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Publications and conference presentations

Below is a list of publications and conference presentations by the research team with the acknowledgement of the support of the IES grant.

Publications

  1. Yuan, K.-H., & Zhang, Z. (2024). Modeling Data with Measurement Errors but without Predefined Metrics: Fact versus Fallacy. Journal of Behavioral Data Science, 4(2), 1-28. https://doi.org/10.35566/jbds/yuan
  2. Yuan, K.-H., Ling, L., & Zhang, Z. (accepted). Scale-invariance, equivariance and dependency of structural equation models. Structural Equation Modeling: A Multidisciplinary Journal. https://doi.org/10.1080/10705511.2024.2353168
  3. Yuan, K.-H., Zhang Z., & Wang, L. (in press). Signal-to-Noise Ratio in Estimating and Testing the Mediation Effect: Structural Equation Modeling versus Path Analysis with Weighted Composites. Psychometrika. https://doi.org/10.1007/s11336-024-09975-4
  4. Liu, X., Zhang Z., & Wang, L. (in press). Detecting mediation effects with the Bayes factor: Performance evaluation and tools for sample size determination. Psychological Methods. https://doi.org/10.1037/met0000670
  5. Fang, Y. & Wang, L. (2024). Dynamic Structural Equation Models with Missing Data: Data Requirements on N and T. Structural Equation Modeling: A Multidisciplinary Journal, 5, 891-908. https://doi.org/10.1080/10705511.2023.2287967 ERIC Number: pending
  6. Li, R., & Wang, L. (2024). Investigating weight constraint methods for modeling causal-formative indicators. Behavioral Research Methods, 56, 6485–6497. https://doi.org/10.3758/s13428-024-02365-9 ERIC Number: ED645376
  7. Tong, L., Qu, W., & Zhang, Z. (In press). Comparison of the K1 Rule, Parallel Analysis, and the Bass-Ackward Method on Identifying the Number of Factors in Factor Analysis. Fudan Journal of the Humanities and Social Sciences. https://doi.org/10.1007/s40647-024-00423-2
  8. Hayashi, K., Yuan, K.-H., & Bentler, P. (in press). On the relationship between factor loadings and component loadings when latent traits and specificities are treated as latent factors. Fudan Journal of the Humanities and Social Sciences. https://doi.org/10.1007/s40647-024-00422-3
  9. Qin, X., & Wang, L. (2024). Causal moderated mediation analysis: Methods and software. Behavior Research Methods. 56, 1314–1334. https://doi.org/10.3758/s13428-023-02095-4 ERIC Number: ED645377
  10. Xu, Z., Gao, F., Fa, A., Qu, W., & Zhang, Z. (2024). Statistical Power Analysis and Sample Size Planning for Moderated Mediation Models. Behavior Research Methods, 56, 6130–6149. https://doi.org/10.3758/s13428-024-02342-2 ERIC Number: ED645375
  11. Zhao, S., Zhang, Z., & Zhang, H. (2024). Bayesian Inference of Dynamic Mediation Models for Longitudinal Data. Structural Equation Modeling: A Multidisciplinary Journal, 31(1), 14-26. https://doi.org/10.1080/10705511.2023.2230519 ERIC Number: EJ1431557
  12. Liu, X., Zhang, Z., Valentino, K., & Wang, L. (2024). The impact of omitting confounders in parallel process latent growth curve mediation models: Three sensitivity analysis approaches. Structural Equation Modeling: A Multidisciplinary Journal, 31 (1), 132-150. https://doi.org/10.1080/10705511.2023.2189551 ERIC Number: ED645155
  13. Wyman, A., & Zhang, Z. (2023). API Face Value: Evaluating the Current Status and Potential of Emotion Detection Software in Emotional Deficit Interventions. Journal of Behavioral Data Science, 3(1), 59–69. https://doi.org/10.35566/jbds/v3n1/wyman ERIC Number: ED645163
  14. Zhang, L., Li, X., & Zhang, Z. (2023). Variety and Mainstays of the R Developer Community. R Journal, 15(3), 5-25. https://doi.org/10.32614/RJ-2023-060 ERIC Number: ED645169
