La androginia y el ajuste de pareja

RESUMEN

En este artículo se constrasta la hipótesis de que existe una relación entre las orientaciones de roles sexuales de los miembros de parejas heterosexuales y el ajuste de dichas parejas. Comenzando con la exposición del panorama actual de las investigaciones sobre el tema y tras concretar los objetivos de su estudio, los autores describen el método y resultados del mismo. Finalmente en las conclusiones destacan que se ha visto confirmada la existencia de una posible relación entre las distintas orientaciones hacia el rol sexual de los sujetos y el modo en que éstos estiman su relación de pareja, siendo los hombres femeninos y las mujeres andróginas quienes por lo general informaban de mayor felicidad. Respecto a los tipos de parejas y el ajuste de las mismas, los autores afirman que sus objetivos parecen haberse confirmado ampliamente, ya que las parejas más ajustadas eran aquéllas en donde tanto un miembro como el otro habían sido categorizados como andróginos, o en donde al menos uno presentaba un alto grado de características tanto masculinas como femeninas.     

State of the aRt personality research: A tutorial on network analysis of personality data in R

Network analysis represents a novel theoretical approach to personality. Network approaches motivate alternative ways of analyzing data, and suggest new ways of modeling and simulating personality processes. In the present paper, we provide an overview of network analysis strategies as they apply to personality data. We discuss different ways to construct networks from typical personality data, show how to compute and interpret important measures of centrality and clustering, and illustrate how one can simulate on networks to mimic personality processes. All analyses are illustrated using a data set on the commonly used HEXACO questionnaire using elementary R-code that readers may easily adapt to apply to their own data.     

Modelling pedestrian crossing behaviour in urban roads: A latent variable approach

As pedestrians are the most exposed and vulnerable road users to traffic accidents, urban planners frequently propose alternatives to improve their safety. However, some solutions, such as pedestrian bridges and crosswalks at signalized intersections, usually imply longer walking distances compared to the direct crossing alternative which, in its turn, involves a higher risk.In this article, a hybrid framework is proposed to analyse the pedestrians’ choice on how to cross an urban road where three crossing options are available: crossing directly, crossing by using a pedestrian bridge or using a crosswalk at a signalized intersection. The decision process is modelled as a discrete choice model incorporating latent variables to consider perceptions and psychological factors, using stated preference data coming from a survey applied in Bogotá, Colombia.Results show that the latent variables security/safety and attractiveness of each crossing alternative are relevant to understand the pedestrian crossing behaviour. These latent variables are strongly determined by socioeconomic characteristics of the individual (age, gender, level of study) and conditioned by the circumstances of the trip (main mode of transport, walking or not with children). It was found that a longer walking distance to a pedestrian bridge or a signalized crosswalk increases the probability of direct crossing, having a more relevant effect in the case of the pedestrian bridge.     

Stepwise Latent Class Models for Explaining Group-Level Outcomes Using Discrete Individual-Level Predictors

Explaining group-level outcomes from individual-level predictors requires aggregating the individual-level scores to the group level and correcting the group-level estimates for measurement errors in the aggregated scores. However, for discrete variables it is not clear how to perform the aggregation and correction. It is shown how stepwise latent class analysis can be used to do this. First, a latent class model is estimated in which the scores on a discrete individual-level predictor are used to construct group-level latent classes. Second, this latent class model is used to aggregate the individual-level predictor by assigning the groups to the latent classes. Third, a group-level analysis is performed in which the aggregated measures are related to the remaining group-level variables while correcting for the measurement error in the class assignments. This stepwise approach is introduced in a multilevel mediation model with a single individual-level mediator, and compared to existing methods in a simulation study. We also show how a mediation model with multiple group-level latent variables can be used with multiple individual-level mediators and this model is applied to explain team productivity (group level) as a function of job control (individual level), job satisfaction (individual level), and enriched job design (group level).     

