Scale Development

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Overview. Exploratory factor analysis (EFA)FunctionProcessConfirmatory factor analysis (CFA)FunctionProcess. EFA Functions. Determine how many latent variables underlie responses to a set of itemsDefine the factors underlying responses to a set of itemsRepresent the information in a set of it

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Scale Development

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1. Scale Development Chapter 6 Factor Analysis

2. Overview Exploratory factor analysis (EFA) Function Process Confirmatory factor analysis (CFA) Function Process

3. EFA Functions Determine how many latent variables underlie responses to a set of items Define the factors underlying responses to a set of items Represent the information in a set of items with a small number of factor scores

4. EFA Process Extracting factors Rotating factors Interpreting factors

5. Extracting Factors EFA first determines the linear combination of the items that best represents variability in the scale Then determines the linear combination of the items that best represents whatever variability is left over Keeps doing this until all variability is explained For k items, will extract k factors

6. Extracting Factors (continued) Need to decide how many factors are needed Scree plot: Look for elbow Eigenvalues > 1 Percent variance explained

7. Rotating Factors Initial factor scores are often difficult to interpret. Rotation relaxes some assumptions to provide more interpretable factors What makes factors more interpretable? Items clearly are or are not related to each factor Each item is only related to one or a small number of factors

8. Rotating Factors (continued) Initial extraction has uncorrelated factors Orthogonal rotations will also have uncorrelated factors Oblique rotations allows for correlated factors Important to intelligently decide whether you want an orthogonal or oblique rotation

9. Interpreting Factors Each factor has a set of factor loadings that indicate how strongly each item is related to that factor Similar to regression coefficients To interpret a factor Identify items with strong factor loadings See what all these items have in common Interpreting factors is subjective

10. CFA functions Verifies that a proposed factor structure fits the observed data well Allows you to compare the fit of different factor structures

11. Sample Size Issues EFA 5 to 10 subjects per item up to 300 subjects After that point, ratio can be relaxed CFA 5 subjects per parameter in the model with a minimum of 100 subjects

12. CFA process CFA is performed using structural equation modeling Cannot be done in SPSS requires SEM software such as AMOS or LISREL Determines how well the correlations/covariances implied by your structural model match the observed correlations in the data

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