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Understanding MASEM vs. Univariate Meta-Analysis in Social Research

Compare MASEM and Univariate Meta-Analysis methods in social research to understand their differences, advantages, and applications. Learn through real data analysis examples.

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Understanding MASEM vs. Univariate Meta-Analysis in Social Research

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  1. Univariate Meta-Analysis vs. Meta-Analytic Structural Equation Modeling Tessa van den Berg, University of AmsterdamSuzanne Jak, University of AmsterdamMachteld Hoeve, University of AmsterdamVeroni Eichelsheim, Netherlands Institute for the Study of Crime and Law Enforcement (NSCR)

  2. Meta-Analysis • Integrate research findings  summary effect size (Glass, 1976) • Univariate meta-analysis • 1 association between 2 variables • Meta-Analytic Structural Equation Modeling (MASEM) • Combination of meta-analysis and SEM (Viswesvaran & Ones, 1995) MASEM vs. Univariate Meta-Analysis

  3. SEM • Investigates set of variables at once • Model based on theory • Model fit • Regression coefficients • Controlling for other variables in the model • MASEM • Answers questions that univariate meta-analysis cannot answer • Primary studies don’t need to include all associations • Can answer RQ’s that were not explored before MASEM vs. Univariate Meta-Analysis

  4. Research Question • Differences between univariate meta-analysis and MASEM • Advantages and disadvantages of both methods MASEM vs. Univariate Meta-Analysis

  5. Application MASEM vs. Univariate Meta-Analysis

  6. Project • Former meta-analysis k = 161 (Hoeve, Dubas, Eichelsheim, Van Der Laan, Smeenk, & Gerris, 2009) Subset: • k = 88 • N = 154,176 MASEM vs. Univariate Meta-Analysis

  7. Methods • Univariate meta-analysis • Summary effect sizes (pooled correlation) • R package metafor • Random effects model • MASEM: Two Stage SEM approach(Cheung, 2014) • R package metaSEM • Random effects model MASEM vs. Univariate Meta-Analysis

  8. Methods • MASEM • Step 1: Pooled correlation matrix • Step 2: Fit hypothesized model to matrix MASEM vs. Univariate Meta-Analysis

  9. Methods • MASEM • Step 1: Pooled correlation matrix • Step 2: Fit hypothesized model to matrix MASEM vs. Univariate Meta-Analysis

  10. Methods • MASEM • Step 1: Pooled correlation matrix • Step 2: Fit hypothesized model to matrix MASEM vs. Univariate Meta-Analysis

  11. Results – Univariate Meta-Analysis k = 6, r = −0.02 [−0.04; 0.01] Support Parental delinquency Parental delinquency Authoritarian control k = 2, r = 0.08 [0.01; 0.15] Parental delinquency k = 6, r = −0.03 [−0.06; 0.001] Behavioral control k = 1, r = 0.04 [−0.01; 0.09] Parental delinquency Psychological control k = 2, r = −0.07 [−0.11; −0.04] Parental delinquency Indirect parenting MASEM vs. Univariate Meta-Analysis

  12. Results – Univariate Meta-Analysis k = 6, r = −0.02 [−0.04; 0.01] Support Parental delinquency Parental delinquency Authoritarian control k = 2, r = 0.08 [0.01; 0.15] Parental delinquency k = 6, r = −0.03 [−0.06; 0.001] Behavioral control k = 1, r = 0.04 [−0.01; 0.09] Parental delinquency Psychological control k = 2, r = −0.07 [−0.11; −0.04] Parental delinquency Indirect parenting MASEM vs. Univariate Meta-Analysis

  13. Results – Univariate Meta-Analysis Juvenile delinquency k = 19, r = 0.19 [0.13; 0.25] Parental delinquency k = 39, r = −0.15 [−0.19; −0.11] Support Juvenile delinquency Authoritarian control Juvenile delinquency k = 10, r = 0.15 [0.10; 0.20] Juvenile delinquency Behavioral control k = 39, r = −0.16 [−0.21; −0.12] Juvenile delinquency k = 7, r = 0.08 [0.02; 0.14] Psychological control Indirect parenting Juvenile delinquency k = 25, r = −0.18 [−0.25; −0.11] MASEM vs. Univariate Meta-Analysis

