Analysis of the Structural Validity of the Reduced Version of Metacognitive Awareness of Reading Strategies Inventory.

The application of metacognitive strategies is considered a basic skill of the student at any educational level. In the present study, we evaluate the reduced version of the Metacognitive Awareness of Reading Strategies Inventory (MARSI-R) in Spanish, a self-report instrument designed to measure the metacognitive awareness of students and their perception of the strategies that they use while they are reading school materials. MARSI-R is formed by three subscales: (a) global reading strategies (GRS), (b) problem-solving strategies, and (c) strategies to support reading. We conducted a confirmatory factor analysis (CFA) in a Spanish student sample (N = 570) and the results shown relative inadequate fit for the proposed theoretical three-factor model. More important, the three subscales presented a high level of inter-correlation, which raises the need to assess to what extent the construct should be considered as unidimensional. We conducted two additional CFA models: a unidimensional model and a bifactor S-1 model, and the results indicated the presence of a strong general factor related to the GRS subscale. These results have important implications, since they imply that it is more appropriate to use the total score of the instrument derived of the S-1 model instead of the scores derived from each subscale. The bifactor S-1 model has allowed us to develop a closer approximation between the psychometric model and the theoretical model.

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.

Application of S-1 Bifactor Model to Evaluate the Structural Validity of TMMS-24.

(1) Background: Recent studies have shown that the internal structure of TMMS-24 can be conceptualized as a bifactor. However, these studies, based exclusively on the evaluation of the fit of the model, fail to show the existence of a general factor of strong emotional intelligence and have neglected the evaluation of the specific factors of attention, clarity and repair. The main goal of this work is to evaluate the degree of determination and reliability of the specific factors of TMMS-24 using a bifactor S-1 model. (2) Methods: We administered TMMS-24 to a sample of 384 students from middle and high schools (58.1% girls; mean age = 15.5; SD = 1.8). (3) Results: The specific TMMS-24 factors are better determined and present a higher internal consistency than the general factor. Furthermore, the bifactor S-1 model shows the existence of a hierarchical relationship between the attention factor and the clarity and repair factors. The S-1 bifactor model is the only one that was shown to be invariant as a function of the sex of the participants. (4) Conclusions: The S-1 bifactor model has proven to be a promising tool for capturing the structural complexity of TMMS-24. Its application indicates that it is not advisable to use the sum score of the items, since it would be contaminated by the attention factor. In addition, this score would not be invariant either, that is, comparisons by sex would be invalid.

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.

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

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