We are interested in quantitative methods in social research. Currently, our lab is developing different methods for the analysis of complex effects (e.g., mediated moderation) in path analysis and structural equation modeling. This is an important topic in psychometrics because in reality, the relationship among the variables are often more complicated than expected and standard models are therefore unable to describe the underlying psychological process adequately.

To develop better research tools for applied researchers, investigate into research topics in quantitative psychology, and provide education to research students.

Address:	Room 338, Wong Foo Yuan Building, The Chinese University of Hong  Kong
Phone:	3943 4382
Email:	vs@psy.cuhk.edu.hk

Meta-analysis is becoming very popular in behavioral and medical research. We are interested in using the method in applied research to solve real problems.

So, S. H.-W., Siu, N. Y.-F., Wong, H.-L., Chan, W., & Garety, P. A. (2016).  ‘Jumping to conclusions’ data-gathering bias in psychosis and other psychiatric disorders — Two meta-analyses of comparisons between patients and healthy individuals.  Clinical Psychology Review, 46, 151-167.

The chi-square difference test plays a key role in multiple-group structural equation modeling (SEM) for testing invariance.  We argue that the traditional approach of using the chi-square test for comparing two nested models sequentially would fail to control either type I or type II errors.  Analysis and example show why chi-square-difference tests are easily misused in the context of multi-group analysis, and Monte Carlo results support the analysis.  To overcome this issue, our study further proposes a test of equivalence as an alternative.

Yuan, K.-H., & Chan, W. (2016).  Measurement invariance via multigroup SEM: Issues and solutions with chi-square-difference tests.  Psychological Methods, 21, 405-426.

Yuan, K.-H., Chan, W., Marcoulides, G. A., & Bentler, P. M. (2016).  Assessing structural equation models by equivalence testing with adjusted fit indices.  Structural Equation Modeling, 23, 319-330.

Different indices have been proposed for measuring reliability of a test, including coefficient alpha, coefficient omega, the greatest lower bound reliability, and others. Among these, the coefficient alpha has been most widely used.  However, it is known that coefficient alpha under-estimates the true reliability unless the items are tau-equivalent, and coefficient omega is deemed as a practical alternative to coefficient alpha in estimating measurement reliability of the total score.  To see if the difference between alpha and omega is practically ignorable, we developed a method to examine the difference of coefficient alpha and omega statistically.

The R code for testing the difference between reliability coefficients alpha and omega (Deng & Chan, 2017)

Deng, L., & Chan, W. (2017).  Testing the difference between reliability coefficients alpha and omega.  Educational and Psychological Measurement, 77, 185-203.

• An SAS/IML program for evaluating the statistical significance of congruence coefficients with Procrustes rotation (Chan, Ho, Leung, Chan, & Yung, 1999)
• A Fortran program for performing the exact unconditional test for linear trend in dose-response studies (Tang, Chan, & Chan, 2000)