Statistical Methods

Is Your Data Ready?

Before conducting Factor Analysis, we must pass the "gatekeepers." Master the KMO and Bartlett's tests in minutes.

1

Kaiser-Meyer-Olkin (KMO)

Measure of Sampling Adequacy

The KMO statistic evaluates whether the partial correlations among variables are small. Essentially, it tells us if the variables share enough in common to warrant grouping them into factors.

Rule of Thumb: KMO ≥ 0.60

KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure... .810 Bartlett's Test Approx. Chi-Square 412.67 df 45 Sig. .000

👆 Example: .810 is "Meritorious"

📊 KMO Value Decoder

Reference Guide

0.90 - 1.00 Marvelous 👑
0.80 - 0.89 Meritorious
0.70 - 0.79 Middling
0.60 - 0.69 Mediocre
< 0.60 Unacceptable ❌
2

Bartlett’s Test of Sphericity

Is it just an Identity Matrix?

This test checks if your correlation matrix is significantly different from an identity matrix. An identity matrix means all variables are perfectly independent (correlation = 0), which is bad for factor analysis.

  • Goal

    We want significant relationships.

  • The Magic Number

    Significance (p-value) < .05

Visual Concept

Bad for Analysis

Good (Significant)

Bartlett's test confirms your data looks like the right side.

Quick Recap

KMO Test

What it tests

Sample Adequacy

Target Value

> 0.60
Ensures variables share common variance.

Bartlett's Test

What it tests

Matrix Correlation

Target Value

Sig. < 0.05
Confirms significant relationships exist.