Can anybody tell if SPSS is capable to perform Robust principal component analysis and Geographically Weighted Regression? If Yes, how can we apply it to the dataset for doing the analysis?
Dear Salman Ahmed, thanks for asking such an important question. This question is very important. We can define principal components analysis (PCA) as a variable-reduction technique that shares many similarities to exploratory factor analysis. It aims to reduce a larger set of variables into a smaller set of 'artificial' variables, called 'principal components', which account for most of the variance in the original variables. Currently, PCA is interested in data analysis systems in order to reduce large dimensions into the small components. Alternatively, it’s possible to analyze PCA via software like Python, R and SPSS. But here I’m going to share with you how to perform PCA using SPSS based on your question. Since PCA has some assumptions, you can also use Categorical Principal Component Analysis (CATPCA). Anyway, to analyze your data follow these steps in SPSS: Click Analyze > Dimension Reduction > Factor... on the main menu, as shown below link. Please have a look this link: https://statistics.laerd.com/spss-tutorials/principal-components-analysis-pca-using-spss-statistics.php
You can assess my paper here also:
https://www.researchgate.net/publication/364349821_Study_on_categorization_of_factors_affecting_smallholder_dairy_production_in_Siltie_Zone_Southern_Ethiopia_applying_multivariate_analysis_approaches
Kind regards
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