Estimation procedures for latent variable models with psychological traits

The starting point for this thesis is a concrete problem: to measure, using statistical models, aspects of subjective perceptions and assessments and to understand their dependencies. The objective is to study the statistical properties of some estimators of the parameters of regression models with variables affected by measurement errors. These models are widely used in surveys based on questionnaires developed to detect subjective assessments and perceptions with Likert-type scales. It is a highly debated topic, as many of the relevant aspects in this field are not directly observable and therefore the variables used to estimate them are affected by measurement errors. The models with measurement errors were very thorough in literature. In this work we will developed two of the most used approaches that the authors have with this topic. Obviously, according to the approach chosen, different models were proposed to estimate the relationships between variables affected by measurement error. After exposing the main features of these models, the thesis focuses on providing an original contribution to comparative analysis of the two presented approaches.