Bayesian networks have been gaining prominence among scientists over the recent decade and the new insights generated by this powerful research method can now be found in studies that circulate well beyond the academic communities. As a result, many practitioners and managerial decision-makers see more and more references to Bayesian networks in all kinds of scientific and business research, ranging from biostatistics to marketing analytics.
It is not surprising that the new Bayesian network paradigm prompts comparisons to more conventional methods. In the field of market research, for instance, long-established methods, such as factor analysis remain in daily use today. Given that there exists a direct counterpart to factor analysis in the Bayesian network framework, we want to highlight similarities as well as fundamental differences. The goal of this paper is to present both methods side-by-side and thus help researchers to correctly compare and understand the respective results. More specifically, we want to establish the semantic equivalents between the traditional statistical factor analysis approach and BayesiaLab’s method based on Bayesian networks, which we refer to as Probabilistic Latent Factor Induction.
Download the white paper here (4.6 MB)