Integrated assessment of financial knowledge through a latent profile analysis

Knowledge is defined as a multi-faceted latent variable that is not directly measurable but through manifest variables, i.e., items. Latent variable models are, therefore, widely used in this context to analyze latent traits from items, usually expressed by ordinal variables. Finding homogeneous groups of units according to their knowledge levels turns out helpful to policymakers and to any other who has to take decisions into the domain. As a result, latent variables models are combined within integrated approaches to find homogeneous groups. The present work proposes a coordinated strategy combining the item response theory (IRT) models with the archetypal analysis (AA). The proposed method is applied to a data set of 625 Italian respondents to a survey conducted within the European project “Fintech and Artificial Intelligence in Finance”. Empirical evidence demonstrates that the proposed method is an effective and helpful tool to get homogeneous groups and their respective profiles according to the knowledge levels of the respondents based on their responses to the survey.