How to Conduct a Meta-analysis: A Practical Guide

Identify a topic area for your meta-analysis and narrow it to an answerable review question using the funnel or inverted triangle approach, illustrated by servant leadership in hospital settings.

Explore the six types of review questions in meta-analysis, learn to distinguish open-ended from closed questions, and craft unbiased, researchable inquiries.

Explore how to formulate comprehensive, specific review questions for meta-analysis using PICO, POA, and PIO frameworks, identifying population, intervention, comparative intervention, and outcomes through practical examples.

Explore how to clarify preliminaries in meta-analysis by distinguishing problem statements, review questions, aims, and objectives, and illustrate with a scoliosis treatment comparison.

Plan a systematic search that balances sensitivity and specificity, identify key concepts and keywords, and search multiple databases to transparently report study identification and relevance to evidence.

Learn to use boolean operators to refine searches by combining terms with and, or, and not, and apply proximity, phrase, and truncation to improve relevance.

Define inclusion and exclusion criteria to set the boundaries of a systematic review and ensure transparency. Determine timelines, study designs, populations, and settings to guide the search and reporting.

Learn how to choose, convert, and compare effect size measures across fields, including odds ratios, risk ratios, standardized mean differences, correlations, and regression coefficients.

Convert study findings to a common effect size using conversion formulas to harmonize diverse measures for meta-analysis. Employ online calculators to derive the primary effect size.

Univariate meta-analysis serves as a starting point to assess general relationships or intervention effects, using standardized metrics such as SMD or log odds ratio and subgroup analyses to identify moderators.

Identify factors driving heterogeneity across studies by performing a multivariate meta-regression, testing multiple moderators such as sample size, study design, and quality, and adjusting weights to reduce bias.

Explore meta-analytic structural equation modeling (masm) to assess direct and indirect effects and moderating effects, using a pooled correlation matrix to fit a path model.

Explore how qualitative meta analysis systematically reviews qualitative studies to identify themes and patterns, codes data into a meta synthesis protocol, and build a meta causal network.

Learn how to choose and use meta-analysis software, with a stata walkthrough of data preparation, the metan command, heterogeneity checks, forest plots, and sensitivity analyses.

Install and run SPSS macros to perform meta analysis, loading macros and configuring options for effect size and variance. Interpret results with plots and confidence intervals to inform research question.

Import and prepare data in SAS for meta-analysis, then install and run a macro to define variables, select analysis options, and produce forest plots and summary statistics.

Develop a coding sheet to organize data for a meta-analysis, code effect sizes, and include moderator variables; learn input formats for univariate meta-analysis and how to treat multiple effect sizes.

Identify and justify the inclusion of moderators and control variables based on strong theoretical rationales, and distinguish substantive from methodological moderators in meta-analysis.

Learn how to handle multiple effect sizes in meta-analysis in management research, balancing independence, heterogeneity, and robustness through sensitivity analyses, with approaches like multilevel models and robust variance estimation.

Explore how outliers and publication bias affect meta-analytic results, and how sensitivity analysis and preliminary checks assess robustness, bias, and potential moderators.

Compare fixed and random effects models in meta-analysis, and explain when heterogeneity guides model choice. Demonstrate how mixed effects meta regression with moderator variables clarifies effect-size variations and improves generalizability.

Report meta-analysis results clearly with checklist-guided steps, including effect sizes, confidence intervals, and heterogeneity. Present tables, figures, and narrative discussion on moderators and open science.

Learn open science practices in meta-analysis, including public datasets and code in open repositories, live updates, and templates to pre-register and share results for transparent decision making.