Mike Cheung

Table of Contents

1 My information


Essentially, all models are wrong, but some are useful. George E. P. Box

I am a quantitative psychologist and an associate professor at Department of Psychology of National University of Singapore (see my curriculum vitae and Research Interview).

Contact information:

2 Research


2.1 new.jpg

2.2 Research interests

My research areas center around structural equation modeling, meta-analysis, and multilevel modeling. My current research interest is to integrate meta-analysis into the SEM. The following figure summarizes my research interests.


2.3 Statistical consultation

I occasionally provide statistical consultation and workshops on the following topics:

  • Structural equation modeling
  • Meta-analysis
  • Multilevel modeling
  • Mediation analysis
  • Longitudinal data analysis
  • Missing data analysis
  • Bootstrap methods
  • General linear models
  • Psychometric methods
  • R statistical platform

The followings are some workshops and talks that I have conducted before. They are provided here for educational and learning purposes. Please send errors and comments to me at mikewlcheung (at) nus.edu.sg.

3 The metaSEM package

4 Teaching

4.1 Current academic year 2015-2016

  • Semester 1
    • On leave
  • Semester 2
    • PL5222 Multivariate Statistics in Psychology
    • PL5225 Structural Equation Modeling

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)

4.2 Thesis topics that I may supervise

My research area is quantitative psychology–the statistical modeling of psychological data. If you are planning to study quantitative psychology, I suggest you to learn R, an open source statistical environment. You may get familiar with the area of quantitative psychology by reading a few recent issues in the following journals.

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

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:
    • Becker, B. J. (2009). Model-based meta-analysis. In H. Cooper, L. V. Hedges, & J. C. Valentine (Eds.), The handbook of research synthesis and meta-analysis (2nd ed., pp. 377-395). New York: Russell Sage Foundation.
    • Cheung, M.W.-L. (2014). Fixed- and random-effects meta-analytic structural equation modeling: Examples and analyses in R. Behavior Research Methods, 46 29-40.
    • Cheung, M.W.-L., & Chan, W. (2009). A two-stage approach to synthesizing covariance matrices in meta-analytic structural equation modeling. Structural Equation Modeling, 6, 28-53.
    • Cheung, M.W.-L., & Chan, W. (2005). Meta-analytic structural equation modeling: A two-stage approach. Psychological Methods, 10, 40-64.
    • Cheung, M.W.-L., & Chan, W. (2005). Classifying correlation matrices into relatively homogeneous subgroups: A cluster analytic approach. Educational and Psychological Measurement, 65, 954-979.

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, 425-438.
    • Cheung, M.W.-L. (2009). Constructing approximate confidence intervals for parameters with structural equation models. Structural Equation Modeling, 16, 267-294.
    • 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, 609-634.
    • Cheung, M.W.-L. (2007). Comparison of approaches to constructing confidence intervals for mediating effects using structural equation models. Structural Equation Modeling, 14, 227-246.
    • Cheung, M.W.-L., & Chan, W. (2004). Testing dependent correlation coefficients via structural equation modeling. Organizational Research Methods, 7, 206-223.
    • MacKinnon, D.P., Fairchild, A.J., Fritz, M.S. (2007). Mediation analysis. Annual Review of Psychology, 58, 593 -614.


  • 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
  • Background readings:
    • Cheung, M.W.-L. (2015). metaSEM: An R Package for Meta-Analysis using Structural Equation Modeling. Frontiers in Psychology, 5 (1521). http://journal.frontiersin.org/Journal/10.3389/fpsyg.2014.01521/abstract.
    • Cheung, M.W.-L. (2014). Modeling dependent effect sizes with three-level meta-analyses: A structural equation modeling approach. Psychological Methods, 19, 211-229.
    • Cheung, M.W.-L. (2013). Multivariate meta-analysis as structural equation models. Structural Equation Modeling, 20, 429-454.
    • Cheung, M.W.-L. (2013). Implementing restricted maximum likelihood estimation in structural equation models. Structural Equation Modeling, 20, 157-167.
    • Cheung, M.W.-L. (2010). Fixed-effects meta-analyses as multiple-group structural equation models. Structural Equation Modeling, 17, 481-509.
    • Cheung, M.W.-L. (2008). A model for integrating fixed-, random-, and mixed-effects meta-analyses into structural equation modeling. Psychological Methods, 13, 182-202.
    • Cheung, M.W.-L., Ho, R.C.M., Lim, Y., & Mak, A. (2012). Conducting a meta-analysis: basics and good practices. International Journal of Rheumatic Diseases, 15, 129-135.
    • Hedges, L.V., & Vevea, J.L. (1998). Fixed- and random-effects models in meta-analysis. Psychological Methods, 3, 486-504.
    • Raudenbush, S.W. (2009). Analyzing effect sizes: random effects models. In H. M. Cooper, L. V. Hedges, & J. C. Valentine (Eds.), The handbook of research synthesis and meta-analysis (2nd ed., pp. 295-315). New York: Russell Sage Foundation.

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, 12, 598-619.
    • 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, 522-541.
    • Klein, K.J., Dansereau, F., & Hall, R.J. (1994). Levels issues in theory development, data collection, and analysis. Academy of Management Review, 19, 195-229.
    • van de Vijver, F.J.R., & Leung, K. (1997). Methods and data analysis for cross-cultural research. Thousand Oaks, CA: Sage.
    • van de Vijver, F.J.R., & Poortinga, Y.H. (2002). Structural equivalence in multilevel research. Journal of Cross-Cultural Psychology, 33, 141-156.

5 Research tools

5.1 Open-source software (free as in free speech and in free beer) that I find useful in my work:

5.2 Proprietary software that I occasionally use in my work:

Author: Mike W.-L. Cheung

Created: 2015-11-13 Fri 09:28