A revision of the causality concept in the confirmatory factor analysis framework

En Psicología el Análisis Factorial Confirmatorio (AFC) es ampliamente utilizado en el proceso de elaboración de tests y escalas, siendo una técnica definida formalmente como potencial generadora de modelos causales de medida. No obstante, en numerosos estudios la aplicación del AFC se elabora a partir de diseños de investigación no experimental, en donde muchos investigadores realizan rutinariamente atribuciones sobre los modelos y los instrumentos que van más allá de una perspectiva estrictamente relacional o predictiva. En este trabajo se presenta una revisión del concepto de causalidad desarrollado dentro del marco de los Modelos de Ecuaciones Estructurales (MEE) y del AFC, con varias recomendaciones de carácter teórico y práctico dirigidas a los investigadores aplicados. Se discute sobre el estatus de las relaciones causales en los diseños no experimentales y sobre la necesidad de pensar en términos causales con el fin de potenciar el alcance explicativo de los modelos AFC en Psicología.Depto. de Psicobiología y Metodología en Ciencias del ComportamientoFac. de PsicologíaTRUEpu

Contribution of the Confirmatory Factor Models to the Evaluation of Internal Structure from the Validity Perspective

En el presente trabajo se reexaminan algunas de las contribuciones más importantes del Análisis Factorial Confirmatorio (AFC) aplicado a la evaluación de estructura interna. Nuestro objetivo es mostrar la utilidad que tienen estas técnicas para conectar el modelo factorial con el modelo teórico (atendiendo al contenido de los ítems, a la finalidad de los tests, y al uso de las puntuaciones derivadas (total vs subescalas)). En la primera sección caracterizamos el tipo de aplicaciones que aparecen en la literatura científica. En las siguientes secciones a) discutimos las ventajas que tiene la aproximación confirmatoria sobre la exploratoria, b) valoramos las ventajas e inconvenientes del modelo bifactor confirmatorio, y c) introducimos el modelo bifactor S-1 como una prometedora alternativa. En la última sección ilustramos con un ejemplo empírico la utilidad de los distintos modelos AFC examinados en este trabajo.Ministerio de Ciencia, Innovación y Universidades (MCIN-AEI)Depto. de Psicobiología y Metodología en Ciencias del ComportamientoFac. de PsicologíaTRUEpu

Scale validation conducting confirmatory factor analysis: a monte carlo simulation study with LISREL

When psychologists are going to test their theoretical models (at the time of planning the research study), several questions may arise regarding the quality and potential accuracy of the estimation of Confirmatory Factor Analysis (CFA) models under certain applied conditions. For example, one question is the minimum sample size (N) and/or the number of indicators per factor (p/k) that is needed to estimate the CFA models properly. Many of these questions can be answered through simulation studies, because the magnitudes of the population factor loadings (λik) are known in advance. Monte Carlo simulation uses random sampling and statistical modeling to estimate mathematical functions, and is a key tool for studying analytically intractable problems (Harrison, 2010). It is quite frequent to find in the literature simulation studies that use CFA to fit measurement models. However, there is a lack of technical information in the published research to replicate this type of studies, probably due to length limitations. Furthermore, researchers and scholars must often go to numerous and technically complex sources of information to understand the laborious simulation and estimation process. In this paper we present complete technical information to conduct Monte Carlo simulation CFA-studies with PRELIS and LISREL programs. The LISREL program, apart from being one of the most used (and validated) software programs, is historically linked to CFA (Brown, 2015). Although it is a commercial program, there is a free student version that allows performing all the simulation tools and the analysis techniques shown in this work. Through a simulation study, we have evaluated the necessary conditions to test the CFA models fit, so we have chosen population structures formed by a single common factor to design the simulation study and low-moderate population factor loadings. It should be noted that these types of studies generate a considerable volume of information, even when using a simple unifactorial model. This approach was adopted for this study for ease of understanding the principles. Included, however, are the PRELIS and LISREL syntax examples for multidimensional factor structures. In the Supplementary Material (https://figshare.com/s/18eb0e998150d39bc952) we have attached both the simulated data and the CFA results (parameter estimates, standard errors and goodness-of-fit measures).Depto. de Psicobiología y Metodología en Ciencias del ComportamientoFac. de PsicologíaTRUEpu

Reconsidering the Conditions for Conducting Confirmatory Factor Analysis

There is a series of conventions governing how Confirmatory Factor Analysis gets applied, from minimum sample size to the number of items representing each factor, to estimation of factor loadings so they may be interpreted. In their implementation, these rules sometimes lead to unjustified decisions, because they sideline important questions about a model’s practical significance and validity. Conducting a Monte Carlo simulation study, the present research shows the compensatory effects of sample size, number of items, and strength of factor loadings on the stability of parameter estimation when Confirmatory Factor Analysis is conducted. The results point to various scenarios in which bad decisions are easy to make and not detectable through goodness of fit evaluation. In light of the findings, these authors alert researchers to the possible consequences of arbitrary rule following while validating factor models. Before applying the rules, we recommend that the applied researcher conduct their own simulation studies, to determine what conditions would guarantee a stable solution for the particular factor model in question.Depto. de Psicobiología y Metodología en Ciencias del ComportamientoFac. de PsicologíaTRUEpu

