
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.
https://guides.hsl.virginia.edu/c.php?g=921177&p=6638623
https://researchguides.gonzaga.edu/qualitative/peo
https://researchguides.gonzaga.edu/qualitative/spider
Explore how to clarify preliminaries in meta-analysis by distinguishing problem statements, review questions, aims, and objectives, and illustrate with a scoliosis treatment comparison.
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.
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.
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.
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.
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.
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.
Meta-analysis is a powerful statistical technique that allows researchers to synthesize and integrate findings from multiple studies, providing a more comprehensive and accurate understanding of a research topic. Whether you're a graduate student, academic researcher, or industry professional, this course will provide you with a thorough understanding of the principles and practical skills needed to conduct and interpret meta-analyses.
Master the Principles and Practical Skills of Conducting Meta-Analyses
Understand the purpose, benefits, and limitations of meta-analysis
Learn how to conduct a systematic literature review and identify relevant studies for inclusion
Extract data from primary studies and calculate effect sizes with confidence
Perform meta-analyses using both fixed-effect and random-effects models
Assess heterogeneity and conduct moderator analyses to explore sources of variation
Report meta-analytic results and interpret their practical and theoretical implications
Incorporate open science practices and utilize online resources for collaboration and data sharing
What You’ll Learn in This Course
In this course, you'll gain a deep understanding of the core principles of meta-analysis, starting with its purpose, benefits, and limitations. You'll learn how to conduct a systematic literature review to identify relevant studies and extract data for analysis. We’ll cover the key techniques for performing meta-analyses using both fixed-effect and random-effects models, as well as how to assess heterogeneity and explore sources of variation through moderator analyses.
You'll also learn how to report meta-analytic results and interpret their implications for both practical and theoretical contexts. With a focus on open science practices, you'll understand how to utilize online resources for collaboration and data sharing in meta-analysis.
By the end of the course, you will have the knowledge and skills necessary to confidently conduct your own meta-analyses, evaluate existing meta-analyses, and contribute to advancing your field of study. Whether you’re conducting meta-analyses or interpreting the results, this course will prepare you to navigate the world of meta-analysis with confidence.