Mike W-L Cheung

National University of Singapore | NUS · Department of Psychology

The increasing availability of individual participant data (IPD) in the social sciences offers new possibilities to synthesize research evidence across primary studies. Two-stage IPD meta-analysis represents a framework that can utilize these possibilities. While most of the methodological research on two-stage IPD meta-analysis focused on its perf...

Redundancy analysis (RA) is a multivariate method that maximizes the mean variance of a set of criterion variables explained by a small number of redundancy variates (i.e., linear combinations of a set of predictor variables). However, two challenges exist in RA. First, inferential information for the RA estimates might not be readily available. Se...

A recent meta-analytical paper (Bierwiaczonek & Kunst, 2021) sparked a controversy by demonstrating that cross-sectional associations between acculturation and adaptation are weak and heterogenous, and approach zero when assessed over time. Some responses criticized the paper by arguing that small but robust effects can make a real-life difference...

The extent and nature of cognitive impairment in brief psychotic disorder remains unclear, being rarely studied unlike schizophrenia. The present study hence sought to directly compare the visual cognitive dysfunction and its associated brain networks in brief psychotic disorder and schizophrenia. Data from picture completion (a complex visual task...

Objectives: Autoimmune encephalitis (AE) is a neurological disorder caused by autoimmune attack on cerebral proteins. Experts currently recommend staged immunotherapeutic management, with first-line immunotherapy followed by second-line immunotherapy if response to first-line therapy is inadequate. Meta-analysis of the evidence base may provide hi...

Meta-analytic structural equation modeling (MASEM) is an increasingly popular technique in management and organizational psychology. MASEM refers to fitting structural equation models (such as path models or factor models) to meta-analytic data. The meta-analytic data generally consists of correlations across the variables in the path or factor mod...

Objective. According to the theory of planned behavior, individuals are more likely to act on their behavioral intentions, and report intentions aligned with their attitudes and subjective norm, when their perceived behavioral control (PBC) is high. We tested these predictions meta-analytically by estimating the moderating effect of PBC on the atti...

A correction to this paper has been published: 10.1007/s11336-021-09764-3

Background Automated magnetic resonance imaging (MRI) volumetry is a promising tool to evaluate regional brain volumes in dementia and especially Alzheimer's disease (AD). Purpose To compare automated methods and the gold standard manual segmentation in measuring regional brain volumes on MRI across healthy controls, patients with mild cognitive i...

The increasing availability of individual participant data (IPD) in the social sciences offers new possibilities to synthesize research evidence across primary studies. Two-stage IPD meta-analysis represents a framework that can utilize these possibilities. While most of the methodological research on two-stage IPD meta-analysis focused on its perf...

This article describes source data from a systematic review and meta-analysis of electroencephalography (EEG) and magnetoencephalography (MEG) studies investigating functional connectivity in idiopathic generalized epilepsy. Data selection, analysis and reporting was performed according to PRISMA guidelines. Eligible studies for review were identif...

Objective In systemic lupus erythematosus (SLE), disease activity and glucocorticoid (GC) exposure are known to contribute to irreversible organ damage. We aimed to examine the association between GC exposure and organ damage occurrence. Methods We conducted a literature search (PubMed (Medline), Embase and Cochrane January 1966–October 2021). We...

Objectives This meta-analytic study examined average effect sizes across studies for the association between dispositional mindfulness and gratitude, dispositional mindfulness and emotional intelligence, and emotional intelligence and gratitude. The study also tested a model positing emotional intelligence as a path linking mindfulness and gratitud...

For idiopathic generalized epilepsies (IGE), brain network analysis is emerging as a biomarker for potential use in clinical care. To determine whether people with IGE show alterations in resting-state brain connectivity compared to healthy controls, and to quantify these differences, we conducted a systematic review and meta-analysis of EEG and ma...

Posttraumatic stress disorder (PTSD) researchers have increasingly used psychological network models to investigate PTSD symptom interactions, as well as to identify central driver symptoms. It is unclear, however, how generalizable such results are. We have developed a meta-analytic framework for aggregating network studies while taking between-st...

In this paper, we show how the methods of systematic reviewing and meta-analysis can be used in conjunction with structural equation modeling to summarize the results of studies in a way that will facilitate the theory development and testing needed to advance prevention science. We begin with a high-level overview of the considerations that resear...

Missing data is a common occurrence in confirmatory factor analysis (CFA). Much work had evaluated the performance of different techniques when all observed variables were either continuous or ordinal. However, few have investigated these techniques when observed variables are a mix of continuous and ordinal variables. This study investigated the p...

