Difference between revisions of "Population and the Environment: Analytical Demography and Applied Population Ethics/Modeling complex populations - statistical inference from demographic data"
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| + | |Pre-meeting notes=Summary statistics such as the mortality rate, birth rate, or Total Fertility Rate can be useful for understanding some basic characteristics of a population. Often, however, we’re interested in having a more detailed understanding of how demographic events—fertility, mortality, or morbidity—relate to individual characteristics or environmental conditions. One may want to ask questions such as: Does education predict fertility, controlling for wealth? Does infant mortality vary with proximity to clean water, accounting for household-level differences? Does the interval between births depend on a parent’s age, economic strategy, or social network? This session will introduce a number of statistical models that are useful for answering these kinds of questions. We will discuss generalized regression models (customizable to model yes/no outcomes, count data, or continuous variables) as well as survival analysis (also called event history or duration analysis). These models allow us to estimate demographic rates as a function of multiple predictor variables, control for confounding variables, and take into account individual- or group-level heterogeneity. | ||
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Revision as of 20:23, October 8, 2018
October 13, 2018
2:15 pm - 2:30 pm
- Presenter
- Abstract
Summary statistics such as the mortality rate, birth rate, or Total Fertility Rate can be useful for understanding some basic characteristics of a population. Often, however, we’re interested in having a more detailed understanding of how demographic events—fertility, mortality, or morbidity—relate to individual characteristics or environmental conditions. One may want to ask questions such as: Does education predict fertility, controlling for wealth? Does infant mortality vary with proximity to clean water, accounting for household-level differences? Does the interval between births depend on a parent’s age, economic strategy, or social network? This session will introduce a number of statistical models that are useful for answering these kinds of questions. We will discuss generalized regression models (customizable to model yes/no outcomes, count data, or continuous variables) as well as survival analysis (also called event history or duration analysis). These models allow us to estimate demographic rates as a function of multiple predictor variables, control for confounding variables, and take into account individual- or group-level heterogeneity.
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