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试用DataCamp for Business课程描述
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."
先决条件
Intermediate RFoundations of Inference in R1
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.
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.
Factor Analysis in R
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