How to calculate effect size, is there any formula available for the calculation of effect size in different studies like correlational studies, and comparative studies in the meta-analysis?
Hello Naresh Behera, somewhere but I don't know where it is at present, as we had visitors and books of mine were shifted, but somewhere I have a book by Prof. Paul Glasziou on systematic reviews. Is it in your library? I can look for it if you want me to.
Effect sizes are used to quantify the magnitude of the relationship or difference between variables in various types of studies, including correlational studies and comparative studies. The choice of effect size measure depends on the nature of the data and the research question. Here are some common effect size measures and their formulas for different types of studies:
Correlational Studies: Pearson's r (Pearson Correlation Coefficient): Measures the strength and direction of the linear relationship between two continuous variables. Spearman's ρ (Spearman's Rank Correlation Coefficient): Measures the strength and direction of the monotonic relationship between two variables (ordinal or interval data).
Comparative Studies (e.g., T-tests, ANOVA): Cohen's d (Standardized Mean Difference): Measures the difference between means of two groups in terms of standard deviations. Hedges' g: An adjusted version of Cohen's d for small sample sizes.
Meta-analysis: When conducting a meta-analysis, you'll often encounter various effect size measures depending on the studies you're synthesizing. Some common ones include Cohen's d for comparative studies. Pearson's r for correlational studies. Odds ratios (OR) for binary outcomes. Hazard ratios (HR) for survival data. In a meta-analysis, you need to convert the effect sizes from each study to a common metric (often referred to as the "standardized mean difference" for continuous outcomes) to allow for meaningful comparisons across studies. This is usually done by calculating the effect size and its standard error for each study and then combining these values across studies using appropriate meta-analytic methods.
It's important to note that effect size interpretation can vary depending on the field of study and context. It's a good practice to provide context and benchmarks for interpreting the effect sizes in your specific research area.
Can you provide an example of calculating Cohen's d?