The social and emotional skills of bullies,victims, and bully–victims of Egyptian primary school children

This study examined whether bullies, victims, bully–victims (who are both bullies and victims), and students who reported no or low levels of bullying and victimization differed in their levels of social and emotional skills. Data were collected from 623 children in fifth and sixth grades from four Egyptian elementary schools; their ages ranged from 10 to 12 years. K‐means cluster analysis revealed four groups: bullies (n = 138), victims (n = 178), bully–victims (n = 59), and children who were not involved in bullying behaviour (n = 248). Data were analyzed using multinomial logistic regression. The findings indicated that boys were more involved in bullying behaviour than girls, and both bullies and bully–victims were less likely to adhere to social rules and politeness than children who were not involved in bullying. Both bullies and victims were less aware of the physiological reactions of their emotions than uninvolved children, and were less able to apply social rules in social interaction. Both victims and bully–victims reported less likeability than children not involved in bullying. Verbal sharing, attending to others’ emotions, and analysis of emotions did not have a statistically significant relationship with the probabilities of classifying children to any bullying group versus children not involved in bullying. Social skills were more important than emotional awareness in predicting the likelihood of classifying children in one of the three bullying groups versus children who not involved in bullying. The main conclusion is that social and emotional skills together may provide an effective means of intervention for bullying problems.     

Latent variables should remain as such: Evidence from a Monte Carlo study

Use of subject scores as manifest variables to assess the relationship between latent variables produces attenuated estimates. This has been demonstrated for raw scores from classical test theory (CTT) and factor scores derived from factor analysis. Conclusions on scores have not been sufficiently extended to item response theory (IRT) theta estimates, which are still recommended for estimation of relationships between latent variables. This is because IRT estimates appear to have preferable properties compared to CTT, while structural equation modeling (SEM) is often advised as an alternative to scores for estimation of the relationship between latent variables. The present research evaluates the consequences of using subject scores as manifest variables in regression models to test the relationship between latent variables. Raw scores and three methods for obtaining theta estimates were used and compared to latent variable SEM modeling. A Monte Carlo study was designed by manipulating sample size, number of items, type of test, and magnitude of the correlation between latent variables. Results show that, despite the advantage of IRT models in other areas, estimates of the relationship between latent variables are always more accurate when SEM models are used. Recommendations are offered for applied researchers.     

The personality of meaning in life: Associations between dimensions of life meaning and the Big Five

The goal of the current study was to identify aspects of personality that are associated with different ways in which people find meaning in life. This was achieved using constrained principal component analysis (CPCA) on data from 322 university students, who completed the Sources of Meaning and Meaning in Life questionnaire and the Big Five Aspects Scale. CPCA demonstrated that personality traits and life meaning are associated, but not redundant, with one another. Specifically, respondents with high scores on lower-level aspects of Openness to Experience tended to derive meaning from questioning, learning and challenging tradition, whereas those with high scores on aspects of Conscientiousness and Extraversion tended to derive meaning from success at work, health, and family. Results suggest that personality traits are associated with variations in the domains used to derive meaning in life, and demonstrate the utility of CPCA as an innovative statistical technique for the study of individual differences.     

潜变量交互效应分析方法

简要回顾了分析显变量交互效应的常用方法。详细讨论了目前分析潜变量交互效应的主要方法,包括用潜变量的因子得分做回归分析、分组线性结构方程模型分析、加入乘积项的结构方程模型分析和两步最小二乘回归分析,并比较和评价了这些方法的优缺点。最后归纳了潜变量交互效应分析方法的研究趋势,并介绍了新近进展(包括LMS方法和GAPI方法)。     

父母教养态度与儿童在2-4岁期间的问题行

考察父母的教养态度和儿童在2—4岁间的问题行为的发展变化。采用交叉时序滞后设计,对172名儿童从2岁起进行2年追踪,在被试2岁和4岁时,分别对他们的父母进行间卷调查。结果发现：儿童外显问题行为与父母教养态度的相互作用较强,儿童在2岁时的外显问题行为可以预测4岁时母亲的惩罚性,内隐问题行为与父母教养态度的相互作用较弱;从2岁到4岁之间,儿童外显问题行为具有较高的稳定性,内隐问题行为的稳定性相对略低,父母教养态度也都具有一定的稳定性。     

Can we disregard the whole model? Omnibus non-inferiority testing for R2 in multi-variable linear regression and in ANOVA

Determining a lack of association between an outcome variable and a number of different explanatory variables is frequently necessary in order to disregard a proposed model (i.e., to confirm the lack of a meaningful association between an outcome and predictors). Despite this, the literature rarely offers information about, or technical recommendations concerning, the appropriate statistical methodology to be used to accomplish this task. This paper introduces non-inferiority tests for ANOVA and linear regression analyses, which correspond to the standard widely used F test for and R², respectively. A simulation study is conducted to examine the Type I error rates and statistical power of the tests, and a comparison is made with an alternative Bayesian testing approach. The results indicate that the proposed non-inferiority test is a potentially useful tool for ‘testing the null’.     

