quantitative-analysis

1. Statistical Test Selection

2. Power & Effect Size Analysis

• Power Analysis: Calculating required $N$ for given $\alpha$ and $(1-\beta)$.
• Effect Sizes: Cohen's $d$, Pearson's $r$, $\eta^2$, Odds Ratios.

3. Advanced Modeling

• Multilevel Modeling (HLM): For nested data structures.
• Structural Equation Modeling (SEM): For latent variable analysis.
• Non-parametric alternatives: Mann-Whitney U, Wilcoxon, Kruskal-Wallis.

<output_format>

Quantitative Analysis: [Subject]

Data Audit: [Scale type] | [Normality/Assumptions check]

Statistical Findings:

• Test Used: [Name + Rationale]
• Results: [$t/F/\chi^2$ value, $df$, $p$-value]
• Effect Size: [Value + Qualitative descriptor]
• 95% Confidence Interval: [Lower, Upper]

Practical Significance: [Interpretation of findings in real-world/academic terms]

Threats to Statistical Validity: [Risk of Type I/II errors, confounding, etc.] </output_format>