Yan Wang

Research Interests

Finite mixture modeling, structural equation modeling, multilevel modeling, Bayesian analysis

Education

• Ph D: Measurement & Research, (2018), University of South Florida - Tampa, Fla.
  Supporting Area: Educational Psychology
  Dissertation/Thesis Title:Covariates in factor mixture modeling: Investigating measurement invariance across unobserved groups
  
  
• MA: Curriculum and Instruction, (2013), Boston College - Boston, Mass.
  
  
• BA: English Literature, (2011), Xiamen University - Xiamen, China
  
  
• BS: Economics, (2011), Xiamen University - Xiamen, China

Selected Awards and Honors

• Teaching Excellence Award (2022) - University of Massachusetts Lowell
• Leslie C. Robbins Dean’s Excellence Award (2016) - University of South Florida
• Conference Presentation Travel Grant (2015) - SouthEast SAS Users Group (SESUG)
• University Graduate Fellowship Award (2013) - University of South Florida
• Dean’s Scholarship (2011) - Boston College
• National Scholarship (2008) - Xiamen University

Selected Publications

• Wang, Y., Kim, E., Joo, S-H., Chun, S., Alamri, A., Lee, P., & Stark, S. (2022). Reconsidering multilevel latent class models: Can level-2 latent classes affect item response patterns?Journal of Experimental Education, 90(1), 158-172. [IF = 2.623; 5-year IF = 3.114]
• Wang, Y., Kim, E., & Yi, Z. (2022). Robustness of latent profile analysis to measurement noninvariance between profiles. Educational and Psychological Measurement, 82(1), 5-28. [IF = 2.821; 5-year IF = 3.279]
• Bugos, J., & Wang, Y. (2022). Piano training enhances executive functions and psychosocial outcomes in aging: Results of a randomized controlled trial. Journal of Gerontology: Psychological Sciences. Advance online publication. [IF = 4.077; 5-year IF = 5.060]
• Joo, S-H., Wang, Y., Ferron, J. M., Moeyaert, M., Beretvas, S. N., & van den Noortgate, W. (2022). Comparison of within- and between-series effect estimates in the meta-analysis of multiple baseline studies. Journal of Educational and Behavioral Statistics, 47(2), 131-166. [IF = 3.288; 5-year IF = 4.387]
• Kim, E., Nguyen, D., *Liu, S., & Wang, Y. (2022). Mixed effects of item parceling on performance of factor mixture modeling. Structural Equation Modeling: A Multidisciplinary Journal, 29(2), 207-217. [IF = 6.125; 5-year IF = 8.372]
• Shen, J., & Wang, Y. (2022). Application of second-order growth mixture modeling (SO-GMM) to longitudinal TBI outcome research: 15-year trajectories of life satisfaction in adolescents and young adults (AYA) as an example. Archives of Physical Medicine and Rehabilitation. Advance online publication. [IF = 3.966; 5-year IF = 3.507]
• Shen, J., Wang, Y., *Kurpad, N., & *Schena, D. (2022). A systematic review on the impact of hot and cool executive functions on pediatric injury risks: A meta-analytic structural equation modeling approach. Prevention Science, 23, 366-377. [IF = 3.103; 5-year IF = 3.361]
• *Yi, Z., Chen, Y.-H., *Yin, Y., *Cheng, K., Wang, Y., *Nguyen, D. T., Pham, T. V., & Kim, E. S. (2022). Brief research report: A comparison of robust tests for homogeneity of variance in factorial ANOVA. Journal of Experimental Education, 90(2), 505-520. [IF = 2.623; 5-year IF = 3.114]
• Wang, Y., Hsu, H.-Y., & Kim, E. (2021). Investigating the impact of covariate inclusion on sample size requirements of factor mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling: A Multidisciplinary Journal, 28(5), 716-724. [IF = 6.125; 5-year IF = 8.372]
• Wang, Y., Kim, E., Ferron, J. M., Dedrick, R., Tan, T. X., & Stark, S. (2021). Testing measurement invariance across unobserved groups: The role of covariates in factor mixture modeling. Educational and Psychological Measurement, 81(1), 61-89. [IF = 2.821; 5-year IF = 3.279]
• Chenane, J. L., Wright, E. M., & Wang, Y. (2021). The effects of police contact and neighborhood context on delinquency and violence. Victims & Offenders, 16(4), 495-518. [IF = 1.789; 5-year IF = 2.373]
• *Jenkins, A., *Thoman, S., Wang, Y., von der Embse, N., Kilgus, S., & Reynolds, F. (2021). Identifying type and evaluating stability of behavioral risk. Psychology in the Schools, 58(11), 2225-2237. [IF = 1.774]
• Ramlackhan, K., & Wang, Y. (2021). Urban school district performance: A longitudinal analysis of achievement. Urban Education. Advance online publication. [IF = 4.382; 5-year IF = 4.655]
• Tan, T. X., Wang, Y., *Hao, S-W, & Li, Y. (2021). Female adopted Chinese-American youth’s sense of exclusion and short-and long-term adjustment. American Journal of Orthopsychiatry, 91(5), 671-681. [IF = 1.769; 5-year IF = 2.629]
• *Yi, Z., Wang, Y., & Tan, T. X. (2021). Evidence for a higher-order ESEM structure of ADHD in a sample of Chinese children. Journal of Psychopathology and Behavioral Assessment, 43, 376-387. [IF = 2.645; 5-year IF = 3.479]