The analytic initiation: the effect of the archetype of initiation on the personal unconscious

The author proposes the term 'analytic initiation1' to describe a rite of passage from childhood to adult life effected by psychotherapy. He suggests this passage is based upon a personal regression within the embrace of an archetypal symbolic process. Archetypal symbols orientate the analysand towards the transformative potential of a regressive experience. They also contain him as he separates from parental complexes and tolerates their dissolution. The analysand's growth depends on his submitting to an intrapsychic and interpersonal experience of the initiatory process with its attending regression. Correspondingly, it relies on the analyst being available for both the personal and impersonal transference/countertransference interaction. Over time, the analysand's participation in this process brings his ego in greater relationship to the Self.
The most important outcome of a successfully constellated analytic initiation is not the dissolution of a group of complexes. Rather, it is the establishment of a symbolic process that allows the individual to continue to evolve. The author supports his findings with a critical review of Jung's writings on initiation and additional case material.     

A two‐part mixed effects model for cigarette purchase task data

The Cigarette Purchase Task is a behavioral economic assessment tool designed to measure the relative reinforcing efficacy of cigarette smoking across different prices. An exponential demand equation has become a standard model for analyzing purchase task data, but its utility is compromised by its inability to accommodate values of zero consumption. We propose a two‐part mixed effects model that keeps the same exponential demand equation for modeling nonzero consumption values, while providing a logistic regression for the binary outcome of zero versus nonzero consumption. Therefore, the proposed model can accommodate zero consumption values and retain the features of the exponential demand equation at the same time. As a byproduct, the logistic regression component of the proposed model provides a new demand index, the “derived breakpoint”, for the price above which a subject is more likely to be abstinent than to be smoking. We apply the proposed model to data collected at baseline from college students (N = 1,217) enrolled in a randomized clinical trial utilizing financial incentives to motivate tobacco cessation. Monte Carlo simulations showed that the proposed model provides better fits than an existing model. We note that the proposed methodology is applicable to other purchase task data, for example, drugs of abuse.     

Regression Mixture Models: Does Modeling the Covariance Between Independent Variables and Latent Classes Improve the Results?

Regression mixture models are increasingly used as an exploratory approach to identify heterogeneity in the effects of a predictor on an outcome. In this simulation study, we tested the effects of violating an implicit assumption often made in these models; that is, independent variables in the model are not directly related to latent classes. Results indicate that the major risk of failing to model the relationship between predictor and latent class was an increase in the probability of selecting additional latent classes and biased class proportions. In addition, we tested whether regression mixture models can detect a piecewise relationship between a predictor and outcome. Results suggest that these models are able to detect piecewise relations but only when the relationship between the latent class and the predictor is included in model estimation. We illustrate the implications of making this assumption through a reanalysis of applied data examining heterogeneity in the effects of family resources on academic achievement. We compare previous results (which assumed no relation between independent variables and latent class) to the model where this assumption is lifted. Implications and analytic suggestions for conducting regression mixture based on these findings are noted.     

A Comparative Investigation of Confidence Intervals for IndependentVariables in Linear Regression

In linear regression, the most appropriate standardized effect size for individual independent variables having an arbitrary metric remains open to debate, despite researchers typically reporting a standardized regression coefficient. Alternative standardized measures include the semipartial correlation, the improvement in the squared multiple correlation, and the squared partial correlation. No arguments based on either theoretical or statistical grounds for preferring one of these standardized measures have been mounted in the literature. Using a Monte Carlo simulation, the performance of interval estimators for these effect-size measures was compared in a 5-way factorial design. Formal statistical design methods assessed both the accuracy and robustness of the four interval estimators. The coverage probability of a large-sample confidence interval for the semipartial correlation coefficient derived from Aloe and Becker was highly accurate and robust in 98% of instances. It was better in small samples than the Yuan-Chan large-sample confidence interval for a standardized regression coefficient. It was also consistently better than both a bootstrap confidence interval for the improvement in the squared multiple correlation and a noncentral interval for the squared partial correlation.