In Chapter 1, you will learn how to conduct an EFA to examine the statistical properties of a measure designed around one construct.

Introduction to Exploratory Factor Analysis (EFA)

Starting out with a unidimensional EFA

Viewing and visualizing the factor loadings

Interpreting individuals' factor scores

Overview of the measure development process

Descriptive statistics of your dataset

Splitting your dataset

Comparing the halves of your dataset

Measure features: correlations and reliability

Viewing and testing correlations

Internal reliability

When to use EFA

Evaluating your measure with factor analysis

This chapter will show you how to extend the single-factor EFA you learned in Chapter 1 to multidimensional data.

Determining dimensionality

Splitting the BFI dataset

Calculating eigenvalues

Creating a scree plot

Interpreting the scree plot

Understanding multidimensional data

Conducting a multidimensional EFA

Interpreting the results

Investigating model fit

Interpret absolute model fit statistics

Selecting the best model

Multidimensional EFA

This chapter will cover conducting CFAs with the sem package. Both theory-driven and EFA-driven CFA structures will be covered.

Setting up a CFA

Creating CFA syntax from EFA results

Creating CFA syntax from theory

Understanding the sem() syntax

Components of sem() syntax

Run a CFA and interpret loadings

Examine item loadings

Absolute fit statistics

Relative fit statistics

Confirmatory Factor Analysis

This chapter will reinforce the difference between EFAs and CFAs and offer suggestions for improving your model and/or measure.

EFA vs. CFA revisited

Differences in estimated factor loadings

Plotting differences in persons' factor scores

Adding loadings to improve fit

Add loadings to improve fit

Compare original model to model with added loadings

Evaluate added loadings with relative fit stats

Improving fit by removing loadings

Remove loadings to improve fit

Compare original model to model with deleted loadings

Evaluate deleted loadings with relative fit stats

Wrap-Up Video

Refining your measure and/or model

Generic Conspiracist Beliefs Scale (GCBS) dataset

The world is full of unobservable variables that can't be directly measured. You might be interested in a construct such as math ability, personality traits, or workplace climate. When investigating constructs like these, it's critically important to have a model that matches your theories and data. This course will help you understand dimensionality and show you how to conduct exploratory and confirmatory factor analyses. With these statistical techniques in your toolkit, you'll be able to develop, refine, and share your measures. These analyses are foundational for diverse fields including psychology, education, political science, economics, and linguistics.

<h2>Discover Factor Analysis in R</h2>
The world is full of unobservable variables that can't be directly measured. You might be interested in a construct such as math ability, personality traits, or workplace climate. When investigating constructs like these, it's critically important to have a model that matches your theories and data. 
<br><br>
This course will help you understand dimensionality and show you how to conduct exploratory and confirmatory factor analyses. 
<br><br>
<h2>Learn to Use Exploratory Factor Analysis and Confirmatory Factor Analysis </h2>
You’ll start by getting to grips with exploratory factor analysis (EFA), learning how to view and visualize factor loadings, interpret factor scores, and view and test correlations. 
<br><br>
Once you’re familiar with single-factor EFA, you’ll move on to multidimensional data, looking at calculating eigenvalues, creating screen plots, and more. Next, you’ll discover confirmatory factor analysis (CFAs), learning how to create syntax from EFA results and theory. 
<br><br>
The final chapter looks at EFAs vs CFAs, giving examples of both. You’ll also learn how to improve your model and measure when using them. 
<br><br>
</h2>Develop, Refine, and Share Your Measures<h2>
With these statistical techniques in your toolkit, you'll be able to develop, refine, and share your measures. These analyses are foundational for diverse fields, including psychology, education, political science, economics, and linguistics."

Intermediate R

Foundations of Inference in R

Start this four-hour course today to discover exploratory factor analysis and confirmatory factor analysis in R to explore latent variables such as personality.

Factor Analysis in R

Explore latent variables, such as personality, using exploratory and confirmatory factor analyses.

Statisticien en R

Components of sem() syntax

Factor Analysis in R

Exercice interactif pratique