  15. Wilcox, K. T., Jacobucci, R., Zhang, Z., & Ammerman, B. A. (2023). Supervised Latent Dirichlet Allocation with Covariates: A Bayesian Structural and Measurement Model of Text and Covariates. Psychological Methods, 28 (5), 1178-1206. https://doi.org/10.1037/met0000541 ERIC Number: ED644986
  16. Liu, X., Wang, L., & Zhang, Z. (2023). Bayesian hypothesis testing of mediation: Methods and the impact of prior odds specifications. Behavioral Research Methods, 55, 1108–1120. https://doi.org/10.3758/s13428-022-01860-1 ERIC Number: ED627293
  17. Xu, Z., Hai, J., Yang, Y., & Zhang, Z. (2023). Comparison of Methods for Imputing Social Network Data. Journal of Data Science, 21(3), 599–618.  https://doi.org/10.6339/22-JDS1045 ERIC Number: ED627304
  18. Deng, L., & Yuan, K.-H. (2023). Which method is more powerful in testing the relationship of theoretical constructs? A meta comparison of structural equation modeling and path analysis with weighted-composites. Behavior Research Methods, 55, 1460–1479. 10.3758/s13428-022-01838-z ERIC Number: ED626847
  19. Yuan, K.-H. (2023). Comments on the article “Marketing or methodology? Exposing the fallacies of PLS with simple demonstrations” and PLS-SEM in general. European Journal of Marketing, 57 (6), 1618-1625(8). https://doi.org/10.1108/EJM-07-2021-0472
  20. Yuan, K.-H., & Deng, L. (2023). A reply to “Structural parameters under partial least squares and covariance-based structural equation modeling: A comment on Yuan and Deng (2021)” by Schuberth, Rosseel, R¨onkk¨o, Trichera, Kline, and Henseler (2023). Structural Equation Modeling, 30(3), 346-348. 10.1080/10705511.2022.2134141
  21. Yuan, K.-H., & Fang, Y. (2023). Replies to comments on “Which method delivers greater signal-to-noise ratio: Structural equation modelling or regression analysis with weighted composites?” by Yuan and Fang (2023). British Journal of Mathematical and Statistical Psychology, 76, 695–704. 10.1111/bmsp.12323
  22. Yuan, K.-H., & Fang, Y. (2023). Which method delivers greater signal-to-noise ratio: Structural equation modeling or regression analysis with weighted composites? British Journal of Mathematical and Statistical Psychology. 76 (3), 646-678. 10.1111/bmsp.12293 ERIC Number: ED645154
  23. Yuan, K-H., & Zhang, Z. (2023). Statistical and psychometric properties of three weighting schemes of the PLS-SEM methodology. In H. Latan, J.F. Hair, & R. Noonan (Eds.), Partial least squares path modeling: Basic concepts, methodological issues, and applications (2nd ed.). Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-031-37772-3_4 ERIC Number: ED627190
  24. Yuan, K.-H., Wen, Y., & Tang, J. (2023). Sensitivity analysis of the weights of the composites under partial least-squares approach to structural equation modeling. Structural Equation Modeling, 30(1), 53–69. 10.1080/10705511.2022.2101460 ERIC Number: ED627193
  25. Mai, Y., Xu, Z., Zhang, Z., & Yuan, K.-H. (2023). An Open Source WYSIWYG Web Application for Drawing Path Diagrams of Structural Equation Models. Structural Equation Modeling: A Multidisciplinary Journal, 30(2), 328-335. https://doi.org/10.1080/10705511.2022.2101460 ERIC Number: ED627194
  26. Xu, Z. (2022). Handling Ignorable and Non-ignorable Missing Data through Bayesian Methods in JAGS. Journal of Behavioral Data Science, 2(2), 99-126. https://doi.org/10.35566/jbds/v2n2/xu ERIC Number: ED645073
  27. Liu, H., Qu, W., Zhang, Z., & Wu, H. (2022). A New Bayesian Structural Equation Modeling Approach with Priors on the Covariance Matrix Parameter. Journal of Behavioral Data Science, 2(2), 1–24. https://doi.org/10.35566/jbds/v2n2/p2 ERIC Number: ED627196
  28. Lu, L., & Zhang, Z. (2022). How to Select the Best Fit Model among Bayesian Latent Growth Models for Complex Data. Journal of Behavioral Data Science, 2(1), 1–24. https://doi.org/10.35566/jbds/v2n1/p2 ERIC Number: pending
  29. Zhang, Z., & Zhang, D. (2021). What is Data Science? An Operational Definition based on Text Mining of Data Science Curricula. Journal of Behavioral Data Science, 1(1), 1–16. https://doi.org/10.35566/jbds/v1n1/p1 ERIC Number: ED616783
  30. Liu, H., & Zhang, Z. (2021). Birds of a Feather Flock Together and Opposites Attract: The Nonlinear Relationship Between Personality and Friendship. Journal of Behavioral Data Science, 1(1), 34–52. https://doi.org/10.35566/jbds/v1n1/p3 ERIC Number: ED616784

Conference Presentations

  1. Zhang, Z. (2024, August). Factor analysis with text data through Universal Sentence Encoder. Paper presented at the 2024 Annual Convention of the American Psychological Association, Seattle, WA.
  2. Zhang, Z. (2024, July). Mediation Analysis with Text Data. Paper presented at the IMPS 2024 Annual Meeting, Prague, Czech.
  3. Zhang, Z. (2024, August). New statistical methods for variable selection and text data analysis. Paper presented at the 2024 Annual Convention of the American Psychological Association, Seattle, WA.
  4. Zhang, Z. (2024, July). New insights in longitudinal data and mediation analysis. Symposium organized at the IMPS 2024 Annual Meeting, Prague, Czech.
  5. Zhang, Z. (2024, July). Introduction to an online app for SEM analysis with text data. Invited talk at the 2024 Annual Meeting of ISDSA, Vienna, Austria.
  6. Zhang, Z. (2024, June). Structural Equation Modeling with Network Data. Invited talk at the 2024 ICSA Applied Statistics Symposium, Nashville, TN.
  7. Zhang, Z. (2024, June). A Two-Stage Method to Utilize Text Information in Structural Equation Modeling. Invited talk at the Beijing Normal University, Beijing, China.
  8. Zhang, Z. (2024, May 17). Structural Equation Modeling with Text Data. Invited talk at the Nanjing University of Posts and Telecommunications, Nanjing, China.
  9. Zhang, Z. (2024, May 15). Prevalence, Influences, and Handling Methods of Non-normal Data. Invited talk at the Tsinghua University, Beijing, China.
  10. Yuan, K.-H., Ling, L., & Zhang, Z. (2024, Feburary,). Scale-Invariance, Equivariance and Dependency of Structural Equation Models. Paper presented at the Quantitaitve Study Group seminar. Notre Dame, IN, USA.
  11. Wang, L., and Li, R. (October 2023). Investigating data quality in daily diary data: A case study. Talk presented at the 2023 Annual Society of Multivariate Experimental Psychology (SMEP) meeting.
  12. Li, R., and Wang, L. (October 2023). An analytical comparison of the cross-lagged, latent growth curve, and latent difference score models for longitudinal mediation analysis. Poster presented at the 21st Annual Society of Multivariate Experimental Psychology (SMEP) Graduate Student Conference, Iowa City, Iowa.
  13. Fang, Y., and Wang, L. (October 2023). A comparison of approaches to modeling intraindividual variability as predictors in longitudinal studies. Poster presented at the 21st Annual Society of Multivariate Experimental Psychology (SMEP) Graduate Student Conference, Iowa City, Iowa.
  14. Xu, Z., Gao, F., Fa, A., Qu, W., & Zhang, Z. (2023, September). Statistical Power Analysis and Sample Size Planning for Moderated Mediation Models. Paper presented at the Quantitaitve Study Group seminar. Notre Dame, IN, USA.
