Statistical methods for the analysis of Polytomous response data in non-cognitive tests

Abstract

In psychology, education, and other social science disciplines, questionnaires and surveys are useful instruments to measure latent variables such as behaviors, ability, or perceptions about specific constructs. Measuring latent traits, abilities, and in general, any type of nonobservable variables is much more complicated than measuring observable features. Latent variables cannot be measured directly but only indirectly through multiple observed variables called indicators (i.e., observed variables of either polytomous or dichotomous type). The scores on items in the questionnaires can be considered indicators of latent variables and are thus used to measure the unobserved constructs of interest. The main theme of this dissertation is the study and implementation of statistical models and methods for the analysis of polytomous response data in non-cognitive tests. Polytomous data arise when items are scored in more than two categories (e.g., strongly disagree, disagree, agree, strongly agree), as in surveys and questionnaires. We have adapted and extended existing statistical models and methods to meet the requirements of various approaches based on polytomous data. The empirical data sets used for the applications of the models are meant as exemplars of a broader category and a more extensive range of domains.