My teaching

Current academic year 2024-2025

• PL5225 Structural Equation Modeling (postgraduate)

Courses taught in previous academic years

• PL1101E Introduction to Psychology (team teaching)
• PL2131 Research and Statistical Methods I (undergraduate)
• PL2132 Research and Statistical Methods II (undergraduate)
• PL5221 Analysis of Psychological Data using GLM (postgraduate)
• PL5222 Multivariate Statistics in Psychology (postgraduate)
• PL5223 Psychometrics and Psychological Testing (postgraduate)
• PL5225 Structural Equation Modeling (postgraduate)

Thesis topics that I may supervise

My research area is quantitative psychology–the statistical modeling of psychological data. A related area is psychometrics.

Sample thesis topics I have supervised

• Do we know why people stay? A meta-analysis of job embeddedness and turnover
• Only the hottest need apply: A meta-analysis investigating the effects of lookism on evaluative workplace outcomes
• A simulation study on the boundary conditions of fitting the Thurstonian IRT model to forced-choice data
• Meta-analytic structural equation modeling: Advancing theories of cognitive vulnerabilities and depression
• Determinants of cashless payment adoption: A meta-anaytic structural equation modelling approach
• What makes them help?: A meta-analysis on factors influencing bystanders behavioural responses in cyberbullying
• A simulation study of the conversion of effect sizes between correlation and standardized mean difference in meta-analysis
• Meta-analysis of evidence for the motivation-facilitation model and confluence model of sexual offending
• A meta-analysis of cognitive behavioural therapy (CBT) for bulimic nervosa (BN) disorder
• A meta-analysis of emotional labor and burnout
• Applying nonlinear time-series analysis to psychological data
• A meta-analysis on the treatment efficacy of psychological intervention for social anxiety disorder (SAD)

Journals related to quantitative methods

• Behavior Research Methods
• British Journal of Mathematical and Statistical Psychology
• Educational and Psychological Measurement
• Journal of Educational and Behavioral Statistics
• Multivariate Behavioral Research
• Organizational Research Methods
• Psychological Methods
• Research Synthesis Methods
• Sociological Methodology
• Sociological Methods and Research
• Structural Equation Modeling: A Multidisciplinary Journal

I am interested in supervising topics in one of the followings areas.

