https://centre.santafe.edu/complextime/w/api.php?action=feedcontributions&user=PaulHooper&feedformat=atomComplex Time - User contributions [en]2022-05-22T05:10:52ZUser contributionsMediaWiki 1.35.6https://centre.santafe.edu/complextime/w/index.php?title=Mortality_experience_of_Tsimane_Amerindians_of_Bolivia:_Regional_variation_and_temporal_trends&diff=2755Mortality experience of Tsimane Amerindians of Bolivia: Regional variation and temporal trends2018-10-16T18:15:54Z<p>PaulHooper: Created page with "{{Reference Material |Meeting=Population and the Environment: Analytical Demography and Applied Population Ethics/Modeling complex populations - statistical inference from dem..."</p>
<hr />
<div>{{Reference Material<br />
|Meeting=Population and the Environment: Analytical Demography and Applied Population Ethics/Modeling complex populations - statistical inference from demographic data<br />
|title=Mortality experience of Tsimane Amerindians of Bolivia: Regional variation and temporal trends<br />
|Mendeley id=81aea944-cf58-3479-af7b-8706fc4faa52<br />
}}</div>PaulHooperhttps://centre.santafe.edu/complextime/w/index.php?title=File:Hooper_-_Statistical_inference_from_demographic_data.pdf&diff=2754File:Hooper - Statistical inference from demographic data.pdf2018-10-16T18:14:08Z<p>PaulHooper: User created page with UploadWizard</p>
<hr />
<div>=={{int:filedesc}}==<br />
{{Information<br />
|description={{Presentation file for|Population and the Environment: Analytical Demography and Applied Population Ethics/Modeling complex populations - statistical inference from demographic data}}<br />
|date=<br />
|source={{own}}<br />
|author=[[User:PaulHooper|PaulHooper]]<br />
|permission=<br />
|other versions=<br />
}}<br />
<br />
=={{int:license-header}}==<br />
{{licensing|generic}}<br />
<br />
[[Category:Uploaded via Campaign:Presentation file]]</div>PaulHooperhttps://centre.santafe.edu/complextime/w/index.php?title=On_mixed-effect_Cox_models,_sparse_matrices,_and_modeling_data_from_large_pedigrees&diff=2752On mixed-effect Cox models, sparse matrices, and modeling data from large pedigrees2018-10-16T15:25:55Z<p>PaulHooper: Created page with "{{Reference Material |Meeting=Population and the Environment: Analytical Demography and Applied Population Ethics/Modeling complex populations - statistical inference from dem..."</p>
<hr />
<div>{{Reference Material<br />
|Meeting=Population and the Environment: Analytical Demography and Applied Population Ethics/Modeling complex populations - statistical inference from demographic data<br />
|title=On mixed-effect Cox models, sparse matrices, and modeling data from large pedigrees<br />
|Mendeley id=87d22682-5066-37bd-84ca-698d8a2129eb<br />
}}</div>PaulHooperhttps://centre.santafe.edu/complextime/w/index.php?title=Multilevel_Analysis&diff=2750Multilevel Analysis2018-10-16T15:18:21Z<p>PaulHooper: Created page with "{{Reference Material |Meeting=Population and the Environment: Analytical Demography and Applied Population Ethics/Modeling complex populations - statistical inference from dem..."</p>
<hr />
<div>{{Reference Material<br />
|Meeting=Population and the Environment: Analytical Demography and Applied Population Ethics/Modeling complex populations - statistical inference from demographic data<br />
|title=Multilevel Analysis<br />
|Mendeley id=f2bd98d6-e516-3fa2-bbcd-7a92d27f89e9<br />
}}</div>PaulHooperhttps://centre.santafe.edu/complextime/w/index.php?title=Data_analysis_using_regression_and_multilevel/hierarchical_models&diff=2749Data analysis using regression and multilevel/hierarchical models2018-10-16T15:16:18Z<p>PaulHooper: Created page with "{{Reference Material |Meeting=Population and the Environment: Analytical Demography and Applied Population Ethics/Modeling complex populations - statistical inference from dem..."</p>
<hr />
<div>{{Reference Material<br />
|Meeting=Population and the Environment: Analytical Demography and Applied Population Ethics/Modeling complex populations - statistical inference from demographic data<br />
|title=Data analysis using regression and multilevel/hierarchical models<br />
|Mendeley id=9122d993-7214-3112-8c7e-5111c9db9ca6<br />
