alterlab-rma-statistics-guide

Selecting the correct statistical test involves mapping your research questions and variable types to a systematic decision framework. This process identifies the appropriate analysis method, checks assumptions, and generates a complete plan for your specific data structure.

A complete analysis plan defines hypotheses, variable roles, test types, and power considerations. It also outlines data preparation steps, assumption checks, and provides APA-style reporting templates to ensure your graduate thesis meets rigorous academic standards.

Software guidance for SPSS, R, Excel, and JASP provides specific instructions for executing statistical tests in each platform. This ensures you can accurately perform data analysis and generate ready-to-report results using your preferred statistical software environment.

Reporting statistical results in APA format requires ready-to-report templates that structure your findings with appropriate test statistics, effect sizes, and p-values. This ensures your data analysis output meets standard academic reporting guidelines for publication.

Power analysis works for course projects and dissertations by calculating required sample sizes based on expected effect sizes and significance levels. This ensures your research design has adequate statistical power to detect meaningful effects before data collection begins.

Data preparation steps include cleaning your dataset, handling missing values, and verifying assumption checks like normality and variance homogeneity. These steps ensure your analysis plan executes correctly and yields valid statistical results for your research.