DataCamp
FreeFactor Analysis in R
4.8(158)12k studentsADVANCEDEnglish
Korshub's take
The Korshub Take
This advanced R course equips you with rigorous statistical methods to measure latent, unobservable variables such as personality traits, climate metrics, or cognitive abilities. It covers both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) to validate complex constructs.
Who It's For
Advanced researchers, quantitative analysts, and social scientists who need to measure unobservable constructs and evaluate measurement models in R.
Key Takeaways
- Exploratory Factor Analysis (EFA): Inspect factor loadings, interpret factor scores, examine correlations, calculate eigenvalues, and create scree plots.
- Confirmatory Factor Analysis (CFA): Translate EFA results into structural syntax and test hypothesis-driven measurement models.
- Model Refinement: Evaluate fit metrics, compare EFA versus CFA frameworks, and refine measurement scales across diverse domains.
About this course
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."
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