SELECTED Publications
Data and preprints are linked where available; if no preprint is linked, please contact us for a copy.
Submitted manuscripts:
van Bork, R. Marsman, M., Rhemtulla, M., Epskamp, S., Kruis, J., & Borsboom, D. (under review). Common effect models: Positive or negative manifold?
Wang, Y. A., & Rhemtulla, M. (under revision). Power analysis for parameter estimation in structural equation
modeling: A discussion and tutorial. Advances in Methods and Practices for Psychological Science.
de Bolt, M. C.*, Rhemtulla, M., & Oakes, L. M. (under revision). Robust data and power in infant
looking time research: Number of infants and number of trials. Infancy
Wysocki, A., & Rhemtulla, M. (under revision). On penalty parameter selection for estimating network models. Multivariate Behavioral Research.
van Bork, R., Rhemtulla, M., Waldorp, L., Kruis, J., Rezvanifar, S., & Borsboom, D. (under revision). Latent variable models and networks: Statistical equivalence and testability. Multivariate Behavioral Research.
Published / In press :
Rhemtulla, M., van Bork, R., & Borsboom, D. (in press). Worse than measurement error: Consequences of inappropriate latent variable models. Psychological Methods. preprint
Williams, D., Rhemtulla, M., Wysocki, A., & Rast, P. (in press). 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. (in press). 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
Wysocki, A. C. & Rhemtulla, M. 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.
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
van Bork, R. Marsman, M., Rhemtulla, M., Epskamp, S., Kruis, J., & Borsboom, D. (under review). Common effect models: Positive or negative manifold?
Wang, Y. A., & Rhemtulla, M. (under revision). Power analysis for parameter estimation in structural equation
modeling: A discussion and tutorial. Advances in Methods and Practices for Psychological Science.
de Bolt, M. C.*, Rhemtulla, M., & Oakes, L. M. (under revision). Robust data and power in infant
looking time research: Number of infants and number of trials. Infancy
Wysocki, A., & Rhemtulla, M. (under revision). On penalty parameter selection for estimating network models. Multivariate Behavioral Research.
van Bork, R., Rhemtulla, M., Waldorp, L., Kruis, J., Rezvanifar, S., & Borsboom, D. (under revision). Latent variable models and networks: Statistical equivalence and testability. Multivariate Behavioral Research.
Published / In press :
Rhemtulla, M., van Bork, R., & Borsboom, D. (in press). Worse than measurement error: Consequences of inappropriate latent variable models. Psychological Methods. preprint
Williams, D., Rhemtulla, M., Wysocki, A., & Rast, P. (in press). 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. (in press). 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
Wysocki, A. C. & Rhemtulla, M. 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.
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