• Meta-analysis
  • Fixed- vs. random-effects models
  • Methods addressing missing covariates
  • Correction for artifacts, e.g., unreliability and range restriction
  • Multivariate meta-analysis
  • Three-level meta-analysis
  • Robust test statistics
• Background readings:
  • Cheung, M. W.-L. (2023). Structural equation modeling (SEM)-based meta-analysis. In R. H. Hoyle (Ed.), Handbook of Structural Equation Modeling} (2nd ed.). New York: Guilford Press.
  • Cheung, M.W.-L. (2018). Computing multivariate effect sizes and their sampling covariance matrices with structural equation modeling: Theory, examples, and computer simulations. Frontiers in Psychology, 9(1387). https://doi.org/10.3389/fpsyg.2018.01387
  • Cheung, M.W.-L. (2015). metaSEM: an R package for meta-analysis using structural equation modeling. Frontiers in Psychology, 5(1521). https://doi.org/10.3389/fpsyg.2014.01521
  • Cheung, M.W.-L. (2014). Modeling dependent effect sizes with three-level meta-analyses: A structural equation modeling approach. Psychological Methods, 19(2), 211–229. https://doi.org/10.1037/a0032968
  • Cheung, M.W.-L. (2013). Multivariate meta-analysis as structural equation models. Structural Equation Modeling: A Multidisciplinary Journal, 20(3), 429–454. https://doi.org/10.1080/10705511.2013.797827
  • Cheung, M.W.-L. (2008). A model for integrating fixed-, random-, and mixed-effects meta-analyses into structural equation modeling. Psychological Methods, 13(3), 182–202. https://doi.org/10.1037/a0013163
• Meta-analytic structural equation modeling (MASEM)
  • Evaluating goodness-of-fit indices in MASEM
  • Applications of MASEM in applied settings
  • Exploring heterogeneity in MASEM
• Background readings:
  • Cheung, M. W.-L. (2021). Meta-analytic structural equation modeling. In Oxford Research Encyclopedia of Business and Management. Oxford University Press. https://doi.org/10.1093/acrefore/9780190224851.013.225
  • Cheung, M.W.-L. (2019). Some reflections on combining meta-analysis and structural equation modeling. Research Synthesis Methods, 10(1), 15–22. https://doi.org/10.1002/jrsm.1321
  • Cheung, M.W.-L. (2014). Fixed- and random-effects meta-analytic structural equation modeling: Examples and analyses in R. Behavior Research Methods, 46(1), 29–40. https://doi.org/10.3758/s13428-013-0361-y
  • Cheung, M.W.-L., & Chan, W. (2005). Meta-analytic structural equation modeling: A two-stage approach. Psychological Methods, 10(1), 40–64. https://doi.org/10.1037/1082-989X.10.1.40
  • Cheung, M.W.-L., & Cheung, S. F. (2016). Random-effects models for meta-analytic structural equation modeling: Review, issues, and illustrations. Research Synthesis Methods, 7(2), 140–155. https://doi.org/10.1002/jrsm.1166
  • Cheung, M.W.-L., & Hafdahl, A.R. (2016). Special issue on meta-analytic structural equation modeling: Introduction from the guest editors. Research Synthesis Methods, 7(2), 112–120. https://doi.org/10.1002/jrsm.1212
  • Cheung, M.W.-L., & Hong, R. Y. (2017). Applications of meta-analytic structural equation modelling in health psychology: examples, issues, and recommendations. Health Psychology Review, 11(3), 265–279. https://doi.org/10.1080/17437199.2017.1343678
  • Valentine, J. C., Cheung, M. W.-L., Smith, E. J., Alexander, O., Hatton, J. M., Hong, R. Y., Huckaby, L. T., Patton, S. C., Pössel, P., & Seely, H. D. (2022). A primer on meta-analytic structural equation modeling: The case of depression. Prevention Science, 23, 346-365. https://doi.org/10.1007/s11121-021-01298-5
• Structural equation modeling
  • Constructing confidence intervals with SEM approach
  • Testing mediating effect
  • Testing moderating effect
  • Latent growth models
• Background readings:
  • Cheung, M.W.-L. (2009). Comparison of methods for constructing confidence intervals of standardized indirect effects. Behavior Research Methods, 41(2), 425–438. https://doi.org/10.3758/BRM.41.2.425
  • Cheung, M.W.-L. (2009). Constructing approximate confidence intervals for parameters with structural equation models. Structural Equation Modeling: A Multidisciplinary Journal, 16(2), 267–294. https://doi.org/10.1080/10705510902751291
  • Cheung, M.W.-L. (2007). Comparison of methods of handling missing time-invariant covariates in latent growth models under the assumption of missing completely at random. Organizational Research Methods, 10(4), 609–634. https://doi.org/10.1177/1094428106295499
  • Cheung, M.W.-L. (2007). Comparison of approaches to constructing confidence intervals for mediating effects using structural equation models. Structural Equation Modeling: A Multidisciplinary Journal, 14(2), 227–246. https://doi.org/10.1080/10705510709336745
  • Cheung, M.W.-L., & Chan, W. (2004). Testing dependent correlation coefficients via structural equation modeling. Organizational Research Methods, 7(2), 206–223. https://doi.org/10.1177/1094428104264024
• Multilevel models in cross-cultural research
  • Multilevel issues in cross-cultural research
  • Structural equivalence between level-1 and level-2 constructs
• Background readings:
  • Cheung, M.W.-L., & Au, K. (2005). Applications of multilevel structural equation modeling to cross-cultural research. Structural Equation Modeling: A Multidisciplinary Journal, 12(4), 598. https://doi.org/10.1207/s15328007sem1204_5
  • Cheung, M.W.-L., Leung, K., & Au, K. (2006). Evaluating multilevel models in cross-cultural research an illustration with social axioms. Journal of Cross-Cultural Psychology, 37(5), 522–541. https://doi.org/10.1177/0022022106290476