Basics of Meta-Analysis

Dr. Muhammad Imran Kurashi introduces the meta-analysis series, aiming to simplify the complex statistical method for both novices and those familiar with basic statistics. As the Founding Director of Connecting Asia and editor of the Journal of Management in 4N, he invites participants to engage through their website or social media. This workshop is an extension of previous ones, focusing on the basics of meta-analysis, a quantitative approach that requires understanding before using advanced software. The session will cover the fundamentals from a recommended textbook and address common struggles researchers face in writing meta-analysis papers.

The second paragraph delves into what meta-analysis entails, which is a quantitative analysis that combines results from previous studies to provide a summary effect. It highlights the importance of meta-analysis across various fields, including business and engineering, not just medicine. The paragraph explains that meta-analysis can improve the precision of estimations, highlight exact findings, and help in generalizing results across different studies. It also emphasizes the method's rigor and wide applicability, as well as its significance in publishing research in reputable journals.

This section outlines the four clear steps involved in conducting a meta-analysis: conducting a systematic literature review (SLR), recording results from previous studies, calculating the effect size, and interpreting the results. The paragraph emphasizes the importance of the SLR as the foundation for a meta-analysis and mentions the inclusion of various sources, such as unpublished material and theses. It also touches on the concept of publication bias and the need to include all forms of study results in a meta-analysis.

The focus shifts to preparing for a meta-analysis by defining a clear research question and identifying keywords. The process involves defining a search strategy and extracting data, with an emphasis on including different sources beyond published material. The paragraph also discusses the importance of preparing a result sheet that includes the correlation coefficient (r) and sample size (n), which are crucial for calculating effect sizes in meta-analysis.

This paragraph discusses the importance of considering the types of data and analysis included in the studies for a meta-analysis. It explains that different statistical tests, such as t-tests, chi-square, and ANOVA, can be used, but they must be converted into effect sizes for meta-analysis. The paragraph also mentions the importance of understanding the data type, whether binary, continuous, or correlational, as this influences the choice of effect size calculation.

The concept of effect size is introduced as a quantitative measure of the magnitude of an experimental effect, indicating the strength of the relationship between variables. The paragraph explains how different types of data require different statistical treatments, such as log odds ratios for binary data or standardized mean differences for continuous data. It also introduces the terms 'fixed effect' and 'random effect' models in meta-analysis, which are crucial for understanding the variation between studies.

This section clarifies the use of fixed and random effects models in meta-analysis. Fixed effects models are used when samples are drawn from a similar population, emphasizing studies with larger sample sizes. In contrast, random effects models account for variation between different populations or groups and are used when there is heterogeneity in the data. The paragraph also discusses the importance of sample size in determining the weight of a study's effect size in meta-analysis.

The speaker introduces software tools used for conducting meta-analysis, such as Excel with add-ins like GEOMEAN and R. The paragraph focuses on the use of the JMEV software for meta-analysis, explaining how to install and run it for a fixed effects model. It provides a step-by-step guide on inputting data, selecting the appropriate module for meta-analysis, and interpreting the results presented by the software, including effect sizes and confidence intervals.

The final paragraph addresses the issue of heterogeneity in meta-analysis and the use of moderator variables. It explains that heterogeneity can indicate that a random effects model is more appropriate than a fixed effects model. The paragraph also introduces the concept of moderator variables, which can be used to differentiate populations in a meta-analysis, and mentions that understanding whether these variables are dichotomous or continuous is crucial for proper analysis.

In the closing segment, the speaker opens the floor for questions, inviting participants to post their inquiries in the comment box for follow-up. The session concludes with a reminder to like, share, and subscribe for more content, emphasizing the value of the meta-analysis workshop as a starting point for further learning and exploration of the topic.