Fife, D. A., Brunwasser, S., & Merkle, E. C. (in press). Seeing the impossible: Visualizing latent variable models with flexplavaan. Psychological Methods. (DOI: 10.1037/met0000468)

Wiedermann, W., Frick, U., & Merkle, E. C. (in press). Detecting heterogeneity of intervention effects in comparative judgments. Prevention Science. (DOI: 10.1007/s11121-021-01212-z)


Flores, R. D., Sanders, C. A., Duan, S. X., Bishop-Chrzanowski, B. M., Oyler, D. L., Shim, H., Clocksin, H. E., Miller, A. P., & Merkle, E. C. (2022). Before/after Bayes: A comparison of frequentist and Bayesian mixed-effects models in applied psychological research. British Journal of Psychology, 113, 1164–1194. (DOI: 10.1111/bjop.12585)

Wang, T., Graves, B., Rosseel, Y., & Merkle, E. C. (2022). Computation and application of generalized linear mixed model derivatives using lme4. Psychometrika, 87, 1173–1193. (DOI: 10.1007/s11336-022-09840-2)

Debelak, R., Pawel, S., Strobl, C., & Merkle, E. C. (2022). Score-based measurement invariance checks for Bayesian maximum-a-posteriori estimates in item response theory. British Journal of Mathematical and Statistical Psychology, 75, 728–752. (DOI: 10.1111/bmsp.12275)

Graves, B. & Merkle, E. C. (2022). Identification constraints and fit indices in Bayesian latent variable models. Behavior Research Methods, 54, 795–804. (DOI: 10.3758/s13428-021-01649-8)

Merkle, E. C., Fitzsimmons, E., Uanhoro, J., & Goodrich, B. (2021). Efficient Bayesian structural equation modeling in Stan. Journal of Statistical Software, 100(6), 1–22.

Garnier-Villarreal, M., Merkle, E. C., & Magnus, B. E. (2021). Between-item multidimensional IRT: How far can the estimation methods go? Psych, 3, 404–421. (DOI: 10.3390/psych3030029)

Wang, T., Merkle, E. C., Anguera, J., & Turner B. M. (2021). Score-based tests for detecting heterogeneity in linear mixed models. Behavior Research Methods, 53, 216–231. (DOI: 10.3758/s13428-020-01375-7)


Merkle, E. C., Saw, G., & Davis-Stober, C. (2020). Beating the average forecast: Regularization based on forecaster attributes. Journal of Mathematical Psychology, 98, 102419. (DOI: 10.1016/

Motschman, C. A., Hatz, L. E., McCarty, K. N., Merkle, E. C., Trull, T. J., & McCarthy, D. M. (2020). Event-level predictors of alcohol-impaired driving intentions. Journal of Studies on Alcohol and Drugs, 81(5), 647–654. (DOI: 10.15288/jsad.2020.81.647)

Haaf, J. M., Merkle, E. C., & Rouder, J. N. (2020). Do items order? The psychology in IRT models. Journal of Mathematical Psychology, 98, 102398. (DOI: 10.1016/

Scofield, J. E., Price, M. H., Flores, A., Merkle, E. C., & Johnson, J. D. (2020). Repetition attenuates the influence of recency on recognition memory: Behavioral and eletrophysiological evidence. Psychophysiology, 57, e13601. (DOI: 10.1111/psyp.13601)

Schneider, L., Chalmers, R. P., Debelak, R., & Merkle, E. C. (2020). Model selection of nested and non-nested item response models using Vuong tests. Multivariate Behavioral Research, 55(5), 664–684. (DOI: 10.1080/00273171.2019.1664280)

Miller, A. P., Merkle, E. C., Galenkamp, H., Stronks, K., Derks, E. M., & Gizer, I. R. (2019). Differential item functioning analysis of the CUDIT and relations with alcohol and tobacco use among men across five ethnic groups: The HELIUS study. Psychology of Addictive Behaviors, 33(8), 697–709. (DOI: 10.1037/adb0000521)

Merkle, E. C., Furr, D., & Rabe-Hesketh, S. (2019). Bayesian comparison of latent variable models: Conditional versus marginal likelihoods. Psychometrika, 84(3), 802–829. (DOI: 10.1007/s11336-019-09679-0)


Wang, T., & Merkle, E. C. (2018). Derivative computations and robust standard errors for linear mixed effects models in lme4. Journal of Statistical Software, 87(c01), 1–16.

Merkle, E. C., & Rosseel, Y. (2018). blavaan: Bayesian structural equation models via parameter expansion. Journal of Statistical Software, 85(4), 1–30.

