Selected Publications

Data and reprints are linked where available; if no preprint is linked, please contact us and we will be happy to share a copy. 

Submitted Manuscripts: 

Herrera-Bennett, A., & Rhemtulla, M. (under review). Network Replicability & Generalizability: Exploring the Effects of Sampling Variability, Scale Variability, and Node Reliability. Multivariate Behavioral Research. preprint

Wysocki, A., Lawson, K. M., & Rhemtulla, M. (under review). Statistical control requires causal justification. Advances in Methods and Practices for Psychological Science.

Published/In-Press

Borsboom, D., Deserno, M. K., Rhemtulla, M., Epskamp, S., Fried, E. I., McNally, R. J., Robinaugh, D. J., Perugini, M., Dalege, J., Costantini, G., Isvoranu, A.-M., Wysocki, A. C., van Borkulo, C. D., van Bork, R., & Waldorp, L. J. (2021). Network analysis of multivariate data in psychological science. Nature Reviews Methods Primers, 1, 58-75, https://doi.org/10.1038/s43586-021-00055-w.

Sun, J*., Rhemtulla, M., & Vazire, S. (2020). Eavesdropping on missing data: What are university students doing when they miss experience sampling reports? Personality and Social Psychology Bulletin. Advanced online publication. https://doi.org/10.1177/0146167220964639.

Chen, L.*, Savalei, V., & Rhemtulla, M. (2020). Two-stage maximum likelihood approach for item-level missing data in regression. Behavior Research Methods. https://doi.org/10.3758/s13428-020-01355-x.

Johal, S. K.*, & Rhemtulla, M. (2021). Comparing estimation methods for psychometric networks with ordinal data. Psychological Methods. https://doi.org/10.1037/met0000449. preprint

Liu, S., & Rhemtulla, M. (2022). Treating random effects as observed versus latent predictors: The bias variance tradeoff in small samples. British Journal of Mathematical and Statistical Psychology, 75, 151-181.

Luck, S. J., Stewart, A. X., Simmons, A. M., & Rhemtulla, M. (2021). Standardized Measurement Error: A universal metric of data quality for averaged event-related potentials. Psychophysiology, 58:c13793. doi: 10.1111.psyp.13793

Wang, Y. A., & Rhemtulla, M. (2021). Power analysis for parameter estimation in structural equation modeling: A discussion and tutorial. Advances in Methods and Practices for Psychological Science. preprint

Wysocki, A., & Rhemtulla, M. (2021). Incorporating Stability Information into Cross-sectional Estimates. Multivariate Behavioral Research. (Abstract). 

Wysocki, A.,* & Rhemtulla, M. (2019). On penalty parameter selection for estimating network models. Multivariate Behavioral Research, 56, 288-302, doi: 10.1080/00273171.2019.1672516

Wysocki, A.,* & Rhemtulla, M. (2021). Incorporating Stability Information into Cross-sectional Estimates. Multivariate Behavioral Research. (Abstract). 

van Bork, R., Rhemtulla, M., Waldorp, L., Kruis, J., Rezvanifar, S., & Borsboom, D. (2019). Latent variable models and networks: Statistical equivalence and testability. Multivariate Behavioral Research. 

de Bolt, M. C.*, Rhemtulla, M., & Oakes, L. M. (2020). Robust data and power in infant looking time research: Number of infants and number of trials. Infancy, 25(4), 393-419. https://doi.org/10.1111/infa.12337

Rhemtulla, M., van Bork, R., & Borsboom, D. (2020). Worse than measurement error: Consequences of inappropriate latent variable models. Psychological Methods, 20, 30-45. doi:10.1037/met0000220. preprint

Williams, D., Rhemtulla, M., Wysocki, A., & Rast, P. (2019). On Non-Regularized Estimation of Psychological Networks. Multivariate Behavioral Research. preprint

Schott, E., Rhemtulla, M., & Byers-Heinlein, K. (2019). Should I test more babies? Solutions for transparent data peeking. Infant Behavior and Development, 54, 166-176.  preprint

van Bork, R., Wijsen, L., & Rhemtulla, M. (2017). Toward a causal interpretation of the common factor model. Disputatio.

Borsboom, D., Robinaugh, D. J., The Psychosystems Group, Rhemtulla, M., & Cramer, A. O. J. (2018). Robustness and replicability of psychopathology networks. World Psychiatry, 17, 143-144.

