创伤暴露程度对中学生创伤后成长的影响——复原力的调节作用

采用修订后的创伤后成长问卷（PTGI）、复原力量表（CD-RISC）以及自编的震后创伤暴露程度调查表对汶川地震后异地复学的166名中学生进行问卷调查,探讨复原力在创伤暴露程度和创伤后成长关系中的调节作用。结果表明：主观害怕程度对创伤后成长有正向预测作用,主观害怕程度越高,创伤后成长的水平越高;复原力对主观害怕程度和创伤后成长之间的关系起增强作用;复原力对房屋损毁和创伤后成长之间的关系有一定的增强作用。     

结构方程建模中的题目打包策略

结构方程建模中题目打包法的优缺点包括：指标数据质量变好、模型拟合程度提高; 估计偏差不大, 可校正; 估计稳定, 但降低了敏感性与可证伪性。打包法的前提条件是单维、同质, 适合结构模型分析, 不适合测量模型分析。对于单维测验, 给出了一个打包流程。对于通常的多个子量表(多维结构)测验, 推荐在子量表内打包, 每个子量表打包成1个指标或者3个指标, 用于结构方程建模。     

Perceiving Benefits in the Cancer Experience

The potential for benefits to be construed under stressful circumstances has become a topic of interest for researchers studying psychological reactions to adversity. This paper examines benefit-construal following one such stressful event—cancer. A simple framework is presented to organize qualitative and quantitative data from various literatures, including psychology, nursing, oncology, and social work, into 3 areas in which cancer survivors frequently report deriving benefits: life perspective, interpersonal relationships, and the self. This typology helps to clarify the extent and nature of benefit-finding in cancer survivors and provides directions for future research.     

Opinion leadership types or continuous opinion leadership traits?

Opinion leadership is typically conceptualised as a continuous personality trait. However, many authors adhere to the view of qualitatively different opinion leadership types and apply arbitrary criteria to split continuous trait scores into two groups (i.e., opinion leaders vs. non‐leaders). The present study is the first to empirically evaluate this approach. A sample of N = 3812 adults (67% women) was administered a validated opinion leadership scale. Finite mixture models examined whether the latent trait distribution can be represented by a set of discrete trait levels that reflected distinct opinion leadership types. The results did not give support to a discrete typology that distinguished leaders from non‐leaders. Rather, opinion leadership was best characterised as a continuous trait.     

Dynamics of Religious Group Growth and Survival

We model and analyze the dynamics of religious group membership and size. A group is distinguished by its strictness, which determines how much time group members are expected to spend contributing to the group. Individuals differ in their rate of return for time spent outside of their religious group. We construct a utility function that individuals attempt to maximize, then find a Nash equilibrium for religious group participation with a heterogeneous population. We then model dynamics of group size by including birth, death, and switching of individuals between groups. Group switching depends on the strictness preferences of individuals and their probability of encountering members of other groups. We show that in the case of only two groups—one with finite strictness and the other with zero—there is a parameter combination that determines whether the nonzero strictness group can survive over time, which is more difficult at higher strictness levels. We also show that a high birth rate can allow even the strictest groups to survive. Finally, we consider cases of several groups, gaining insight into strategic choices of strictness values and displaying the rich behavior of the model.     

Application of Latent Semantic Analysis to Divergent Thinking is Biased by Elaboration

Scoring divergent‐thinking response sets has always been challenging because such responses are not only open‐ended in terms of number of ideas, but each idea may also be expressed by a varying number of concepts and, thus, by a varying number of words (elaboration). While many current studies have attempted to score the semantic distance in divergent‐thinking responses by applying latent semantic analysis (LSA), it is known from other areas of research that LSA‐based approaches are biased according to the number of words in a response. Thus, the current article aimed to identify and demonstrate this elaboration bias in LSA‐based divergent‐thinking scores by means of a simulation. In addition, we show that this elaboration bias can be reduced by removing the stop words (for example, and, or, for and so forth) prior to analysis. Furthermore, the residual bias after stop word removal can be reduced by simulation‐based corrections. Finally, we give an empirical illustration for alternate uses and consequences tasks. Results suggest that when both stop word removal and simulation‐based bias correction are applied, convergent validity should be expected to be highest.     

