Statistical Analysis and Interpretation

Apply Bayesian statistical methods to research problems: prior selection, posterior inference, credible intervals, MCMC, and model comparison using Bayes factors.

Apply causal inference methods — difference-in-differences, instrumental variables, regression discontinuity, propensity scores — to observational and quasi-experimental data.

Expert statistical support for clinical trial design, sample size calculation, randomization, interim analysis, and regulatory-compliant reporting.

Analyze repeated-measures and panel data with mixed models, GEE, growth curve analysis, and expert handling of missing data and time-varying covariates.

Conduct and report meta-analyses with expert guidance on effect size pooling, heterogeneity, publication bias, forest plots, and PRISMA-compliant reporting.

Expert guidance on PCA, factor analysis, cluster analysis, MANOVA, discriminant analysis, and other multivariate methods for complex, high-dimensional research data.

Calculate and justify sample sizes for experiments, surveys, and clinical studies using power analysis — covering ANOVA, t-tests, regression, and complex designs.

Expert guidance on linear, logistic, multilevel, and advanced regression models — from assumption checking and model selection to coefficient interpretation and reporting.

Translate statistical output into clear, accurate results sections and reports for academic papers, regulatory documents, and non-technical stakeholder audiences.

Analyze survey and questionnaire data with expert guidance on weighting, Likert scale analysis, non-response bias, and meaningful results interpretation.