Ecological social capital does not predict geographical variance in increases in depression following the 2008 financial crisis

Research suggests that the financial crisis of 2008 and its aftermath were associated with an increase in mental health problems, but there has been little research into potential protective factors. Ecological social capital is a plausible candidate given evidence of its protective status following natural disasters. Pre-crisis area-level estimates of generalized trust and sense of belonging were computed from the 2004 to 2006 waves of the Living in Wales survey (N = 43,473) for 413 neighbourhoods in Wales, using multilevel regression with post-stratification, a technique for disaggregating survey data into small area estimates. Area estimates and the planned analysis were preregistered using Open Science Framework. Data (N = 180,462) from the Welsh Health Survey (2003–2015) were then used to model whether social capital was protective against depression in general and whether it moderated the increase in depression prevalence, associated with the financial crisis. Depression rates increased post-crisis (odds ratio [OR] = 1.271), and trust was a protective factor against depression (OR = 0.775). The hypothesized interaction, however, was not significant (OR = 1.033), nor was sense of belonging (OR = 0.934) or its interaction with the crisis (OR = 1.024). Although ecological generalized trust appears to be a protective factor against depression, it did not buffer against the mental health impact of the financial crisis.     

在什么情况下员工会汇报差错?基于秘密分享视角

差错在工作场所中并不鲜见, 然而有关差错诊断的研究相对匮乏。基于此, 以员工主动差错汇报为切入点, 分析影响员工差错汇报的因素。结合秘密分享框架, 提出差错特点(差错严重性与可见性)对员工的焦虑感、反刍和汇报责任感产生正向影响; 而焦虑感、反刍和汇报责任感会促使员工采取差错汇报行为, 由此上述个体感知中介差错特点与差错汇报之间的关系; 此外, 个体责任心、领导容错性以及团队心理安全会作为边界条件进一步影响上述员工犯错后的感知, 并最终影响其差错汇报行为。     

纵向数据的中介效应分析

目前中介效应检验主要是基于截面数据,但许多时候截面数据的中介分析不适合进行因果推断,因而需要收集历时性的纵向数据,进行纵向数据的中介分析。评介了基于交叉滞后面板模型、多层线性模型和潜变量增长模型的纵向数据的中介分析方法及其四个发展。第一,中介效应随时间变化,如连续时间模型、多层时变系数模型。第二,中介效应随个体变化,如随机效应的交叉滞后面板模型和多层自回归模型。第三,中介模型的整合,如交叉滞后面板模型与多层线性模型整合为多层自回归模型。第四,中介检验方法的发展,建议使用Monte Carlo、Bootstrap和贝叶斯法进行纵向数据的中介分析。总结出一个纵向数据的中介分析流程并给出相应的Mplus程序。随后展望了纵向数据的中介分析的拓展方向。     

The Impact of Crime on Social Ties and Civic Participation

The present study examined associations of neighbourhood crime with residents' social ties and civic participation using multilevel models. We hypothesized that crime is indirectly associated with residents' low civic participation by negatively relating to their acquaintanceship ties because of fear of neighbours. By contrast, we predicted that crime is indirectly related to frequent civic participation by positively associating with more intimate friendship ties as a response to combat external threats. Additionally, we hypothesized that high crime rates in the neighbourhood increases the importance of generalized trust towards others. Therefore, we examined the interaction effects of neighbourhood crime and trust on social ties and participation. The study is based on a postal questionnaire mailed to residents aged between 20 and 69 years, residing in Musashino City and Kiyose City, in Tokyo. Rates of larceny reported by the Tokyo Metropolitan Police Department were used as indices of neighbourhood crime. As hypothesized, our results showed that crime is positively associated with friendship ties and is negatively related to acquaintanceship ties. Through these opposing relationships, crime showed both positive and negative associations with civic participation. Moreover, we found that generalized trust buffered the adverse relationships between crime, broader social ties and participation. Copyright © 2015 John Wiley & Sons, Ltd.     

Actor‐Partner Interdependence Models (APIM) and Voting Behavior: Methodology and Applications

Recently, the social sciences have witnessed a rising interest in dyadic design, as an efficient way to disentangle mechanisms of interpersonal influence. Despite the relevance of this design to political research, few efforts have been made to collect and efficiently analyze dyadic data. In this article, we suggest the Actor‐Partner Interdependence Model as a useful tool to test bidirectional effects in dyadic data on political attitudes and behaviors. The model explicitly assumes that members of a dyad (reciprocally identified as actor and partner) involved in political communication are interdependent and influence each other. We apply the model to estimate the effect of partner's party identification on actor's vote choice, using 1996 Indianapolis–St. Louis dyadic data. Results show that partner's party identification is significantly associated with vote choice. Moreover, we show that influence between dyad members is moderated by their intimacy and that an increased difference in socioeconomic status between dyad members tips the balance of the effect in favor of the individual with more resources. Our conclusions point to the effectiveness of APIM in modeling interdependent asymmetric relations and call for increasing efforts in collecting dyadic data and in developing proper tools for their analysis.     