• Zhang, F. F., Wang, Y., McCabe, A., & Fan, H. H. (2021). On referential abilities in personal narratives of Chinese children aged three to six years. Lingua: International Review of General Linguistics, 260, 1-13. [IF = 0.643; 5-year IF = 0.975]
• Kim, E., Wang, Y., & *Liu, S. (2020). The impact of measurement noninvariance on latent change score modeling: A Monte Carlo simulation study. Structural Equation Modeling: A Multidisciplinary Journal, 27(6), 918-930. [IF = 3.638; 5-year IF = 5.993]
• *Pham, T. V., Kromrey, J. D., Chen, Y-H., Kim, E. S., *Nguyen, D. T., & Wang, Y. (2020). ANOVA robust: A SAS macro for parametric tests of mean differences in one-factor ANOVA models. Journal of Statistical Software, Code Snippets, 95(2), 1-16. [IF = 13.642; 5-year IF = 21.629]
• *Nguyen, D. T., Kim, E. S., Wang, Y., *Pham, T. V., Kromrey, J., & Chen, Y-H. (2019). Empirical comparisons of tests for one-factor ANOVA under heterogeneity and non-normality: A Monte Carlo study. Journal of Modern Applied Statistical Methods, 18(2), eP2906.
• Tan, X. T., *Yi, Z., Kamphaus, R., Wang, Y., *Li, Z., & *Cheng, K. (2019). Testing the reliability and construct validity of the Chinese translation of BASC-3-SRP for 12-18-year old youth. Journal of Psychoeducational Assessment, 38(5), 599-612. [IF = 1.256; 5-year IF = 1.626]
• Zhang, F., McCabe, A., Ye, J., Wang, Y., & Li, X. (2019). A developmental study of the narrative components and patterns of Chinese children aged three to six years. Journal of Psycholinguistic Research, 48(2), 477-500. [IF = .655; 5-year IF = .791]
• Wang, Y., *Pham, T. V., *Nguyen, D. T., Kim, E. S., Chen, Y-H., Kromrey, J. D., *Yi, Z., & *Yin, Y. (2018). Evaluating the efficacy of conditional analysis of variance under heterogeneity and non-normality. Journal of Modern Applied Statistical Methods, 17(2), eP2701.
• Kim, E. S. & Wang, Y. (2018). Investigating sources of heterogeneity with 3-step multilevel factor mixture modeling: Beyond testing measurement invariance in cross-national studies. Structural Equation Modeling: A Multidisciplinary Journal, 26(2), 165-181. [IF = 4.426; 5-year IF = 6.742]
• Tan, T. X., Wang, Y., Cheah, C., & Wang, G. H. (2018). Reliability and construct validity of Children's Sleep Habits Questionnaire in Chinese kindergartners. Sleep Health: Journal of the National Sleep Foundation, 4(1), 104-109.
• Wang, Y., Kim, E. S., Dedrick, R., Ferron, J., & Tan, T. X. (2017). A multilevel bifactor approach to construct validation of the mixed-format Students Confident in Mathematics Scale. Educational and Psychological Measurement, 78(2), 253-271.
• Tan, T. X., Wang, Y., & Ruggerio, A. (2017). Childhood adversity and children's academic functioning: Role of parenting stress and neighborhood support. Journal of Child and Family Studies, 26(10), 2742-2752.
• Kim, E. S., Wang, Y. (2017). Class enumeration and parameter recovery of growth mixture modeling and second-order growth mixture modeling in the presence of measurement noninvariance. Frontiers in Psychology,8(1499).
• Wang, Y., Rodriguez de Gil, P., Chen, Y-H., Kromrey, J. D., Kim, E. S., Nguyen, D. T., Pham, T. V., Romano, J. (2017). Comparing the performance of approaches for testing the Homogeneity of Variance assumption in one-factor ANOVA models. Educational and Psychological Measurement,77(2) 305-329.
• Wang, Y., Kim, E.S. (2017). Evaluating model fit and structural coefficient bias: A Bayesian approach to multilevel bifactor model misspecification. Structural Equation Modeling: A Multidisciplinary Journal,24(5) 699-713.
• Kim, E. S., Cao, C., Wang, Y., Nguyen, D. T. (2017). Measurement invariance testing with many groups: A comparison of five approaches. Structural Equation Modeling: A Multidisciplinary Journal,24(4) 524-544.
• St. John Walsh, A., Wesley, K. L., Tan, S. Y., Lynn, C., Wang, Y., O'Leary, K., Rodriguez, C.A., et. al. (2017). Screening for depression among youth with HIV in an integrated care setting. AIDS Care,29(7) 851-857.
• Kim, E. S., Wang, Y., & Kiefer, S. (2017). Cross-level group measurement invariance when groups are at different levels of multilevel data. Educational and Psychological Measurement, 78(6), 973-997.
• Li, I., Chen, Y-H., Wang, Y., Rodriguez de Gil, P., Pham, T. V., Nguyen, D. T., Kromrey, J. D., et. al. (2017). ANOVA_HOV: A SAS macro for testing homogeneity of variance in one-factor ANOVA models. Journal of Modern Applied Statistical Methods, 16(2), 506-539.
• Kim, E. S., Joo, S-H., Lee, P., Wang, Y., & Stark, S. (2016). Measurement invariance testing across between-level latent classes using multilevel factor mixture modeling. Structural Equation Modeling: A Multidisciplinary Journal, 23(6), 870-887

