, H. (2009). Intensity profiles of emotional experience over time. Cognition & Emotion, 23, 1427-1443. doi:10.1080/02699930902949031
• Verguts, T., Storms, G., & Tuerlinckx, F. (2003). Decision bound theory and the influence of familiarity. Psychonomic Bulletin & Review, 10, 141-148.
• Vigo, D.E., Ogrinz, B., Wan, L., Bersenev, E., Tuerlinckx, F., van den Bergh, O., & Aubert, A. (in press). Sleep-wake differences in heart rate variability during a 105-day simulated mission to Mars. Aviation, Space and Environmental Medicine.
• Wan, L., Ogrinz, B., Vigo, D., Bersenev, E., Tuerlinckx, F., van den Bergh, O., & Aubert, A. (2011). Cardiovascular autonomic adaptation to long-term confinement during a 105-day simulated Mars mission. Aviation, Space and Environmental Medicine, 82, 711-716. doi:10.3357/ASEM.2986.2011
• Wetzels, R., Vandekerckhove, J., Tuerlinckx, F., & Wagenmakers, E.-J. (2008). Bayesian parameter estimation in the Expectancy Valence model of the Iowa gambling task. Journal of Mathematical Psychology, 54, 14-27. doi:10.1016/j.jmp.2008.12.001

Chapters in books

• Rijmen, F., Tuerlinckx, F., Meulders, M., Smits, D. J. M., & Balázs, K. (2006). Mixed model estimation methods for item response models. In E. V. Smith & R. M. Smith (Eds.), Rasch Measurement: Advanced and Specialized Applications. Chicago: University of Illinois.
• Tuerlinckx, F. (2010). Weibull distribution. In N. Salkind (Ed.), Encyclopedia of Research Design. Thousand Oaks, CA: Sage.
• Tuerlinckx, F., & De Boeck, P. (2004). Models for residual dependencies. In P. De Boeck & M. Wilson (Eds.), Explanatory item response models: A generalized linear and nonlinear approach (pp. 289-316). New York: Springer.
• Tuerlinckx, F., Rijmen, F., Molenberghs, G., Verbeke, G., Briggs, D., Van den Noortgate, W., Meulders, M., & De Boeck, P. (2004). Estimation and software. In P. De Boeck & M. Wilson (Eds.), Explanatory item response models: A generalized linear and nonlinear approach (pp. 343-373). New York: Springer.
• Tuerlinckx, F., & Wang, W. C. (2004). Models for polytomous data. In P. De Boeck & M. Wilson (Eds.), Explanatory item response models: A generalized linear and nonlinear approach (pp. 75-109). New York: Springer.
• Vandekerckhove, J., Tuerlinckx, F., & Lee, M. D. (2008). A Bayesian approach to diffusion process models of decision-making. In V. Sloutsky, B. Love, & K. McRae (Eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society (pp. 1429-1434). Austin, TX: Cognitive Science Society.

Software

Teaching