Sex Differences in Emotional and Behavioral Responses to HIV+ individuals’ Expression of Distress

Two studies examined the influence of HIV+ individual’s expression of distress on perceivers’ emotional and behavioral reactions. In Study 1 (N = 224), HIV+ individuals’ expression of distress was experimentally manipulated by means of vignettes. Men and women reacted differently when persons with HIV conveyed distress: women reported stronger feelings of pity, whereas men reported stronger feelings of anger. Study 2 (N = 136) replicated this study in a realistic experimental setting with additional behavioral measures. Similarly, women reported stronger pro-social behavior than men when confronted with a person with HIV who conveyed distress. Results of the present study shed additional light to the self-presentational dilemma of ill persons. Conveying moderate levels of distress may evoke prosocial responses in women, but not in men

The Highlander: Volume 2, Number 10- November 16, 1936

https://digitalmaine.com/highlander/1009/thumbnail.jp

Waarneming van het stelsel van Nederlandse politieke partijen

It is commonly assumed that the Dutch electorate views its political parties in terms of a progressive-conservative and a denominational-nondenominational division. This assumption was tested by interviewing a sample of 126 voters in a Dutch municipality. Respondents were asked to rank 12 political parties according to preference. Additional data were collected, a.o. progressiveness, frequency of church attendance, authoritarianism. Because of non-response and incomplete ranking the rank orders of 9 parties (N _ 46) were used. These data were analysed by means of a principal components analysis of the matrix of product-moment correlations between the parties. After varimax rotation of the two first components individual component scores were calculated. By means of additional data the two components, contributing 28% and 23% to the total variance, could be safely interpreted as representing a progressiveness dimension and a denominational dimension respectively

Protestantisme en progressiviteit opnieuw bezien

The data on which this analysis is based were assembled by Dr. A. Hoogerwerf; I a random sample of 912 persons in Delft was used. The authors investigated the influence of several variables on the independent i variable of social-political-progressiveness (spp). A matrix of correlations between fifteen items of the original questionnaire was computed; eight items which were highly related to each other were found. These items were used as indicators for social-political-progressiveness by summing the answers of each respondent on every item. On these scores an analysis of variance was carried out with five independent variables; religion, age, income, level of education, and sex. The following findings were obtained; — Religion had a significant influence on spp-scores; respondents who do not have any specific religion tend to be more progressive than others. Furthermore, Catholics tend to be more progressive than Protestants and Dutch-Reformed. — It was also shown that both the level of education and income had significant influences on spp-scores; the higher the income or the educational level, the lower the progressiveness-score. — The variables age and sex did not have significant influences on spp-scores. — The five variables included in the design explained 32% of the total variance in the spp-scores. Of the remaining 68% about 27% can be explained in terms 2 of error-variance. This means that about 41% of the total variance in spp-score.I has to be explained by factors which are not included in the design

Modeling Population Growth in R with the biogrowth Package

The growth of populations is of interest in a broad variety of fields, such as epidemiology, economics or biology. Although a large variety of growth models are available in the scientific literature, their application usually requires advanced knowledge of mathematical programming and statistical inference, especially when modelling growth under dynamic environmental conditions. This article presents the biogrowth package for R, which implements functions for modelling the growth of populations. It can predict growth under static or dynamic environments, considering the effect of an arbitrary number of environmental factors. Moreover, it can be used to fit growth models to data gathered under static or dynamic environmental conditions. The package allows the user to fix any model parameter prior to the fit, an approach that can mitigate identifiability issues associated to growth models. The package includes common S3 methods for visualization and statistical analysis (summary of the fit, predictions, . . . ), easing result interpretation. It also includes functions for model comparison/selection. We illustrate the functions in biogrowth using examples from food science and economy

Naar een steekproef landschap; ontwerp van een methode en pilotstudie

Kwantitatieve gegevens over de veranderingen in het landschap ontbreken vrijwel op nationaal niveau. Dit rapport beschrijft een methode om veranderingen in de graadmeter landschapswaarde te volgen. Het geeft inventarisatieprotocollen voor de variabelen ruimtegebruik, groen-blauwe dooradering, beheerstoestand kleine landschapselementen, informatiewaarde van terreinvormen en ontginningsgeschiedenis, en maat van de ruimte. De methode is ontwikkeld en getest in vier gebiedjes (Tilburg-Zuidwest, Zieuwent,Abcoude, Nisse)

Can we gain precision by sampling with probabilities proportional to size in surveying recent landscape changes in the Netherlands?

Seventy-two squares of 100 ha were selected by stratified random sampling with probabilities proportional to size (pps) to survey landscape changes in the period 1996-2003. The area of the plots times the urbanization pressure was used as a size measure. The central question of this study is whether the sampling with probabilities proportional to size leads to gain in precision compared to equal probability sampling. On average 1.03 isolated buildings per 100 ha have been built, while 0.90 buildings per 100 ha have been removed, leading to a net change of 0.13 building per 100 ha. The area with unspoiled natural relief has been reduced by 2.3 ha per 100 ha, and the length of linear relicts by 137 m per 100 ha. On average 74 m of linear green elements have been planted per 100 ha, while 106 m have been removed, leading to a net change of -31 m per 100 ha. For the state variables 'unspoiled natural relief', 'linear relicts', 'removed linear green elements', and 'new - removed linear green elements' there is a gain in precision due to the pps-sampling. For the remaining state variables there is no gain or even a loss of precision (`new buildings', 'removed buildings', 'new - removed buildings', 'new linear green elements'). Therefore, if many state variables must be monitored or when interest is not only in the change but also in the current totals, we recommend to keep things simple, and to select plots with equal probability