Testing reciprocity in social interactions: A comparison between the directional consistency and skew-symmetry statistics

In the present article, we focus on two indices that quantify directionality and skew-symmetrical patterns in social interactions as measures of social reciprocity: the directional consistency (DC) and skew-symmetry indices. Although both indices enable researchers to describe social groups, most studies require statistical inferential tests. The main aims of the present study are first, to propose an overall statistical technique for testing null hypotheses regarding social reciprocity in behavioral studies, using the DC and skew-symmetry statistics (Phi) at group level; and second, to compare both statistics in order to allow researchers to choose the optimal measure depending on the conditions. In order to allow researchers to make statistical decisions, statistical significance for both statistics has been estimated by means of a Monte Carlo simulation. Furthermore, this study will enable researchers to choose the optimal observational conditions for carrying out their research, since the power of the statistical tests has been estimated.     

On the asymptotic standard error of a class of robust estimators of ability in dichotomous item response models

In item response theory, the classical estimators of ability are highly sensitive to response disturbances and can return strongly biased estimates of the true underlying ability level. Robust methods were introduced to lessen the impact of such aberrant responses on the estimation process. The computation of asymptotic (i.e., large‐sample) standard errors (ASE) for these robust estimators, however, has not yet been fully considered. This paper focuses on a broad class of robust ability estimators, defined by an appropriate selection of the weight function and the residual measure, for which the ASE is derived from the theory of estimating equations. The maximum likelihood (ML) and the robust estimators, together with their estimated ASEs, are then compared in a simulation study by generating random guessing disturbances. It is concluded that both the estimators and their ASE perform similarly in the absence of random guessing, while the robust estimator and its estimated ASE are less biased and outperform their ML counterparts in the presence of random guessing with large impact on the item response process.     

In medio stat virtus: theoretical and methodological extremes regarding reciprocity will not explain complex social behaviors

Guala contests the validity of strong reciprocity as a key element in shaping social behavior by contrasting evidence from experimental games to that of natural and historic data. He suggests that in order to understand the evolution of social behavior researchers should focus on natural data and weak reciprocity. We disagree with Guala's proposal to shift the focus of the study from one extreme of the spectrum (strong reciprocity) to the other extreme (weak reciprocity). We argue that the study of the evolution of social behavior must be comparative in nature, and we point out experimental evidence that shows that social behavior is not cooperation determined by a set of fixed factors. We argue for a model that sees social behavior as a dynamic interaction of genetic and environmental factors.     

Multilevel Modeling with Correlated Effects

When there exist omitted effects, measurement error, and/or simultaneity in multilevel models, explanatory variables may be correlated with random components, and standard estimation methods do not provide consistent estimates of model parameters. This paper introduces estimators that are consistent under such conditions. By employing generalized method of moments (GMM) estimation techniques in multilevel modeling, the authors present a series of estimators along a robust to efficient continuum. This continuum depends on the assumptions that the analyst makes regarding the extent of the correlated effects. It is shown that the GMM approach provides an overarching framework that encompasses well-known estimators such as fixed and random effects estimators and also provides more options. These GMM estimators can be expressed as instrumental variable (IV) estimators which enhances their interpretability. Moreover, by exploiting the hierarchical structure of the data, the current technique does not require additional variables unlike traditional IV methods. Further, statistical tests are developed to compare the different estimators. A simulation study examines the finite sample properties of the estimators and tests and confirms the theoretical order of the estimators with respect to their robustness and efficiency. It further shows that not only are regression coefficients biased, but variance components may be severely underestimated in the presence of correlated effects. Empirical standard errors are employed as they are less sensitive to correlated effects when compared to model-based standard errors. An example using student achievement data shows that GMM estimators can be effectively used in a search for the most efficient among unbiased estimators. This research was supported by the National Academy of Education/Spencer Foundation and the National Science Foundation, grant number SES-0436274. We thank the editor, associate editor, and referees for detailed feedback that helped improve the paper.     

“投桃报李”——互惠理论的组织行为学研究述评

互惠是一种存在于各种社会文化中的人际交往规范.组织行为学中许多研究都是通过互惠理论来解释变量之间的内部作用机制.文章回顾并讨论了互惠的涵义,不同互惠类型的特点及互惠的测量方式.此外.还论述了不同类型互惠对社会交换方式产生的影响.依据现有的互惠研究成果,分析了互惠发挥影响的内在作用机制是通过:互惠各方的价值观、互惠过程中各方感知到的风险、各方冲突的程度这三种途径来实现的.最后,根据现有互惠理论的研究成果提出该领域的未来发展应从研究设计和研究内容两方面进行扩展.     

