# Enter your data for Factor Analysis

Stat > Multivariate > Factor Analysis

Specify the data for your analysis, enter the number of factors to calculate, and specify the extraction method and type of rotation.

In Variables, specify the columns of data that you want to analyze. You must have two or more columns of numeric data, with each column representing a different measurement. If a missing value exists in any column, Minitab ignores the entire row.
###### Note

If you want to enter a stored correlation or covariance matrix, or the loadings from a previous analysis, instead of using raw data, click Options.

In this worksheet, each column contains measurements of characteristics of each job applicant.

C1 C2 C3 C4 C5 C6 C7 C8
Company fit Communication Self-confidence Academic record Resume Experience Attitude Organization
5 9 8 2 2 5 4 8
10 9 5 10 8 5 5 4
4 7 6 6 5 8 7 2
2 2 3 4 4 7 8 4
8 4 3 8 9 2 4 9
7 5 9 5 7 9 8 7

## Number of factors to extract

Enter the number of factors to extract from the data. The number of factors must be at least 1 and cannot exceed the total number of variables. For best results, you should not have more than one factor for every 3 variables in your data. For example, if you have 12 variables, you should extract, at most, 4 factors.

If you do not know the number of factors to extract, leave the field blank and specify principal components as the method of extraction. Click Graphs and display the Scree plot. Minitab calculates the maximum number of factors, which equals the number of variables that you entered. Use the results to determine the number of factors to extract, and then enter that number when you repeat the analysis. For more information, go to Step 1: Determine the number of factors.

If you use maximum likelihood as the method of extraction, you must enter the number of factors. The maximum number of factors with maximum likelihood is one less than the number of variables in your data.

## Method of Extraction

Select a method to extract the factors.
• Principal components: Select this option if you do not know the number of factors to extract, if you cannot assume that the factors and errors obtained after fitting the factor model follow a normal distribution, or you do not have a large number of observations.
• Maximum likelihood: Select this option if you know the number of factors, you can assume that the factors and the errors obtained after fitting the factor model follow a normal distribution, and you have a reasonably large data set.
###### Note

When you know the number of factors, Maximum likelihood often gives factors that fit the data better (have smaller residuals). However, for some data, the factor loadings from the maximum likelihood method are sensitive to the choice of initial communalities and convergence criterion. The principal components method works in many cases when the maximum likelihood method does not.

## Type of Rotation

Select an option to orthogonally rotate the initial factor loadings. Minitab rotates the axes to give you a different perspective, which can help you to interpret the factors.

The original factor loadings are often difficult to interpret. Rotation usually creates a simpler factor structure and makes the factors more clearly distinguishable. Rotation also tends to remove general factors that load highly on all variables.

Minitab rotates the loadings to minimize a simplicity criterion. A parameter, gamma (γ), within this criterion, is determined by the rotation method. If you use a method with a low value of gamma, the rotation tends to simplify the rows of the loadings. If you use a method with a high value of gamma, the rotation tends to simplify the columns of the loadings.