A growing number of publications focus on estimating Gaussian graphical models (GGM, networks of partial correlation coefficients). At the same time, generalizibility and replicability of these highly parameterized models are debated, and sample sizes typically found in datasets may not be sufficient for estimating the underlying network structure....

Aims A mediator is a variable that explains the underlying mechanism between an independent variable and a dependent variable. The indirect effect indicates the effect from the predictor to the outcome variable via the mediator. In contrast, the direct effect represents the predictor's effort on the outcome variable after controlling for the mediat...

Meta-analytic structural equation modeling (MASEM) refers to fitting structural equation models (such as path models or factor models) to eta-analytic data. Currently, fitting MASEMs may be challenging for researchers that are not accustomed to working with R software and packages. Therefore, we developed webMASEM; a web application for MASEM. This...

Structural equation modeling (SEM) and meta-analysis are two popular techniques in the behavioral, medical, and social sciences. They have their own research communities, terminologies, models, software packages, and even journals. This chapter introduces SEM-based meta-analysis, an approach to conduct meta-analyses using the SEM framework. By conc...

The current study examines the relationship between bilingual children's dual language experience (i.e. language input, language output and vocabulary proficiency), and their social-emotional and behavioral skills. Data were analysed from 805 Singaporean bilingual preschoolers (ages 4; 1–5; 8 years), who are learning English and either Mandarin (n...

Missing data is a common occurrence in confirmatory factor analysis (CFA). Much work had evaluated the performance of different techniques when all observed variables were either continuous or ordinal. However, few have investigated these techniques when observed variables are a mix of continuous and ordinal variables. This study investigated the p...

Early childhood is a crucial period for human development that has long-term implications for one’s life trajectories. During the years before formal schooling, brain size and structures, as well as cognitive abilities, undergo rapid development. Children’s cognitive abilities develop by leaps and bounds and show great malleability. Cognitive devel...

Post-traumatic stress disorder (PTSD) researchers have increasingly used psychological network models to investigate PTSD symptom interactions, as well as to identify central driver symptoms. It is unclear, however, how generalizable such results are. We have developed a meta-analytic framework for aggregating network studies while taking between-s...

A mediator is a variable that explains the underlying mechanism between an independent variable and a dependent variable. The indirect effect indicates the effect from the predictor to the outcome variable via the mediator. In contrast, the direct effect represents the effect of the predictor on the outcome variable after controlling for the mediat...

Meta-analytic Structural Equation Modeling (MASEM) refers to fitting structural equation models (such as path models or factor models) to meta-analytic data. Currently, fitting MASEMs may be challenging for researchers that are not accustomed to working with R software and packages. Therefore, we developed webMASEM; a web application for MASEM. Thi...

Meta-analysis and structural equation modeling (SEM) are two popular statistical models in the social, behavioral, and management sciences. Meta-analysis summarizes research findings to provide an estimate of the average effect and its heterogeneity. Moderators may be used to explain the heterogeneity in the data. On the other hand, SEM includes se...

This quantitative review systematically integrates the antecedents and outcomes of psychological ownership (PO) and examines its incremental validity and explanatory power compared with two other forms of workplace attachment (i.e., organizational commitment and organizational identification). Across 141 studies published over 20 years, our meta-an...

A growing number of publications focuses on estimating Gaussian graphical models (GGMs, networks of partial correlation coefficients). At the same time, generalizibility and replicability of these highly parameterized models are debated, and sample sizes typically found in datasets may not be sufficient for estimating the underlying network structu...

Machine learning methods have become very popular in diverse fields due to their focus on predictive accuracy, but little work has been conducted on how to assess the replicability of their findings. We introduce and adapt replication methods advocated in psychology to the aims and procedural needs of machine learning research. In Study 1, we illus...

Conventional meta-analytic procedures assume that effect sizes are independent. When effect sizes are not independent, conclusions based on these conventional procedures can be misleading or even wrong. Traditional approaches, such as averaging the effect sizes and selecting one effect size per study, are usually used to avoid the dependence of the...

Objective The association between cigarette smoking and the risk of systemic lupus erythematosus (SLE) remains controversial. Additionally, the impact of the change of smokers’ demographics on the risk of development of SLE over time was not formally addressed. We aimed to examine the association between cigarette smoking and the risk of SLE by per...

