Evaluating and comparing profiles of burglaries developed using three statistical classification techniques: cluster analysis, multidimensional scaling, and latent class analysis

Abstract

While there are a variety of statistical classification techniques available, the most prominent in the behavioral sciences are Cluster Analysis (CA), Multidimensional Scaling (MDS), and Latent Class Analysis (LCA). Researchers often rely on person-oriented statistical classification techniques to identify and understand the latent heterogeneity in the complex individuals that we study. Using data on 405 randomly selected solved burglaries committed in Florida, this study is the first to conduct a head-to-head comparison of the benefits and weakness of each analysis and evaluate the resultant typologies and predictive validity when all three analyses are applied to the same dataset. Findings suggest that the number and nature of resultant subtypes differ depending on the statistical classification technique employed. We conclude that LCA is superior to MDS and CA due to its ability to objectively evaluate model fit and handle missing data, balance of parsimony and complexity in the results, and reliability and accuracy stemming from the first test of predictive validity among the three statistical classification techniques. Implications for future research and the application and testing of statistical classification techniques are also discussed.