Bayesian adaptive estimation of arbitrary points on a psychometric function

Bayesian adaptive methods have been extensively used in psychophysics to estimate the point at which performance on a task attains arbitrary percentage levels, although the statistical properties of these estimators have never been assessed. We used simulation techniques to determine the small‐sample properties of Bayesian estimators of arbitrary performance points, specifically addressing the issues of bias and precision as a function of the target percentage level. The study covered three major types of psychophysical task (yes‐no detection, 2AFC discrimination and 2AFC detection) and explored the entire range of target performance levels allowed for by each task. Other factors included in the study were the form and parameters of the actual psychometric function Ψ, the form and parameters of the model function M assumed in the Bayesian method, and the location of Ψ within the parameter space. Our results indicate that Bayesian adaptive methods render unbiased estimators of any arbitrary point on ψ only when M=Ψ, and otherwise they yield bias whose magnitude can be considerable as the target level moves away from the midpoint of the range of Ψ. The standard error of the estimator also increases as the target level approaches extreme values whether or not M=Ψ. Contrary to widespread belief, neither the performance level at which bias is null nor that at which standard error is minimal can be predicted by the sweat factor. A closed‐form expression nevertheless gives a reasonable fit to data describing the dependence of standard error on number of trials and target level, which allows determination of the number of trials that must be administered to obtain estimates with prescribed precision.     

Effect sizes in ANCOVA and difference-in-differences designs

It is common practice in both randomized and quasi-experiments to adjust for baseline characteristics when estimating the average effect of an intervention. The inclusion of a pre-test, for example, can reduce both the standard error of this estimate and—in non-randomized designs—its bias. At the same time, it is also standard to report the effect of an intervention in standardized effect size units, thereby making it comparable to other interventions and studies. Curiously, the estimation of this effect size, including covariate adjustment, has received little attention. In this article, we provide a framework for defining effect sizes in designs with a pre-test (e.g., difference-in-differences and analysis of covariance) and propose estimators of those effect sizes. The estimators and approximations to their sampling distributions are evaluated using a simulation study and then demonstrated using an example from published data.     

Closeness, support and reciprocity: a study of attachment styles in adolescence

In the context of boarding schools, the transactions between adolescents internal working models of self and others, their peers perceptions of them, as well as the reciprocity between perceptions of others and by others were explored. Findings supported the assumed effect of models of the other on a persons social environment, as well as the expected relations between the congruency of models of self and other and the actual reciprocity between perceptions of others and others perceptions of self. Patterns of reciprocity in interpersonal relationships were found to characterize secure (positive reciprocity) and fearful (negative reciprocity) attachment styles, while non-reciprocal relations appeared more frequently among preoccupied and dismissing persons. These findings suggest specific person-environment transactions that may be involved in securing the continuity of styles of relating.     

计算式互惠机制探讨——认知和动机的物种比较

与灵长类动物相比, 人类社会中存在广泛的互惠行为, 这种广泛性表现为人类具有独特的计算式互惠。本文从计算式互惠的认知和动机机制两方面对人类儿童和灵长类动物进行比较, 以期从近因机制的角度为人类互惠行为的复杂性提供解释依据。未来研究需要重视从个体发展和个体差异的视角探寻认知和动机因素对计算式互惠的影响, 并注重从互动双方各自的角度对各影响因素进行区别和分析, 从而更全面地提供有关人类互惠近因机制的证据。     

Altruistic behavior and cooperation: The role of intrinsic expectation when reputational information is incomplete

Altruistic behavior is known to be conditional on the level of altruism of others. However, people often have no information, or incomplete information, about the altruistic reputation of others, for example when the reputation was obtained in a different social or economic context. As a consequence, they have to estimate the other's altruistic intentions. Using an economic game, we showed that without reputational information people have intrinsic expectations about the altruistic behavior of others, which largely explained their own altruistic behavior. This implies that when no information is available, intrinsic expectations can be as powerful a driver of altruistic behavior as actual knowledge about other people's reputation. Two strategies appeared to co-exist in our study population: participants who expected others to be altruistic and acted even more altruistically themselves, while other participants had low expected altruism scores and acted even less altruistically than they expected others to do. We also found evidence that generosity in economic games translates into benefits for other social contexts: a reputation of financial generosity increased the attractiveness of partners in a social cooperative game. This result implies that in situations with incomplete information, the fitness effects of indirect reciprocity are cumulative across different social contexts.     

