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Publications

List refreshed 2026-11-08.

2026

Zhang, X., Wu, H., Shi, L., & Savalei, V. (2026). Model identification made clear: From conceptual foundations to CFA identification rules [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/pqa6y_v1

Siegel, D., Savalei, V., & Rhemtulla, M. (2026). Nested model comparisons between common factors and composites. Psychological Methods, 31(2), 363–383. https://doi.org/10.1037/met0000806

Li, S., & Savalei, V. (2026). Calculating and interpreting maximal reliability in bifactor models. Multivariate Behavioral Research, 61(3), 469–490. https://doi.org/10.1080/00273171.2025.2612035

Zhang, X., Huang, M., Sun, J., & Savalei, V. (2026). Improving the measurement of the Big Five via alternative formats for the BFI-2. Journal of Personality Assessment, 108(2), 250–271. https://doi.org/10.1080/00223891.2025.2531187

Higgins, W. C., Savalei, V., Polito, V., & Ross, R. M. (2026). Reading the Mind in the Eyes Test scores demonstrate poor structural properties in nine large non-clinical samples. Assessment, 33(2), 204–220. https://doi.org/10.1177/10731911251328604

2025

Li, S., & Savalei, V. (2025). Evaluating statistical fit of confirmatory bifactor models: Updated recommendations and a review of current practice. Psychological Methods. https://doi.org/10.1037/met0000730

Savalei, V., & Huang, M. (2025). Fit indices are insensitive to multiple minor violations of perfect simple structure in confirmatory factor analysis. Psychological Methods. https://doi.org/10.1037/met0000718

Rhemtulla, M., & Savalei, V. (2025). Estimated factor scores are not true factor scores. Multivariate Behavioral Research, 60(3), 598–619. https://doi.org/10.1080/00273171.2024.2444943

2024

Zhang, X., & Savalei, V. (2024). An overview of alternative formats to the Likert format: A comment on Wilson et al. (2022). Psychological Methods, 29(3), 606–612. https://doi.org/10.1037/met0000631

Folk, D. P., & Savalei, V. (2024). Examining the performance of the chi-square difference test when the unrestricted model is slightly misspecified. Behavior Research Methods, 56(7), 6687–6706. https://doi.org/10.3758/s13428-024-02384-6

Savalei, V., Brace, J. C., & Fouladi, R. T. (2024). We need to change how we compute RMSEA for nested model comparisons in structural equation modeling. Psychological Methods, 29(3), 480–493. https://doi.org/10.1037/met0000537

2023

Higgins, W. C., Savalei, V., Polito, V., & Ross, R. M. (2023). Validation of the reading the mind in the eyes test requires an interpretable factor model. Proceedings of the National Academy of Sciences, 120(52), e2303706120. https://doi.org/10.1073/pnas.2303706120

Chen, L., Savalei, V., & Rhemtulla, M. (2023). Pay attention to the ignorable missing data mechanisms! An exploration of their impact on the efficiency of regression coefficients. Multivariate Behavioral Research, 58(6), 1134–1159. https://doi.org/10.1080/00273171.2023.2193600

Zhang, X., Zhou, L., & Savalei, V. (2023). Comparing the psychometric properties of a scale across three Likert and three alternative formats: An application to the Rosenberg Self-Esteem Scale. Educational and Psychological Measurement, 83(4), 649–683. https://doi.org/10.1177/00131644221111402

Zhang, X., & Savalei, V. (2023). New computations for RMSEA and CFI following FIML and TS estimation with missing data. Psychological Methods, 28(2), 263–283. https://doi.org/10.1037/met0000445

2022

Byers‐Heinlein, K., Bergmann, C., & Savalei, V. (2022). Six solutions for more reliable infant research. Infant and Child Development, 31(5). https://doi.org/10.1002/icd.2296

