Plantear y formular un problema de investigación: un ejercicio de razonamiento / Raise and formulate a research problem: an exercise of reasoning / Apresentar e formular um problema de pesquisa: um exercício de raciocínio

Resumen Introducción: Plantear un problema de investigación requiere aclarar la diferencia entre problema real, problemática, problematización y problema de investigación. A su vez requiere pensar el investigador como fuente del problema y de razonamiento, considerando los tres tipos de razonamiento: deductivo, inductivo y abductivo. Propone cuatro criterios para construir un problema de investigación: 1. Descripción del fenómeno 2. Descripción del desequilibrio 3. Descripción espacio-temporal 4. Descripción de los sujetos de investigación. Finalmente se considera la diferencia entre los paradigmas en la construcción de problemas de investigación.

Abstract Introduction. Raising a research problem requires clarifying the difference between real problem, problem, problematization and research problem. At the same time, it requires thinking of the researcher as the source of the problem and of reasoning, considering the three types of reasoning: deductive, inductive and abductive. It proposes four criteria to build a research problem: 1. Description of the phenomenon 2. Description of the imbalance 3. Description of the time space 4. Description of the research subjects. Finally, the difference between the paradigms in the construction of research problems is considered.

Resumo Introdução: Colocar um problema de pesquisa requer esclarecer a diferença entre problema real, problemático, problematização e problema de pesquisa. Por sua vez, o pesquisador precisa pensar como fonte do problema e do raciocínio, considerando os três tipos de raciocínio: dedutivo, indutivo e abdutivo. Ele propõe quatro critérios para construir um problema de pesquisa: 1. Descrição do fenômeno 2. Descrição do desequilíbrio 3. Descrição espaço-temporal 4. Descrição dos sujeitos da pesquisa. Por fim, considera-se a diferença entre paradigmas na construção de problemas de pesquisa.

Componentes de dificultad de tareas de razonamiento deductivo aplicando el modelo LLTM de Fischer / Difficulty components of deductive reasoning tasks by applying Fischer's LLTM model

El razonamiento silogístico es parte importante del razonamiento deductivo. El análisis de las fuentes de error en la resolución de silogismos originó, dentro de la psicología cognitiva, explicaciones como el efecto atmósfera, el sesgo de la figura y la conversión ilícita. En este trabajo se ajustó el modelo LLTM de Fischer para identificar componentes de dificultad de silogismos y estimar sus efectos. Se administraron 46 ítems con un diseño de enlace a tres grupos, con un total de 1074 estudiantes universitarios. Para cada par de premisas se debía escoger un esquema de conclusión y completarlo con los términos extremos o reconocer la falta de conclusión válida. El modelo de Rasch se ajustó sobre un subconjunto de 20 silogismos y se aplicó el modelo LLTM de Fischer. Se identificaron, aumentando la dificultad, cuatro componentes: efecto atmósfera y sesgo de figura (cuando éstos están en dirección contraria a la conclusión o no hay conclusión válida), figura II y figura III. El carácter reversible de la conclusión (modos universal negativo y particular afirmativo) y la falta de conclusión válida fueron componentes facilitadores. La correlación entre las estimaciones de los parámetros de dificultad bajo el modelo de Rasch y el LLTM fue 0,96.

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.

Efecto de la introducción de negaciones sobre la dificultad de los argumentos condicionales simbólicos / Effect of the introduction of negations on the difficulty of symbolic conditional arguments

En este trabajo se estudió la dificultad para evaluar la validez de los argumentos condicionales Modus Ponens (MP), Modus Tollens (MT), Negación del Antecedente (NA) y Afirmación del Consecuente (AC) de contenido simbólico cuando se introducen negaciones explícitas en el antecedente y/o el consecuente de la premisa mayor. No se encontraron diferencias significativas entre los porcentajes de aceptación en MP y AC de acuerdo a la presencia o no de negaciones. En MT el argumento con antecedente y consecuente negado obtuvo un porcentaje de aceptación significativamente menor a los que tienen negado un sólo componente. En NA el argumento con ambas cláusulas negadas tiene un porcentaje de aceptación menor al resto. Se discuten las implicaciones teóricas de estos resultados a partir de su comparación con los patrones propuestos en Schroyens, Schaeken & D'Ydewalle (2001).

This paper studies the difficulty to evaluate the validity of conditional arguments Modus Ponens (MP), Modus Tollens (MT), Denying the Antecedent (NA) and Affirming the Consequent (AC) of symbolic content when explicit negations are introduced into the antecedent and/or the consequent of the main premise. No significant differences were found among the acceptance percentages in MP and AC according to the presence or absence of negations. In MT, the argument with antecedent and consequent denied obtained a significantly lower acceptance percentage than arguments with only one component denied. In NA, the argument with both clauses denied has a lower acceptance percentage than the rest. The theoretical implications of these results are discussed on the basis of their comparison with the patterns proposed in Schroyens, Schaeken & D'Ydewalle (2001).

