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Abstract

In a multinational study (61 countries; N =15,039), we examined how collective narcissists, both agentic (ACN) and communal (CCN), reacted cognitively (through endorsement of unfounded conspiracy and health beliefs) and behaviorally (via prevention, hoarding, and prosociality) to the pandemic. Higher ACN and CCN predicted greater endorsement of COVID-19 unfounded beliefs and higher likelihood of having recently engaged in pandemic-related prevention, hoarding, and prosociality. The predictive effects of ACN and CCN were inde pendent, suggesting construct separability. Fear positively predicted endorsement of unfounded beliefs and behaviors, but the slope of that relation was flattened when ACN and CCN were particularly high. Finally, the relation between ACN or CCN and outcomes changed across countries varying in collective fear.

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Recalling a memory often prompts an emotional response. Research examining the fading affect bias (FAB) indicates that the emotional response prompted by positive memories often tends to be stronger than the emotional response prompted by negative memories. This chapter presents an overview of research that has explored the FAB effect. This overview indicates that the FAB reflects two trends: (1) over time, the affect associated with positive memories tends to fade more slowly from event occurrence to event recall than the affect associated with negative memories, and (2) it is more often the case that events that were negative at their occurrence will ultimately come to prompt positive affect-at-recall than it is the case that events that were positive at their occurrence will come to prompt negative affect-at-recall. Research has also revealed that the FAB can be altered by event moderators, situational moderators, and individual-difference moderators. The chapter uses this research review to highlight several needed directions for future research, suggest some theoretical ideas that may underlie the FAB, and discuss some ways in which the FAB might be important to life in a social world.
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Two aspects of translation were investigated: (1) factors that affect translation quality, and (2) how equivalence between source and target versions can be evaluated. The variables of language, content, and difficulty were studied through an analysis of variance design. Ninety-four bilinguals from the University of Guam, representing ten languages, translated or back-translated six essays incorporating three content areas and two levels of difficulty. The five criteria for equivalence were based on comparisons of meaning or predictions of similar responses to original or translated versions. The factors of content, difficulty, language and content-language interaction were significant, and the five equivalence criteria proved workable. Conclusions are that translation quality can be predicted, and that a functionally equivalent translation can be demonstrated when responses to the original and target versions are studied.
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Survey and longitudinal studies in the social and behavioral sciences generally contain missing data. Mean and covariance structure models play an important role in analyzing such data. Two promising methods for dealing with missing data are a direct maximum-likelihood and a two-stage approach based on the unstructured mean and covariance estimates obtained by the EM-algorithm. Typical assumptions under these two methods are ignorable nonresponse and normality of data. However, data sets in social and behavioral sciences are seldom normal, and experience with these procedures indicates that normal theory based methods for nonnormal data very often lead to incorrect model evaluations. By dropping the normal distribution assumption, we develop more accurate procedures for model inference. Based on the theory of generalized estimating equations, a way to obtain consistent standard errors of the two-stage estimates is given. The asymptotic efficiencies of different estimators are compared under various assumptions. We also propose a minimum chi-square approach and show that the estimator obtained by this approach is asymptotically at least as efficient as the two likelihood-based estimators for either normal or nonnormal data. The major contribution of this paper is that for each estimator, we give a test statistic whose asymptotic distribution is chi-square as long as the underlying sampling distribution enjoys finite fourth-order moments. We also give a characterization for each of the two likelihood ratio test statistics when the underlying distribution is nonnormal. Modifications to the likelihood ratio statistics are also given. Our working assumption is that the missing data mechanism is missing completely at random. Examples and Monte Carlo studies indicate that, for commonly encountered nonnormal distributions, the procedures developed in this paper are quite reliable even for samples with missing data that are missing at random.
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This article examines the adequacy of the “rules of thumb” conventional cutoff criteria and several new alternatives for various fit indexes used to evaluate model fit in practice. Using a 2‐index presentation strategy, which includes using the maximum likelihood (ML)‐based standardized root mean squared residual (SRMR) and supplementing it with either Tucker‐Lewis Index (TLI), Bollen's (1989) Fit Index (BL89), Relative Noncentrality Index (RNI), Comparative Fit Index (CFI), Gamma Hat, McDonald's Centrality Index (Mc), or root mean squared error of approximation (RMSEA), various combinations of cutoff values from selected ranges of cutoff criteria for the ML‐based SRMR and a given supplemental fit index were used to calculate rejection rates for various types of true‐population and misspecified models; that is, models with misspecified factor covariance(s) and models with misspecified factor loading(s). The results suggest that, for the ML method, a cutoff value close to .95 for TLI, BL89, CFI, RNI, and Gamma Hat; a cutoff value close to .90 for Mc; a cutoff value close to .08 for SRMR; and a cutoff value close to .06 for RMSEA are needed before we can conclude that there is a relatively good fit between the hypothesized model and the observed data. Furthermore, the 2‐index presentation strategy is required to reject reasonable proportions of various types of true‐population and misspecified models. Finally, using the proposed cutoff criteria, the ML‐based TLI, Mc, and RMSEA tend to overreject true‐population models at small sample size and thus are less preferable when sample size is small.
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To determine the accuracy of self-reported health care utilization and absence reported on health risk assessments against administrative claims and human resource records. Self-reported values of health care utilization and absenteeism were analyzed for concordance to administrative claims values. Percent agreement, Pearson's correlations, and multivariate logistic regression models examined the level of agreement and characteristics of participants with concordance. Self-report and administrative data showed greater concordance for monthly compared with yearly health care utilization metrics. Percent agreement ranged from 30% to 99% with annual doctor visits having the lowest percent agreement. Younger people, males, those with higher education, and healthier individuals more accurately reported their health care utilization and absenteeism. Self-reported health care utilization and absenteeism may be used as a proxy when medical claims and administrative data are unavailable, particularly for shorter recall periods.
Collective narcissism: How being narcissistic about your groups shapes politics, group processes and intergroup relations
  • Eker
Hoarding in the age of COVID-19
  • Baddeley