After doing these courses, I feel confident creating professional visualizations and dashboards
Shoebur RahmanProcurement Specialist, Giz Bangladesh
Course Description
Discover Factor Analysis in R
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.
Learn to Use Exploratory Factor Analysis and Confirmatory Factor Analysis
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.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.
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.
Develop, Refine, and Share Your Measures
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."
Prerequisites
Curriculum
Course outline
1
Evaluating your measure with factor analysis
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)50 XP
- Starting out with a unidimensional EFA100 XP
- Viewing and visualizing the factor loadings100 XP
- Interpreting individuals' factor scores100 XP
- Overview of the measure development process50 XP
- Descriptive statistics of your dataset100 XP
- Splitting your dataset100 XP
- Comparing the halves of your dataset100 XP
- Measure features: correlations and reliability50 XP
- Viewing and testing correlations100 XP
- Internal reliability100 XP
- When to use EFA50 XP
2
Multidimensional EFA
This chapter will show you how to extend the single-factor EFA you learned in Chapter 1 to multidimensional data.
3
Confirmatory Factor Analysis
This chapter will cover conducting CFAs with the sem package. Both theory-driven and EFA-driven CFA structures will be covered.
4
Refining your measure and/or model
This chapter will reinforce the difference between EFAs and CFAs and offer suggestions for improving your model and/or measure.
R
Factor Analysis in R
Course
Complete
