Regression with Dummy Variables / Edition 1

by Melissa A Hardy

Overview

It is often necessary for social scientists to study differences in groups, such as gender or race differences in attitudes, buying behavior, or socioeconomic characteristics. When the researcher seeks to estimate group differences through the use of independent variables that are qualitative, dummy variables 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.

Product Details

ISBN-13:	9780803951280
Publisher:	SAGE Publications
Publication date:	02/25/1993
Series:	Quantitative Applications in the Social Sciences , #93
Edition description:	New Edition
Pages:	96
Product dimensions:	5.50(w) x 8.50(h) x 0.20(d)

About the Author

RESEARCH a ND TEACHING INTERESTS

Quantitative Analysis Techniques, Longitudinal Methods; Aging & Social Change;
Public Policy and Aging.

Social Inequality, Social Sciences and Humanities Research Council of Canada. “Workforce aging in the new economy: A comparative study of information technology employment” (Julie A. Mc Mullin, PI; Victor Marshall, Joann Marshall, University of North Carolina; Neil Charness, Florida State University). October 2002 – September 2006.

Work and Retirement, including the influence of organizational incentives, pensions, family decision-making, health, job satisfaction; Public Policy, including Social Security, the Age Discrimination and Employment Act, the Employee Retirement Income Security Act, Medicare and Medicaid; Political Attitudes, including analysis of social change and individual aging; Women’s Issues and Aging, including poverty and financial security, caregiving, long-term care, intergenerational assistance, and health; Cognitive Components of Saving and Investment behavior, including planning horizon, discounting, risk tolerance, deferred gratification; Older Workers, including training, displacement, work schedules, job demands, organizational incentive structures.

Table of Contents

Introduction
Creating Dummy Variables
Using Dummy Variables as Regressors
Assessing Group Differences in Effects
Alternative Coding Schemes for Dummy Variables
Special Topics in the Use of Dummy Variables
Conclusions