Meta-analytic structural equation modeling (MASEM) is a statistical technique to fit hypothesized models on the combined data of multiple independent studies. Lv and Maeda (2019) present a simulation study on the performance of three fixed-effects correlation-based MASEM methods with varying levels of data missing completely at random (MCAR). In th...

Meta-analytic structural equation modeling (MASEM) is an increasingly popular meta-analytic technique that combines the strengths of meta-analysis and structural equation modeling. MASEM facilitates the evaluation of complete theoretical models (e.g., path models or factor analytic models), accounts for sampling covariance between effect sizes, and...

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Conventional meta-analytic procedures assume that effect sizes are independent. When effect sizes are non-independent, conclusions based on these conventional models can be misleading or even wrong. Traditional approaches, such as averaging the effect sizes and selecting one effect size per study, are usually used to remove the dependence of the ef...

This study investigates the mechanism between ethical leadership and employees' organizational citizenship behavior (OCB) through a justice perspective. We propose that interactional justice serves as a conduit that induces employees' OCB in response to leaders' ethical behaviors. The explanatory powers of interactional justice on two forms of OCB...

Meta-analytic structural equation modeling (MASEM) is an increasingly popular meta-analytic technique that combines the strengths of meta-analysis and structural equation modeling. MASEM facilitates the evaluation of complete theoretical models (e.g. path models or factor models), accounts for sampling covariance between effect sizes, and provides...

Machine learning tools are increasingly used in social sciences and policy fields due to their increase in predictive accuracy. However, little research has been done on how well the models of machine learning methods replicate across samples. We compare machine learning methods with regression on the replicability of variable selection, along with...

Meta‐analysis and structural equation modeling (SEM) are two of the most prominent statistical techniques employed in the behavioral, medical, and social sciences. They each have their own well‐established research communities, terminologies, statistical models, software packages, and journals (Research Synthesis Methods and Structural Equation Mod...

In the social and behavioral sciences, it is recommended that effect sizes and their sampling variances be reported. Formulas for common effect sizes such as standardized and raw mean differences, correlation coefficients, and odds ratios are well known and have been well studied. However, the statistical properties of multivariate effect sizes hav...

The mathematical connection between canonical correlation analysis (CCA) and covariance structure analysis was first discussed through the Multiple Indicators and Multiple Causes (MIMIC) approach. However, the MIMIC approach has several technical and practical challenges. To address these challenges, a comprehensive COSAN modeling approach is propo...

This study used the latent profile transition analysis (LPTA) to analyze whether emotional labor profiles change across time and how these profiles relate to occupational well-being (i.e., job satisfaction, quality of work life, psychological distress, and work–family conflict). A total of 155 full-time Chinese employees completed the questionnaire...

Meta-analytic structural equation modeling (MASEM) is becoming increasingly popular for testing theoretical models from a pool of correlation matrices in management and organizational studies. One limitation of the conventional MASEM approaches is that the proposed structural equation models are only tested on the average correlation matrix. It rem...

Meta-analytic structural equation modeling (MASEM) is a statistical technique to pool correlation matrices and test structural equation models on the pooled correlation matrix. In Stage 1 of MASEM, correlation matrices from independent studies are combined to obtain a pooled correlation matrix, using fixed- or random-effects analysis. In Stage 2, a...

Internet gaming disorder (IGD) has been viewed by scholars as (a) a pathology that co-occurs with psychological problems (comorbidity hypothesis), (b) maladaptive coping with abundant interpersonal problems (interpersonal impairment hypothesis), and (c) deficient self-regulation with the underlying motive to restore psychosocial well-being (dilutio...

Meta-analysis and structural equation modeling (SEM) are two of the most prominent statistical techniques employed in the behavioral, medical, and social sciences. They each have their own well-established research communities, terminologies, statistical models, software packages, and journals (Research Synthesis Methods and Structural Equation Mod...

Meta-analytic structural equation modeling (MASEM) is increasingly applied to advance theories by synthesizing existing findings. MASEM essentially consists of two stages. In Stage 1, a pooled correlation matrix is estimated based on the reported correlation coefficients in the individual studies. In Stage 2, a structural model (such as a path mode...

Meta-analytic structural equation modeling (MASEM) is becoming increasingly popular for testing theoretical models from a pool of correlation matrices in management and organizational studies. One limitation of the conventional MASEM approaches is that the proposed structural equation models are only tested on the average correlation matrix. It rem...