The impact of omitting the interaction between crossed factors in cross‐classified random effects modelling

Cross‐classified random effects modelling (CCREM) is a special case of multi‐level modelling where the units of one level are nested within two cross‐classified factors. Typically, CCREM analyses omit the random interaction effect of the cross‐classified factors. We investigate the impact of the omission of the interaction effect on parameter estimates and standard errors. Results from a Monte Carlo simulation study indicate that, for fixed effects, both coefficients estimates and accompanied standard error estimates are not biased. For random effects, results are affected at level 2 but not at level 1 by the presence of an interaction variance and/or a correlation between the residual of level two factors. Results from the analysis of the Early Childhood Longitudinal Study and the National Educational Longitudinal Study agree with those obtained from simulated data. We recommend that researchers attempt to include interaction effects of cross‐classified factors in their models.     

Point-biserial correlation: Interval estimation,hypothesis testing,meta-analysis,and sample size determination

The point-biserial correlation is a commonly used measure of effect size in two-group designs. New estimators of point-biserial correlation are derived from different forms of a standardized mean difference. Point-biserial correlations are defined for designs with either fixed or random group sample sizes and can accommodate unequal variances. Confidence intervals and standard errors for the point-biserial correlation estimators are derived from the sampling distributions for pooled-variance and separate-variance versions of a standardized mean difference. The proposed point-biserial confidence intervals can be used to conduct directional two-sided tests, equivalence tests, directional non-equivalence tests, and non-inferiority tests. A confidence interval for an average point-biserial correlation in meta-analysis applications performs substantially better than the currently used methods. Sample size formulas for estimating a point-biserial correlation with desired precision and testing a point-biserial correlation with desired power are proposed. R functions are provided that can be used to compute the proposed confidence intervals and sample size formulas.     

Statistical properties of the rWG(J) index of agreement

L. R. James, R. G. Demaree, and G. Wolf (1984) introduced rWG(J) to estimate interrater agreement for a group. This index is calculated by comparing an observed group variance with an expected random variance. As researchers have gained experience using this index, several questions have arisen. What are the consequences of replacing values beyond the unit interval by 0? What is the dependence of rWG(J) on the group size? The authors' simulations show that a positive bias is caused by the truncation, but for large population values of rWG(J) it is negligible. Also, in this case, the group size has no effect on the expected value of rWG(J). For inference on rWG(J), researchers can exploit the availability of computers to simulate data from the hypothesized distribution and then compare the simulation results for rWG(J) with the actual values. In addition, it is shown how the bootstrap method can be used for comparing the indices of 2 groups.     

Statistical analysis of binary relational data: Parameter estimation

Binary interaction data, measuring the presence or absence of a relation between pairs of actors in a “dyadic interaction situation,” are commonly gathered to study the social structure of the group of actors. Recent developments have made the statistical analysis of such data statistically easier and more substantively sophisticated. These developments allow researchers to simultaneously study several sociometric structural properties, such as reciprocity, differential popularity, and equivalence of actors. Building on this research, we review the stochastic models responsible for this breakthrough, and discuss methods for estimating expected values and model parameters. Throughout, we also highlight recent advances designed to incorporate nodal or actor attribute data into the relational data analysis. We conclude with an example illustrating these ideas based on conversational activities among actors in a group of eight people.     

Preventive interventions among children exposed to trauma of armed conflict: a literature review

Increasing research is available on the preconditions for child mental health and optimal development in traumatic conditions, whereas less is known how to translate the findings into effective interventions to help traumatized children. This literature review analyses the effectiveness of psychosocial preventive interventions and treatments and their theoretical bases among children traumatized in the context of armed conflicts (war, military violence, terrorism and refugee). The first aim is to evaluate the effectiveness of preventive interventions in preventing emotional distress and impairment and promoting optimal emotional‐cognitive and social development. The second task is to analyze the nature of the underlying mechanisms for the success of preventive interventions, and the theoretical premises of the choice of intervention techniques, procedures and tools. We found 16 relevant published studies, but an examination of them revealed that only four of them had experimental designs strong enough that they could be included in the meta‐analysis. While the subjective reports of the researchers suggested that systematic preventive interventions were effective in decreasing PTSD and depressive symptoms among children traumatized due to armed conflict, the more objective results of the meta‐analysis and the weaknesses in designs uncovered during the meta‐analysis undermine such a conclusion. Additionally, a majority of the reported preventive interventions focused only on children's biased cognitive processes and negative emotions, while only a few aimed at influencing multiple domains of child development and improving developmental functioning on emotional, social and psychophysiological levels. It is concluded that substantial additional work needs to be done in developing effective preventive interventions and treatments for children traumatized by exposure to war and violence. Aggr. Behav. 36:95–116, 2010. © 2009 Wiley‐Liss, Inc.     

