Here is a detailed explanation of how to conduct an exploratory factor analysis test in SPSS and how to interpret the results.
What is an EFA’s primary goal, to begin with?
An EFA’s goal is to simplify the number of variables needed to describe a multidimensional data collection. Following the validation of a questionnaire, a method known as confirmatory factor analysis can be applied. A “sister” software to SPSS called AMOS supports this. (Source)
- Identification and comprehension of the fundamental notion
- if there are too many variables in a study, some of which overlap because they have similar meanings and behaviors, the number of variables is reduced.
Assumptions of exploratory factor analysis:
- Sample size (N > 150)
- Eligibility of correlation matrix for factorization
- Linearity
- No outliers
Now to the business of the day
- Select a cell in the dataset.
- Click Dimension Reduction, followed by Factors, under the Statistical Analyses group on the Analyse -it ribbons tab.
- Select the variables from the Variables list.
- Type the number of underlying factors to try and extract in the Factors to extract edit box.
Optional: Select the Color maps check box to highlight the coefficients based on their sign and magnitude.
Optional: Select the type of rotation in the Rotation drop-down list, choose the rotation in the Method drop-down list, type any other parameters that are needed, and rotate the extracted factors to facilitate simpler understanding.
Select Extraction from the menu. Principal components is the option selected in the Method box. Select the Correlation matrix in the Analyze box.
Click the Options button. In the Missing value box, choose Exclude cases pairwise.
Make the selections Sorted by size and Suppress absolute values less than in the Coefficient Display Format box. Please enter 0.30 in the Suppress box.
providing an example, describe how to carry out an exploratory factor analysis test in SPSS statistical software.
Exploratory Factor Analysis Output Results: Explanation Step by Step
We should move forward with exploratory factor analysis if the Kaiser-Meyer-Olkin Measure of Sampling Adequacy is equal to or better than 0.60; this indicates that the sample utilized was suitable. We should move forward with the exploratory factor analysis if Bartlett’s test of sphericity is significant (p <0.05).
How to Report Total Variance explained Table in SPSS Output?
The Initial Eigenvalues are shown in the table. Only components with Total Initial Eigenvalues larger than 1 should be considered. Only two components in our instance have Total Initial Eigenvalues that are higher than 1. These two elements account for 63.41% of the variation. We can therefore infer that there are two components. But we also need to consider the Scree storyline.
How to Report Scree Plot in SPSS Output?
We have two components, according to the scatter plot.
How to Report Pattern Matrix Table in SPSS output?
The factor weights are displayed in the table. The first element consists of anxious, ashamed, afraid, upset, and irritable feelings. The second element consists of positive emotions including joy, inspiration, focus, excitement, and pride.
How to report Component Correlation Matrix in SPSS Output?
There is no significant correlation between the elements, which is beneficial for our analysis, according to the table component correlation matrix.
For more information, please visit our Reporting Factor Analysis Page.
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