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Rで学ぶ因子分析

上級スキルレベル

更新日 2020/10

性格などの潜在変数を、探索的因子分析と確認的因子分析で学び、活用しましょう。

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RProbability & Statistics

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45 演習

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コース説明

前提条件

Intermediate R Foundations of Inference in R

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)

Starting out with a unidimensional EFA

Viewing and visualizing the factor loadings

Interpreting individuals' factor scores

Overview of the measure development process

Descriptive statistics of your dataset

Splitting your dataset

Comparing the halves of your dataset

Measure features: correlations and reliability

Viewing and testing correlations

Internal reliability

When to use EFA

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2

Multidimensional EFA

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

Determining dimensionality

Splitting the BFI dataset

Calculating eigenvalues

Creating a scree plot

Interpreting the scree plot

Understanding multidimensional data

Conducting a multidimensional EFA

Interpreting the results

Investigating model fit

Interpret absolute model fit statistics

Selecting the best model

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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.

Setting up a CFA

Creating CFA syntax from EFA results

Creating CFA syntax from theory

Understanding the sem() syntax

Components of sem() syntax

Run a CFA and interpret loadings

Examine item loadings

Investigating model fit

Absolute fit statistics

Relative fit statistics

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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.

EFA vs. CFA revisited

Differences in estimated factor loadings

Plotting differences in persons' factor scores

Adding loadings to improve fit

Add loadings to improve fit

Compare original model to model with added loadings

Evaluate added loadings with relative fit stats

Improving fit by removing loadings

Remove loadings to improve fit

Compare original model to model with deleted loadings

Evaluate deleted loadings with relative fit stats

Wrap-Up Video

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Rで学ぶ因子分析

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