Asian culture in transition: is it related to reported parenting styles and transitivity of simple choices?

Does culture shape reported parenting styles and cognitive processes like transitive reasoning, of choosing A over B, B over C, and then A over C (transitivity)? Asian‐American, Caucasian‐American, and Indian university students differed significantly in transitivity and in reported parental styles. India participants were more intransitive and, contrary to traditional findings in the literature, reported their parents as more laissez‐faire, individualistic, and competitive than did Caucasian‐Americans. Recent technological and industrial advances in India likely explain some of these obtained differences. Predictions from Adlerian theory and work of Kurt Lewin, that parenting styles would relate to transitivity of choices, were indirectly supported. Stronger evidence was found that culture impacts both reported parental styles and transitivity of simple choices.     

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.     

Children in psychodynamic psychotherapy: changes in global functioning

This study was part of the Erica Process and Outcome Study. The aim was to investigate if children's global functioning improves after psychodynamic psychotherapy. Variables that may predict changes in global functioning were examined both statistically and qualitatively, for example, the child's age and gender; diagnosis and comorbidity; treatment variables. The sample consisted of 33 children (five to 10 years of age) who participated in psychodynamic psychotherapy with parallel work with parents. Twenty-nine children had at least one DSM-IV diagnosis, and 15 children had comorbid conditions. At the start of psychotherapy, the most frequent diagnoses were attention disorder and disruptive behaviour. Child psychotherapists rated the children's global functioning using CGAS and HCAM. Large effect sizes were obtained (d = 1.80 and d = 1.98). However, no statistical correlations were found between global functioning and the selected variables. In order to illuminate the complex connection between process and outcome the study was complemented with in-depth case studies where data were taken from questionnaires, completed by the child therapists every third month. Two child therapies were selected: one in which the therapist rated a large change and one in which a small change was rated (CGAS). The analysis showed that important individual change, for example, attainment of formulated goals, was not always reflected in the change rated using the CGAS. Findings suggest that psychodynamic child psychotherapy can be beneficial. However, further research is needed to identify factors that contribute to change in children's global functioning.     

Reconsidering Advertising Literacy as a Defense Against Advertising Effects

It is widely assumed that advertising literacy makes children less susceptible to advertising effects. However, empirical research does not provide convincing evidence for this view. In this article, we explain why advertising literacy as it is currently defined (i.e., conceptual knowledge of advertising) is not effective in reducing children's advertising susceptibility. Specifically, based on recent insights on children's advertising processing, we argue that due to the affect-based nature of contemporary advertising, children primarily process advertising under conditions of low elaboration and, consequently, are unlikely to use their advertising knowledge as a critical defense. Moreover, literature on cognitive development suggests that children's ability to use advertising knowledge as a defense will be further limited by their immature executive functioning and emotion regulation abilities. Therefore, we argue that the current conceptualization of advertising literacy needs to be extended with two dimensions: advertising literacy performance, which takes into account the actual use of conceptual advertising knowledge, and attitudinal advertising literacy, which includes low-effort, attitudinal mechanisms that can function as a defense under conditions of low elaboration. We conclude our article with specific directions for future research and implications for the ongoing societal and political debate about children and advertising.     

CHull as an alternative to AIC and BIC in the context of mixtures of factor analyzers

Mixture analysis is commonly used for clustering objects on the basis of multivariate data. When the data contain a large number of variables, regular mixture analysis may become problematic, because a large number of parameters need to be estimated for each cluster. To tackle this problem, the mixtures-of-factor-analyzers (MFA) model was proposed, which combines clustering with exploratory factor analysis. MFA model selection is rather intricate, as both the number of clusters and the number of underlying factors have to be determined. To this end, the Akaike (AIC) and Bayesian (BIC) information criteria are often used. AIC and BIC try to identify a model that optimally balances model fit and model complexity. In this article, the CHull (Ceulemans & Kiers, 2006) method, which also balances model fit and complexity, is presented as an interesting alternative model selection strategy for MFA. In an extensive simulation study, the performances of AIC, BIC, and CHull were compared. AIC performs poorly and systematically selects overly complex models, whereas BIC performs slightly better than CHull when considering the best model only. However, when taking model selection uncertainty into account by looking at the first three models retained, CHull outperforms BIC. This especially holds in more complex, and thus more realistic, situations (e.g., more clusters, factors, noise in the data, and overlap among clusters).     