To develop a consensus on the definition and measurement of Internet gaming disorder (IGD), several recent studies have used the DSM-5's proposed criteria for IGD as the basis in scale construction. This study contributes to this emerging consensus by developing and validating a new Chinese Internet Gaming Disorder Scale (C-IGDS) based on the DSM-5...

A number of novel problematic behaviors have emerged in the information technology era, and corresponding addictions have been proposed for some of these behaviors. Scholars have speculated that a common factor may underlie these information technology addictions, but this theoretical notion has yet to be tested empirically. The present study teste...

Vividness is an aspect of consciousness related to mental imagery and prospective episodic memory. Despite being harshly criticized in the past for failing to demonstrate robust correlations with behavioral measures, currently this construct is attracting a resurgent interest in cognitive neuroscience. Therefore, an updated examination of the valid...

The Looming Maladaptive Style Questionnaire (LMSQ) is a self-report measure designed to assess the looming cognitive style, a tendency to interpret threats as rapidly approaching and increasing in magnitude. To date, no systematic evaluation on the psychometric properties of the LMSQ across diverse cultural contexts has been done. In the present re...

Most individuals identified as ultra high-risk for psychosis (UHR) do not develop frank psychosis. They continue to exhibit subthreshold symptoms, or go on to fully remit. Prior work has shown that the volume of CA1, a subfield of the hippocampus, is selectively reduced in the early stages of schizophrenia. Here, we aimed to determine whether patte...

Purpose The purpose of this paper is to illustrate how international business (IB) researchers can benefit from meta-analytic structural equation modeling (MASEM) by introducing a statistically rigorous approach (i.e. two-stage meta-analytic structural equation modeling or TSSEM) and comparing it with a conventional approach (i.e. the univariate-r...

Health locus of control (HLOC) refers to beliefs regarding how one's health is influenced by oneself, others, or fate. This meta-analysis investigated whether three HLOC dimensions (internality/I-HLOC, powerful others/P-HLOC, chance/C-HLOC) were related to both specific health behaviours and global health appraisal, and whether these relationships...

Meta-analysis is widely accepted as the preferred method to synthesize research findings in various disciplines. This paper provides an introduction to when and how to conduct a meta-analysis. Several practical questions, such as advantages of meta-analysis over conventional narrative review and the number of studies required for a meta-analysis, a...

Meta-analytic structural equation modeling (MASEM) combines the techniques of meta-analysis and structural equation modeling for the purpose of synthesizing correlation or covariance matrices and fitting structural equation models on the pooled correlation or covariance matrix. Both fixed-effects and random-effects models can be defined in MASEM. R...

Big data is a field that has traditionally been dominated by disciplines such as computer science and business, where mainly data-driven analyses have been performed. Psychology, a discipline in which a strong emphasis is placed on behavioral theories and empirical research, has the potential to contribute greatly to the big data movement. However,...

Statistical methods play an important role in behavioral, medical and social sciences. Two recent statistical advances are structural equation modeling (SEM) and meta-analysis. SEM is used to test hypothesized models based on substantive theories, which can be path, confirmatory factor analytic, or full structural equation models. Meta-analysis is...

Cheung, M. W.-L. (in press). Meta-analysis: Fixed effect analysis. In M. Allen (Ed.), The SAGE Encyclopedia of Communication Research Methods. Sage.

Paper accepted for publication in Research Synthesis Methods

Presents a novel approach to conducting meta-analysis using structural equation modeling. Structural equation modeling (SEM) and meta-analysis are two powerful statistical methods in the educational, social, behavioral, and medical sciences. They are often treated as two unrelated topics in the literature. This book presents a unified framework on...

This chapter discusses two advanced topics in the structural equation modeling (SEM)-based meta-analysis. The first topic is the restricted (or residual) maximum likelihood (REML) estimation. We compare the pros and cons of the maximum likelihood (ML) estimation against the REML estimation. A graphical model is proposed to represent the transformat...

Half-Title Page Title Page Copyright Page Dedication Table of Contents Preface Acknowledgments List of abbreviations List of figures List of tables

This chapter begins by introducing the basic ideas of the fixed-effects model. The extension to the random-effects model is then introduced. Conceptual and statistical differences between the fixed- and the random-effects models are discussed. By including study characteristics as moderators, we extend the random-effects model to the mixed-effects...

This chapter extends univariate meta-analysis to a multivariate meta-analysis that allows researchers to analyze more than one effect size per study. We begin the chapter by discussing different types of dependence in the effect sizes and the need for a multivariate meta-analysis to handle multiple effect sizes. Several conventional approaches to c...