Volpert-Esmond, H. I., Merkle, E. C., Levsen, M. P., Ito, T. A., & Bartholow, B. D. (2018). Using trial-level data and multilevel modeling to investigate within-task change in event-related potentials. Psychophysiology, 55, e13044. (DOI: 10.1111/psyp.13044)

Merkle, E. C., & Hartman, R. (2018). Weighted Brier score decompositions for topically heterogenous forecasting tournaments. Judgment and Decision Making, 13, 185–201. (Data and code)

Merkle, E. C., & Wang, T. (2018). Bayesian latent variable models for the analysis of experimental psychology data. Psychonomic Bulletin & Review, 25, 256–270. (DOI: 10.3758/s13423-016-1016-7)

Wang, T., Strobl, C., Zeileis, A., & Merkle, E. C. (2018). Score-based tests of differential item functioning via pairwise maximum likelihood estimation. Psychometrika, 83, 132–155. (DOI: 10.1007/s11336-017-9591-8)

Wiedermann, W., Merkle, E. C., & von Eye, A. (2018). Direction of dependence in measurement error models. British Journal of Mathematical and Statistical Psychology, 71, 117–145. (DOI: 10.1111/bmsp.12111)

Volpert-Esmond, H. I., Merkle, E. C., & Bartholow, B. D. (2017). The iterative nature of person construal: Evidence from event-related potentials. Social Cognitive and Affective Neuroscience, 12, 1097–1107. (DOI: 10.1093/scan/nsx048)

Merkle, E. C., Steyvers, M., Mellers, B., & Tetlock, P. E. (2017). A neglected dimension of good forecasting judgment: The questions we choose also matter. International Journal of Forecasting, 33, 817–832. (DOI: 10.1016/j.ijforecast.2017.04.002)

Turner, B. M., Wang, T., & Merkle, E. C. (2017). Factor analysis linking functions for simultaneously modeling neural and behavioral data. NeuroImage, 153, 28–48. (DOI: 10.1016/j.neuroimage.2017.03.044)


Merkle, E. C., You, D., & Preacher, K. J. (2016). Testing non-nested structural equation models. Psychological Methods, 21, 151-163. (DOI: 10.1037/met0000038)

Merkle, E. C., Steyvers, M., Mellers, B., & Tetlock, P. E. (2016). Item response models of probability judgments: Application to a geopolitical forecasting tournament. Decision, 3, 1–19. (DOI: 10.1037/dec0000032)

Merkle, E. (2016). Discussion of the paper "Of quantiles and expectiles: Consistent scoring functions, Choquet representations and forecast rankings" by Ehm, Gneiting, Jordan, and Kruger. Journal of the Royal Statistical Society B, 78, 550–551 (DOI: 10.1111/rssb.12154).

Holmes-Rovner, M., Montgomery, J. S., Rovner, D. R., Scherer, L., Whitfield, J., Kahn, V. C., Merkle, E. C., Ubel, P. A., & Fagerlin, A. (2015). Informed decision making: Assessment of the quality of physician communication about prostate cancer diagnosis and treatment. Medical Decision Making, 35, 999–1009. (DOI: 10.1177/0272989X15597226)

Mellers, B., Stone, E., Atanasov, P., Rohrbaugh, N., Metz, S. E., Ungar, L., Bishop, M., Horowitz, M., Merkle, E., & Tetlock, P. (2015). The psychology of intelligence analysis: Drivers of prediction accuracy in world politics. Journal of Experimental Psychology: Applied, 21, 1–14. (DOI: 10.1037/xap0000040)


Steyvers, M., Wallsten, T. S., Merkle, E. C., & Turner, B. M. (2014). Evaluating probabilistic forecasts with Bayesian signal detection models. Risk Analysis, 34, 435–452. (DOI: 10.1111/risa.12127)

Turner, B. M., Steyvers, M., Merkle, E. C., Budescu, D. V., & Wallsten, T. S. (2014). Forecast aggregation via recalibration. Machine Learning, 95, 261–289. (DOI: 10.1007/s10994-013-5401-4)

Wang, T., Merkle, E. C., & Zeileis, A. (2014). Score-based tests of measurement invariance: Use in practice. Frontiers in Psychology, 5 (438), 1–11.

