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  3. Factor Analysis in R
  • 1

    Evaluating your measure with factor analysis

    Free

    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.

Initializing

Exercise

Differences in estimated factor loadings

The differences between EFAs and CFAs are evident when examining the factor loadings. Not only are the procedures mathematically different, but the number of estimated parameters is also different. By default, EFAs estimate all possible item/factor pairs, while CFAs only estimate specified item/factor relationships. Let's take a look at a few of those estimated loadings and see how they differ.

Which of these loadings represent the EFA and CFA estimates of the relationship between item A1 and the fourth factor (named MR5)?

Instructions 1/2

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  • Check out the first five rows of loadings in EFA_model. Remember that the EFA estimates all possible item/factor pairs.
  • Look at the first five loadings in EFA_model. This output only estimates specified item/factor relationships.