Latentosuuksinaanalyyseissä

Latentosuuksinaanalyysi, often translated as latent structure analysis, is a statistical technique used to identify underlying unobserved variables or factors that influence observed data. It is a broad category encompassing various methods, including factor analysis and latent class analysis. The core idea is that the relationships among observed variables can be explained by a smaller number of latent variables. These latent variables are not directly measured but are inferred from the patterns of responses or measurements in the observed variables.

In factor analysis, latent variables are assumed to be continuous. The goal is to explain the correlations between a set of observed variables by a smaller set of common factors. For instance, in a psychological assessment, multiple test items might be explained by a single underlying latent trait like "intelligence" or "anxiety." The strength of the relationship between each observed variable and the latent factor is represented by factor loadings.

Latent class analysis, on the other hand, assumes that the latent variables are categorical. It aims to identify distinct subgroups or "classes" within a population, where individuals in the same class share similar patterns of responses to observed variables. For example, a survey on consumer preferences might reveal distinct latent classes of shoppers based on their purchasing habits. Both factor analysis and latent class analysis are valuable tools for simplifying complex datasets and uncovering hidden structures within them, aiding in theory development and data interpretation across various fields like psychology, sociology, marketing, and medicine.