The effect of emotion on statistical reasoning: Findings from a base rates task

In this paper we explore the effect of emotion on statistical reasoning. We employed a base rates task to examine reliance on statistical information in the presence of congruent or incongruent anecdotal information. The impact of emotional content on statistical reasoning was investigated in controls and victims of sexual abuse. Two types of emotional contents were used, one generally emotional, and one related to sexual abuse. The latter category was included to investigate the effect of semantically identical stimuli that could potentially produce different levels of emotion in the victim and control groups. For control participants, emotional contents were associated with decreased use of statistical information and increased use of anecdotal information compared to neutral contents. Results from the victims group showed this effect to be increased for contents related to sexual abuse, suggesting that the effect is linked to the emotional connotation rather than semantic content. Victims perceived contents relating to their experience as more emotional and this was associated with specific reasoning impairments for these topics.     

An empirical evaluation of alternative methods of estimation for confirmatory factor analysis with ordinal data

Confirmatory factor analysis (CFA) is widely used for examining hypothesized relations among ordinal variables (e.g., Likert-type items). A theoretically appropriate method fits the CFA model to polychoric correlations using either weighted least squares (WLS) or robust WLS. Importantly, this approach assumes that a continuous, normal latent process determines each observed variable. The extent to which violations of this assumption undermine CFA estimation is not well-known. In this article, the authors empirically study this issue using a computer simulation study. The results suggest that estimation of polychoric correlations is robust to modest violations of underlying normality. Further, WLS performed adequately only at the largest sample size but led to substantial estimation difficulties with smaller samples. Finally, robust WLS performed well across all conditions.     

BootES: An R package for bootstrap confidence intervals on effect sizes

Bootstrap Effect Sizes (bootES; Gerlanc & Kirby, 2012) is a free, open-source software package for R (R Development Core Team, 2012), which is a language and environment for statistical computing. BootES computes both unstandardized and standardized effect sizes (such as Cohen’s d, Hedges’s g, and Pearson’s r) and makes easily available for the first time the computation of their bootstrap confidence intervals (CIs). In this article, we illustrate how to use bootES to find effect sizes for contrasts in between-subjects, within-subjects, and mixed factorial designs and to find bootstrap CIs for correlations and differences between correlations. An appendix gives a brief introduction to R that will allow readers to use bootES without having prior knowledge of R.     

Mental chronometry and individual differences: Modeling reliabilities and correlations of reaction time means and effect sizes

We used a general stage-based model of reaction time (RT) to investigate the psychometric properties of mean RTs and experimental effect sizes (i.e., differences in mean RTs). Using the model, formulas were derived for the reliabilities of mean RTs and RT difference scores, and these formulas provide guidance about the number of trials per participant needed to obtain reliable estimates of these measures. In addition, formulas were derived for various different types of correlations computed in RT research (e.g., correlations between a mean RT and an external non-RT measure, between two mean RTs, between a mean RT and an RT effect size). The analysis revealed that observed RT-based correlations depend on many parameters of the underlying processes contributing to RT. We conclude that these correlations often fail to support the inferences drawn from them and that their proper interpretation is far more complex than is generally acknowledged.     

有中介的调节模型的拓展及其效应量

传统的有中介的调节(mediated moderation, meMO)模型关于误差方差齐性的假设经常被违背, 应用研究中也缺乏测量meMO效应大小的指标。对于单层数据, 本文借助于两层建模的思想, 提出了一种可用于处理方差非齐性的两层有中介的调节(2meMO)模型; 给出了用于测量meMO分析中总调节效应、直接调节效应和有中介调节效应大小的效应量。通过Monte Carlo模拟研究, 比较了meMO和2meMO模型在参数和效应量估计上的表现。并通过实际案例解释了2meMO模型的应用以及效应量的计算和解释。     

But consider the alternative: The influence of positive affect on overconfidence

Three studies find evidence that positive affect reduces comparative overconfidence (overplacement). This occurs because positive affect attenuates focalism via decreasing people's tendency to overweight information regarding themselves in the light of information concerning others. Specifically, Study 1 provides evidence that positive affect leads to more realistic estimates of comparative ability and that other-focus partially mediates this effect. Then, Study 2 provides causal evidence that positive affect independently influences other-focus and that other-focus, in turn, influences overplacement. Additionally, Study 2 uses an indirect measure of focalism to better capture this attentional process. Finally, Study 3 explores the influence of negative affect on overplacement. In addition, each study finds that positive affect does not influence overconfidence regarding participant's raw performances (overestimation) as this type of overconfidence is not dependent on self-other comparisons.     