Reciprocity of support in coatis (Nasua nasua)

Primate sociality has received much attention and its complexity has been viewed as a driving force for the evolution of cognitive abilities. Improved analytic techniques have allowed primate researchers to reveal intricate social networks based on the exchange of cooperative acts and services. Although nonprimates are known to show similar behavior (e.g., cooperative hunting, food sharing, coalitions) there seems a consensus that social life is less complex than in primates. Here the authors present the first group-level analysis of reciprocity of social interactions in a social carnivore, the ring-tailed coati (Nasua nasua). The authors found that support in aggressive conflicts is a common feature in coatis and that this behavior is reciprocally exchanged in a manner seemingly as complex as in primates. Given that reciprocity correlations persisted after controlling for the effect of spatial association and subunit membership, some level of scorekeeping may be involved. Further studies will be needed to confirm our findings and understand the mechanisms underlying such reciprocity, but our results contribute to the body of work that has begun to challenge primate supremacy in social complexity and cognition.     

When and why is helping others good for well‐being? The role of belief in reciprocity and conformity to society's expectations

This research investigates the relation between informal help and subjective well‐being and its underlying mechanisms using a cross‐national perspective. We focus on two potential mechanisms derived from the self‐determination theory and conformity to the social norms literature. From the standpoint of self‐determination theory, helping others is good for well‐being if it is intrinsically motivated, rather than driven by the expectation of reciprocity. On the other hand, from the perspective of the conformity literature, helping others is associated with a higher well‐being when it is linked to the benefits of social conformity, such as social approval. We tested these hypotheses using the data from a total of 23 countries. The results provided support for both mechanisms. First, we found that the lower individuals' beliefs in reciprocity are, the stronger is the positive effect of self‐reported helping behavior on their well‐being. Second, helping behavior was more strongly related to life satisfaction in countries where providing help represents a strong social norm (measured with two different cultural indicators). We conclude that both individual‐ and culture‐level mechanisms account for the relation between prosocial behavior and well‐being. Copyright © 2015 John Wiley & Sons, Ltd.     

Reputation,Gossip, and Human Cooperation

Why do people cooperate? We address this classic question by analyzing and discussing the role of reputation: people cooperate to maintain a positive reputation in their social environment. Reputation is a key element fueling a system of indirect reciprocity, where cooperators establish a good reputation and are thus more likely to receive future benefits from third parties. The tendencies to monitor, spread, and manage each other's reputation help explain the abundance of human cooperation with unrelated strangers. We review research on the phenomenon of reputation‐based cooperation in the domains of how people manage their reputation in response to varying cues of reputation, when reputation can promote cooperation, and individual differences in reputation management. We also propose three directions for future research: group stability and reputation‐based cooperation, solutions to cope with noise and biased reputation, and the relative efficiency of positive versus negative reputation systems.     

Curious Disappearances: Affectability Imbalances and Process‐Based Invisibility

In this paper, we analyze the recent public scandal involving Nafissatou Diallo and Dominique Strauss‐Kahn to offer an account of the role affectability imbalances play in process‐based invisibility. Process‐based invisibilities, in this paper, refer to predictable narrative gaps within public narratives that can be aptly described as disappearances. We demonstrate that compromised, complex social identities, maladjusted webs of reciprocity, and a failure to fully appreciate basic affectability in large part cause affectability imbalances. Ultimately, we claim that affectability imbalances and the three imbricated conditions that facilitate such imbalances—complex social identities, reciprocity, and basic affectability—are integral features of process‐based invisibility.     

The Dynamic Relationship Between Accuracy and Bias in Social Perception Research

How do perceivers make accurate social judgments? A substantial amount of evidence suggests that perceivers' judgments are often quite accurate even when they do not have direct access to the truth, in part because they make judgments through biased processes. In the present article, we examine the dynamic relationship between bias and accuracy in social perception research. We outline how bias and accuracy are theoretically and empirically distinct processes and describe the importance (and difficulty) of defining and measuring both truth variables and bias variables in order to make empirical conclusions in accuracy research. Additionally, we examine how both bias variables (e.g., stereotypes, perceivers' own beliefs) and truth variables exert an influence on how perceivers make social judgments, as well as the extent to which judgments are accurate. Lastly, we provide steps that researchers can take in order to examine the relationship between bias and accuracy in their own research.