Merkle, E. C., Fan, J., & Zeileis, A. (2014). Testing for measurement invariance with respect to an ordinal variable. Psychometrika, 79, 569–584. (DOI: 10.1007/s11336-013-9376-7)

Merkle, E. C., & Steyvers, M. (2013). Choosing a strictly proper scoring rule. Decision Analysis, 10, 292–304. (DOI: 10.1287/deca.2013.0280)

Merkle, E. C., & Zeileis, A. (2013). Tests of measurement invariance without subgroups: A generalization of classical methods. Psychometrika, 78, 59–82. (DOI: 10.1007/s11336-012-9302-4)

Shaffer, V. A., Probst, C. A., Merkle, E. C., Arkes, H. R., & Medow, M. A. (2013). Why do patients derogate physicians who use a computer-based diagnostic support system? Medical Decision Making, 33, 108-118. (DOI: 10.1177/0272989X12453501)


Shaffer, V. A., Merkle, E. C., Fagerlin, A., Griggs, J. J., Langa, K. M., & Iwashyna, T. J. (2012). Chemotherapy was not associated with cognitive decline in older adults with breast and colorectal cancer: Findings from a prospective cohort study. Medical Care, 50, 849–855. (DOI: 10.1097/MLR.0b013e31825a8bb0)

Warnaar, D. B., Merkle, E. C., Steyvers, M., Wallsten, T. S., Stone, E. R., Budescu, D. V., Yates, J. F., Sieck, W. R., Arkes, H. R., Argenta, C. F., Shin, Y., & Carter, J. N. (2012). The aggregative contingent estimation system: Selecting, rewarding, and training experts in a wisdom of crowds approach to forecasting. In Proceedings of the 2012 Association for the Advancement of Artificial Intelligence Spring Symposium Series (AAAI Technical Report SS-12-06) (pp. 75–76).

Preacher, K. J., & Merkle, E. C. (2012). The problem of model selection uncertainty in structural equation modeling. Psychological Methods, 17, 1–14. (DOI: 10.1037/a0026804)

Merkle, E. C. (2011). A comparison of imputation methods for Bayesian factor analysis models. Journal of Educational and Behavioral Statistics, 36, 257–276. (DOI: 10.3102/1076998610375833)

Merkle, E. C., & Steyvers, M. (2011). A psychological model for aggregating judgments of magnitude. In J. Salerno, S. J. Yang, D. Nau, & S.-K. Chai (eds.), Social computing and behavioral-cultural modeling 2011 (pp. 236–243). Lecture Notes in Computer Science 6589.

Smithson, M., Merkle, E. C., & Verkuilen, J. (2011). Beta regression finite mixture models of polarization and priming. Journal of Educational and Behavioral Statistics, 36, 804–831. (DOI: 10.3102/1076998610396893)

Merkle, E. C., & Shaffer, V. A. (2011). Binary recursive partitioning: Background, methods, and application to psychology. British Journal of Mathematical and Statistical Psychology, 64, 161–181. (DOI: 10.1348/000711010X503129)

Merkle, E. C., Smithson, M., & Verkuilen, J. (2011). Hierarchical models of simple mechanisms underlying confidence in decision making. Journal of Mathematical Psychology, 55, 57–67. (DOI: 10.1016/


Merkle, E. C. (2010). Calibrating subjective probabilities using hierarchical Bayesian models. In S.-K. Chai, J. J. Salerno, & P. L. Mabry (eds.), Social computing, behavioral modeling, and prediction (SBP) 2010 (pp. 13–22). Heidelberg: Springer.

Merkle, E. C. (2009). The disutility of the hard-easy effect in choice confidence. Psychonomic Bulletin and Review, 16, 204–213. (DOI: 10.3758/PBR.16.1.204)

Merkle, E. C., Sieck, W. R., & Van Zandt, T. (2008). Response error and processing biases in confidence judgment. Journal of Behavioral Decision Making, 21, 428–448. (DOI: 10.1002/bdm.597)

Merkle, E. C., Van Zandt, T., & Sieck, W. R. (2008). Rejoinder: Error in confidence judgments. Journal of Behavioral Decision Making, 21, 453–456. (DOI: 10.1002/bdm.605)

Brown, L. D., Shepherd, M. D., Merkle, E. C., Wituk, S. A., & Meissen, G. (2008). Understanding how participation in a consumer-run organization relates to recovery. American Journal of Community Psychology, 42, 167-178. American Journal of Community Psychology, 42, 167–178. (DOI: 10.1007/s10464-008-9184-x)

Sieck, W. R., Merkle, E. C., & Van Zandt, T. (2007). Option fixation: A cognitive contributor to overconfidence. Organizational Behavior and Human Decision Processes, 103, 68–83. (DOI: 10.1016/j.obhdp.2006.11.001)

Merkle, E. C., & Van Zandt, T. (2006). An application of the Poisson race model to confidence calibration. Journal of Experimental Psychology: General, 135, 391–408. (DOI: 10.1037/0096-3445.135.3.391)