Working Paper

  • Savalei, V., & Rhemtulla, M. (2016). Normal theory GLS estimator for missing data: An application to item-level missing data and a comparison to two-stage ML. Unpublished Manuscript.
  • Savalei, V. (2016). A comparison of several approaches for controlling measurement error in small samples. Unpublished Manuscript.
  • Sears, D. O., Savalei, V., & Brace, J. (2016). The even-handedness bias in evaluations of racial and ethnic groups. Unpublished Manuscript.

In Press

  • Brace, J., & Savalei, V. (in press). Type I Error rates and power of scaled chi-square difference tests in investigations of measurement invariance. Psychological Methods.
  • Savalei, V., & Rhemtulla, M. (in press). Normal theory two-stage estimator for models with composites when data are missing at the item level. Journal of Educational and Behavioral Statistics.
  • Whillans, A. V., Seider, S. C., Chen, L., Dwyer, R. J., Novick, S., Graminga, K. J., Mitchell, B. A., Savalei, V., Dickerson, S. S., & Dunn, E. W. (in press). Does volunteering increase well-being? Comprehensive Results in Social Psychology.


  • Rhemtulla, M., Savalei, V., & Little, T. (2016). On the asymptotic relative efficiency of planned missingness designs. Psychometrika, 81, 60-89. doi: 10.1007/s11336-014-9422-0
  • Zhang, X., & Savalei, V. (2016). Bootstrapping confidence intervals for fit indices in structural equation modeling. Structural Equation Modeling, 23, 392-408. doi: 10. 1080/10705511.2015.1118692
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  • Zhang, X., & Savalei, V. (2016). Improving the factor structure of psychological scales: The Expanded format as an alternative to the Likert scale format. Educational and Psychological Measurement, 76, 357-386. doi: 10.1177/0013164415596421
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  • Zhang., X., Noor, R., & Savalei, V. (2016). Examining the effect of reverse worded items on the factor structure of the Need for Cognition scale. PloS ONE 11(6): e0157795. doi: 10.1371/journal.pone.0157795
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  • Chuang, J., Savalei, V., & Falk, C. (2015). Investigation of Type I error rate of three versions of robust chi-square difference tests. Structural Equation Modeling, 22 , 517-530. doi: 10.1080/10705511.2014.938713
  • Savalei, V., & Dunn, E. (2015). Is the call to abandon p-values the red herring of the replicability crisis? Frontiers in Psychology, 6 , 2015. doi: 10.3389/fpsyg.2015.00245
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  • Savalei, V., Bonett, D. G., & Bentler, P. M. (2015). CFA in binary variables in small samples: A comparison of two methods. Frontiers in Psychology, 5 , 1515. doi: 10.3389/fpsyg.2014.01515
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  • Savalei, V. (2014). Understanding robust corrections in structural equation modeling. Structural Equation Modeling, 21, 149-160. doi: 10.1080/10705511.2013.824793
  • Savalei, V., & Falk, C. (2014). Robust two-stage approach outperforms robust FIML with incomplete nonnormal data. Structural Equation Modeling, 21, 280-302. doi: 10.1080/10705511.2014.882692
  • Yuan, K.-H., & Savalei, V. (2014). Consistency, bias and efficiency of the normal-distribution-based MLE: The role of auxiliary variables. Journal of Multivariate Analysis, 124, 353-370. doi: 10.1016/j.jmva.2013.11.006
  • Lickel, B., Kushlev, K., Savalei, V., Matta, S., & Schmader, T. (2014). Self-conscious emotions and motivation to change the self. Emotion, 14, 1049-1061. doi: 10.1037/a0038235
  • Savalei, V., & Falk, C. (2014). Recovering substantive factor loadings in the presence of acquiescence bias: A comparison of three approaches. Multivariate Behavioral Research, 49, 407-424. doi: 10.1080/00273171.2014.931800
  • Brosseau-Liard, P. E., & Savalei, V. (2014). Adjusting relative fit indices for nonnormality. Multivariate Behavioral Research, 49, 460-470. doi: 10.1080/00273171.2014.933697


  • Savalei, V., & Rhemtulla, M. (2013). The performance of robust test statistics with categorical data. British Journal of Mathematical and Statistical Psychology, 66, 201-223. doi: 10.1111/j.2044-8317.2012.02049.x


  • Brosseau-Liard, P., Savalei, V., & Li, L. (2012). An investigation of the sample performance of two non-normality corrections for RMSEA. Multivariate Behavioral Research, 47, 904-930. doi: 10.1080/00273171.2012.715252
  • Rhemtulla, M., Brosseau-Liard, P., & Savalei, V. (2012). How many categories is enough to treat data as continuous? A comparison of robust continuous and categorical SEM estimation methods under a range of non-ideal situations. Psychological Methods, 17, 354-373. doi: 10.1037/a0029315
  • Savalei, V. (2012). The relationship between RMSEA and model misspecification in CFA models. Educational and Psychological Measurement, 72, 910-932. doi: 10.1177/0013164412452564
  • Savalei, V., & Rhemtulla, M. (2012). On obtaining estimates of the fraction of missing information from full information maximum likelihood. Structural Equation Modeling, 19, 477-494. doi: 10.1080/10705511.2012.687669


  • Falk, C., & Savalei, V. (2011). The relationship between unstandardized and standardized alpha, true reliability, and the underlying measurement model. Journal of Personality Assessment, 93, 445-453. doi: 10.1080/00223891.2011.594129
  • Savalei, V. (2011). What to do about zero frequency cells when estimating polychoric correlations? Structural Equation Modeling, 18, 253-273. doi: 10.1080/10705511.2011.557339


  • Biesanz, J. C., Falk, C., & Savalei, V. (2010). Inferences and estimation for indirect effects: Missing data, non-normality, and sample size. Multivariate Behavioral Research, 45, 661-701. doi: 10.1080/00273171.2010.498292
  • Savalei, V. (2010). Expected vs. observed information in SEM with incomplete normal and nonnormal data. Psychological Methods, 15, 352-367. doi: 10.1037/a0020143
  • Savalei, V. (2010). Small sample statistics for incomplete nonnormal data: extensions of complete data formulae and a Monte Carlo comparison. Structural Equation Modeling, 17, 245-268. doi: 10.1080/10705511003659375


  • Savalei, V., & Bentler, P. M. (2009). A two-stage ML approach to missing data: Theory and application to auxiliary variables. Structural Equation Modeling, 16, 477-497. doi: 10.1080/10705510903008238
  • Savalei, V., & Yuan, K.-H. (2009). On the model-based bootstrap with missing data: Obtaining a p-value for a test of exact fit. Multivariate Behavioral Research, 44, 741-763. doi: 10.1080/00273170903333590
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  • Savalei, V. (2008). Is the ML chi-square ever robust to nonnormality? A cautionary note with missing data. Structural Equation Modeling, 15, 1-22. doi: 10.1080/10705510701758091
  • Savalei, V., & Kolenikov, S. (2008). Constrained vs. unconstrained estimation in structural equation modeling. Psychological Methods, 13, 150-170. doi: 10.1037/1082-989X.13.2.150