Principal Component Analysis (PCA) is a statistical technique used to identify patterns and reduce the dimensionality of a dataset. When performing PCA on the covariance matrix in SPSS, the results typically provide information on the principal components, eigenvalues, eigenvectors, and variance explained. Here's how to interpret the output:

To interpret the results of PCA, you typically examine the eigenvalues and the variance explained to determine the number of PCs to retain. You can also explore the eigenvectors to understand the underlying patterns or structures in the data and how variables contribute to these patterns.

It is important to note that the interpretation of PCA results should be based on the specific context and objectives of your analysis. Additionally, it is often helpful to combine PCA results with domain knowledge and further analyses, such as factor loadings, variable contributions, and biplots, to gain a comprehensive understanding of the data patterns and relationships.