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RHuebner1972
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quantitative-analysis

Guide statistical test selection, assumption checking, and APA-style results reporting for quantitative research.

1|Updated Jun 20, 2026
Search Tags:#hypothesis testing#regression#dissertation#statistical analysis#effect size#anova#apa reporting
One-click install
npx skills add https://github.com/RHuebner1972/doctoral-second-brain --skill quantitative-analysis-rhuebner1972
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: quantitative-analysis
Source: https://github.com/RHuebner1972/doctoral-second-brain/tree/main/skills/quantitative-analysis
Command: npx skills add https://github.com/RHuebner1972/doctoral-second-brain --skill quantitative-analysis-rhuebner1972

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Doctoral researchers often struggle to choose the right statistical test, verify assumptions, and report results with proper effect sizes and APA formatting, leading to rejected drafts and methodological critiques. ## Core Features & Use Cases - Test Selection Decision Trees: Maps research questions and designs (groups, predictors, repeated measures) to appropriate tests such as t-tests, ANOVA, regression, and mixed models. - Assumption Checking & Remedies: Provides procedures for testing normality, homogeneity of variance, and independence, with fallback options like Welch's t-test and non-parametric alternatives. - Results Reporting Templates: Supplies worked examples of Results sections with exact p-values, effect sizes (Cohen's d, eta-squared, r), descriptive tables, and sensitivity checks. - Use Case: A doctoral candidate analyzing pretest/posttest intervention data uses the skill to verify assumptions, run an independent samples t-test, report t(88) = 3.45, p = .001, d = 0.73, and draft a complete Results section. ## Quick Start Ask the assistant to help you choose the right statistical test and draft an APA-style Results section for your dataset and research questions.

Frequently Asked Questions about quantitative-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I choose the right statistical test for my research question?▼

Match your design to the test: independent t-test for two groups, ANOVA for three or more groups, correlation for two continuous variables, and multiple regression for several predictors. The skill provides a decision table mapping research questions to appropriate tests.

How do I check statistical assumptions before running a t-test or ANOVA?▼

Test normality with Shapiro-Wilk and Q-Q plots, and homogeneity of variance with Levene's test. If violated, use Welch's t-test, data transformations, or non-parametric alternatives like Mann-Whitney U and Kruskal-Wallis.

What should I report besides the p-value in my Results section?▼

Report descriptive statistics (M, SD, n), the test statistic with degrees of freedom, the exact p-value, and an effect size such as Cohen's d, eta-squared, or r. Effect sizes convey practical significance that p-values alone cannot.

How do I report non-significant results in a dissertation?▼

Report the full statistics including effect size and state that insufficient evidence exists to reject the null, rather than claiming no difference. Note statistical power when available and avoid hiding null findings.

What do I do when my data violates normality assumptions?▼

Try transformations such as log or square root, switch to non-parametric tests, or rely on the Central Limit Theorem with large samples. Running both parametric and non-parametric tests and reporting consistent results strengthens confidence.

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