An Examination of a Functional Mixed-Effects Modeling Approach to the Analysis of Longitudinal Data

Growth curve modeling is one of the main analytical approaches to study change over time. Growth curve models are commonly estimated in the linear and nonlinear mixed-effects modeling framework in which both the mean and person-specific curves are modeled parametrically with functions of time such as the linear, quadratic, and exponential. However, when more complex nonlinear trajectories need to be estimated and researchers do not have a priori knowledge of an appropriate functional form of growth, parametric models may be too restrictive. This paper reviews functional mixed-effects models, a nonparametric extension of mixed-effects models that permit both the mean and person-specific curves to be estimated without assuming a prespecified functional form of growth. Details of the model are presented along with results from a simulation study and an empirical example. The simulation study showed functional mixed-effects models performed reasonably well under various conditions commonly associated with longitudinal panel data, such as few time points per person, irregularly spaced time points across persons, missingness, and nonlinear trajectories. The usefulness of functional mixed-effects models is illustrated by analyzing empirical data from the Early Childhood Longitudinal Study – Kindergarten Class of 1998–1999.     

Nonlinear Indicator-Level Moderation in Latent Variable Models

Linear, nonlinear, and nonparametric moderated latent variable models have been developed to investigate possible interaction effects between a latent variable and an external continuous moderator on the observed indicators in the latent variable model. Most moderation models have focused on moderators that vary across persons but not across the indicators (e.g., moderators like age and socioeconomic status). However, in many applications, the values of the moderator may vary both across persons and across indicators (e.g., moderators like response times and confidence ratings). Indicator-level moderation models are available for categorical moderators and linear interaction effects. However, these approaches require respectively categorization of the continuous moderator and the assumption of linearity of the interaction effect. In this article, parametric nonlinear and nonparametric indicator-level moderation methods are developed. In a simulation study, we demonstrate the viability of these methods. In addition, the methods are applied to a real data set pertaining to arithmetic ability.     

The Relationship between Local Identification,Urban Disorder Sensitivity,and Prejudice Toward Immigrants: The Role of Autochthony

Autochthony is the belief that a place belongs to those who were born there and that they are more entitled. Autochthony and local identification can foster sensitivity to any source of disorder that threatens local stability. The aims of this study were to determine whether: (a) local identification is associated with a higher level of sensitivity to urban disorder and a higher level of prejudice toward immigrants; (b) higher city identifiers use autochthony (entitlement for first comers) as a justification for both of these attitudes. A self‐report questionnaire was administered to 254 adult residents of Turin, Italy. Local identification was found related to autochthony and to urban disorder sensitivity, autochthony was positively associated with both urban disorder sensitivity and prejudice toward immigrants and it mediated the relationship between local identification and prejudice.     

Trait rumination and response to negative evaluative lab-induced stress: neuroendocrine,affective, and cognitive outcomes

Theoretical models of depression posit that, under stress, elevated trait rumination predicts more pronounced or prolonged negative affective and neuroendocrine responses, and that trait rumination hampers removing irrelevant negative information from working memory. We examined several gaps regarding these models in the context of lab-induced stress. Non-depressed undergraduates completed a rumination questionnaire and either a negative-evaluative Trier Social Stress Test (n?=?55) or a non-evaluative control condition (n?=?69), followed by a modified Sternberg affective working memory task assessing the extent to which irrelevant negative information can be emptied from working memory. We measured shame, negative and positive affect, and salivary cortisol four times. Multilevel growth curve models showed rumination and stress interactively predicted cortisol reactivity; however, opposite predictions, greater rumination was associated with blunted cortisol reactivity to stress. Elevated trait rumination interacted with stress to predict augmented shame reactivity. Rumination and stress did not significantly interact to predict working memory performance, but under control conditions, rumination predicted greater difficulty updating working memory. Results support a vulnerability-stress model of trait rumination with heightened shame reactivity and cortisol dysregulation rather than hyper-reactivity in non-depressed emerging adults, but we cannot provide evidence that working memory processes are critical immediately following acute stress.