Our lab's research program consists of three streams: 1) developing new methods for computing point or interval estimates of fit indices in structural equation modelling (SEM); 2) developing new scale formats with better psychometric properties; and 3) improving social science researchers' understanding of statistics by writing a series of tutorial and teaching papers. The first stream is the primary research focus. The second and third streams naturally evolve from the first stream as we discover and address new problems in my research. Our experiences in applied research (e.g., item format projects) give us unique insight into the type of methodological problems that applied researchers encounter, and thus inspire us to conduct methodological research (e.g., SEM fit indices projects) to address these problems. Our methodological research, in turn, makes us realize the misconceptions in the existing literature and thus inspires us to write tutorial and teaching papers to address these misconceptions. Together, with these three research streams, we hope to reach a wide range of audiences and make a substantial impact in both the applied and methodological fields.
Selected Publications
| Zhang, X. & Wu, H. (2024). Investigating Structural Model Fit Evaluation. Structural Equation Modelling, In Print. Zhang, X., & Savalei, V (2023). An overview of alternative formats to the Likert format: A comment on Wilson et al. (2022). Psychological Methods, In Print. Zhang, X. (2023). How to generate missing data for simulation studies. The Quantitative Methods for Psychology, 19, 100-122, https://doi.org/10.20982/tqmp.19.2.p100 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, 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, 263-283. https://doi.org/10.1037/met0000445 Zhang, X., Astivia, O.L.O., Kroc, E., & Zumbo, B.D. (2022). How to think clearly about Central Limit Theorem. Psychological Methods, 28, 1427-1445. https://doi.org/10.1037/met0000448 Zhang, X., & Savalei, V. (2019). Examining the effect of missing data on RMSEA and CFI under the normal theory full information maximum likelihood. Structural Equation Modeling, 27, 219-239. https://doi.org/10.1080/10705511.2019.1642111 Zhang, X., Tse, WW-Y., & 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 Zhang, X., & Savalei, V (2018). Investigating the effect of missing data on the population CFI and RMSEA values. Multivariate Behavioral Research, 53, 147. https://doi.org/10.1080/00273171.2017.1405787 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. (2016a). Bootstrapping confidence intervals for fit indices in structural equation modeling. Structural Equation Modeling, 23, 392-408. https://doi.org/10.1080/10705511.2015.1118692 Zhang, X., & Savalei, V. (2016b). 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. https://doi.org/10.1177/0013164415596421 |