Dynamic reliability models with conditional proportional hazards

A dynamic approach to the stochastic modelling of reliability systems is further explored. This modelling approach is particularly appropriate for load-sharing, software reliability, and multivariate failure-time models, where component failure characteristics are affected by their degree of use, amount of load, or extent of stresses experienced. This approach incorporates the intuitive notion that when a set of components in a coherent system fail at a certain time, there is a jump from one structure function to another which governs the residual lifetimes of the remaining functioning components, and since the component lifetimes are intrinsically affected by the structure function which they constitute, then at such a failure time there should also be a jump in the stochastic structure of the lifetimes of the remaining components. For such dynamically-modelled systems, the stochastic characteristics of their jump times are studied. These properties of the jump times allow us to obtain the properties of the lifetime of the system. In particular, for a Markov dynamic model, specific expressions for the exact distribution functions of the jump times are obtained for a general coherent system, a parallel system, and a series-parallel system. We derive a new family of distribution functions which describes the distributions of the jump times for a dynamically-modelled system.     

Rational-experiential thinking style and rational intergroup cooperation: the moderating role of intergroup conflict / Estilos de pensamiento racional-experiencial y la cooperación intergrupal racional: el rol modulador del conflicto intergrupal

Cooperative relationships between groups are difficult because of the high human capability to differentiate between in-group vs. out-group members. This obstacle exists even when the groups can obtain benefits for themselves from cooperation with other groups (rational cooperation). Based on an interactionist approach, the authors propose that personal (individual differences) and situational (conflicts) factors contribute to rational intergroup cooperation. The authors conducted a preliminary correlational study (Study 1) and an experimental investigation (Study 2). In Study 1, the authors examined, with 105 participants, the psychometric properties of the Spanish version of the Rational–Experiential Inventory (REI), measuring individual differences in information processing. In Study 2, the authors conducted an experimental investigation with 141 participants, testing the role of individual differences and conflict in intergroup rational cooperation. Findings of Study 1 showed that the scale has adequate psychometric properties. Results of Study 2 confirmed that the existence of both manipulated and perceived intergroup conflict reduces the magnitudes of the relationships between rational ability and rational cooperation. In addition, perceived intergroup conflict increased the magnitude of the links from experiential ability and experimental engagement to rational cooperation. Both individual differences and previous conflict have a role in predicting rational intergroup cooperation. According to the interactionist approach, personal and situational factors should be considered in the understanding of intergroup relationships.     

Robust Methods of Building Regression Models—An Application to thè Housing Sector

This article studies robustification strategies for the linear model in the presence of outliers. The advantages of an internal analysis of the robustness of least squares for a given sample are pointed out. The application of this methodology is illustrated by building an explicit model of the determinants of rental housing values in the Madrid Metropolitan Area.     

Materialism in Pedagogy Students in Chile

Many studies have used Richins and Dawson’s (J Consum Res 19: 303–316, 1992) Material Values Scale (MVS), applying it to different types of populations that exhibit a particular psychometric behavior, and showing little stability in their factorial structure. In the present study, 1,070 pedagogy students from the northern, central and southern regions of Chile answered the MVS. This sample was randomly divided in two. Using the first sub-sample (N = 539), an exploratory factorial analysis was carried out, from which a structure of nine items was grouped into two factors called “Social Success” and “Personal Happiness”, which presented adequate reliability. Later, with the second sub-sample (N = 531), the factorial structure indicated above was put to the test through a confirmatory factorial analysis. The data from the model show that the scale contains 8 items in total, grouped into two dimensions. The factorial loads are significant at the level of 1 %, which indicates that the 2-factor structure can be confirmed. Finally—using the proposed structure—the presence of the students’ material values was evaluated.     

Size does matter: Passive sampling in urban parks of a regional bat assemblage

Urban Ecosystems - We studied the occurrence of bats in urban parks in the city of Madrid (Spain), and the resulting patterns were compared with bat occurrence in the surrounding region. In this...     

Life Satisfaction in Brazil: Testing the Psychometric Properties of the Satisfaction With Life Scale (SWLS) in Five Brazilian Samples

The aim of this paper was to validate the Brazilian-Portuguese version of the Satisfaction With Life Scale (SWLS), presenting evidence of its reliability and construct and criterion-related validity. A large Brazilian sample (2,180 participants), from five different populations (undergraduate and high school students, general population, elementary school teachers, and physicians), was considered. The results confirmed the single factorial structure and reliability (0.77 < Cronbach’s α < 0.88, mean α = 0.81) of the SWLS. Supporting its criterion-related validity, the SWLS correlated positively with positive affect and negatively with both negative affect and psychological distress across all five samples. The findings indicate that the Brazilian-Portuguese version of the SWLS is a valid instrument to use with diverse Brazilian samples.     

Temporal disaggregation and restricted forecasting of multiple population time series

This article presents some applications of time-series procedures to solve two typical problems that arise when analyzing demographic information in developing countries: (1) unavailability of annual time series of population growth rates (PGRs) and their corresponding population time series and (2) inappropriately defined population growth goals in official population programs. These problems are considered as situations that require combining information of population time series. Firstly, we suggest the use of temporal disaggregation techniques to combine census data with vital statistics information in order to estimate annual PGRs. Secondly, we apply multiple restricted forecasting to combine the official targets on future PGRs with the disaggregated series. Then, we propose a mechanism to evaluate the compatibility of the demographic goals with the annual data. We apply the aforementioned procedures to data of the Mexico City Metropolitan Zone divided by concentric rings and conclude that the targets established in the official program are not feasible. Hence, we derive future PGRs that are both in line with the official targets and with the historical demographic behavior. We conclude that growth population programs should be based on this kind of analysis to be supported empirically. So, through specialized multivariate time-series techniques, we propose to obtain first an optimal estimate of a disaggregate vector of population time series and then, produce restricted forecasts in agreement with some data-based population policies here derived.     

Assessing Type I error and power of multistate Markov models for panel data—A simulation study

Ordinal outcomes collected at multiple follow-up visits are common in clinical trials. Sometimes, one visit is chosen for the primary analysis and the scale is dichotomized amounting to loss of information. Multistate Markov models describe how a process moves between states over time. Here, simulation studies are performed to investigate the Type I error and power characteristics of multistate Markov models for panel data with limited non-adjacent state transitions. The results suggest that the multistate Markov models preserve the Type I error and adequate power is achieved with modest sample sizes for panel data with limited non-adjacent state transitions.