Savalei, V., & Rosseel, Y. (2022). Computational options for standard errors and test statistics with incomplete normal and nonnormal data in SEM. Structural Equation Modeling: A Multidisciplinary Journal, 29(2), 163–181. https://doi.org/10.1080/10705511.2021.1877548

2021

Chen, L., & Savalei, V. (2021). Three sample estimates of fraction of missing information from full information maximum likelihood. Frontiers in Psychology, 12, 667802. https://doi.org/10.3389/fpsyg.2021.667802

Dunn, E. W., Chen, L., Proulx, J. D. E., Ehrlinger, J. M., & Savalei, V. (2021). Can researchers’ personal characteristics shape their statistical inferences? Personality and Social Psychology Bulletin, 47(6), 969–984. https://doi.org/10.1177/0146167220950522

Savalei, V. (2021). Improving fit indices in structural equation modeling with categorical data. Multivariate Behavioral Research, 56(3), 390–407. https://doi.org/10.1080/00273171.2020.1717922

2020

Chen, L., Savalei, V., & Rhemtulla, M. (2020). Two-stage maximum likelihood approach for item-level missing data in regression. Behavior Research Methods, 52(6), 2306–2323. https://doi.org/10.3758/s13428-020-01355-x

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, 32(7), 698–704. https://doi.org/10.1037/pas0000824

Zhang, X., & Savalei, V. (2020). Examining the effect of missing data on RMSEA and CFI under normal theory full-information maximum likelihood. Structural Equation Modeling: A Multidisciplinary Journal, 27(2), 219–239. https://doi.org/10.1080/10705511.2019.1642111

2019

Zhang, X., Tse, W. 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. https://doi.org/10.3389/fpsyg.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. https://doi.org/10.1525/collabra.247

Savalei, V. (2019). A comparison of several approaches for controlling measurement error in small samples. Psychological Methods, 24(3), 352–370. https://doi.org/10.1037/met0000181

2018

Park, J. L., Silveira, M. M., Elliott, M., Savalei, V., & Johnston, C. (2018). Confirmatory factor analysis of the structure of adult ADHD symptoms. Journal of Psychopathology and Behavioral Assessment, 40(4), 573–585. https://doi.org/10.1007/s10862-018-9698-y

Savalei, V. (2018). On the computation of the RMSEA and CFI from the mean-and-variance corrected test statistic with nonnormal data in SEM. Multivariate Behavioral Research, 53(3), 419–429. https://doi.org/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(1), 147. https://doi.org/10.1080/00273171.2017.1405787

2017

Benjamin, D. J., Berger, J. O., Johannesson, M., Nosek, B. A., Wagenmakers, E., Berk, R. A., Bollen, K. A., Brembs, B., Brown, L., Camerer, C. F., Cesarini, D. A., Chambers, C., Clyde, M. A., Cook, T. D., de Boeck, P. A. L., Dienes, Z. P., Dreber, A., Easwaran, K., Efferson, C. M., … Johnson, V. E. (2017). Redefine statistical significance. Nature Human Behaviour, 2(1), 6–10. https://doi.org/10.1038/s41562-017-0189-z

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. https://doi.org/10.3389/fpsyg.2017.00767

Savalei, V., & Rhemtulla, M. (2017). Normal theory two-stage ML estimator when data are missing at the item level. Journal of Educational and Behavioral Statistics, 42(4), 405–431. https://doi.org/10.3102/1076998617694880

Brace, J. C., & Savalei, V. (2017). Type I error rates and power of several versions of scaled chi-square difference tests in investigations of measurement invariance. Psychological Methods, 22(3), 467–485. https://doi.org/10.1037/met0000097

2016

Whillans, A. V., Seider, S. C., Chen, L., Dwyer, R., Novick, S., Gramigna, K. J., Mitchell, B. A., Savalei, V., Dickerson, S. S., & Dunn, E. W. (2016). Does volunteering improve well-being? Comprehensive Results in Social Psychology, 1(1-3), 35–50. https://doi.org/10.1080/23743603.2016.1273647

