Meta-Analysis for Psychology Review
Budget: ₹1,500 – ₹12,500 INR
I have extracted quantitative outcomes from a series of published psychology studies, but the dataset is not fully prepared for statistical synthesis yet. It will need appropriate formatting, variable alignment, and coding adjustments before conducting the meta-analysis. Once the dataset is structured, I need a complete, hypothesis-driven meta-analysis to identify overarching trends and test specific theoretical expectations, which will directly inform the discussion section of my systematic review.
Here is what I expect:
• Prepare and structure the dataset into a suitable format for meta-analysis, ensuring consistent effect size coding and variable alignment.
• Run a full meta-analysis (random- and fixed-effects where appropriate) guided by my research hypotheses rather than conducting generic analyses.
• Report pooled estimates, confidence intervals, heterogeneity statistics (I², Q) and include influence diagnostics to evaluate study-level impact.
• Conduct publication-bias checks—funnel plots, Egger’s test, and trim-and-fill—as appropriate, so potential bias can be addressed in the manuscript.
• Provide publication-ready visualizations, including forest plots, funnel plots, and subgroup or sensitivity analyses that help strengthen the hypothesis-driven narrative.
• Deliver the underlying code or syntax (R, Python, SPSS, or CMA—whichever you prefer) alongside well-labelled output tables so I can reproduce or update the analysis later.
If you are comfortable interpreting psychological instruments and have experience with hypothesis-driven meta-analyses using tools like the metafor or meta packages in R, RevMan, or Comprehensive Meta-Analysis, this should be straightforward. Please briefly outline your experience with both data preparation and meta-analytic modelling, and indicate how quickly you could deliver an initial draft of the results.
Here is what I expect:
• Prepare and structure the dataset into a suitable format for meta-analysis, ensuring consistent effect size coding and variable alignment.
• Run a full meta-analysis (random- and fixed-effects where appropriate) guided by my research hypotheses rather than conducting generic analyses.
• Report pooled estimates, confidence intervals, heterogeneity statistics (I², Q) and include influence diagnostics to evaluate study-level impact.
• Conduct publication-bias checks—funnel plots, Egger’s test, and trim-and-fill—as appropriate, so potential bias can be addressed in the manuscript.
• Provide publication-ready visualizations, including forest plots, funnel plots, and subgroup or sensitivity analyses that help strengthen the hypothesis-driven narrative.
• Deliver the underlying code or syntax (R, Python, SPSS, or CMA—whichever you prefer) alongside well-labelled output tables so I can reproduce or update the analysis later.
If you are comfortable interpreting psychological instruments and have experience with hypothesis-driven meta-analyses using tools like the metafor or meta packages in R, RevMan, or Comprehensive Meta-Analysis, this should be straightforward. Please briefly outline your experience with both data preparation and meta-analytic modelling, and indicate how quickly you could deliver an initial draft of the results.