Saltar al contenido principal
InicioRFactor Analysis in R

# Factor Analysis in R

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

Comience El Curso Gratis
4 Horas13 Videos45 Ejercicios
10.105 AprendicesDeclaración de cumplimiento

## Crea Tu Cuenta Gratuita

o

Al continuar, acepta nuestros Términos de uso, nuestra Política de privacidad y que sus datos se almacenan en los EE. UU.
¿Entrenar a 2 o más personas?Pruebe DataCamp para empresas

## 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."

Empresas

### .css-1goj2uy{margin-right:8px;}Group.css-gnv7tt{font-size:20px;font-weight:700;white-space:nowrap;}.css-12nwtlk{box-sizing:border-box;margin:0;min-width:0;color:#05192D;font-size:16px;line-height:1.5;font-size:20px;font-weight:700;white-space:nowrap;}¿Entrenar a 2 o más personas?

Obtenga acceso de su equipo a la biblioteca completa de DataCamp, con informes centralizados, tareas, proyectos y más
Pruebe DataCamp Para EmpresasPara obtener una solución a medida, solicite una demonstración.
1. 1

### Evaluating your measure with factor analysis

Gratuito

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

Reproducir Capítulo Ahora
Introduction to Exploratory Factor Analysis (EFA)
50 xp
Starting out with a unidimensional EFA
100 xp
100 xp
Interpreting individuals' factor scores
100 xp
Overview of the measure development process
50 xp
100 xp
100 xp
Comparing the halves of your dataset
100 xp
Measure features: correlations and reliability
50 xp
Viewing and testing correlations
100 xp
Internal reliability
100 xp
When to use EFA
50 xp
2. 2

### Multidimensional EFA

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

3. 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. 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.

En las siguientes pistas

Jennifer Brussow

Psychometrician at Ascend Learning

Ver Mas