Factor Analysis Reinvented—Probabilistic Latent Factor Induction with Bayesian Networks and BayesiaLab
Recorded on June 29, 2018.
Bayesian networks have been gaining prominence among scientists over the last decade, and insights generated with this new paradigm can now be found in books and papers that circulate well beyond the academic community. Practitioners and managerial decision-makers see references to Bayesian networks in studies ranging from biostatistics to marketing analytics. Therefore, it is not surprising that the relatively new Bayesian network framework prompts comparisons with more conventional methods, such as Factor Analysis, which remains widely used in many fields of study.
The goal of this webinar is to compare a traditional statistical factor analysis with BayesiaLab's new workflow for Probabilistic Latent Factor Induction using a psychometric example.
Traditional Factor Analysis
Factor Analysis is a statistical method used to describe variability among observed variables in terms of a potentially lower number of unobserved variables called factors. It is possible, for example, that variations in three or four observed variables mainly reflect the variations in a single unobserved variable, or in a reduced number of unobserved variables. The observed variables can be seen as manifestations of abstract underlying (and unobserved) dimensions or (latent) factors.
BayesiaLab's Probabilistic Latent Factor Induction
Probabilistic Latent Factor Induction is a workflow within the BayesiaLab software package, which has the same objective as a traditional factor analysis, i.e., variable reduction, but works entirely within the framework of Bayesian networks and is based on principles derived from information theory. This approach also takes advantage of recent advances in machine learning, especially BayesiaLab's Unsupervised Learning algorithms.
Example: The HEXACO Personality Inventory
Given that factor analysis originated in psychometrics, we shall explore a prototypical psychometric dataset, namely the HEXACO Personality Inventory. The HEXACO model of personality conceptualizes human personality in six dimensions. It was proposed as an alternative to the Big Five/FFM (Five Factor Model):
- Honesty-Humility (H)
- Emotionality (E)
- Extraversion (X)
- Agreeableness (versus Anger) (A)
- Conscientiousness (C)
- Openness to Experience (O)
Our objective is to reexamine the proposed HEXACO factors and present an alternative latent variable structure. For this purpose, we utilize the publicly-available HEXACO dataset from the Open Source Psychometrics Project. Finally, we wish to highlight the speed of the factor induction and validation process with BayesiaLab, which helps researchers focus on the substantive interpretation of results and spend less time dealing with statistical minutiae.