The Hedonic and Arousal Affect Scale (HAAS): a brief adjective checklist to assess affect states

The present study aims to develop a brief instrument to assess self-reported affective experiences, the Hedonic and Arousal Affect Scale (HAAS), rooted in the valence-arousal model of affect. Throughout four different studies, we found that: (1) the 12-item version showed a better goodness-of-fit than an initial longer version (Study 1; n = 259); (2) the two-dimensional model of affect (i.e., four-factor model: positive affect and high arousal, positive affect and low arousal, negative affect and high arousal, and negative affect and low arousal) showed the best fit to our data (Study 2; n = 525); (3) the HAAS showed evidence of concurrent validity with related measures in the field (Study 3; n = 480); and (4) it showed partial support for temporal invariance (Study 4; n = 262). The content and psychometric qualities of the HAAS make it a suitable brief scale to measure affect and could be particularly useful for repeated measures designs such as psychological interventions, experimental studies, or ecological momentary assessment studies.Depto. de Psicobiología y Metodología en Ciencias del ComportamientoFac. de PsicologíaTRUEpu

The Role of Emotional Intelligence, Meta-Comprehension Knowledge and Oral Communication on Reading Self-Concept and Reading Comprehension

Emotional Intelligence (EI) is considered a fundamental variable for a person’s adequate psychosocial adjustment. In education, its importance transcends the level of interpersonal relationships, and has been proposed as a variable that somehow influences academic performance, although there is controversy about whether its effect is direct or, rather, an intermediate variable. The present research analyses, from a sample of 327 students (52.6% female and mean age = 14.5), the relationship of EI with respect to the knowledge and management of oral communication and reading meta-comprehension strategies, which should directly affect different educational outcomes. In order to assess both the direct and indirect effects of these variables, a Partial Least Squares Structural Equation Modelling (PLS-SEM) approach has been proposed, due to its versatility and the possibility of jointly analysing reflective and formative measures. The results show that EI indirectly affects reading self-concept and reading comprehension, as it is involved in the management and handling of both effective oral communication and reading meta-comprehension strategies.Ministry of Science, Innovation and Universities (Spain)Depto. de Psicobiología y Metodología en Ciencias del ComportamientoFac. de PsicologíaTRUEpu

People’s attitudes towards the agrifood system influence the value of ecosystem services of mountain agroecosystems

Studies covering the social valuation of ecosystem services (ES) are increasingly incorporating people’s attitudes, which allows social heterogeneity to be identified. This is especially relevant in mountain areas, where diverse complex interactions occur among the environment, the socioeconomic system, and a wide variety of farming practices. In this context, we aimed to: (i) identify the attitudinal dimensions that build people views about the agrifood system; and (ii) analyse how these attitudinal dimensions influence the value given to ES delivered by mountain agroecosystems of two European countries. We conducted a survey with a sample of 1008 individuals evenly distributed in the Italian Alps and Spanish Mediterranean mountain areas to collect information on people’s attitudes toward: (i) the economy and the environment; (ii) rural development and agricultural intensification; (iii) food quality, production, and consumption; and (iv) agricultural and environmental policies. The survey included a choice experiment to assess the value that individuals attach to the most relevant ES provided by mountain agroecosystems in these areas (i.e., landscape, biodiversity, quality local products, wildfires prevention and water quality). The results showed four common attitudinal dimensions, namely Economy over environment, Mass-Market distribution reliability, Agricultural productivism, and Environmentalism and rural lifestyle. These attitudinal dimensions resulted in six groups of respondents. Most groups positively valued an increase in the delivery of all the analysed ES, which suggests that agricultural policies which aim to promote ES are likely to receive social support in the study areas. However, the differing attitudinal dimensions underlying people’s preferences may result in disagreements about the steps to be taken to achieve the desired increase in ES delivery.Depto. de Psicobiología y Metodología en Ciencias del ComportamientoFac. de PsicologíaTRUEpu

Un asistente pedagógico digital para la reflexión, toma de decisiones y evaluación de la práctica docente

Desarrollo e Implementación de un Sistema de Soporte a la toma de Decisiones orientado a evaluar y fortalecer las competencias comunicativas de docentes y estudiantes con el objetivo de mejorar la motivación, la calidad y el rendimiento académico.Depto. de Psicobiología y Metodología en Ciencias del ComportamientoFac. de PsicologíaFALSEsubmitte