Regression with Dummy Variables

Social scientists are often interested in studying differences in groups, such as gender or race differences in attitudes, buying behaviors, or socioeconomic characteristics. When the researcher seeks to estimate group differences through the use of independent variables that are qualitative (i.e., measured at only the nominal level), dummy variables will allow the researcher to represent information about group membership in quantitative terms without imposing unrealistic measurement assumptions on the categorical variables. Beginning with the simplest model, Hardy probes the use of dummy variable regression in increasingly complex specifications, exploring issues such as: interaction, heteroscedasticity, multiple comparisons and significance testing, the use of effects or contrast coding, testing for curvilinearity, and estimating a piecewise linear regression.

Melissa Hardy is a Distinguished Professor Emeritus of Sociology and Demography at Penn State University in University Park. She is an alumna of Albright College and Indiana University in Bloomington. Her research focused on aging and the life course, retirement and age-stratified transitions, self-assessed health, and political attitudes using longitudinal data and a range of quantitative techniques.  Her published work appears in American Sociological Review, Social Forces, Journal of Health and Social Behavior, and Demography. She enjoyed teaching social statistics and general linear models to graduate and undergraduates students, using everyday experiences to help them understand the meaning of statistical concepts.