  15. Yuan, K.-H. & Zhang, Z. (2023, September). Modeling Data with Measurement Errors but without Predefined Metrics: Fact vs Fallacy. Paper presented at the Quantitaitve Study Group seminar. Notre Dame, IN, USA.
  16. Wang, L., and *Liu. X. (July 2023). Modeling intraindividual variability as a predictor with longitudinal data: Methods and evaluations. Talk presented at the 2023 Applied Data Science Meeting, Shanghai, China.
  17. Xu, Z., & Zhang, Z. (2023, July). Integrating Structural Equation Modeling with Social Networks. The 2023 Applied Data Science Meeting, Shanghai, China.
  18. Zhang, Z. (2023, July). Statistical power for linear and quadratic growth curve models with ignorable and non-ignorable missing data. The 2023 Applied Data Science Meeting, Shanghai, China.
  19. Yuan, K.-H., & Zhang, Z. (2023, July). Modeling Data with Measurement Errors but without Predefined Metrics: Fact vs Fallacy. The 2023 Applied Data Science Meeting, Shanghai, China.
  20. Tong, L., & Zhang, Z. (2023). Evaluation of the Bass-Ackward Method on Identifying the Number of Factors. The 2023 Applied Data Science Meeting, Shanghai, China.
  21. Zhang, Z. (2023, July). Social Network Analysis in the Framework of Structural Equation Modeling. Invited talk at the Nanjing University of Posts and Telecommunications, Nanjing, China.
  22. Wyman, A. & Zhang, Z. (2024, May). Estimating Emotions: Comparing Cross-Sectional, Repeated-Measure, and Emotion Recognition API Approaches. The 2024 APS Annual Convention. San Francisco, California.
  23. Tong, L. & Zhang, Z. (2023, March).Permutation test of Importance-Weighted Autoencoder for Factor Analysis. Paper presented at the Quantitaitve Study Group seminar. Notre Dame, IN, USA.
  24. Mai Y., Xu, Z., Zhang, Z., & Yuan, K.-H. (2022, October). An open source WYSIWYG web application for drawing path diagrams of structural equation models. Paper presented at the 2022 Annual Meeting of the Society of Multivariate Experimental Psychology (SMEP). Monterey, California, USA.
  25. Yuan, K.-H. & Liu, H. (2022, October). Recent Advancements of Moderation and Mediation Analyses. Paper presented at the 2022 Annual Meeting of the Society of Multivariate Experimental Psychology (SMEP). Monterey, California, USA.
  26. Zhang, Z. (2022, August, Chair). Methods and Applications of Network Science in Psychology. Invited symposium conducted at the 2022 Annual Convention of the American Psychological Association, Minneapolis, MN.
  27. Zhang, Z. (2022, April). Prevalence, Influences, and Handling Methods of Non-normal Data. Invited talk at the University of Southern California
  28. Wyman, A., & Zhang, Z. (2022, October). API Face Value: Enhancing Emotional Deficit Interventions with Emotion Detection Software. Presented at the ninety-third annual conference of the Indiana Association of the Social Sciences, Gary, IN, United States.
  29. Xu, Z., Hai, J., Yang, Y., & Zhang, Z. (May, 2022). Comparison of Methods for Imputing Social Network Data. Paper presented at the 2022 Annual Convention of the American Psychological Association, Minneapolis, MN.
  30. Xu, Z., Hai, J., Yang, Y., & Zhang, Z. (May, 2022). Comparison of Methods for Imputing Social Network Data. Paper presented at the 2022 Annual Meeting of the International Society for Data Science and Analytics, Notre Dame, IN, USA.
  31. Zhang, Z. (May, 2022). Social Network Analysis in the Framework of Structural Equation Modeling. Paper presented at the 2022 Annual Meeting of the International Society for Data Science and Analytics, Notre Dame, IN, USA.
  32. Zhang, Z. (2022, January). A New Sentiment Lexicon for Student Evaluation of Teaching Text Data. IES PI Meeting. 
  33. Zhang, Z. (2021, November). Social Network Analysis in the Framework Of Structural Equation Modeling. Invited talk by Data Analytics Colloquium.
 
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