This chapter covers meta-analytic structural equation modeling (MASEM), a technique that combines meta-analysis and structural equation modeling (SEM) to synthesize correlation or covariance matrices and to fit structural equation models on the pooled correlation (covariance) matrix. We begin this chapter with a discussion on the need to synthesize...

Most users of structural equation modeling (SEM) are familiar with at least one popular SEM package, such as, Mplus, LISREL or EQS. This chapter illustrates how to analyze the meta-analytic models introduced in previous chapters with Mplus. We show how Mplus can be used to conduct univariate, multivariate, and three-level meta-analyses using a tran...

This chapter reviews selected topics in structural equation modeling (SEM) that are relevant to the SEM-based meta-analysis. It provides a quick introduction to SEM for those who are less familiar with the techniques. This chapter begins by introducing three different model specifications---path diagrams, equations and matrix specification. It then...

This chapter reviews issues of non-independent effect sizes in a meta-analysis and some of the conventional approaches used to address these issues. A three-level meta-analysis is then put forward to address the problem of dependence in the effect sizes. A model and analyses of three-level meta-analysis are introduced. This chapter also seeks to ex...

This chapter covers how to estimate common effect sizes and their sampling variances in a meta-analysis. We begin by introducing the formulas to compute effect sizes and their sampling variances for a univariate meta-analysis. Formulas to calculate effect sizes for a multivariate meta-analysis are then introduced. This chapter then introduce a gene...

The metaSEM package provides functions to conduct univariate, multivariate, and three-level meta-analyses using a structural equation modeling (SEM) approach via the OpenMx package in the R statistical platform. It also implements the two-stage SEM approach to conducting fixed- and random-effects meta-analytic SEM on correlation or covariance matri...

This paper illustrates how management researchers can benefit from a two- stage meta-analytic structural equation modeling (TSSEM) method. We address this by introducing this method in stages as well as presenting an empirical example that employs this method to examining a strategic decision on an international investment with Transaction Cost Eco...

Cognitive theories of psychopathology posit that maladaptive patterns of cognitions confer elevated risks to individuals in the development of psychological disorders. This meta-analysis examined the extent to which six cognitive vulnerabilities associated with depression (i.e., pessimistic inferential style, dysfunctional attitudes, and ruminative...

This multinational study simultaneously tested three prominent hypotheses—universal disposition, cultural relativity, and livability—that explained differences in subjective well-being across nations. We performed multilevel structural equation modeling to examine the hypothesized relationships at both individual and cultural levels in 33 nations....

Meta-analysis is an indispensable tool used to synthesize research findings in the social, educational, medical, management, and behavioral sciences. Most meta-analytic models assume independence among effect sizes. However, effect sizes can be dependent for various reasons. For example, studies might report multiple effect sizes on the same constr...

Multivariate meta-analysis has become increasingly popular in the educational, social, and medical sciences. It is because the outcome measures in a meta-analysis can involve more than one effect size. This article proposes 2 mathematically equivalent models to implement multivariate meta-analysis in structural equation modeling (SEM). Specifically...

Meta-analytic structural equation modeling (MASEM) combines the ideas of meta-analysis and structural equation modeling for the purpose of synthesizing correlation or covariance matrices and fitting structural equation models on the pooled correlation or covariance matrix. Cheung and Chan (Psychological Methods 10:40-64, 2005b, Structural Equation...

Structural equation modeling (SEM) is now a generic modeling framework for many multivariate techniques applied in the social and behavioral sciences. Many statistical models can be considered either as special cases of SEM or as part of the latent variable modeling framework. One popular extension is the use of SEM to conduct linear mixed-effects...

Meta-analysis is a statistical method to compare and combine effect sizes from a pool of relevant empirical studies. It is now a standard approach to synthesize research findings in many disciplines, including medical and healthcare research. This paper is the third paper of a mini-series introducing systematic review and meta-analysis. First, comm...

To assess systemically with meta-analysis the trend of survival and its determinants, which are hindering further improvement of survival of patients with systemic lupus erythematosus (SLE) over the past 5 decades. Retrospective, cross-sectional, and prospective observational studies addressing survival and damage in SLE patients published between...

In this study, the authors tested four cultural models—independence, interdependence, conflict, and integration—that describe the hypothesized relationships between dimensions of self-construal and components of subjective well-being among individualistic and collectivistic countries. Collectivistic countries that have undergone rapid socioeconomic...