CROWDING,TASK PERFORMANCE,AND COMMUNICATIVE INTERACTION IN YOUTH AND OLD AGE

Following Altman's privacy regulation model, this research was based on the assumption that the relationship between achieved and desired levels of privacy is a major determinant of the experience of crowding in small groups. Further, on the basis of evidence that advancing age is associated with a chronic lack of communicative interaction, it was assumed that the achieved level of privacy among elderly persons generally is greater than that experienced by young adults. Given these assumptions, it was predicted that the crowding threshold of young adults would be significantly lower than that of older persons. To test this hypothesis, the research varied conversation distance (close, far) and density (small room, large room) and assessed the quality of communicative interaction and task performance among small groups of college-age females and retired women over 60 years of age. Results showed that task performance among younger subjects deteriorated in close interaction conditions, whereas the performance of older subjects was improved by spatial intrusion. Further, older subjects exhibited positive communication behaviors in response to close conversation, high-density conditions, while younger subjects responded either with negative communication behaviors or were unaffected by spatial restriction. Finally, the research confirmed previous findings that conversation distance is a more important determinant of the experience of crowding in small groups than physical density. The significance of the results for Altman's privacy regulation model and their implications for the study of human communication behaviors were discussed.     

Choosing Between Parametric and Nonparametric Tests

A fundamental analysis decision confronting researchers in psychology and education is the choice between parametric and nonparametric tests. Despite the statistical and substantive implications of this important decision, many researchers unerringly employ parametric tests and thus ignore the advantages of their nonparametric counterparts. One justification for this behavior has been the absence of guidelines for choosing between these procedures. A second has been the lack of a comprehensive nonparametric test that is computationally manageable. In this article, the author discusses several statistical and substantive criteria that can be used to choose between parametric and nonparametric tests. A non-parametric test capable of testing a number of statistical hypotheses using existing computer packages is also presented. Recommendations are made encouraging researchers to routinely use nonparametric tests in their data analytic work.