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Publications with MplusAutomation

lalarabbit edited this page Jun 13, 2015 · 7 revisions

Papers that cite MplusAutomation


  1. Narayanan, A. (2012). A review of eight software packages for structural equation modeling. The American Statistician, 66(2), 129-138. DOI:10.1080/00031305.2012.708641

  2. Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1-36. URL:

  3. van de Schoot, R., Hoijtink, H. Hallquist, M. N., & Boelen, P. A. (2012). Bayesian Evaluation of Inequality-Constrained Hypotheses in SEM Models Using Mplus, Structural Equation Modeling: A Multidisciplinary Journal, 19:4, 593-609, DOI: 10.1080/10705511.2012.713267


  1. Marshall, G. N., Schell, T. L., & Miles, J. N. (2013). A multi-sample confirmatory factor analysis of PTSD symptoms: what exactly is wrong with the DSM-IV structure?. Clinical psychology review, 33(1), 54-66. doi: 10.1016/j.cpr.2012.10.004

  2. Passalacqua, N. V., Zhang, Z., & Pierce, S. J. (2013). Sex determination of human skeletal populations using latent profile analysis. American journal of physical anthropology, 151(4), 538-543. doi:10.1002/ajpa.22295

  3. van de Schoot, R., Verhoeven, M., & Hoijtink, H. (2013). Bayesian evaluation of informative hypotheses in SEM using M plus: A black bear story. European Journal of Developmental Psychology, 10(1), 81-98. DOI: 10.1080/17405629.2012.732719

  4. Wang, Z. (2013). Examining big-fish-little-pond-effects across 49 countries: a multilevel latent variable modelling approach. Educational Psychology, (ahead-of-print), 1-24. DOI: 10.1080/01443410.2013.827155


  1. Bei, B. (2014). Sleep, mood, and cognitive vulnerability in adolescents: a naturalistic study over restricted and extended sleep opportunities.

  2. Brown, L. A., Wiley, J. F., Wolitzky‐Taylor, K., Roy‐Byrne, P., Sherbourne, C., Stein, M. B., ... & Craske, M. G. (2014). Changes in self-efficacy and outcome expectancy as predictors of anxiety outcomes from the CALM study. Depression and Anxiety. DOI: 10.1002/da.22256

  3. Bryant, C., Bei, B., Gilson, K. M., Komiti, A., Jackson, H., & Judd, F. (2014). Antecedents of Attitudes to Aging: A Study of the Roles of Personality and Well-being. The Gerontologist, doi:10.1093/geront/gnu041

  4. Chen, J., Choi, J., Weiss, B. A., & Stapleton, L. (2014). An Empirical Evaluation of Mediation Effect Analysis With Manifest and Latent Variables Using Markov Chain Monte Carlo and Alternative Estimation Methods. Structural Equation Modeling: A Multidisciplinary Journal, 21(2), 253-262. doi: 10.1080/10705511.2014.882688

  5. Djukic, M., Kovner, C. T., Brewer, C. S., Fatehi, F., & Greene, W. H. (2014). Exploring Direct and Indirect Influences of Physical Work Environment on Job Satisfaction for Early‐Career Registered Nurses Employed in Hospitals. Research in Nursing & Health. doi: 10.1002/nur.21606

  6. Feng, G. C. (2014). Estimating intercoder reliability: a structural equation modeling approach. Quality & Quantity, 1-15. DOI: 10.1007/s11135-014-0034-7

  7. Foldnes, N., Hagtvet, K. A. (2014). The choice of product indicators in latent variable interaction models: Post hoc analyses. Psychological Methods, 19(3), 444-457. doi: 10.1037/a0035728

  8. Guenole N., & Brown, A. (2014). The Consequences of Ignoring Measurement Invariance for Path Coefficients in Structural Equation Models. Front. Psychol. 5:980. doi: 10.3389/fpsyg.2014.00980

  9. Johnson, A. R., van de Schoot, R., Delmar, F., & Crano, W. D. (2014). Social Influence Interpretation of Interpersonal Processes and Team Performance Over Time Using Bayesian Model Selection. Journal of Management. doi: 10.1177/0149206314539351

  10. Koch, T., Schultze, M., Eid, M., & Geiser, C. (2014). A longitudinal multilevel CFA-MTMM model for interchangeable and structurally different methods. Frontiers in Psychology, 5. doi: 10.3389/fpsyg.2014.00311

  11. Morgan, Grant B. Mixed Mode Latent Class Analysis: An Examination of Fit Index Performance for Classification. Structural Equation Modeling. ahead-of-print (2014): 1-11.

  12. Schweig, J. (2014). Multilevel Factor Analysis by Model Segregation New Applications for Robust Test Statistics. Journal of Educational and Behavioral Statistics, 39(5), 394-422. DOI: 10.3102/1076998614544784

  13. Thoemmes, F., & Rose, N. (2014). A Cautious Note on Auxiliary Variables That Can Increase Bias in Missing Data Problems. Multivariate Behavioral Research, 49(5), 443-459. DOI: 10.1080/00273171.2014.931799

  14. Toman, A. (2014). Robust confirmatory factor analysis based on the forward search algorithm. Statistical Papers, 55(1), 233-252. DOI: 10.1007/s00362-013-0525-y

  15. Zerwas, S., Holle, A. V., Watson, H., Gottfredson, N., & Bulik, C. M. (2014). Childhood anxiety trajectories and adolescent disordered eating: Findings from the NICHD study of early child care and youth development. International Journal of Eating Disorders. DOI: 10.1002/eat.22318


  1. Bei, B., Wiley, J. F., Allen, N. B., & Trinder, J. (2015). A cognitive vulnerability model of sleep and mood in adolescents under restricted and extended sleep opportunities. Sleep, 38(3), 453-461. DOI: 10.5665/sleep.45081

  2. Bei, B., Ong, J., Rajaratnam, S. M. W., & Manber, R. (2015) Chronotype and improved sleep quality independently predict depressive symptom reduction after group cognitive behavioral therapy for insomnia. Journal of Clinical Sleep Medicine. ahead-of-print.

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