9
Quantitative MethodsModule 9 of 11

Parametric and Non-Parametric Tests of Independence

3

Concepts

3

Formulas

1

Decisions

3

Quiz Questions

Key Concepts

3 concepts covered in this module.

Parametric Correlation Test

Tests whether population correlation ρ = 0. t = r√(n-2) / √(1-r²) with df = n-2.

Spearman Rank Correlation

Non-parametric alternative. Ranks data, then calculates correlation of ranks. Robust to outliers and non-linearity.

Contingency Table Tests

Chi-square test of independence using observed vs expected frequencies. χ² = Σ(O-E)²/E.

Formulas

3 essential formulas for this module.

Parametric t-test for Correlation

t = r × √(n-2) / √(1-r²)

Where: r = sample correlation, df = n-2

Chi-Square Statistic

χ² = Σ (Oi - Ei)² / Ei

Where: O = observed frequency, E = expected frequency

Expected Frequency

Eij = (Row total × Column total) / Grand total

Where: For contingency tables

Decision Frameworks

1 decision frameworks to guide your analysis.

Parametric vs Non-parametric correlation?

  • Parametric: when data is approximately normal
  • Spearman: when data is ordinal, has outliers, or non-linear relationship

Mind Map

Visual overview of how concepts connect in this module.

Tests of Independence
Parametric Correlation
Test H0: ρ = 0
t-statistic with df = n-2
Assumes normality
Spearman Rank
Rank the data
Correlate ranks
Non-parametric
Robust to outliers
Contingency Tables
Chi-square test
Observed vs Expected
df = (r-1)(c-1)
Free Study Dashboard

Don't just read Parametric and Non-Parametric Tests of Independence — practice it

Everything on this page becomes interactive on the study dashboard, free.

  • 7 interactive flashcards built from this page
  • 3 exam-style quiz questions with instant scoring
  • Progress tracking across all 11 Quantitative Methods modules
Open Study Dashboard

No signup required. Create an account anytime to save progress.

Try it right here

Flashcard

Parametric Correlation Test

Tap to reveal the answer

Answer
Tests whether population correlation ρ = 0. t = r√(n-2) / √(1-r²) with df = n-2.
Sample 1 of 3
Study all 7 flashcards on the dashboard

Previous Module

Hypothesis Testing

Next Module

Simple Linear Regression