Mike Cheung's Homepage

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, Research Interview, ResearcherID, Google Scholar, Microsoft Academic Search, and Mendeley).

Contact information:

  • note.gif Department of Psychology, Faculty of Arts and Social Sciences, National University of Singapore, Singapore 117570
  • phone.gif Tel: (65) 6516-3702; Fax: (65) 6773-1843
  • mailbox.gif mikewlcheung (at) nus.edu.sg

2 new.jpg


2.1 Cheung, M. W.-L. (forthcoming in 2015). Meta-Analysis: A Structural Equation Modeling Approach. Wiley.

2.2 Special issue on meta-analytic structural equation modeling

  1. Research Synthesis Methods
  2. Call for papers
  3. Deadline for submission: 31 August 2014

3 Research

3.1 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.


3.2 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

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.

4 The metaSEM package

5 Teaching

5.1 Current academic year 2014-2015

  • Semester 1
    • PL2132 Research and Statistical Methods II
  • 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)
  • 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)

5.2 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. If you are looking for thesis/dissertation topics, I suggest to read 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. (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. (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.

6 Research tools

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

Mailing lists and discussion forums that I occasionally participate in:

Author: Mike W.-L. Cheung

Created: 2014-12-02 Tue 15:27

Emacs (Org mode 8.2.10)