PhD program R Software 16 April 2026

Introduction to
Meta-Analysis
with Application in R

PhD program in Psychological Sciences
University of Padova

Author Margherita Calderan

Department ?

Course Contents

Meta-analysis is a statistical method that allows for the quantitative synthesis of results from multiple studies. These studies can involve existing literature or can be pre-planned studies following the same protocol, such as multi-lab studies.

This course presents various statistical models of meta-analysis from both a theoretical and applied perspective, using R software (via metafor package, Viechtbauer (2010)).

Learning Goals

01

Conceptualize research questions within a meta-analytic framework and compute effect sizes.

02

Fit appropriate statistical models using R.

03

Interpret synthesized findings and critically evaluate heterogeneity.

Schedule

Thursday, 16 April 2026 · Room 4R — Psico 2

Time Session
10:30 – 13:30 Theory and meta-analytic models
13:30 – 14:30 Lunch break
14:30 – 16:30 Critical evaluation · Discussion · Practice in R

Resources

1. Slides are available here: html.

This presentation draws in part on workshop materials on meta-analysis by Filippo Gambarota and Gianmarco Altoè, course slides by Prof. Altoè (A.A. 2024/2025), and resources including Harrer et al. (2021) book and the metafor documentation.

2. Practice in R: solution

The following texts and articles are recommended as companion reading for this course.

References

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Borenstein, Michael. 2022. “In a Meta-Analysis, the i-Squared Statistic Does Not Tell Us How Much the Effect Size Varies.” Journal of Clinical Epidemiology 152 (December): 281–84. https://doi.org/10.1016/j.jclinepi.2022.10.003.
———. 2024. “Avoiding Common Mistakes in Meta-Analysis: Understanding the Distinct Roles of q, i-Squared, Tau-Squared, and the Prediction Interval in Reporting Heterogeneity.” Research Synthesis Methods 15 (2): 354–68. https://doi.org/10.1002/jrsm.1678.
Borenstein, Michael, Larry V. Hedges, Julian P. T. Higgins, and Hannah R. Rothstein. 2009. Introduction to Meta-Analysis. 1st ed. Wiley. https://doi.org/10.1002/9780470743386.
———. 2021. Introduction to Meta-Analysis. 2nd ed. John Wiley & Sons.
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Gambarota, Filippo, and Gianmarco Altoè. 2024. “Understanding Meta-Analysis Through Data Simulation with Applications to Power Analysis.” Advances in Methods and Practices in Psychological Science 7 (1): 25152459231209330. https://doi.org/10.1177/25152459231209330.
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