Regression with Dummy Variables
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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.
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Contents
Introduction
Introduction
Creating Dummy Variables
Creating Dummy Variables
Using Dummy Variables as Regressors
Using Dummy Variables as Regressors
Assessing Group Differences in Effects
Assessing Group Differences in Effects
Alternative Coding Schemes for Dummy Variables
Alternative Coding Schemes for Dummy Variables
Special Topics in the Use of Dummy Variables
Special Topics in the Use of Dummy Variables
Conclusions
Conclusions
Description
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.
Learn more about "The Little Green Book" - QASS Series! Click Here
Contents
Introduction
Introduction
Creating Dummy Variables
Creating Dummy Variables
Using Dummy Variables as Regressors
Using Dummy Variables as Regressors
Assessing Group Differences in Effects
Assessing Group Differences in Effects
Alternative Coding Schemes for Dummy Variables
Alternative Coding Schemes for Dummy Variables
Special Topics in the Use of Dummy Variables
Special Topics in the Use of Dummy Variables
Conclusions
Conclusions
February 1993 | 96 pages | Sage US
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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.
Learn more about "The Little Green Book" - QASS Series! Click Here
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