Example for Factor Analysis

A human resources manager wants to identify the underlying factors that explain the 12 variables that the human resources department measures for each applicant. Human resources employees rate each job applicant on various characteristics using a 1 (low) through 10 (high) scale. The manager collects the ratings for 50 job applicants.

Previous analysis determined that 4 factors account for most of the total variability in the data.

1. Open the sample data set, JobApplicants.MTW.
2. Choose Stat > Multivariate > Factor Analysis.
3. In Variables, enter C1-C12.
4. In Number of factors to extract, enter 4.
5. Under Method of Extraction, select Maximum likelihood.
6. Under Type of Rotation, select Varimax.
7. Click OK.

Interpret the results

Minitab calculates the factor loadings for each variable in the analysis. The loadings indicate how much a factor explains each variable. Large loadings (positive or negative) indicate that the factor strongly influences the variable. Small loadings (positive or negative) indicate that the factor has a weak influence on the variable.

• Company Fit (0.778), Job Fit (0.844), and Potential (0.645) have large positive loadings on factor 1, so this factor describes employee fit and potential for growth in the company.
• Appearance (0.730), Likeability (0.615), and Self-confidence (0.743) have large positive loadings on factor 2, so this factor describes personal qualities.
• Communication (0.802) and Organization (0.889) have large positive loadings on factor 3, so this factor describes work skills.
• Letter (0.947) and Resume (0.789) have large positive loadings on factor 4, so this factor describes writing skills.

Together, all four factors explain 0.754 or 75.4% of the variation in the data.

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