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 sixfactor model) are provided. Copyright © 2014 European Association of Personality Psychology Key words: lexical studies; cross-cultural research; statistical methods In order to arrive at an estimation of cross-cultural tenability of a trait structural model, generally two different routes can be followed. The first is that items based on a trait structure from one language or culture are translated and tested in another language for its applicability. This approach is often referred to by cross-cultural psychologists as the etic or even imposed-etic (Berry, 1969) approach. The second route, typically followed in the psycholexical approach to personality (De Raad, 2000), is that trait structures that are different in terms of number and nature of variables from different languages or cultures are compared as follows: (i) content-wise and/or (ii) by using psychometric means. Content-wise comparisons generally yield higher estimates of the number of cross-culturally valid factors than assessments through psychometric means (e.g. Brokken, 1978; cf. De Raad, Barelds, Levert, et al., 2010). This paper is positioned between the two forces of this second route, aiming to keep a balance between what is clear and valid in terms of content and what is psychometrically wise. In what follows, we come across more forces that are pushing and pulling with reference to what a proper cross-culturally valid model of personality traits should or could be. Some relate to an interest in what lies beyond the Big Five, some to a strict cross-cultural tenability, some to a belief in an early phrased Big Five model, and some to cultural-contextual informative accounts of personality dimensions. The interest of most cross-cultural studies is in both what is common to the trait structures under investigation and in how they differ, often with an emphasis on one or the other. The present study has its primary interest indeed in what may be seen as the common kernel to all.De Raad, Barelds, Levert, et al. (2010) pairwise compared t...

The experience of positive emotion is closely linked to subjective well-being. For this reason, campaigns aimed at promoting the value of positive emotion have become widespread. What is rarely considered are the cultural implications of this focus on happiness. Promoting positive emotions as important for "the good life" not only has implications for how individuals value these emotional states, but for how they believe others around them value these emotions also. Drawing on data from over 9,000 college students across 47 countries we examined whether individuals' life satisfaction is associated with living in contexts in which positive emotions are socially valued. The findings show that people report more life satisfaction in countries where positive emotions are highly valued and this is linked to an increased frequency of positive emotional experiences in these contexts. They also reveal, however, that increased life satisfaction in countries that place a premium on positive emotion is less evident for people who tend to experience less valued emotional states: people who experience many negative emotions, do not flourish to the same extent in these contexts. The findings demonstrate how the cultural value placed on certain emotion states may shape the relationship between emotional experiences and subjective well-being.

When time-intensive longitudinal data are used to study daily-life dynamics of psychological constructs (e.g., well-being) within persons over time (e.g., by means of experience sampling methodology), the measurement model (MM)-indicating which constructs are measured by which items-can be affected by time-or situation-specific artifacts (e.g., response styles and altered item interpretation). If not captured, these changes might lead to invalid inferences about the constructs. Existing methodology can only test for a priori hypotheses on MM changes, which are often absent or incomplete. Therefore, we present the exploratory method "latent Markov factor analysis" (LMFA), wherein a latent Markov chain captures MM changes by clustering observations per subject into a few states. Specifically, each state gathers validly comparable observations, and state-specific factor analyses reveal what the MMs look like. LMFA performs well in recovering parameters under a wide range of simulated conditions, and its empirical value is illustrated with an example.

Older adults are often described as being more emotionally competent than younger adults, and higher levels of affect complexity are seen as an indicator of this competence. We argue, however, that once age differences in affect variability are taken into account, older adults' everyday affective experiences will be characterized by lower affect complexity when compared with younger adults'. In addition, reduced affect complexity seems more likely from a theoretical point of view. We tested this hypothesis with a study in which younger and older adults reported their momentary affect on 100 days. Affect complexity was examined using clusterwise simultaneous component analysis based on covariance matrices to take into account differences in affect variability. We found that in the majority of older adults (55%), structures of affect were comparatively simpler than those of younger adults because they were reduced to a positive affect component. Most remaining older adults (35%) were characterized by differentiated rather than undifferentiated affective responding, as were a considerable number of younger adults (43%). When affect variability was made comparable across age groups, affect complexity also became comparable across age groups. It is interesting that individuals with the least complex structures had the highest levels of well-being. We conclude that affective experiences are not only less variable in the majority of older adults, but also less complex. Implications for understanding emotions across the life span are discussed.

To explore structural differences and similarities in multivariate multiblock data (e.g., a number of variables have been measured for different groups of subjects, where the data for each group constitute a different data block), researchers have a variety of multiblock component analysis and factor analysis strategies at their disposal. In this article, we focus on three types of multiblock component methods-namely, principal component analysis on each data block separately, simultaneous component analysis, and the recently proposed clusterwise simultaneous component analysis, which is a generic and flexible approach that has no counterpart in the factor analysis tradition. We describe the steps to take when applying those methods in practice. Whereas plenty of software is available for fitting factor analysis solutions, up to now no easy-to-use software has existed for fitting these multiblock component analysis methods. Therefore, this article presents the MultiBlock Component Analysis program, which also includes procedures for missing data imputation and model selection.