  14. Results – Univariate Meta-Analysis Juvenile delinquency k = 19, r = 0.19 [0.13; 0.25] Parental delinquency k = 39, r = −0.15 [−0.19; −0.11] Support Juvenile delinquency Authoritarian control Juvenile delinquency k = 10, r = 0.15 [0.10; 0.20] Juvenile delinquency Behavioral control k = 39, r = −0.16 [−0.21; −0.12] Juvenile delinquency k = 7, r = 0.08 [0.02; 0.14] Psychological control Indirect parenting Juvenile delinquency k = 25, r = −0.18 [−0.25; −0.11] MASEM vs. Univariate Meta-Analysis

  15. Results – MASEM k = 88 N = 154,176 MASEM vs. Univariate Meta-Analysis

  16. Results – MASEM k = 88 N = 154,176 MASEM vs. Univariate Meta-Analysis

  17. Results – MASEM Indirect effects Support: β = 0.002 [−0.0002; 0.004] Authoritarian: β = 0.008 [0.001; 0.019] Behavioral: β = 0.003 [−0.0002; 0.009] Psychological: β = −0.001 [−0.008; 0.004] Indirect parenting: β = 0.007 [−0.0002; 0.015] MASEM vs. Univariate Meta-Analysis

  18. Results – Compared MASEM vs. Univariate Meta-Analysis

  19. Results – Compared MASEM vs. Univariate Meta-Analysis

  20. Results – Compared MASEM vs. Univariate Meta-Analysis

  21. Results – Compared MASEM vs. Univariate Meta-Analysis

  22. Moderator Analysis • Univariate meta-analysis • All kinds of moderator variables MASEM vs. Univariate Meta-Analysis

  23. Moderator Analysis • MASEM • Subgroup analysis (Jak & Cheung, in press)  compare groups of studies • Continuous moderator variable  create subgroups • Loss of information Group 1 Group 2 MASEM vs. Univariate Meta-Analysis

  24. Moderator Analysis • Continuous: Age • Univariate: significant effect on some associations • MASEM: no significant effects • Categorical: SES • Univariate: no significant effects • MASEM: no significant effects MASEM vs. Univariate Meta-Analysis

  25. Conclusion • Different methods  different effect sizes  different conclusions MASEM vs. Univariate Meta-Analysis

  26. Conclusion • Different methods  different effect sizes  different conclusions MASEM vs. Univariate Meta-Analysis

  27. Conclusion • Different methods  different effect sizes  different conclusions MASEM vs. Univariate Meta-Analysis

  28. Conclusion • Different methods  different effect sizes  different conclusions MASEM vs. Univariate Meta-Analysis

  29. Conclusion • Different methods  different effect sizes  different conclusions MASEM vs. Univariate Meta-Analysis

  30. Conclusion • Different methods  different effect sizes  different conclusions MASEM vs. Univariate Meta-Analysis

  31. Conclusion • Different methods  different effect sizes  different conclusions MASEM vs. Univariate Meta-Analysis

  32. Conclusion • Different methods  different effect sizes  different conclusions MASEM vs. Univariate Meta-Analysis

  33. Conclusion • Different methods  different effect sizes  different conclusions MASEM vs. Univariate Meta-Analysis

  34. Conclusion • Different methods  different effect sizes  different conclusions MASEM vs. Univariate Meta-Analysis

  35. References Cheung, M. W. L. (2014). Fixed-and random-effects meta-analytic structural equation modeling: Examples and analyses in R. Behavior Research Methods, 46(1), 29-40. Glass, G. V. (1976). Primary, secondary, and meta-analysis of research. Educational Researcher, 5, 3-8. doi:10.3102/0013189X005010003 Hoeve, M., Dubas, J. S., Eichelsheim, V. I., Van Der Laan, P. H., Smeenk, W., & Gerris, J. R. (2009). The relationship between parenting and delinquency: A meta-analysis. Journal of Abnormal Child Psychology, 37, 749-775. Jak, S. (2015). Meta-Analytic Structural Equation Modeling. Springer International Publishing. doi:10.1007/978-3-319-27174-3 Jak, S., & Cheung, M. W.-L. (in press). Testing moderator hypotheses in meta-analytic structural equation modeling using subgroup analysis. Behavior Research Methods. Viswesvaran, C., & Ones, D. (1995). Theory testing: Combining psychometric meta-analysis and structural equations modeling. Personnel Psychology, 48, 865-885. doi:10.1111/j.1744-6570.1995.tb01784.x MASEM vs. Univariate Meta-Analysis

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