Bowling Green State University

About: Bowling Green State University is a education organization based out in Bowling Green, Ohio, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 8315 authors who have published 16042 publications receiving 482564 citations. The organization is also known as: BGSU.

Facial expressions of emotion influence interpersonal trait inferences

Abstract: Theorists have argued that facial expressions of emotion serve the interpersonal function of allowing one animal to predict another's behavior. Humans may extend these predictions into the indefinite future, as in the case of trait inference. The hypothesis that facial expressions of emotion (e.g., anger, disgust, fear, happiness, and sadness) affect subjects' interpersonal trait inferences (e.g., dominance and affiliation) was tested in two experiments. Subjects rated the dispositional affiliation and dominance of target faces with either static or apparently moving expressions. They inferred high dominance and affiliation from happy expressions, high dominance and low affiliation from angry and disgusted expressions, and low dominance from fearful and sad expressions. The findings suggest that facial expressions of emotion convey not only a target's internal state, but also differentially convey interpersonal information, which could potentially seed trait inference.

Measuring Attraction to Organizations

Abstract: Organizational attractionmeasures are commonly used as surrogate assessments of organizational pursuit. Despite the range in content often encompassed by such instruments, no research has systematically examined the assumptions underlying their use. The authors address this issue by empirically distinguishing items assessing attractiveness, prestige, and behavioral intentions and by modeling their effects on organization pursuit. Undergraduates (N= 305) were randomly assigned to recruitment literature from one of five wellknown companies and were asked to respond to a series of items commonly used in past research. Analyses of the itemresponses suggested that three components of organizational attraction can be reliably distinguished and that their relation to organization-pursuit behavior corresponds to Fishbein and Ajzen’s theory of reasoned action.

On the interchangeability of objective and subjective measures of employee performance: a meta-analysis

Abstract: A meta-analysis of studies containing both objective and subjective ratings of employee performance resulted in a corrected mean correlation of .389. This value, although significantly greater than zero, indicates that objective and subjective performance measures should not be used interchangeably. Moreover, in no moderator subgroup examined did the correlation suggest convergent validity. After discussing issues related to resolving the previous anomalies of primary and meta-analytic results, a secondary analysis suggested that objective and subjective measures of the same construct at the same level may be used inter-changeably. The secondary analysis, however, was based on a very limited sample. Future research should address the appropriate dimensionality of employee performance.

Hierarchical Clustering via Joint Between-Within Distances: extending Ward's Minimum Variance Method

Abstract: We propose a hierarchical clustering method that minimizes a joint between-within measure of distance between clusters. This method extends Ward's minimum variance method, by defining a cluster distance and objective function in terms of Euclidean distance, or any power of Euclidean distance in the interval (0,2]. Ward's method is obtained as the special case when the power is 2. The ability of the proposed extension to identify clusters with nearly equal centers is an important advantage over geometric or cluster center methods. The between-within distance statistic determines a clustering method that is ultrametric and space-dilating; and for powers strictly less than 2, determines a consistent test of homogeneity and a consistent clustering procedure. The clustering procedure is applied to three problems: classification of tumors by microarray gene expression data, classification of dermatology diseases by clinical and histopathological attributes, and classification of simulated multivariate normal data.