Faculty Profile
Amy Steinbugler
(she/her/hers)Associate Professor of Sociology (2008)
Contact Information
Denny Hall Room 106
717.254.8140
https://dickinson.academia.edu/ACSteinbugler
Bio
Amy C. Steinbugler's research and teaching focus on neighborhoods, social networks, family, race/ethnicity, stratification, gender, and sexuality. She is interested in how individuals construct and maintain social relationships across systems of inequality. In 2020, with a grant from the Spencer Foundation, she began a mixed-method network study that explores how connected Philadelphia parents are to their neighbors and their neighborhoods. She also collaborates on a longer-term project with colleagues at Bryn Mawr and the University of Pennsylvania on parents' school and neighborhood networks. Dr. Steinbugler is the recipient of the Distinguished Book Award from the Sexualities Section and the William J. Goode Book Award from the Family Section of the American Sociological Association for Beyond Loving: Intimate Racework in Lesbian, Gay, and Straight Interracial Relationships (Oxford University Press). Her writing has been published in Contexts, DuBois Review, Ed Researcher, Ethnic and Racial Studies, Gender and Society, Sexualities and Sociology of Education.
Education
- B.A., Evergreen State College, 1998
- M.A., Temple University, 2002
- Ph.D., 2007
2021-2022 Academic Year
Spring 2022
SOCI 244 Quantitative Research Methods
Quantitative Research Methods introduces students to basic principles of sociological research methodologies and statistical analysis. Students learn to conceptualize a research question, operationalize key concepts, identify relevant literature, and form research hypotheses. Then, using elementary tools of descriptive and inferential statistics, they choose appropriate statistical methods, analyze data, and draw meaningful conclusions. Special emphasis is given to interpreting numbers with clear, persuasive language, in both oral and written formats. Students will become proficient in using quantitative software for data analysis. Two and a half hours classroom and three hours laboratory a week. Prerequisite: 110.
