We are living in an information-overloaded society. There are tons of academic papers published every year, we must summarize and integrate these findings before making sense of them. Meta-analysis is the de facto standard in many fields to integrate research findings. Many of us believe that meta-analysis can only synthesize simple effect sizes, such as standardized mean difference and correlation coefficient. This presentation gives an overview of how we can integrate effect sizes from simple to complex models.
Prof Mike Cheung received his PhD degree in Psychology from the Chinese University of Hong Kong. He is currently a Full Professor at the Department of Psychology, the National University of Singapore. His research area is quantitative methods, including structural equation modeling, meta-analysis, and multilevel modeling. His primary research topic is the integration of meta-analysis and structural equation modeling. He has published over 70 articles in international journals and one book titled “Meta-Analysis: A Structural Equation Modeling Approach.” He is an Associate Editor of Research Synthesis Methods and Neuropsychology Review, and in the editorial boards of Psychological Bulletin, Journal of Management, Health Psychology Review, and Methods in Psychology. See http://mikewlcheung.github.io for his profile.
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