Why does service with a smile make employees happy? A social interaction model

Drawing on the social interaction model, we examine the mediating role that the customer's display of positive emotions plays on the relationship between the employee's display of positive emotions and the employee's positive mood. We also examine the moderating role that the customer's personality traits-agreeableness, extraversion, and emotional stability-play on the relationship between the employee's display of positive emotions and the customer's display of positive emotions. The results show that the customer's display of positive emotions mediates the relationship between the employee's display of positive emotions and the employee's positive mood. In addition, the customer's personality traits moderate the relationship between the employee's display of positive emotions and the customer's display of positive emotions. The customer's display of positive emotions depends less on the employee's display of positive emotions when the customer has high levels of agreeableness and emotional stability than when the customer has low levels of agreeableness and emotional stability. (PsycINFO Database Record (c) 2012 APA, all rights reserved).     

A test of the circumvention-of-limits hypothesis in scientific problem solving: the case of geological bedrock mapping

Sources of individual differences in scientific problem solving were investigated. Participants representing a wide range of experience in geology completed tests of visuospatial ability and geological knowledge, and performed a geological bedrock mapping task, in which they attempted to infer the geological structure of an area in the Tobacco Root Mountains of Montana. A Visuospatial Ability × Geological Knowledge interaction was found, such that visuospatial ability positively predicted mapping performance at low, but not high, levels of geological knowledge. This finding suggests that high levels of domain knowledge may sometimes enable circumvention of performance limitations associated with cognitive abilities.     

Manipulating the role of cognitive control while taking the implicit association test

The Implicit Association Test (IAT) is one of the most widely used methods for measuring attitudes in the behavioral and social sciences. Recent studies have found that individual differences in cognitive control correlate with IAT scores. However, these studies did not collect independent measures of attitude, which makes it difficult to isolate the construct of attitude separate from cognitive control. Furthermore, no study has examined whether the role of cognitive control can be manipulated, which is necessary to establish a causal link between cognitive control and IAT performance. By collecting independent measures of attitude (explicit attitude ratings and the Affect Misattribution Procedure: AMP), Experiment 1 factored out the role of attitude for two different IATs and still found a relationship between IAT scores and cognitive control (Stroop and stop-signal). Experiments 2 and 3 manipulated the role of cognitive control through instructions and feedback regarding the race IAT's measurement goal. These manipulations increased average IAT scores (i.e., stronger preference for whites), increased the relationship with cognitive control (Stroop), and decreased the relationship with attitude (AMP). These results demonstrate that cognitive control influences IAT performance rather than merely correlating with IAT performance.     

The role of perfectionism in daily self-esteem, attachment, and negative affect

This study of university students (64 men, 99 women) examined the role of self-critical (SC) and personal standards (PS) higher order dimensions of perfectionism in daily self-esteem, attachment, and negative affect. Participants completed questionnaires at the end of the day for 7 consecutive days. Trait and situational influences were found in the daily reports of self-esteem, attachment, and affect. In contrast to PS perfectionism, SC perfectionism was strongly related to aggregated daily reports of low self-esteem, attachment fears (fear of closeness, fear of dependency, fear of loss), and negative affect as well as instability indexes of daily self-esteem, attachment, and negative affect. Multilevel modeling indicated that both SC and PS perfectionists were emotionally reactive to decreases in self-esteem, whereas only SC perfectionists were emotionally reactive to increases in fear of closeness with others. These results demonstrate the dispositional and moderating influences of perfectionism dimensions on daily self-esteem, attachment, and negative affect.     

The psychology of inferring conditionals from disjunctions: A probabilistic study

There is a new probabilistic paradigm in the psychology of reasoning that is, in part, based on results showing that people judge the probability of the natural language conditional, if $A$then $B$, $P\left(ifAthenB\right)$, to be the conditional probability, $P(B\mid A)$. We apply this new approach to the study of a very common inference form in ordinary reasoning: inferring the conditional if not-$A$then $B$ from the disjunction $A$ or $B$. We show how this inference can be strong, with $P$(if not-$A$then $B$) “close to” $P\left(AorB\right)$, when $A$ or $B$ is non-constructively justified. When $A$ or $B$ is constructively justified, the inference can be very weak. We also define suitable measures of “closeness” and “constructivity”, by providing a probabilistic analysis of these notions.     

