Componentes de dificultad de tareas de razonamiento deductivo aplicando el modelo LLTM de Fischer
Syllogistic reasoning is an important part of deductive reasoning. In cognitive psychology, the analysis of error sources in solving syllogisms produced explanations such as the atmosphere effect, figure bias and wrong conversion. The Fischer Linear Logistic Test Model (LLTM) was fitted on a set of...
- Autores:
-
Galibert, María Silvia
Abal, Facundo
Auné, Sofía
Lozzia, Gabriela Susana
Aguerri, María Ester
- Tipo de recurso:
- Fecha de publicación:
- 2015
- Institución:
- Universidad Santo Tomás
- Repositorio:
- Universidad Santo Tomás
- Idioma:
- spa
- OAI Identifier:
- oai:repository.usta.edu.co:11634/40364
- Acceso en línea:
- https://revistas.usantotomas.edu.co/index.php/diversitas/article/view/2676
http://hdl.handle.net/11634/40364
- Palabra clave:
- syllogism
deductive reasoning
cognitive psychology
Rasch model
LLTM model.
silogismo
razonamiento deductivo
psicología cognitiva
modelo de Rasch
modelo LLTM.
- Rights
- License
- http://purl.org/coar/access_right/c_abf2
Summary: | Syllogistic reasoning is an important part of deductive reasoning. In cognitive psychology, the analysis of error sources in solving syllogisms produced explanations such as the atmosphere effect, figure bias and wrong conversion. The Fischer Linear Logistic Test Model (LLTM) was fitted on a set of syllogisms in order to identify their difficulty components and estimate their effects. Forty six items were administered with a link design to three groups of 1074 university students. The task consisted in choosing, for each pair of premises, one conclusion scheme and complete it with the suitable terms, if a valid conclusion existed; otherwise, examinees had to select the option of no valid conclusion. The Rasch model was fitted to a subset of 20 syllogisms on which Fischer’s LLTM was applied. Four components were identified that increase syllogistic difficulty: atmosphere effect, figure bias (when they follow the opposite direction of the conclusion or when there is no valid conclusion), figure II and figure III. Two components were found that make the task easier: reversibility of conclusion (universal negative and particular affirmative modes) and lack of valid conclusion. Linear correlation between the estimates of difficulty parameters obtained with Rasch and LLTM models was .96. |
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