1 / 11

Understanding Factor Analysis: Techniques, Assumptions, and Practical Applications

Factor analysis is a statistical method used to reduce a large set of variables into a smaller number of factors through the examination of correlations among variables. It helps in theory development, identifying individual differences, and refining measurement scales. The process includes factor extraction, rotation to interpret factors better (commonly using VARIMAX), and determining the number of factors based on eigenvalues. Proper reporting involves evaluating the percentage of variance explained and using the scree plot. Factor analysis can also enhance predictive models in areas like leadership ability linked to emotional intelligence.

starr
Download Presentation

Understanding Factor Analysis: Techniques, Assumptions, and Practical Applications

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Overview of Factor Analysis • Construct combinations of quantitative variables. • Reduce a large set of variables to a smaller number of factors.

  2. Uses of Factor Analysis • Develop and test theories • Describe differences between individuals • Determine which variables/measurements may be dropped from a scale or battery

  3. Extraction of Factors • Based on correlations among variables • The first factor will explain the most variance in the original scores, with each factor explaining less variance.

  4. Factor Rotation • Transform the loadings to make it easier to interpret what the factors represent. • A loading indicates how important a variable is for that particular factor. • VARIMAX is the most popular method: • Maximizes variability in factor loadings within factors • Maintains orthogonal factors

  5. Assumptions for Factor Analysis • Linear relationships among variables • Multivariate normal distributions

  6. Reporting Factor Analysis • Decide on number of factors based on factor extraction (principal components analysis). • The eigenvalue for a factor represents the amount of variance in the original scores explained by the factor. • Also look at percentage of variance explained.

  7. Reporting Factor Analysis • One way to decide on the number of factors is to only use those with eigenvalues greater than one. • The other way is to examine the scree plot and discard factors after the plot flattens out.

  8. Reporting Factor Analysis • Look at the rotated factor loadings to interpret and name the factors. • A confirmatory factor analysis can be done as a follow-up.

  9. Review Question! What are the two steps in factor analysis?

  10. Choosing Stats A business school obtains a sample of students who have taken an emotional intelligence test and a leadership inventory. They would like to use information from this data set to develop a way of predicting leadership ability for students who have known scores on the emotional intelligence test.

More Related