Selected Presentations

• Wang, Y. (2022 April). Incorporating machine learning into factor mixture modeling: identification of covariate interactions to explain population heterogeneity. Paper presented in an invited session for Division D Mini-Grant Awardees at the annual meeting of American Educational Research Association.
• *Karaca, M., Geraci, L., & Wang, Y. (2022 April). Do older adults remember more positive memories when they feel older than their chronological age? Paper presented at the 2022 Cognitive Aging Conference.
• Cao, C., Wang, Y., & Kim, E. (2022 April). Multilevel factor mixture modeling: illustrations for multilevel constructs. Paper presented at the annual meeting of American Educational Research Association.
• Ramlackhan, K. & Wang, Y. (2022 January). Impact of school leadership on student achievement: An equity and social justice framed analysis. Paper presented virtually at the annual convention of the International Congress for School Effectiveness and Improvement.
• Kim, E., Cao, C., *Liu, S., & Wang, Y. (2021 November). Testing measurement invariance over time in intensive longitudinal data: Demonstration of four approaches. Paper presented at the Florida Educational Research Association.
• *Armagan, M., Ho, I., & Wang, Y. (2021 August). Perceptions about refugees and other out-group members in the US: A Latent Class Analysis. Poster presented at the American Psychological Association (APA).
• Wang, Y., Hsu, H.-Y., & Kim, E. (2021 April). Investigating impact of covariate inclusion on sample size requirements of factor mixture modeling. Paper presented at the annual meeting of American Educational Research Association (AERA).
• Wang, Y., Cao, C., & Kim, E. (2021 April). Evaluating performance of factor mixture modeling and MIMIC in measurement invariance testing with multiple covariates. Paper presented at the annual meeting of American Educational Research Association (AERA).
• *Sutherland, K. T., Smalarz, L., Wilford, M. M., Wang, Y., & Pascale, M. (2021 March). Does knowledge of coercive plea offers reduce the tendency to infer guilt from a guilty plea? Poster accepted for presentation at the meeting of the American Psychology-Law Society.

Selected Contracts, Fellowships, Grants and Sponsored Research

• Virtual Reality-based Rehabilitation for Children with Traumatic Brain Injuries (R00 Phase) (2020), Grant - Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)
  Shen, J. (Principal), Wang, Y. (Co-Investigator)