An Introduction to Factor Analysis

Name: An Introduction to Factor Analysis
Rating: 4.4 (80 reviews)

Theory and Application

Created byNajib Mozahem

Last updated 10/2019

English

English [Auto],

What you'll learn

Extrating Factors
Determing Reliability
Rotating Factors
Understanding Scores

Course content

3 sections • 18 lectures • 1h 31m total length

Introduction3:31
Explore when to use factor analysis to construct a single time management score from responses to multiple questions, turning abstract skills into a measurable variable.
Example: Masculinity2:10
Explore how factor analysis uses surveys to measure masculinity as a latent construct, employing a 1–7 rating scale on traits like assertiveness, competitiveness, dominance, and individualism, while ensuring reliability.
Reliability3:59
Learn how to assess reliability in a questionnaire using Cronbach's alpha, interpret values above or below 0.7, and improve reliability by removing problematic questions with software guidance.
Calculating the Value of the Construct3:34
After establishing reliability and alpha above 0.7, calculate the construct's value from responses. Factor analysis weighs items by relevance rather than averaging, highlighting traits like assertiveness and dominance in masculinity.
Factor Analysis - Extracting the Factors8:02
Learn how factor analysis extracts the latent construct masculinity from item loadings, and compare principle component analysis with common factor analysis to decide how many factors to retain.
Factor Analysis - Eigenvalues4:55
Explore factor analysis through eigenvalues, item loadings, and variance explained; show five items yielding only one factor with eigenvalue above one, with all items loading strongly on it.
The Scores5:01
Compute factor scores from a one-factor solution using item loadings and interpret standardized masculinity scores (mean zero, sd one), including gender comparisons.

Extracting Two Factors5:11
Extract two factors from a mixed item set, separating masculinity and femininity with eigenvalues greater than one and clear loadings. Rotation will be covered in the next lecture.
Rotation7:47
Rotate principal components to reveal distinct factor groups, aligning loadings so masculine traits cluster on one factor and feminine traits on another.
Types of Rotation6:02
Learn how rotation refines multiple-factor results by allowing factors to account for distinct item sets. Decide between orthogonal and oblique rotations based on independence, using varimax or oblimin/promax as appropriate.

Introduction1:49
Explore the theory of factor analysis and apply it to a case study on gender imbalances in education and stem majors, with an upcoming discussion of social cognitive theory.
The Theory5:32
Examine social cognitive theory's explanation for why many females avoid STEM, focusing on self efficacy and four information sources: mastery experience, vicarious experience, social persuasion, and physiological state.
The Survey5:12
Explore how researchers measure four social cognitive theory constructs—mastery experience, vicarious experience, social persuasion, and physiological state—with a six-question-per-construct 24-item survey across genders.
Reliability1:23
Establish reliability with Cronbach's alpha across four constructs; master experience 0.85, vicarious experience 0.79, social persuasion 0.91, physiological state 0.87. All exceed 0.7, enabling factor analysis.
Extracting the Factors5:11
Explore factor analysis with principal component analysis, using eigenvalues to decide how many factors to retain, and rotate to interpret loadings across four constructs.
Rotating the Factors8:22
Use oblique rotation with promax to reveal four correlated factors in factor analysis. Interpret loadings for social persuasion, mastery experience, physiological state, and vicarious experience, and compute factor scores.
The Scores9:54
Compute and compare four factor scores: mastery experience, vicarious experience, social persuasion, and physiological state from rotated loadings and standardized scores, and examine gender and age patterns using histograms.
Conclusion3:27
Shows how factor analysis, specifically principal component analysis, quantifies unseen variables, producing four constructs with oblique rotation and factor scores, revealing age and gender differences that support social cognitive theory.

Requirements

None

Description

Included in this course is an e-book and a set of slides. The purpose of the course is to introduce students to factor analysis, when it is used and how it is used. The course does not assume the use of any specific statistical software. Therefore, this course should be of use to anyone intending interested in factor analysis. The theory is explained in an intuitive way while keeping the math at a minimum. The course starts with a simple one-dimensional example where the concepts of reliability, loadings, and eigenvalues are explained. The course then moves to two-dimensions where the concept of rotation is explained. Different rotation techniques are discussed in addition to the differences between them.

In the second part of the course, students walk through a case study in a step-by-step approach in order to see how the techniques are applied and what sort of logic is used in each step. In this part, students will walk through a large project in order to understand the type of questions that are raised throughout the process.

Who this course is for:

Beginner students who want to learn about factor analysis

An Introduction to Factor Analysis

What you'll learn

Explore related topics

Course content

One Dimension7 lectures • 31min

Two Dimensions3 lectures • 19min

Case Study8 lectures • 41min

Requirements

Description

Who this course is for: