Cohort differences in Big Five personality factors over a period of 25 years

The notion of personality traits implies a certain degree of stability in the life span of an individual. But what about generational effects? Are there generational changes in the distribution or structure of personality traits? This article examines cohort changes on the Big Five personality factors Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness to Experience, among first-year psychology students in The Netherlands, ages 18 to 25 years, between 1982 and 2007. Because measurement invariance of a personality test is essential for a sound interpretation of cohort differences in personality, we first assessed measurement invariance with respect to cohort for males and females separately on the Big Five personality factors, as measured by the Dutch instrument Five Personality Factors Test. Results identified 11 (females) and 2 (males) biased items with respect to cohort, out of a total of 70 items. Analyzing the unbiased items, results indicated small linear increases over time in Extraversion, Agreeableness, and Conscientiousness and small linear decreases over time in Neuroticism. No clear patterns were found on the Openness to Experience factor. Secondary analyses on students from 1971 to 2007 of females and males of different ages together revealed linear trends comparable to those in the main analyses among young adults between 1982 onward. The results imply that the broad sociocultural context may affect personality factors.     

A Closer Look at the Personality Characteristics of Outplaced Executives

The present study examines more closely the findings and recommendations presented recently by Austin and Murray (1993) regarding the personality characteristics of outplaced executives. The analysis of a much larger national sample that does not exclude females shows that the previously reported findings may not be representative of the population of outplaced executives and that important gender differences also are present. Finally, the suggestion that counselors should help individuals change their negative predispositions is questioned. It is recommended here that personality differences between employed and outplaced executives may have more important implications for organizational practices and counseling pertaining to person-job fit     

Towards a Pan‐cultural Personality Structure: Input from 11 Psycholexical Studies

The purpose of the present study is to find the common kernel of different trait taxonomic studies and find out how the individual structures relate to this common kernel. Trait terms from 11 psycholexically based taxonomies were all translated into English. On the basis of the commonalities in English, the 11 matrices were merged into a joint matrix with 7104 subjects and 1993 trait terms. Untranslatable terms produced large areas with missing data. To arrive at the kernel structure of the joint matrix, a simultaneous component analysis was applied. In addition, the kernel structures were compared with the individual taxonomy trait structures, obtained via principal component analysis. The findings provide evidence of a structure consisting of three components to stand out as the core of the taxonomies included in this study; those components were named dynamism, affiliation, and order. Moreover, the relations between these three kernel components and those of a six‐component solution (completing the six‐factor model) are provided. Copyright © 2014 European Association of Personality Psychology     

Sexual harassment in the context of double male dominance

It has been suggested that numerically male-dominated workplaces propagate cultural norms that support sexual bravado, sexual posturing, and the denigration of feminine behaviour (Sbraga & O'Donohue, 2000 Sbraga, T. P. and O'Donohue, W. 2000. Sexual harassment. Annual Review of Sex Research, 11: 258–285. [Taylor & Francis Online] , [Google Scholar]). These cultural norms are features of normative male dominance, which have been shown to increase the risk of sexual harassment. This implies that the effect of numerical male dominance on sexual harassment may be mediated by the level of normative male dominance in the work environment. The aim of this study was to test this assumption. Our sample consisted of 1295 police women, who filled out an Internet questionnaire. The results suggest that normative male dominance indeed mediates the relation between numerical male dominance and sexual harassment. We add to the sexual harassment literature by building on Gruber's concept of double dominance. Our study helps to make clear why women are at greater risk of sexual harassment in work situations where men outnumber women than in more gender-balanced workplaces.     

The Hull Method for Selecting the Number of Common Factors

A common problem in exploratory factor analysis is how many factors need to be extracted from a particular data set. We propose a new method for selecting the number of major common factors: the Hull method, which aims to find a model with an optimal balance between model fit and number of parameters. We examine the performance of the method in an extensive simulation study in which the simulated data are based on major and minor factors. The study compares the method with four other methods such as parallel analysis and the minimum average partial test, which were selected because they have been proven to perform well and/or they are frequently used in applied research. The Hull method outperformed all four methods at recovering the correct number of major factors. Its usefulness was further illustrated by its assessment of the dimensionality of the Five-Factor Personality Inventory (Hendriks, Hofstee, &; De Raad, 1999 Hendriks, A. A. J., Hofstee, W. K. B. and De Raad, B. 1999. The Five-Factor Personality Inventory (FFPI). Personality and Individual Differences, 27: 307–325. [Crossref], [Web of Science ®] , [Google Scholar]). This inventory has 100 items, and the typical methods for assessing dimensionality prove to be useless: the large number of factors they suggest has no theoretical justification. The Hull method, however, suggested retaining the number of factors that the theoretical background to the inventory actually proposes.     

Modeling Differences in the Dimensionality of Multiblock Data by Means of Clusterwise Simultaneous Component Analysis

Given multivariate multiblock data (e.g., subjects nested in groups are measured on multiple variables), one may be interested in the nature and number of dimensions that underlie the variables, and in differences in dimensional structure across data blocks. To this end, clusterwise simultaneous component analysis (SCA) was proposed which simultaneously clusters blocks with a similar structure and performs an SCA per cluster. However, the number of components was restricted to be the same across clusters, which is often unrealistic. In this paper, this restriction is removed. The resulting challenges with respect to model estimation and selection are resolved.     

Subspace K-means clustering

To achieve an insightful clustering of multivariate data, we propose subspace K-means. Its central idea is to model the centroids and cluster residuals in reduced spaces, which allows for dealing with a wide range of cluster types and yields rich interpretations of the clusters. We review the existing related clustering methods, including deterministic, stochastic, and unsupervised learning approaches. To evaluate subspace K-means, we performed a comparative simulation study, in which we manipulated the overlap of subspaces, the between-cluster variance, and the error variance. The study shows that the subspace K-means algorithm is sensitive to local minima but that the problem can be reasonably dealt with by using partitions of various cluster procedures as a starting point for the algorithm. Subspace K-means performs very well in recovering the true clustering across all conditions considered and appears to be superior to its competitor methods: K-means, reduced K-means, factorial K-means, mixtures of factor analyzers (MFA), and MCLUST. The best competitor method, MFA, showed a performance similar to that of subspace K-means in easy conditions but deteriorated in more difficult ones. Using data from a study on parental behavior, we show that subspace K-means analysis provides a rich insight into the cluster characteristics, in terms of both the relative positions of the clusters (via the centroids) and the shape of the clusters (via the within-cluster residuals).     

A dilemma for Epicureanism

Philosophical Studies - Perhaps death’s badness is an illusion. Epicureans think so and argue that agents cannot be harmed by death when they’re alive (because death hasn’t...     

Four simultaneous component models for the analysis of multivariate time series from more than one subject to model intraindividual and interindividual differences

A class of four simultaneous component models for the exploratory analysis of multivariate time series collected from more than one subject simultaneously is discussed. In each of the models, the multivariate time series of each subject is decomposed into a few series of component scores and a loading matrix. The component scores series reveal the latent data structure in the course of time. The interpretation of the components is based on the loading matrix. The simultaneous component models model not only intraindividual variability, but interindividual variability as well. The four models can be ordered hierarchically from weakly to severely constrained, thus allowing for big to small interindividual differences in the model. The use of the models is illustrated by an empirical example.This research has been made possible by funding from the Netherlands Organization of Scientific Research (NWO) to the first author. The authors are obliged to Tom A.B. Snijders, Jos M.F. ten Berge and three anonymous reviewers for comments on an earlier version of this paper, and to Kim Shifren for providing us with her data set, which was collected at Syracuse University.     

Violence and race in professional baseball: Getting better or getting worse?

The purpose of this study was to examine the relationship between race and violence in the context of professional baseball. Specifically, I used the number of times a batter was hit by a pitch (per plate appearance) as an indicator of being the victim of an indirect violent act. Archival data were gathered from 4,273 players from 1950 to 1997, yielding 27,022 individual records. Even after controlling for player ability and league rules, race was a significant predictor of being hit. Specifically, from 1950 to 1997, the rate at which Blacks were hit was approximately 7.5% greater than the rate for Whites. The rate at which Hispanics were hit was approximately 7.6% greater than the rate for Whites. Testing year as a moderator revealed that the Black‐White difference was much greater in the 1950s and 1960s. From 1970 to 1989, race was not a significant predictor of being hit. Contrary to the hypotheses, from 1990 to 1997, Whites and Hispanics were hit at a significantly higher rate than Blacks (23.0% and 29.0%, respectively). Additional analyses revealed no relationship between pitcher race and batter race in hit‐by‐pitch events from 1997 to 1999. The findings are discussed with respect to group threat theory and the distinction between covert and overt aggression. Aggr. Behav. 28:109–116, 2002. © 2002 Wiley‐Liss, Inc.