Aplicación del modelo LLTM de Fischer al análisis de las fuentes de dificultad de ítemes de razonamiento deductivo / Application of the Fischer LLTM model to the analysis of the sources of difficulty of deductive reasoning items

El Modelo Logístico Lineal de Rasgo Latente (LLTM) de Fischer permite descomponer la dificultad de un ítem como suma de los efectos debidos a las fuentes de dificultad predichas por las teorías cognitivas, decidir si éstos son significativos y estimarlos. En el estudio que se informa se diseñaron y elaboraron 24 ítemes de razonamiento deductivo teniendo en cuenta las fuentes de dificultad predichas por las teorías cognitivas y por la experiencia educacional. Se administraron a 251 estudiantes de la Carrera de Psicología de la Universidad de Buenos Aires (UBA). Se describe el procedimiento para seleccionar un subconjunto de los mismos al cual ajuste el modelo LLTM. El objetivo de este trabajo fue verificar la pertinencia de las fuentes de dificultad consideradas y orientar la construcción de nuevos ítemes. Se logró un buen ajuste del modelo de Rasch (p = .89) y del modelo LLTM (p = .11) sobre 12 de ellos. Los valores z de Wald resultaron no significativos para los 12 ítemes mencionados. La correlación de los parámetros de dificultad estimados en ambos modelos fue: r = .99. Se consideraron cinco componentes que resultaron significativos. Éstos fueron, en orden decreciente de dificultad, la presencia de: (a) falacias de afirmación del consecuente y de negación del antecedente, (b) negación afectando a la disyunción o conjunción, (c) contenido abstracto o simbólico, (d) cuantificadores y (e) condicionales. Se verificaron los supuestos de invariancia para los parámetros de los ítemes y de los sujetos. Los resultados de esta etapa exploratoria alientan a seguir construyendo ítemes tomando en cuenta las fuentes de dificultad halladas.

The processes involved in deductive reasoning have been studied by Cognitive Psychology since the seventies. Many hypotheses have been put forward to explain the difficulties in solving simple reasoning problems when considering their logical connectives, content and context of the tasks in which they are presented. These hypotheses have led to the development of different theories of reasoning like those based on the formal inference rules approach (Braine, 1978; Braine & O'Brien, 1991; Braine & Rumain, 1983; Rips, 1994), the Pragmatic Schemas Theory (Cheng & Holyoak, 1985) and the theory of semantic mental models (Johnson-Laird, 1983, Johnson-Laird & Byrne, 1991). The componential models of the Item Response Theory have allowed Psychometry to explain said these processes (Embretson, 1994). Thus, for instance, the Linear Logistic Latent Trait Model (LLTM) (Fischer, 1973, 1997), an extension of the Rasch model, expresses item difficulty as the sum of the effects due to the sources of difficulty predicted by the mentioned cognitive theories, which enables us to decide whether these effects are significant and estimate them. In other words, the Rasch item parameters β1 are linearly decomposed in the form where p is the number of components considered, αl -the basic parameters of the model, expresses the difficulty of each component l, w il is the weight of αl with respect to the difficulty of the item i and c is an arbitrary normalization constant. Formula (1) implies that the application of the LLTM model makes sense only when the Rasch model fits the data. On the other hand, if the proposed components were sufficiently exhaustive to explain the differences between the items, formula (1) would allow us, once the basic parameters αl have been estimated, to recover estimates similar to those obtained directly by the application of the Rasch model, which would imply a high correlation between the parameters estimated under both models. The identification of the difficulty components and the estimate of their effects may be useful to generate items with preset difficulty parameters. This paper describes the process to find a subset of deductive reasoning items to which the LLTM model fits well. A set of 24 deductive reasoning items were designed and created considering the sources of difficulty predicted by cognitive theories and educational practice. The objective is to verify the suitability of such sources and to guide the construction of new items. Each item may consist of one, two or three premises and one conclusion. The individual must decide whether the conclusion is true or false. Nine items are made of concrete content, neutral to avoid any bias due beliefs or opinions, and the remaining ones have abstract or symbolic content. They were administered to a sample of 251 students of Psychology (Universidad de Buenos Aires - Argentina), composed of 24% males and 76% females, whose average age is 22.68 (DS = 6.35). Good fit for the Rasch model (p = .89) and for the LLTM model (p = .11) were obtained for 12 of them. The Wald z-values were not significant for the 12 items mentioned before. The linear correlation between the parameters estimated under both models was r = .99. Five components that turned out to be significant were considered. These components are listed in a decreasing level of difficulty: (a) affirmation of the consequent and negation of antecedent fallacies, (b) negation when affecting disjunction / conjunction, (c) abstract or symbolic content, (d) quantifiers and (e) conditionals. The two assumptions that refer to both, the item and subject parameter invariance, were checked. The results of this exploratory step encourage us to go on constructing new items taking into account the sources of difficulty that were found.