Publications

In Press

  • Chen, L., & Savalei, V. (accepted). Two-stage maximum likelihood approach for item-level missing data in regression. Behavior Research Methods.

2020

  • Savalei, V. (2020). Improving fit indices in structural equation modeling with categorical data. Multivariate Behavioral Research. Advance online publication. doi: 10.3389/fpsycg.2019.01286
  • Normand, S., Mikami, A. Y., Savalei, V., & Guiet, J. (2020).A Multiple Indicators Multiple Causes (MIMIC) Model of Friendship Quality and Comorbidities in Children with Attention-Deficit/Hyperactivity Disorder. Psychological Assessment. Advance online publication. doi: 10.1037/pas0000824
  • Zhang, X., & Savalei, V. (2020). Examining the effect of missing data on RMSEA and CFI under the normal theory full-information maximum likelihood. Structural Equation Modeling, 27, 219-239. doi: 10.1080/10705511.2019.1642111

2019

  • Savalei, V. (2019). A comparison of several approaches for controlling measurement error in small samples. Psychological Methods, 24, 352-370. doi: 10.1037/met0000181
  • Zhang, X., Tse, W., & Savalei, V.(2019). Improved properties of the Big Five Inventory and the Rosenberg Self-Esteem Scale in the Expanded format relative to the Likert format. Frontiers in Psychology, 10:1286. doi: 10.3389/fpsycg.2019.01286
  • Savalei, V., & Reise, S. P. (2019). Don't Forget the Model in Your Model-based Reliability Coefficients: A Reply to McNeish (2018). Collabra: Psychology, 5(1) 36. doi: 10.1037/met0000181

2018

  • Savalei, V. (2018). On the computation of the RMSEA and its confidence interval to accompany the mean-and-variance corrected test statistic with nonnormal data. Multivariate Behavioral Research, 53, 419-429. doi: 10.1080/00273171.2018.1455142
  • Zhang, X. & Savalei, V. (2018). Investigating the effect of missing data on the population CFI and RMSEA values. Multivariate Behavioral Research, 53, 147-147. doi: 10.1080/00273171.2017.1405787
  • Savalei, V. (2018). A comparison of several approaches for controlling measurement error in small samples. Psychological Methods, 24, 352-370. doi: 10.1037/met0000181
  • Park, J. L., Silveira, M., Elliot, M., Savalei, V., & Johnston, C. (2018). Confirmatory factor analysis of the factor structure of adult ADHD symptoms. Journal of Psychopathology and Behavioral Assessment, 40, 573-583. doi: 10.1007/s10862-018-9698-y

2017

  • Brace, J., & Savalei, V. (2017). Type I Error rates and power of scaled chi-square difference tests in investigations of measurement invariance. Psychological Methods. 22, 467-485. doi: 10.1037/met0000097
  • 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. doi: 10.3102/1076998617694880
  • 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. (2017). Does volunteering increase well-being? Comprehensive Results in Social Psychology, 1, 35-50. doi: 10.1080/23743603.2016.1273647

2016

  • 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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2015

  • 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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2014

  • 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

2013

  • 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

2012

  • 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). When can categorical variables be treated as continuous? A comparison of robust continuous and categorical SEM estimation methods under suboptimal conditions. 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

2011

  • 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

2010

  • 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

2009

  • 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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2008

  • 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