}}</div>PaulHooperhttps://centre.santafe.edu/complextime/w/index.php?title=Population_and_the_Environment:_Analytical_Demography_and_Applied_Population_Ethics/Modeling_complex_populations_-_statistical_inference_from_demographic_data&diff=2748Population and the Environment: Analytical Demography and Applied Population Ethics/Modeling complex populations - statistical inference from demographic data2018-10-16T14:47:00Z<p>PaulHooper: </p>
<hr />
<div>{{Agenda item<br />
|Start time=October 16, 2018 09:55:00 AM<br />
|End time=October 16, 2018 10:40:00 AM<br />
|Presenter=PaulHooper<br />
|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.<br />
|Post-meeting notes=It's clear that an extended version of this course should include treatment of inequality (and more generally the distribution of the benefits and costs of environmental impacts within societies) and conflict between and within states. The #1 highlight is of course the group of people assembled here.<br />
|Reference material notes=The optional reading Kaplan, Hooper, Stieglitz & Gurven (2015) [[The Causal Relationship between Fertility and Infant Mortality: Prospective analyses of a population in transition]] provides worked examples of analyzing fertility data (using Cox proportional hazards to model time to next birth) and infant mortality data (using logistic regression).<br />
<br />
<nowiki>***</nowiki> Access the Emory CASAS Cancer Survival Analysis Suite here: http://bbisr.shinyapps.winship.emory.edu/CASAS/ ***<br />
}}</div>PaulHooperhttps://centre.santafe.edu/complextime/w/index.php?title=Population_and_the_Environment:_Analytical_Demography_and_Applied_Population_Ethics/Modeling_complex_populations_-_statistical_inference_from_demographic_data&diff=2747Population and the Environment: Analytical Demography and Applied Population Ethics/Modeling complex populations - statistical inference from demographic data2018-10-16T14:46:36Z<p>PaulHooper: </p>
<hr />
<div>{{Agenda item<br />
|Start time=October 16, 2018 09:55:00 AM<br />
|End time=October 16, 2018 10:40:00 AM<br />
|Presenter=PaulHooper<br />
|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.<br />
|Post-meeting notes=It's clear that an extended version of this course should include treatment of inequality (and more generally the distribution of the benefits and costs of environmental impacts within societies) and conflict between and within states. The #1 highlight is of course the group of people assembled here.<br />
|Reference material notes=The optional reading Kaplan, Hooper, Stieglitz & Gurven (2015) [[The Causal Relationship between Fertility and Infant Mortality: Prospective analyses of a population in transition]] provides worked examples of analyzing fertility data (using Cox proportional hazards to model time to next birth) and infant mortality data (using logistic regression).<br />
<br />
Access the Emory CASAS Cancer Survival Analysis Suite here: http://bbisr.shinyapps.winship.emory.edu/CASAS/<br />
}}</div>PaulHooperhttps://centre.santafe.edu/complextime/w/index.php?title=File:First_Birth_Data_For_Course.csv&diff=2746File:First Birth Data For Course.csv2018-10-16T14:45:16Z<p>PaulHooper: User created page with UploadWizard</p>
<hr />
<div>=={{int:filedesc}}==<br />
{{Information<br />
|description={{Related page|Population and the Environment: Analytical Demography and Applied Population Ethics/Modeling complex populations - statistical inference from demographic data}}<br />
|date=<br />
|source={{own}}<br />
|author=[[User:PaulHooper|PaulHooper]]<br />
|permission=<br />
|other versions=<br />
}}<br />
<br />
=={{int:license-header}}==<br />
{{licensing|generic}}<br />
<br />
[[Category:Uploaded via Campaign:Add image to page]]</div>PaulHooperhttps://centre.santafe.edu/complextime/w/index.php?title=File:Hooper_%26_Dasgupta_-_Economic_development_and_demographic_choices.pdf&diff=2745File:Hooper & Dasgupta - Economic development and demographic choices.pdf2018-10-16T13:24:05Z<p>PaulHooper: User created page with UploadWizard</p>
<hr />
<div>=={{int:filedesc}}==<br />
{{Information<br />
|description={{Presentation file for|Population and the Environment: Analytical Demography and Applied Population Ethics/Economic development and demographic choices}}<br />
