# 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**:

- Department of Psychology, Faculty of Arts and Social Sciences, National University of Singapore, Singapore 117570
- mikewlcheung (at) nus.edu.sg
- Google Scholar
- ResearcherID
- Scopus
- ResearchGate
- Academia
- Loop
- Publons
- GitHub
- StackExchange
- OpenMx
- Meta-analysis resources

## 2 Research

### 2.1 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.

- Cheung, M. W.-L. (2015). Meta-Analysis: A Structural Equation Modeling Approach. Wiley, Amazon and Google Book.
- A two-day workshop based on my book: SEM-based meta-analysis and MASEM.

### 2.2 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
- 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.

## 4 Teaching

### 4.1 Current academic year 2016-2017

- PL5222 Multivariate Statistics in Psychology (postgraduate)
- 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)

### 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.

- 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-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.

- Becker, B. J. (2009). Model-based meta-analysis. In H. Cooper, L. V. Hedges, & J. C. Valentine (Eds.),

**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.

- Cheung, M.W.-L. (2009). Comparison of methods for constructing confidence intervals of standardized indirect effects.

**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
- 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.

- Cheung, M.W.-L. (2015). metaSEM: An R Package for Meta-Analysis using Structural Equation Modeling.

**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.

- Cheung, M.W.-L., & Au, K. (2005). Applications of multilevel structural equation modeling to cross-cultural research.

## 5 Research tools

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

- Operating system: Linux (Linux Mint, Debian, and Fedora)
- Unix-like environment in Windows: Cygwin
- Statistical environments: R, BUGS, JAGS, and Julia
- Structural equation modeling: OpenMx, and lavaan in R
- Multilevel modeling: nlme, and lme4 in R
- Meta-analysis: metafor, and metaSEM in R
- Generating diagrams in DOT language: Graphviz, and dot2tex
- Synchronizing files among computers: rsync, and Syncthing
- Version control system: Subversion, Git, and GitHub
- Building files automatically: GNU make
- Programming languages: C and Perl
- Preparing documents: LibreOffice with Zotero, LaTeX with Sweave, Pandoc with knitr
- Editors/IDE: Vim, Emacs with ESS and Org mode, RStudio