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. https://doi.org/10.1371/journal.pone.0157795

Zhang, X., & Savalei, V. (2016). Bootstrapping confidence intervals for fit indexes in structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal, 23(3), 392–408. https://doi.org/10.1080/10705511.2015.1118692

Zhang, X., & Savalei, V. (2016). Improving the factor structure of psychological scales. Educational and Psychological Measurement, 76(3), 357–386. https://doi.org/10.1177/0013164415596421

Rhemtulla, M., Savalei, V., & Little, T. D. (2016). On the asymptotic relative efficiency of planned missingness designs. Psychometrika, 81(1), 60–89. https://doi.org/10.1007/s11336-014-9422-0

2015

Chuang, J., Savalei, V., & Falk, C. F. (2015). Investigation of Type I error rates of three versions of robust chi-square difference tests. Structural Equation Modeling: A Multidisciplinary Journal, 22(4), 517–530. https://doi.org/10.1080/10705511.2014.938713

Savalei, V., & Dunn, E. W. (2015). Is the call to abandon p-values the red herring of the replicability crisis? Frontiers in Psychology, 6, 245. https://doi.org/10.3389/fpsyg.2015.00245

Savalei, V., Bonett, D. G., & Bentler, P. M. (2015). CFA with binary variables in small samples: A comparison of two methods. Frontiers in Psychology, 5, 1515. https://doi.org/10.3389/fpsyg.2014.01515

2014

Brosseau-Liard, P. É., & Savalei, V. (2014). Adjusting incremental fit indices for nonnormality. Multivariate Behavioral Research, 49(5), 460–470. https://doi.org/10.1080/00273171.2014.933697

Savalei, V., & Falk, C. F. (2014). Recovering substantive factor loadings in the presence of acquiescence bias: A comparison of three approaches. Multivariate Behavioral Research, 49(5), 407–424. https://doi.org/10.1080/00273171.2014.931800

Savalei, V., & Falk, C. F. (2014). Robust two-stage approach outperforms robust full information maximum likelihood with incomplete nonnormal data. Structural Equation Modeling: A Multidisciplinary Journal, 21(2), 280–302. https://doi.org/10.1080/10705511.2014.882692

Savalei, V. (2014). Understanding robust corrections in structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal, 21(1), 149–160. https://doi.org/10.1080/10705511.2013.824793

Lickel, B. A., Kushlev, K., Savalei, V., Matta, S., & Schmader, T. (2014). Shame and the motivation to change the self. Emotion, 14(6), 1049–1061. https://doi.org/10.1037/a0038235

Yuan, K., & 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. https://doi.org/10.1016/j.jmva.2013.11.006

2013

Savalei, V., & Rhemtulla, M. (2013). The performance of robust test statistics with categorical data. British Journal of Mathematical and Statistical Psychology, 66(2), 201–223. https://doi.org/10.1111/j.2044-8317.2012.02049.x

2012

Brosseau-Liard, P. É., Savalei, V., & Li, L. (2012). An investigation of the sample performance of two nonnormality corrections for RMSEA. Multivariate Behavioral Research, 47(6), 904–930. https://doi.org/10.1080/00273171.2012.715252

Savalei, V. (2012). The relationship between root mean square error of approximation and model misspecification in confirmatory factor analysis models. Educational and Psychological Measurement, 72(6), 910–932. https://doi.org/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: A Multidisciplinary Journal, 19(3), 477–494. https://doi.org/10.1080/10705511.2012.687669

Rhemtulla, M., Brosseau-Liard, P. É., & 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(3), 354–373. https://doi.org/10.1037/a0029315

2011

Falk, C. F., & Savalei, V. (2011). The relationship between unstandardized and standardized alpha, true reliability, and the underlying measurement model. Journal of Personality Assessment, 93(5), 445–453. https://doi.org/10.1080/00223891.2011.594129

