Meta Analysis Calculator Guide
Meta analysis helps researchers combine results from several studies. This calculator uses inverse variance methods to pool compatible effects. It accepts effect estimates and standard errors. These values may represent mean differences, standardized differences, or log ratios.
Why Pooled Evidence Matters
One study can be small or unstable. Several studies can show a clearer pattern. Pooling gives larger evidence weight to precise studies. It also reveals disagreement across results. That balance supports cautious interpretation.
Fixed Effect Model
The fixed effect model assumes one true effect. Every study estimates the same underlying value. Differences happen because of sampling error. Study weight equals one divided by variance. Smaller standard errors receive higher weights. This model is useful when studies are very similar.
Random Effects Model
The random effects model allows real differences between studies. It adds between study variance to each study variance. The DerSimonian Laird method estimates that extra variance. Wider confidence intervals often appear. This is expected when evidence varies.
Heterogeneity Review
Heterogeneity measures disagreement among studies. Cochran Q tests whether variation exceeds chance. I squared shows the percent of variation beyond sampling error. A higher I squared value suggests more inconsistency. It does not prove poor quality. It tells users to review design, population, and measurement differences.
Practical Use
Enter each study name, effect estimate, and standard error. Use log odds ratios or log risk ratios for ratio measures. Select the log scale option when ratios should be shown after exponentiation. Review fixed and random summaries together. Export the table when you need documentation.
Good Reporting Habits
Always mention the effect scale. State the number of included studies. Report the pooled estimate, confidence interval, tau squared, Q, and I squared. Explain any strong heterogeneity. Do not treat the pooled value as automatic truth. Check study quality, bias, and clinical meaning.
Limits
This tool supports generic inverse variance analysis. It does not replace specialist review. It also does not judge study eligibility. Users should prepare data carefully before entry. Consistent direction matters. Positive and negative effects must mean the same thing across all included studies. Careful preparation makes the summary stronger.
Use sensitivity checks when needed. Keep original extraction sheets available for review during peer review.