A probability-based measure of effect size: robustness to base rates and other factors

Calculating and reporting appropriate measures of effect size are becoming standard practice in psychological research. One of the most common scenarios encountered involves the comparison of 2 groups, which includes research designs that are experimental (e.g., random assignment to treatment vs. placebo conditions) and nonexperimental (e.g., testing for gender differences). Familiar measures such as the standardized mean difference (d) or the point-biserial correlation (rpb) characterize the magnitude of the difference between groups, but these effect size measures are sensitive to a number of additional influences. For example, R. E. McGrath and G. J. Meyer (2006) showed that rpb is sensitive to sample base rates, and extending their analysis to situations of unequal variances reveals that d is, too. The probability-based measure A, the nonparametric generalization of what K. O. McGraw and S. P. Wong (1992) called the common language effect size statistic, is insensitive to base rates and more robust to several other factors (e.g., extreme scores, nonlinear transformations). In addition to its excellent generalizability across contexts, A is easy to understand and can be obtained from standard computer output or through simple hand calculations.     

The Stanford-Binet: An evaluation of the technical data available since the 1972 restandardization

A review of the technical data available on the 1972 norms edition of the Stanford-Binet is presented. Data on the standardization sample, reliability and validity are considered. This review demonstrates how inadequate these data are. It is contended that the Stanford-Binet should not continue to be used in important decision making processes unless this weakness is corrected.     

The influence of the minority on perception: A note on a possible alternative explanation

This research note raises some concerns about possible alternative explanations to studies using perceptual change as indicative of minority vs majority influence. Having failed to replicate the original study (S. Moscovici & B. Personnaz, Journal of Experimental Social Psychology 1980, 16, 270–282), using the same and an alternate measuring procedure, inspection of additional data introduces the role of suspiciousness in explaining this and previous findings. An informal study adds further credence to this explanation.     

An alternative approach to relapse analysis: Using Monte Carlo methods and proportional rates of response

Relapse is the recovery of a previously suppressed response. Animal models have been useful in examining the mechanisms underlying relapse (e.g., reinstatement, renewal, reacquisition, resurgence). However, there are several challenges to analyzing relapse data using traditional approaches. For example, null hypothesis significance testing is commonly used to determine whether relapse has occurred. However, this method requires several a priori assumptions about the data, as well as a large sample size for between‐subjects comparisons or repeated testing for within‐subjects comparisons. Monte Carlo methods may represent an improved analytic technique, because these methods require no prior assumptions, permit smaller sample sizes, and can be tailored to account for all of the data from an experiment instead of some limited set. In the present study, we conducted reanalyses of three studies of relapse (Berry, Sweeney, & Odum, 2014 ; Galizio et al., 2018 ; Odum & Shahan, 2004 ) using Monte Carlo techniques to determine if relapse occurred and if there were differences in rate of response based on relevant independent variables (such as group membership or schedule of reinforcement). These reanalyses supported the previous findings. Finally, we provide general recommendations for using Monte Carlo methods in studies of relapse.     

The influence of correlations between noncritical features and reinforcement on stimulus generalization

One strategy to program for generalization is to vary noncritical features in teaching exemplars, thereby avoiding noncritical features from being highly correlated with reinforcement and thus gaining faulty stimulus control. In the current translational evaluation, 2 groups of adults of typical development were taught to respond to arbitrary stimuli with experimenter‐defined critical and noncritical features in a matching‐to‐sample task. The teaching arrangement used for 1 group programmed for low correlation between noncritical features and reinforcement; the teaching arrangement used for the other group programmed for high correlation between noncritical features and reinforcement. Participants in the former group displayed (a) faster acquisition of matching, (b) less variability in correct responding, and (c) a decreased likelihood of faulty stimulus control developing during training. The results contribute towards advancing the study of stimulus control and developing an explicit technology of generalization to better serve consumers of the application of our science.     

The effect of category learning on sensitivity to within-category correlations

A salient property of many categories is that they are not just sets of independent features but consist of clusters of correlated features. Although there is much evidence that people are sensitive to between-categories correlations, the evidence about within-category correlations is mixed. Two experiments tested whether the disparities might be due to different learning and test tasks. Subjects learned about categories either by classifying items or by inferring missing features of items. Their knowledge of the correlations was measured with classification, prediction, typicality, and production tests. The inference learners, but not the classification learners, showed sensitivity to the correlations, although different tests were differentially sensitive. These results reconcile some earlier disparities and provide a more complete understanding of people's sensitivities to within-category correlations.     

