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ERIC Number: EJ1056177
Record Type: Journal
Publication Date: 2015-Jan
Pages: 6
Abstractor: As Provided
Reference Count: 15
ISBN: N/A
ISSN: EISSN-1941-3432
Predicting Social Trust with Binary Logistic Regression
Adwere-Boamah, Joseph; Hufstedler, Shirley
Research in Higher Education Journal, v27 Jan 2015
This study used binary logistic regression to predict social trust with five demographic variables from a national sample of adult individuals who participated in The General Social Survey (GSS) in 2012. The five predictor variables were respondents' highest degree earned, race, sex, general happiness and the importance of personally assisting people in trouble. The objective of the data analysis was to assess the impact of the predictors on the likelihood that respondents would report that they have low social trust. The results of binary logistic regression analysis of the data showed that the full logistic regression model containing all the five predictors was statistically significant. The strongest predictor of low social trust was education or degree earned. It recorded an odds ratio of 12.7 indicating that when holding all the other predictors constant, a person who left or dropped out of high school is 12.7 times more likely to have low social trust than a person with a graduate degree. In summary, females are less trustful than males, African Americans are less trustful than Whites, less educated individuals are less trustful than educated individuals and less happy people are less trustful than happy people.
Publisher Info: Academic and Business Research Institute. 147 Medjool Trail, Ponte Vedra, FL 32081. Tel: 904-435-4330; e-mail: editorial.staff@aabri.com; Web site: http://www.aabri.com
Publication Type: Journal Articles; Reports - Evaluative
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Assessments and Surveys: General Social Survey