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Abstract Increased interest in the study of the relationships between human and nonhuman animals over the past few decades has often focused on what factors shape people’s attitudes and treatment towards nonhuman animals. Some scholars have focused on features of the nonhuman animals (cuteness, intelligence, and utility) and how those features predict human behavior while others have concentrated more on possible human individual differences that influence their attitudes and positive or negative actions towards nonhuman animals. In this study, with 978 participants from five different cross-cultural study sites (Arizona, California, Costa Rica, Mexico, and Spain), we utilized three superordinate categories of animal species representing three “Concentric Circles” surrounding our own (from Kith-Kin, to Domestic, to Wild Animals) to investigate the influence of individual differences and local ecological conditions on empathy and harm avoidance scores across these categories. Specifically, these variables were as follows: (1) a set of variables representing the cross-cultural study sites; (2) a second set of sociodemographic predictors; (3) a developmental Animal Exposure factor; (4) a Dark Inventory factor; (5) an Interpersonal Aggression factor; (6) a KithKin-Empathy factor, measuring emotional and cognitive empathy towards Kith-Kin animals; (7) a Domestic-Empathy factor, measuring emotional and cognitive empathy towards Domestic animals; (8) a Wild-Empathy factor, measuring emotional and cognitive empathy towards Wild animals; and (9) a Harm Avoidance factor. A Cascade Model approach allowed us to integrate many factors (individual differences and local ecological conditions) together in explaining empathy and harm avoidance that have previously been examined only in mutual isolation.

How to lead the investigated patient with respiratory symptoms through the first level of care in pandemic times?

In this study, we conducted the translation and validation of the K-SF-42 in Japan (Figueredo, 2017). The K-SF-42 is a new short form of the Arizona Life History Battery. We obtained empirical evidence that the original seven-factor structure could be applied to the Japanese translated version of K-SF-42 (K-SF-42-J). We also observed good internal consistency of the seven scales of K-SF-42-J. The multi-group confirmatory factor analysis findings suggest that the K-SF-42-J can be used in both sexes and diverse age groups. The K-SF-42-J scales showed similar correlates as the English original, with higher scores of other life history strategy measurement, trait emotional intelligence, well-being, and cultural and social resources in childhood. Use of the K-SF-42-J will allow researchers with Japanese speaking samples to integrate their findings with the existing life history strategy research literature. The brevity of the K-SF-42-J will be appealing to researchers who are concerned about taxing the time and motivation of their participants.

Structural Equation Modeling (SEM) is used to analyze the causal relationships between observable and unobservable variables. Among the assumptions considered, but not essential, for the application of the SEM are the presence of multivariate normality between the data, and the need for a large number of observations, in order to obtain the variances and covariances between the variables. It is not always possible to have access to a sufficiently large number of observations to enable the calculation of parameters, and the convergence of the iterative algorithm is one of the problems in obtaining the results. This work investigates the convergence of iterative algorithms, which minimize the variation of parameters, through a stipulated convergence rate, using the Maximum Likelihood (ML) and Generalized Least Squares (GLS) estimation methods on structural equation models using confirmatory factor analysis (CFA) and regression models. Convergences were evaluated in relation to the number of observations, in order to obtain a minimum quantity sufficient for a convergence rate above 50%. The calculations were performed in the statistical environment R® version 3.4.4, and the results obtained showed a convergence rate above 50% for models estimated by GLS, even with the data showing lack of multivariate normality, kurtosis and accentuated asymmetry. Thus, it was possible to define a minimum number of observations necessary for an adequate convergence of the iterative algorithms in obtaining the necessary parameters. [ABSTRACT FROM AUTHOR]

Structural Equation Modeling (SEM) is used to analyze the causal relationships between observable and unobservable variables. Among the assumptions considered, but not essential, for the application of the SEM are the presence of multivariate normality between the data, and the need for a large number of observations, in order to obtain the variances and covariances between the variables. It is not always possible to have access to a sufficiently large number of observations to enable the calculation of parameters, and the convergence of the iterative algorithm is one of the problems in obtaining the results. This work investigates the convergence of iterative algorithms, which minimize the variation of parameters, through a stipulated convergence rate, using the Maximum Likelihood (ML) and Generalized Least Squares (GLS) estimation methods on structural equation models using confirmatory factor analysis (CFA) and regression models. Convergences were evaluated in relation to the number of observations, in order to obtain a minimum quantity sufficient for a convergence rate above 50%. The calculations were performed in the statistical environment R® version 3.4.4, and the results obtained showed a convergence rate above 50% for models estimated by GLS, even with the data showing lack of multivariate normality, kurtosis and accentuated asymmetry. Thus, it was possible to define a minimum number of observations necessary for an adequate convergence of the iterative algorithms in obtaining the necessary parameters. [ABSTRACT FROM AUTHOR]