Book contents
- Applied Regression Models in the Social Sciences
- Applied Regression Models in the Social Sciences
- Copyright page
- Brief Contents
- Contents
- Figures
- Tables
- Preface
- 1 Introduction
- 2 Undertaking Statistical Analysis Using Stata
- 3 Undertaking Statistical Analysis Using R
- 4 Descriptive Statistics and the Normal Distribution
- 5 Statistical Tables and Cross-Tabulations
- 6 Bivariate Regression and Correlation and Statistical Inference
- 7 Multiple Regression and Correlation
- 8 Regression Assumptions and Diagnostics and Robust Regression
- 9 Missing Data
- 10 Issues of Survey Design
- 11 Binomial Logistic Regression
- 12 Ordinal Logistic Regression
- 13 Multinomial Logistic Regression
- 14 Count Regression
- 15 Survival Analysis
- 16 Multilevel Models
- 17 Other Issues and Final Thoughts
- References
- Index
14 - Count Regression
Published online by Cambridge University Press: 17 August 2023
Book contents
- Applied Regression Models in the Social Sciences
- Applied Regression Models in the Social Sciences
- Copyright page
- Brief Contents
- Contents
- Figures
- Tables
- Preface
- 1 Introduction
- 2 Undertaking Statistical Analysis Using Stata
- 3 Undertaking Statistical Analysis Using R
- 4 Descriptive Statistics and the Normal Distribution
- 5 Statistical Tables and Cross-Tabulations
- 6 Bivariate Regression and Correlation and Statistical Inference
- 7 Multiple Regression and Correlation
- 8 Regression Assumptions and Diagnostics and Robust Regression
- 9 Missing Data
- 10 Issues of Survey Design
- 11 Binomial Logistic Regression
- 12 Ordinal Logistic Regression
- 13 Multinomial Logistic Regression
- 14 Count Regression
- 15 Survival Analysis
- 16 Multilevel Models
- 17 Other Issues and Final Thoughts
- References
- Index
Summary
In this chapter we cover the modeling of a dependent variable that is neither continuous nor categorical, but is a count of the number of events. Dependent count variables measure the number of times an event has occurred. An example from demography is the number of children ever born to a woman or man in their lifetimes. Frequently, count variables are treated as though they are continuous and unbounded, and ordinary least squares (OLS) models are used to estimate the effects of independent variables on their occurrence. But if the OLS assumptions we discussed in Chapter 8 are not met, then the use of OLS for count outcomes may result in inefficient, inconsistent, and biased estimates. There are many kinds or classes of models that may be used to estimate count dependent variables. In this chapter we consider five models: (1) the Poisson regression model; (2) the negative binomial regression model; (3) the zero-inflated count model; (4) the zero-truncated count model; and (5) the hurdle regression model.
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- Applied Regression Models in the Social Sciences , pp. 389 - 430Publisher: Cambridge University PressPrint publication year: 2023
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