Evaluating dimensionality reduction strategies on mixed-type datasets: a comparative analysis using Python, R and SPSS

Multidimensional and multivariate datasets encompassing diverse data types provide researchers a platform to apply a range of dimensionality reduction methods. This study assessed principal components analysis, factor analysis, multiple correspondence analysis, categorical principal components analysis and factor analysis for mixed data. We examined different strategies based on input variable measurement scale selection and variable value coding. The objectives were to highlight the importance of applying different analysis strategies, ascertain the applicability of these methods to multidimensional mixed-type data, compare outcomes, and evaluate execution times from three different statistical software to identify notable computational and interpretive drawbacks. Significant issues included the 'curse of dimensionality' concerning the determination of crucial dimensions, the need for increased computing power, the absence of software code for some methods and criteria, discrepancies in results' calculations across software packages, and the inability of some software packages to handle numerous variables or binary-coded variables and perform parallel analysis.