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| Author(s): |
Tiago de Miranda Fragoso
Total Authors: 1
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| Document type: | Master's Dissertation |
| Press: | São Carlos. |
| Institution: | Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB) |
| Defense date: | 2010-08-04 |
| Examining board members: |
Mariana Cúri;
Mário de Castro Andrade Filho;
Caio Lucidius Naberezny Azevedo
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| Advisor: | Mariana Cúri |
| Abstract | |
Educational evaluations, psychological testing and market surveys are examples of studies aiming to quantify an underlying construct of interest through multiple choice item tests. Item Response Theory (IRT) is a class of models used to analyse such data. There are several IRT models already being used in applied studies to such end, either for dichotomical answers (right/wrong, present/ absent, Yes/No) or for itens with nominal or ordinal answers. However, the large majority of those models make the assumption that only one latent trait is sufficient to explain the probability of a correct answer to an item (unidimensional models). Since many situations in practice are characterized by multiple aptitudes (latent traits) in uencing such probabilities, multidimensional models that take such traits into consideration gain great importance. In the present work, after a thorough review of the litterature regarding multidimensional IRT models, we studied in depth one model: the two parameter multidimensional logistic model for dichotomical items. The marginal maximum likelihood method used to estimate the item parameters and the maximum likelihood method used for the latent traits as well as bayesian methods for parameter estimation were studied, compared, implemented in the R software and then applied to a real dataset to infere depression using the Beck Depression Inventory(BDI)and the Exame Nacional do Ensino Médio (ENEM) (AU) | |
| FAPESP's process: | 08/02528-4 - A multidimensional model for Item Response Theory |
| Grantee: | Tiago de Miranda Fragoso |
| Support Opportunities: | Scholarships in Brazil - Master |