A tutorial on Bayesian nonparametric models

A key problem in statistical modeling is model selection, that is, how to choose a model at an appropriate level of complexity. This problem appears in many settings, most prominently in choosing the number of clusters in mixture models or the number of factors in factor analysis. In this tutorial, we describe Bayesian nonparametric methods, a class of methods that side-steps this issue by allowing the data to determine the complexity of the model. This tutorial is a high-level introduction to Bayesian nonparametric methods and contains several examples of their application.     

Natural ambiguities: Racial categorization of multiracial individuals

Understanding the perception of multiracial persons is increasingly important in today's diverse society. The present research investigated the process of categorizing multiracial persons as “Multiracial.” We hypothesized that perceivers would make fewer Multiracial categorizations of multiracials and that these categorizations would take longer than monoracial categorizations. We found support for these hypotheses across six experiments. Experiment 1 demonstrated that perceivers did not categorize morphed Black–White faces as Multiracial with the same frequency with which they categorized Black and White faces as Black and White (respectively), and that categorizations of multiracials as Multiracial took longer than monoracial categorizations. Experiment 2 replicated and extended these effects to real Black–White faces. Experiment 3 showed that these findings generalized to Asian–White faces. We used pixel variance analysis to show that these effects were not due to increased variance among Multiracial faces. The image analysis showed that the Black–White morphs and real biracials were actually less varied than either the Black or White sets of faces. Experiments 4 and 5 demonstrated that cognitive load and time constraints detrimentally affected multiracial, but not monoracial, categorizations. Experiment 6 showed that imbuing monoracial categories with importance decreases the use of the Multiracial category. Implications of these findings for understanding perceptions of multiracial persons are discussed.     

Treating stimuli as a random factor in social psychology: a new and comprehensive solution to a pervasive but largely ignored problem

Throughout social and cognitive psychology, participants are routinely asked to respond in some way to experimental stimuli that are thought to represent categories of theoretical interest. For instance, in measures of implicit attitudes, participants are primed with pictures of specific African American and White stimulus persons sampled in some way from possible stimuli that might have been used. Yet seldom is the sampling of stimuli taken into account in the analysis of the resulting data, in spite of numerous warnings about the perils of ignoring stimulus variation (Clark, 1973; Kenny, 1985; Wells & Windschitl, 1999). Part of this failure to attend to stimulus variation is due to the demands imposed by traditional analysis of variance procedures for the analysis of data when both participants and stimuli are treated as random factors. In this article, we present a comprehensive solution using mixed models for the analysis of data with crossed random factors (e.g., participants and stimuli). We show the substantial biases inherent in analyses that ignore one or the other of the random factors, and we illustrate the substantial advantages of the mixed models approach with both hypothetical and actual, well-known data sets in social psychology (Bem, 2011; Blair, Chapleau, & Judd, 2005; Correll, Park, Judd, & Wittenbrink, 2002).     

Why and when peer prediction is superior to self-prediction: the weight given to future aspiration versus past achievement

Peer predictions of future behavior and achievement are often more accurate than those furnished by the self. Although both self- and peer predictions correlate equally with future outcomes, peers tend to avoid the degree of overoptimism so often seen in self-predictions. In 3 studies, the authors tested whether this differential accuracy arises because people give more weight to past behavior when predicting others, but emphasize agentic information, in particular data about their aspiration level, when predicting the self. Studies 1 and 3 showed that the exact same participants rated past behavior more diagnostic of future performance when predicting another person but viewed aspiration-level data as more valuable when someone else was trying to predict them. In Studies 2 and 3 (predicting an upcoming exam score and performance in a lab task, respectively), participants gave greater weight in self-predictions to aspiration-level data than did a yoked peer, who instead gave greater weight to evidence of past achievement. This differential weighting explained why peer predictions tended to be less optimistic and, thus, more accurate. Discussion centers on strategies for predicting future behavior and why people may remain ignorant of their own incompetence despite feedback.     

Religious Involvement Measurement Model in a National Sample of African Americans

This study examined the factor structure of a brief measure of religious involvement developed previously in research with African American women. Telephone interview methods were used with a national sample of both African American women and men (N = 2,370). Confirmatory factor analyses supported the distinction between religious beliefs and religious behaviors factors and indicated that the factor loadings were equivalent for women and men. Women reported higher levels of religious involvement than men. These results support the validity of this relatively brief instrument for assessing these two dimensions of religious involvement for both African American women and men.