罗夏测验在西方近10年的最新发展

近10年来, 罗夏综合系统不断发展, 新增了XA%与WDA%、知觉−思维指数PTI、GHR:PHR等多个指标, 修改了8个群变量, 删除了精神分裂SCZI指标。这些新调整虽然不是本质性的, 但使得罗夏综合系统内容更加完善, 信度和效度不断提高, 更加精确化和标准化, 成为西方不可或缺的心理测验工具。尽管罗夏综合系统不断发展, 但仍然存在着操作过于复杂、功能还需要进一步完善等问题。罗夏综合系统作为一个代表性的投射测验, 与其它客观测验组成了一个完整的心理测量体系, 会发挥越来越大的作用。     

Influence of school-level and family-level variables on Chinese college students’ aggression

With the frequent occurrence of campus violence, scholars have devoted increasing attention to college students’ aggression. This study aims to estimate the prevalence of aggression in Chinese university students and identify factors that could influence their aggression. We can thus find methods to reduce the incidence of college students’ aggression in the future. A multi-stage stratified sampling procedure was used to select university students (N = 4565) aged 16–25 years in Harbin. The Aggression Questionnaire, the Adolescent Self-Rating Life Events Checklist and the Social Support Revalued Scale were used to collect data. Females reported lower levels of aggression than males (p < .001). A multiple linear regression analysis was conducted to determine the influence of factors of aggression, and the model was highly significant (R² = .233, Ad R² = .230, p < .01). The results show that the aggression is affected by gender, family-level and school-level variables. Aggression scores are significantly correlated with not only family-level or school-level variables independently, but their combination as well. We find that the risk factors for aggression include a dissatisfying profession, higher levels of study pressure, poor parental relationships, poor interpersonal relationships, the presence of siblings, punishment, health maladjustment, less subjective support, and lower levels of utilization of social support.     

Bayesian Estimation of Circumplex Models Subject to Prior Theory Constraints and Scale-Usage Bias

This paper presents a hierarchical Bayes circumplex model for ordinal ratings data. The circumplex model was proposed to represent the circular ordering of items in psychological testing by imposing inequalities on the correlations of the items. We provide a specification of the circumplex, propose identifying constraints and conjugate priors for the angular parameters, and accommodate theory-driven constraints in the form of inequalities. We investigate the performance of the proposed MCMC algorithm and apply the model to the analysis of value priorities data obtained from a representative sample of Dutch citizens. We wish to thank Michael Browne and two anonymous reviewers for their comments. The data for this study were collected as part of the project AIR2-CT94-1066, sponsored by the European Commission.     

Analysis of structural equation model with ignorable missing continuous and polytomous data

The main purpose of this article is to develop a Bayesian approach for structural equation models with ignorable missing continuous and polytomous data. Joint Bayesian estimates of thresholds, structural parameters and latent factor scores are obtained simultaneously. The idea of data augmentation is used to solve the computational difficulties involved. In the posterior analysis, in addition to the real missing data, latent variables and latent continuous measurements underlying the polytomous data are treated as hypothetical missing data. An algorithm that embeds the Metropolis-Hastings algorithm within the Gibbs sampler is implemented to produce the Bayesian estimates. A goodness-of-fit statistic for testing the posited model is presented. It is shown that the proposed approach is not sensitive to prior distributions and can handle situations with a large number of missing patterns whose underlying sample sizes may be small. Computational efficiency of the proposed procedure is illustrated by simulation studies and a real example.The work described in this paper was fully supported by a grant from the Research Grants Council of the HKSAR (Project No. CUHK 4088/99H). The authors are greatly indebted to the Editor and anonymous reviewers for valuable comments in improving the paper; and also to D. E. Morisky and J.A. Stein for the use of their AIDS data set.     

Distinguishing Constructs from Variables in Designing Research

Current research practices often conflate theoretical constructs and explanatory hypotheses with variables and predicted effects, to the detriment of research progress. This has led to the use of procedures such as manipulation checks, mediation analysis, and boundary conditions predicated on the idea that matching constructs to variables is necessary to validate that a theory corresponds to an effect . An alternative perspective, Inference to the Best Explanation (IBE), calls for designing research to exploit the power of distinguishing constructs from variables, hypotheses from predictions, and theory from effects. IBE calls for stating hypotheses (Hs) about construct‐to‐construct relationships and, separately, the predictions (Ps) about variable‐to‐variable effects that are explained by the hypotheses. In addition, articles should include disparate effects, a single explanation covering all studies, and a discussion of the use of the research in specific problem contexts. The application of IBE is illustrated with research investigating when judgments are based on a feeling about the ease of information retrieval versus the information content itself.