The resident researcher: An alternative career model centered on community

Considers the potential benefits and costs of an alternative career model for community researchers, one in which professionals specialize in the community where they live. By virtue of their local familiarity, resident researchers can make more informed judgments about research problems and methods. They can employ longitudinal designs to assess change and be available to assist in interpretation and implementation of research. Potential costs include the possibility of provincialism, unintended researcher effects, ethical dilemmas, the need to cultivate community relations, and role conflicts. Interdisciplinary training programs and research field stations can contribute to the development of the resident researcher role. Researchers in the community can help generate grass roots support for social science research. We acknowledge Louise Shedd Barker's helpful comments and suggestions on an earlier version of this article.     

The effects of base rates and individuating information on judgments about another person

Past research suggests that in judging a person's category membership, people largely ignore the population frequency of membership in the category (base rates) in favor of individuating information about the particular person. This study tested the hypothesis that base rates will be utilized to the extent that the usefulness of the individuating information for diagnosing category membership is diminished. Subjects were given problems in which both base rates of membership in each of two categories and individuating information about a target person were presented. Then, in each case, they were asked to assess the probability that the target person belonged to each category. In three sets of problems, the diagnostic usefulness of the individuating information (personality characteristics) was diminished by including individuating information that was either inconsistent or irrelevant, or by increasing the similarity of the two alternative membership categories. In a fourth set of problems, the individualistic information included consistent, relevant personal characteristics and the membership categories were dissimilar. As expected, base rates were used in each of the first three sets of problems but were ignored in the fourth set. The results were interpreted in terms of informational factors that induce a shift away from a habitual, spontaneous reliance on a source of information (e.g., personality traits) for which one has well-developed rules (e.g., intuitive personality theories), and toward a more controlled, deliberate mode of thinking wherein other sources of information (e.g., base rates) are attended to and incorporated into judgment through less frequently used rules (e.g., sampling rules).     

The influence of economic business cycles on United States suicide rates

A number of social science investigators have shown that a downturn in the economy leads to an increase in the suicide rate. However, the previous works on the subject are flawed by the fact that they employ years as their temporal unit of analysis. This time period is so large that it makes it difficult for investigators to precisely determine the length of the lag effect, while at the same time removing the autocorrelation effects. Also, although most works on suicide and the business cycle employ unemployment as a measure of a downturn in the business cycle, the average duration of unemployment represents a better measure for determining the social impact of an economic downturn. From 1947 to 1977 the average monthly duration of unemployment is statistically related to the suicide rate using multivariate time-series analysis. From 1910 to 1939 the Ayres business index, a surrogate measure for movement in the business cycle, is statistically related to the monthly suicide rate. An examination of the findings confirms that in most cases a downturn in the economy causes an increase in the suicide rate.     

解释框架对文化混搭产品评价的影响——比较焦点和解释策略的调节效应

文化混搭产品因涉及到不同国家的文化符号的同时呈现, 可能导致母国文化的完整性与生命力受到威胁, 引发外国文化入侵母国文化的感知进而降低消费者的产品评价。本研究从文化混搭产品的营销传播视角出发, 基于前人的研究基础之上, 探讨了不同信息表述框架策略的采用会如何影响消费者的文化入侵感知进而影响他们对文化混搭产品的评价, 并重点探究了它可能存在的两个边界条件, 即消费者的比较焦点和企业的不同解释策略。两个实验的结果不仅再一次验证了前人所得出的研究结论, 即文化混搭产品采用“外国文化-母国文化”的框架策略时, 会使消费者产生外国文化改变了母国文化的感知, 导致消费者对产品的评价要显著低于采用“母国文化-外国文化”的框架策略, 其中感知文化入侵起着完全中介的作用。更重要的是, 深入揭示出上述的这种不同文化混搭框架效应只有在启动的是消费者的差异性聚焦以及企业采用的是属性解释策略时才存在, 而当启动的是消费者的相似性聚焦以及企业采用关系解释策略时, 这一效应将会消失。本研究不仅深入剖析出了引发消费者对文化混搭现象产生排斥性反应的内在机理, 拓展了前人研究结论的普适性, 也为企业的文化混搭产品的营销推广提供了有用的实践启示。