1,052
Views
18
CrossRef citations to date
0
Altmetric
Original Articles

Segmenting an Audience into the Own, the Wise, and Normals: A Latent Class Analysis of Stigma-Related Categories

Pages 257-265 | Published online: 31 Oct 2012
 

Abstract

Goffman introduced a classification scheme of three stigma-related categories of people: the own, the wise, and normals. This study presents the first known empirical test of this taxonomy using latent class analysis. Participants (N = 144) completed a survey. Latent class analysis was used to analyze the data. The results showed that a four-class model best fit the data. The profiles of the stigmatizer and stigmatized were very similar to Goffman's descriptions of the normal and the own; the wise (labeled supporters) were split into two categories based on their encouragement of educating stigmatizers and challenging stigmatization. The stigma groups considered by participants and participants’ social networks were significant covariates of class membership. Understanding how many audience segments exist and which indicators differentiate them could provide critical information for anti-stigma campaigns, such as those that attempt to reduce stigmatization by influencing stigmatizers to become supporters.

Acknowledgments

My thanks to Michelle Baker for her feedback on early versions of this manuscript, and Josie Moore and Eric DiMuzio for their assistance with data collection. This project was supported by Award Number P50-DA010075-15 from the National Institute on Drug Abuse. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute on Drug Abuse or the National Institutes of Health.

Notes

Note. Boldface type indicates the selected model, which has the lowest AIC and BIC. df = degrees of freedom; AIC = Akaike's Information Criterion; Adj BIC = Bayesian Information Criterion using Rissanen's sample size adjustment.

Note. The highest scores for each indicator are bolded.

Additional information

Notes on contributors

Rachel A. Smith

Rachel A. Smith (PhD, Michigan State University, 2003) is an associate professor in the Department of Communication Arts & Sciences and Human Development & Family Studies at The Pennsylvania State University.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 53.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.