Illustration of Multilevel Explanatory IRT Model DIF Testing with the Creative Thinking Scale

Creativity has been well studied in the past several decades, and numerous measures have been developed to assess creativity. However, validity evidence associated with each measure is often mixed. In particular, the social consequence aspect of validity has received little attention. This is partly due to the difficulty of testing for differential item functioning (DIF) within the traditional classical test theory framework, which still remains the most popular approach to assessing creativity. Hence, this study provides an example of examining differential item functioning using multilevel explanatory item response theory models. The Creative Thinking Scale was tested for DIF in a sample of 1043 10th–12th graders. Results revealed significant uniform and non-uniform DIF for some items. Differentially functioning items are able to produce measurement bias and should be either deleted or modeled. The detailed implications for researchers and practitioners are discussed.     

Human values and ideological beliefs as predictors of attitudes toward immigrants across 20 countries: The country-level moderating role of threat

Immigration is a worldwide subject of interest, and studies about attitudes toward immigrants have been frequent due to immigration crises in different locations across the globe. We aimed at understanding individual-level effects of human values and ideological beliefs (Right-Wing Authoritarianism—RWA, and Social Dominance Orientation—SDO) on attitudes toward immigrants, and whether country-level variables (perception of Islamic fundamentalism as a threat, perception of immigrants as a threat, and international migrant stock) moderate these relations. With representative samples from 20 countries (N = 21,362; the Americas, Europe, Asia, and Oceania), and using Multilevel Bayesian regressions, results showed the negative effect of RWA, SDO, and existence values on attitudes toward immigrants, and the positive effects of suprapersonal and interactive values. Cross-level interactions indicated that the effects of RWA, SDO, and suprapersonal and existence values were intensified in countries with societally high levels of perceiving Islamic fundamentalism as a threat. International migrant stock served as a country-level moderator for the effects of SDO and RWA only. When country-level moderators were included simultaneously, Islamic fundamentalism as a threat was the most consistent moderator. Framing theory is offered as a plausible explanation of these results.     

多层次研究的数据聚合适当性检验:文献评价与关键问题试解

组织管理领域的多层次研究经常需要测量共享单位特性构念, 常用方法是将单位内若干个体成员的评分聚合到单位层次, 确保聚合后的分数具有充分代表性的统计前提是通过聚合适当性检验。聚合适当性检验的常用指标是组内一致性r_WG和组内信度ICC(1)、ICC(2), 但目前学界对于这两类指标何者更优、r_WG的原分布选择和数据清理、各指标的划界值等关键问题存在诸多争议。为此, 首先对国内9份管理学、心理学期刊2014年以来发表的166篇包含聚合适当性检验的论文进行内容分析, 并以Journal of Applied Psychology上的85篇论文为对比, 查明常规实践中的共性问题, 进而提出实践建议:(1)明确功能定位, 将r_WG作为聚合适当性指标, ICC(1)和ICC(2)分别作为效度、信度指标。(2)计算r_WG时审慎选择原分布, 排除组内一致性过低的组。(3)为各指标设定更加合理、有适度灵活性的划界值, 停止使用武断、粗糙的经验标准。最后, 强调研究者在模型构建和聚合决策中应加强理论考量, 避免片面依赖统计检验结果。     

A primer for using multilevel models to meta-analyze single case design data with AB phases

Meta-analytic methods provide a way to synthesize data across treatment evaluation studies. However, these well-accepted methods are infrequent with behavior analytic studies. Multilevel models may be a promising method to meta-analyze single-case data. This technical article provides a primer for how to conduct a multilevel model with single-case designs with AB phases using data from the differential-reinforcement-of-low-rate behavior literature. We provide details, recommendations, and considerations for searching for appropriate studies, organizing the data, and conducting the analyses. All data sets are available to allow the reader to follow along with this primer. The purpose of this technical article is to minimally equip behavior analysts to complete a meta-analysis that will summarize a current state of affairs as it relates to the science of behavior analysis and its practice. Moreover, we aim to demonstrate the value of analyses of this sort for behavior analysis.     

Bayesian estimation of a multilevel IRT model using gibbs sampling

In this article, a two-level regression model is imposed on the ability parameters in an item response theory (IRT) model. The advantage of using latent rather than observed scores as dependent variables of a multilevel model is that it offers the possibility of separating the influence of item difficulty and ability level and modeling response variation and measurement error. Another advantage is that, contrary to observed scores, latent scores are test-independent, which offers the possibility of using results from different tests in one analysis where the parameters of the IRT model and the multilevel model can be concurrently estimated. The two-parameter normal ogive model is used for the IRT measurement model. It will be shown that the parameters of the two-parameter normal ogive model and the multilevel model can be estimated in a Bayesian framework using Gibbs sampling. Examples using simulated and real data are given.