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).     

CHull: A generic convex-hull-based model selection method

When analyzing data, researchers are often confronted with a model selection problem (e.g., determining the number of components/factors in principal components analysis [PCA]/factor analysis or identifying the most important predictors in a regression analysis). To tackle such a problem, researchers may apply some objective procedure, like parallel analysis in PCA/factor analysis or stepwise selection methods in regression analysis. A drawback of these procedures is that they can only be applied to the model selection problem at hand. An interesting alternative is the CHull model selection procedure, which was originally developed for multiway analysis (e.g., multimode partitioning). However, the key idea behind the CHull procedure—identifying a model that optimally balances model goodness of fit/misfit and model complexity—is quite generic. Therefore, the procedure may also be used when applying many other analysis techniques. The aim of this article is twofold. First, we demonstrate the wide applicability of the CHull method by showing how it can be used to solve various model selection problems in the context of PCA, reduced K-means, best-subset regression, and partial least squares regression. Moreover, a comparison of CHull with standard model selection methods for these problems is performed. Second, we present the CHULL software, which may be downloaded from http://ppw.kuleuven.be/okp/software/CHULL/, to assist the user in applying the CHull procedure.     

Quantitative work demands,emotional demands,and cognitive stress symptoms in surgery nurses

In surgery, cognitive stress symptoms, including problems in concentrating, deciding, memorising, and reflecting are risks to patient safety. Recent evidence points to social stressors as antecedents of cognitive stress symptoms in surgery personnel. The current study tests whether cognitive stress symptoms are positively associated with emotional abuse, emotional- and task-related demands and resources in surgery work. Forty-eight surgery nurses from two hospitals filled out the Copenhagen Psychosocial Questionnaire in its German version. Task-related and emotional demands were positively related to cognitive stress symptoms. In a stepwise, multiple, linear regression of cognitive stress symptoms on task-related and emotional demands, emotional abuse and emotional demands were unique predictors (p < .05). Efforts to increase patient safety should address emotional abuse, emotional demands, and, therefore, communication and cooperation team climate in surgery personnel.     

Encountering the ‘unexpected’ in phenomenological research. Faith and belief as expressions of spirituality in a qualitative study

A study of 12 secondary schools students in Victoria, Australia explored the spirituality of young people in the context of their everyday lives through a hermeneutic phenomenological investigation. In developing an understanding of what spirituality meant to the participants in this study, it was apparent that spirituality was deeply abiding and embedded within the participants’ religious traditions. This was influenced by the exposure participants had to religious and spiritual teachings. Despite the fact that the participants expressed a range of religious beliefs about God, the afterlife and transcendence towards others as significant ways in which they understood spirituality, the findings were not generalisable across the Australian population. These themes will be explored throughout the paper proving implications for educators working with specific groups of students from faith backgrounds and the possibility of investigating what young people from non-religious backgrounds believe.     

How do Curiosity,Meaning in Life,and Search for Meaning Predict College Students’ Daily Emotional Exhaustion and Engagement?

College students face numerous academic demands on a daily basis. The resources of the University and of the students to cope with these demands are essential to explain students’ levels of well-being. The purpose of this investigation is to explore the role of day-level curiosity and meaning in life in the explanation of students’ levels of engagement and emotional exhaustion at night. Two-hundred and nine college students participated in a daily study of five consecutive academic days, completing measures of curiosity and meaning in life in the afternoon and measures of engagement and emotional exhaustion at night. Data were analyzed using MLwiN software from a hierarchical linear modeling and daily approach. Curiosity in the afternoon showed a positive relationship with levels of engagement at night, and a negative relationship with levels of emotional exhaustion at night. Moreover, the interaction of curiosity and search for meaning and emotional exhaustion was positive. Although curiosity drives to exploring opportunities and challenges, decreases exhaustion and promotes daily engagement, when curiosity interacts with other emotional loads (such as search for meaning), it can become an emotional overload favoring exhaustion. From this perspective, it is necessary to continue investigating the mechanisms that predict students’ well-being and to create academic environments that stimulate curiosity and support students in their search for meaning in life.