Savalei, V. (2011). What to do about zero frequency cells when estimating polychoric correlations. Structural Equation Modeling: A Multidisciplinary Journal, 18(2), 253–273. https://doi.org/10.1080/10705511.2011.557339

2010

Biesanz, J. C., Falk, C. F., & Savalei, V. (2010). Assessing mediational models: Testing and interval estimation for indirect effects. Multivariate Behavioral Research, 45(4), 661–701. https://doi.org/10.1080/00273171.2010.498292

Savalei, V. (2010). Small sample statistics for incomplete nonnormal data: Extensions of complete data formulae and a Monte Carlo comparison. Structural Equation Modeling: A Multidisciplinary Journal, 17(2), 241–264. https://doi.org/10.1080/10705511003659375

Bentler, P. M., & Savalei, V. (2010). Analysis of correlation structures: Current status and open problems. In S. Kolenikov, L. Thombs, & D. Steinley (Eds.), Recent Methodological Developments in Social Science Statistics (pp. 1–36). Wiley. https://doi.org/10.1002/9780470583333.ch1

Savalei, V., & Bentler, P. M. (2010). Structural equation modeling. In I. Weiner & E. Craighead (Eds.), Corsini’s Encyclopedia of Psychology (4th ed., pp. 1714–1716). Wiley. https://doi.org/10.1002/9780470479216.corpsy0953

Savalei, V. (2010). Expected versus observed information in SEM with incomplete normal and nonnormal data. Psychological Methods, 15(4), 352–367. https://doi.org/10.1037/a0020143

2009

Savalei, V., & Yuan, K. (2009). On the model-based bootstrap with missing data: Obtaining a p-value for a test of exact fit. Multivariate Behavioral Research, 44(6), 741–763. https://doi.org/10.1080/00273170903333590

Savalei, V., & Bentler, P. M. (2009). A two-stage approach to missing data: Theory and application to auxiliary variables. Structural Equation Modeling: A Multidisciplinary Journal, 16(3), 477–497. https://doi.org/10.1080/10705510903008238

2008

Savalei, V. (2008). Is the ML chi-square ever robust to nonnormality? A cautionary note with missing data. Structural Equation Modeling: A Multidisciplinary Journal, 15(1), 1–22. https://doi.org/10.1080/10705510701758091

Savalei, V., & Kolenikov, S. (2008). Constrained versus unconstrained estimation in structural equation modeling. Psychological Methods, 13(2), 150–170. https://doi.org/10.1037/1082-989x.13.2.150

2007

Savalei, V., & Bentler, P. M. (2007). A two-stage ML approach to missing data: Theory and application to auxiliary variables. eScholarship. [link]

2006

Sears, D. O., & Savalei, V. (2006). The political color line in America: Many “Peoples of Color” or Black exceptionalism? Political Psychology, 27(6), 895–924. https://doi.org/10.1111/j.1467-9221.2006.00542.x

Savalei, V. (2006). Logistic approximation to the normal: The KL rationale. Psychometrika, 71(4), 763–767. https://doi.org/10.1007/s11336-004-1237-y

Kolenikov, S., Bollen, K. A., & Savalei, V. (2006). Specification tests with Heywood cases. Proceedings of the American Statistical Association.

Savalei, V., & Bentler, P. M. (2006). Structural equation modeling. In R. Grover & M. Vriens (Eds.), The Handbook of Marketing Research: Uses, Misuses, and Future Advances (pp. 330–364). Sage.

2005

Savalei, V. (2005). On asymptotic robustness of NT methods with missing data. eScholarship. [link]

Savalei, V., & Bentler, P. M. (2005). A statistically justified pairwise ML method for incomplete nonnormal data: A comparison with direct ML and pairwise ADF. Structural Equation Modeling: A Multidisciplinary Journal, 12(2), 183–214. https://doi.org/10.1207/s15328007sem1202_1