# EFA

Exploratory factor analysis (EFA) identifies the underlying relationships between a large number of interrelated variables when there are no prior hypotheses about factors or patterns amongst the variables.

EFA is a technique based on the common factor model which describes the measured variables by a function of the common factors, unique factors, and error of measurements. Common factors are those that influence two or more measured variables, while unique factors influence only one measured variable.

## Pattern matrix

The factor pattern matrix loadings are the linear combinations of the factors that make up the original standardized variables.

## Structure matrix

The factor structure matrix loadings are the correlation coefficients between the factors and the variables.

## Correlation matrix

The factor correlation matrix coefficients are the correlation coefficients between the factors.

**Available in Analyse-it Editions**

Standard edition

Method Validation edition

Quality Control & Improvement edition

Ultimate edition

- What is Analyse-it?
- What's new?
- Administrator's Guide
- User's Guide
- Statistical Reference Guide
- Distribution
- Compare groups
- Compare pairs
- Contingency tables
- Correlation and association
- Principal component analysis (PCA)
- Factor analysis (FA)
- EFA
- Factor rotation
- Matrix rotations
- Extracting factors
- Item reliability
- Fit model
- Method comparison / Agreement
- Measurement systems analysis (MSA)
- Reference interval
- Diagnostic performance
- Survival/Reliability
- Control charts
- Process capability
- Pareto analysis
- Study Designs
- Bibliography

Version 6.15

Published 18-Apr-2023