课程
Factor Analysis in R
高级技能水平
更新时间 2020年10月
RProbability & Statistics4小时13 视频45 道练习3,600 XP12,213成就证明
创建您的免费帐户
继续使用 Google显示更多选项或
继续操作即表示您接受我们的《使用条款》和《隐私政策》，并同意您的数据存储在美国。
深受数千家公司学习者的喜爱
需要团队培训？
企业版试用课程描述
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
课程完成 加入超过19百万学习者，今天就开始Factor Analysis in R！
创建您的免费帐户
继续使用 Google显示更多选项或
继续操作即表示您接受我们的《使用条款》和《隐私政策》，并同意您的数据存储在美国。
通过 DataCamp for Mobile 提升您的数据技能
随时随地通过我们的移动课程和每日 5 分钟编程挑战提升技能。