Advances in Computerized Adaptive Measurement of personality
Author
Nieto, María DoloresEntity
UAM. Departamento de Psicología Social y MetodologíaDate
2019-07-12Subjects
Personalidad - Evaluación - Tesis doctorales; PsicologíaNote
Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Facultad de Psicología, Departamento de Psicología Social y Metodología. Fecha de lectura: 12-07-2019Esta tesis tiene embargado el acceso al texto completo hasta el 12-01-2021
Esta obra está bajo una licencia de Creative Commons Reconocimiento-NoComercial-SinObraDerivada 4.0 Internacional.
Abstract
Personality traits remain a primary focus of study in many psychological areas.
Notwithstanding the advances achieved with the consolidation of the Big Five model as a
common framework of study, personality assessment still presents some limitations that
need to be addressed. First, traditional paper-and-pencil questionnaires are quite long for
modern evaluation settings where several instruments are administered or testing time is
very limited. Second, although some attempts have been made to measure personality
more efficiently through computerized adaptive testing (CAT), they have completely
ignored the hierarchical nature of domains and facets of personality traits. Third, most
personality research and assessment relies on self-report measures, which is well known
are sensitive to the influence of item wording effects that can distort research results.
Accordingly, this dissertation sought to address these limitations by means of three
studies. Study 1 presents the process of construction and calibration of a wide pool to
measure the Big Five facets. Results from a post-hoc simulation study demonstrated that
the adaptive administration of the items produced accurate facet scores using only a third
of the total of the items in the pool. Study 2 goes one step further and illustrates the
construction of a CAT based on the bifactor model, which allows to approach the study of
the Big Five while considering its hierarchical nature. A post-hoc simulation study
demonstrated that the CAT based on the bifactor model is more advantageous to assess
the Big Five personality traits than other traditional competing approaches. Finally, Study
3 used Monte Carlo methods to evaluate the impact of three types of item wording effects
(careless, item verification difficulty, and acquiescence) on person score estimates and
other aspects (model fit, factor loadings, and structural validity) in the context of
unidimensional fixed-length texts. Two models were evaluated to this end: the random
intercept item factor analysis (RIIFA) model and the traditional model with one
substantive factor (1F). Results revealed that, although the RIIFA model was consistently
superior in terms of model fit to the 1F model, it was not able to better estimate the
uncontaminated person scores and other parametes for any type of wording effect than the
1F model. In conclusion, the three studies included in this dissertation provided a series of
tools to measure personality traits more efficiently and contributed to the advancement of
knowledge in the area of wording effect measurement.
Files in this item
Google Scholar:Nieto, María Dolores
This item appears in the following Collection(s)
Related items
Showing items related by title, author, creator and subject.