We wish to acknowledge the support of the Department of Sociology and Anthropology of Ohio University in the form C. Wright Mills Fund Research Award. We also wish to acknowledge the assistance of Dominika Dittwald, Tracy Fehr, Jared Liebold, Todd Platts, and Lori Sefcik in data preparation and coding. Rick Hoyle, editor of the Journal of Social Issues, and Frank Bellezza of the Ohio University Department of Psychology provided valuable advice for the analyses reported here. We wish to thank them for their assistance. The comments of anonymous reviewers have proved helpful as we prepared the manuscript.
Speech Content and the Emergence of Inequality in Task Groups
Article first published online: 10 APR 2009
© 2009 The Society for the Psychological Study of Social Issues
Journal of Social Issues
Volume 65, Issue 2, pages 307–333, June 2009
How to Cite
Shelly, R. K. and Shelly, A. C. (2009), Speech Content and the Emergence of Inequality in Task Groups. Journal of Social Issues, 65: 307–333. doi: 10.1111/j.1540-4560.2009.01602.x
- Issue published online: 10 APR 2009
- Article first published online: 10 APR 2009
We examine how the content of actors’ speech and the frequency with which they make contributions affect the emergence and legitimation of inequality in task groups. Previous research has focused on classifying acts, the smallest meaningful units of speech, such as providing opportunities to others to speak, making task relevant suggestions, and positively or negatively evaluating the contributions of others. We also employ a classification scheme based on the cognitive complexity of spoken language in the turn. This scheme is based on an interpretation of the cognitive development model posited by Piaget. In addition to the complexity of language, we analyze speech group members employ to organize to solve the task. These classifications rely on the entire content of the turn rather than the more atomistic act. Data analyzed are from 33 groups of students performing different types of tasks. We employ structural equation models to identify how acts and content are related to one another in the observed interaction patterns in groups. Applications of these insights are explored in the discussion.