Savalei, V., & Rhemtulla, M. (2017). Normal theory two-stage estimator for models with composites when data are missing at the item level. Journal of Educational and Behavioral Statistics, 42, 405-431. pdf data

van Bork, R., Epskamp, S., Rhemtulla, M., Borsboom, D., & van der Maas, H. L. J. (2017). What is the p=factor of psychopathology? Some risks of general factor modeling. Theory and Psychology, 27, 759-773. pdf

Savalei, V., & Rhemtulla, M. (2017). Normal theory GLS estimator for missing data: An application to item-level missing data and a comparison to two-stage ML. Frontiers in Psychology, 8:767. 

Epskamp, S., Rhemtulla, M., & Borsboom, D. (2017). Generalized network psychometrics: Combining network and latent variable models. Psychometrika, 82 (4), 904-927.  arXiv

Rhemtulla, M., & Hancock, D. (2016). Planned missing data designs for educational research. Educational Psychologist, 51, 305-316. pdf

van Bork, R., Rhemtulla, M., & Borsboom, D. (2016). Composites can be causal too. Comment on Möttus (2016). European Journal of Personality, 30, 304-340. pdf

Rhemtulla, M. (2016). Population performance of SEM parceling strategies under measurement and structural model misspecification. Psychological Methods, 21, 348-368. pdf

Borsboom, D., Rhemtulla, M., Dolan, C., Cramer, A. O. J., Scheffer, M., & van der Maas, H. L. J. (2016). Kinds versus continua: A review of psychometric approaches to uncover the structure of psychiatric constructs. Psychological Medicine, 46, 1567-1579.

Rhemtulla, M., Fried, E. I., Aggen, S. H., Tuerlinckx, F., Kendler, K., & Borsboom, D. (2016). Network analysis of substance abuse and dependence symptoms. Drug and Alcohol Dependence, 161, 230-237. http://dx.doi.org/10.1016/j.drugalcdep.2016.02.005 pdf

Rhemtulla, M., van Bork, R., & Borsboom, D. (2015). Calling models with causal indicators measurement models implies more than they can deliver. Measurement, 13, 59-62. pdf

Rhemtulla, M., Savalei, V., & Little, T. D. (2016). On the asymptotic relative efficiency of planned missingness designs. Psychometrika, 81, 60-89. pdf

​Rhemtulla, M., Jia, F., Wu, W., & Little, T. D. (2014). Planned missing designs to optimize the efficiency of latent growth parameter estimates. International Journal of Behavioral Development, 38, 5, 423-434. pdf

Savalei, V., & Rhemtulla, M. (2013). The performance of robust test statistics with categorical data. British Journal of Mathematical and Statistical Psychology, 66, 201-223. pdf

Rhemtulla, M., & Little, T. D. (2012). Planned missing data designs for research in cognitive development. Journal of Cognition and Development, 13, 425-438. pdf

Rhemtulla, M., Brosseau-Liard, P., & Savalei, V. (2012). When can categorical variables be treated as continuous? A comparison of robust continuous and categorical SEM estimation methods under sub-optimal conditions. Psychological Methods, 17, 354-373. pdf

Savalei, V., & Rhemtulla, M. (2012). Teachers Corner: On obtaining estimates of the fraction of missing information from FIML. Structural Equation Modeling, 19, 477-494. pdf

Rhemtulla, M., & Tucker-Drob, E. M. (2012). Gene-by-socioeconomic status interaction on school readiness. Behavior Genetics, 42, 549-558. pdf

Rhemtulla, M., & Tucker-Drob, E. M. (2011). Correlated longitudinal changes across linguistic, achievement, and psychomotor domains in early childhood: Evidence for a global dimension of development. Developmental Science, 14, 1245-1254. pdf

Conference Presentations

Johal, S. K., & Rhemtulla, M. (2021). Evaluating network estimation performance with ordinal data. Poster presented at Association for Psychological Science Conference. Poster

Wysocki, A., & Rhemtulla. M. (2021). Incorporating Stability Information into Cross-sectional Estimates. Poster presented at the annual convention for the Society of Multivariate Experimental Psychology.

Wysocki, A., Lawson, K. M. & Rhemtulla. M. (2021) Statistical Control Requires Causal Justification. Paper presented at the Spring Psychology Conference at the University of California, Davis.

Johal, S. K., & Rhemtulla, M. (2020). Comparing estimation methods for psychometric networks with ordinal data. Poster presented at International Meeting of the Psychometric Society. College Park, Maryland. Poster

Wysocki, A. C. & Rhemtulla, M. (2018) On penalty parameter selection for estimating network models. Paper presented at International Meetings of the Psychometric Society. New York, New York. Powerpoint

​Wysocki, A. C., Williams, D. R., & Rhemtulla, M. (2018). On selection methods in network models. Poster presented at Association for Psychological Science Conference. San Francisco, California.