Creating a New Factor Analysis

1. In the Analyst toolbox, expand the Advanced analytics folder, right-click on the Factor Analysis folder and select Insert Factor Analysis (inside) .



Figure 1 - Creating a new factor analysis object

A new Factor Analysis object is created in the folder.

2. Double-click on the new object to open the Factor Analysis designer page .



Figure 2 - The Factor Analysis designer page

The fields are as follows:

• Variables - this field will contain the variables that will be used in the factor analysis. The field can contain one or more variables, and the variables must all be numeric. The supported question types are: Numeric, Numeric List answers, Single with numeric codes/scale and Grid elements with numeric scale. Drag and drop one or more questions from the Data Source toolbox to populate the field.
• Filter - this field may contain a filter to restrict the underlying dataset the factor analysis is performed on. The field can contain only filters of type Filter Expression. Drag and drop a filter expression to populate the field. To review or edit the filter expression, click the "..." button.

Click Show advanced settings to reveal the following dialog .



Figure 3 - The Factor Analysis Advanced Settings dialog

The properties are as follows:

• Matrix - states if the factor analysis is to be based on the correlation matrix or the covariance matrix of the underlying data. The default choice is correlation matrix. If the variables in the underlying data have widely different scales, it is recommended to use the correlation matrix in order to get interpretable results from the factor analysis.
• Minimum eigenvalue / Number of Factors - these settings are used to calculate the number of factors in the factor analysis. The default setting for a factor analysis is to base the calculation on eigenvalue levels, and the default minimum value for an eigenvalue is 1.0. The “Number of factors” setting is used if you want to include a specific number of factors in the factor analysis, rather than letting the number of factors be based on a minimum eigenvalue (go to Determining the Number of Factors to Retain for more information).
• Minimum number of Cases - is used to specify a minimum number of cases (data rows) required to run the multivariate calculation. The default value is 0, imposing no lower limit. If the number of available cases in the calculation is less than the specified limit, an error message will be displayed.
• Display matrix - states if the matrix (correlation or covariance) used as the basis for the factor analysis is shown in the calculation result.
• Rotate factors - is used to perform an orthogonal rotation of the factor loadings, in order to make the interpretation of the factors easier. The orthogonal rotation is based on the Varimax algorithm. A Varimax rotation is an orthogonal rotation, meaning that it results in uncorrelated components. The Varimax rotation will not affect the amount of variance explained by the rotated factors compared to the original factors.
• Scree plot - controls if a scree plot is shown for the factors. The plot shows the eigenvalues corresponding to each factor as a curve, and can be used as a graphical interpretation of how much of the total variance that each factor retains, as well as a means to identify the appropriate number of factors for the model.
• Case selection - this setting specifies how calculations are to be handled if the number of available cases (data rows) exceeds the system limit for the maximum number of cases allowed in multivariate calculations. The limit is 10.000 data rows. The options are:
• Default - The last 10.000 of the available data rows will be used. This corresponds to the latest collected data.
• Random - A random subset containing 10.000 of the available data rows will be used.
• First - The first 10.000 of the available data rows will be used. This corresponds to the earliest collected data.
• Data selection - specifies which data values are to be used for categorical variables in the multivariate calculation. The options are:
• Codes - The variables' answer codes will be used. This requires the codes to be numerical.
• Scores - The variables' answer scores will be used. This requires the answer scores to be specified. Any answers with missing scores will be omitted by list-wise deletion in the calculation.
• Highlight limit - will apply conditional formatting to cells in the correlations in the correlation matrix and the factors table dependent on a defined threshold. Positive correlations over this threshold are highlighted in green, negative correlations are highlighted in red. The highlight limit can be adjusted and applied to tables without creating a new calculation run by changing the values and selecting the Apply button.