EFFECT SIZE ESTIMATION

Mastering the art of meta-analysis may seem straightforward, but achieving excellence in this field is truly challenging. Determining effect sizes is one of the most daunting aspects of conducting a meta-analysis.

The first step in a meta-analysis involves systematic literature review and data extraction. Researchers use database searches, reference lists, and expert consultations to identify relevant studies. Inclusion and exclusion criteria are applied to ensure that only studies addressing the research question and meeting quality standards are included.

By combining data from various sources, meta-analysis can increase statistical power, provide more precise estimates of effect sizes, and identify patterns or moderators across studies. This essay explores the quantitative methods used in meta-analysis, including data collection, effect size estimation, model selection, and assessment of heterogeneity (Haddaway et al., 2022).

The focus of any meta-analysis is the effect size, which measures the strength of how one variable or group of variables influences another. Effect sizes are crucial for understanding the impact of experimental treatments or the relationship between variables in nonexperimental studies. However, calculating effect sizes can be challenging due to the wide range of research designs and the inadequate reporting of statistical information in primary research reports. The d and r measures are commonly used and practical for calculating effect sizes, providing researchers with valuable tools for meta-analysis.

Once relevant studies are identified, extracting and standardising effect sizes is next. The effect size is a numerical measure that indicates the strength of the experimental outcome. Common effect size metrics include:

1. Cohen's d: Measures the difference between two means divided by the pooled standard deviation.
2. Odds Ratio (OR): Used in binary outcomes to measure the odds of an event occurring in one group compared to another.
3. Correlation Coefficient (r): Analyse the intensity and orientation of the connection between two variables.

Standardisation of effect sizes is crucial because it allows the combination of results from studies that use different scales or outcome measures.