|date=<br />
|source={{own}}<br />
|author=[[User:PaulHooper|PaulHooper]]<br />
|permission=<br />
|other versions=<br />
}}<br />
<br />
=={{int:license-header}}==<br />
{{licensing|generic}}<br />
<br />
[[Category:Uploaded via Campaign:Presentation file]]</div>PaulHooperhttps://centre.santafe.edu/complextime/w/index.php?title=Population_and_the_Environment:_Analytical_Demography_and_Applied_Population_Ethics/Modeling_complex_populations_-_statistical_inference_from_demographic_data&diff=2612Population and the Environment: Analytical Demography and Applied Population Ethics/Modeling complex populations - statistical inference from demographic data2018-10-14T20:04:22Z<p>PaulHooper: </p>
<hr />
<div>{{Agenda item<br />
|Start time=October 13, 2018 02:15:00 PM<br />
|End time=October 13, 2018 02:30:00 PM<br />
|Presenter=PaulHooper<br />
|Description=Test2<br />
|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.<br />
|Post-meeting notes=It's clear that an extended version of this course should include treatment of inequality (and more generally the distribution of the benefits and costs of environmental impacts within societies) and conflict between and within states. The #1 highlight is of course the group of people assembled here.<br />
|Reference material notes=The optional reading Kaplan, Hooper, Stieglitz & Gurven (2015) [[The Causal Relationship between Fertility and Infant Mortality: Prospective analyses of a population in transition]] provides worked examples of analyzing fertility data (using Cox proportional hazards to model time to next birth) and infant mortality data (using logistic regression).<br />
}}</div>PaulHooperhttps://centre.santafe.edu/complextime/w/index.php?title=Population_and_the_Environment:_Analytical_Demography_and_Applied_Population_Ethics/Modeling_complex_populations_-_statistical_inference_from_demographic_data&diff=2598Population and the Environment: Analytical Demography and Applied Population Ethics/Modeling complex populations - statistical inference from demographic data2018-10-14T18:12:42Z<p>PaulHooper: </p>
<hr />
<div>{{Agenda item<br />
|Start time=October 13, 2018 02:15:00 PM<br />
|End time=October 13, 2018 02:30:00 PM<br />
|Presenter=PaulHooper<br />
|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. <br />
|Post-meeting notes=It's clear that an extended version of this course should include treatment of inequality (and more generally the distribution of the benefits and costs of environmental impacts within societies) and conflict between and within states. The #1 highlight is of course the group of people assembled here.<br />
|Reference material notes=The optional reading Kaplan, Hooper, Stieglitz & Gurven (2015) [[The Causal Relationship between Fertility and Infant Mortality: Prospective analyses of a population in transition]] provides worked examples of analyzing fertility data (using Cox proportional hazards to model time to next birth) and infant mortality data (using logistic regression).<br />
}}</div>PaulHooperhttps://centre.santafe.edu/complextime/w/index.php?title=Population_and_the_Environment:_Analytical_Demography_and_Applied_Population_Ethics/Modeling_complex_populations_-_statistical_inference_from_demographic_data&diff=2591Population and the Environment: Analytical Demography and Applied Population Ethics/Modeling complex populations - statistical inference from demographic data2018-10-14T18:10:16Z<p>PaulHooper: </p>
<hr />
<div>{{Agenda item<br />
|Start time=October 13, 2018 02:15:00 PM<br />
|End time=October 13, 2018 02:30:00 PM<br />
|Presenter=PaulHooper<br />
|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. <br />
|Post-meeting notes=It's clear that an extended version of this course should include treatment of inequality (and more generally the distribution of the benefits and costs of environmental impacts within societies) and conflict between and within states. The #1 highlight is of course the group of people assembled here.<br />
|Reference material notes=The optional reading Kaplan, Hooper, Stieglitz & Gurven (2015) [[The Causal Relationship between Fertility and Infant Mortality: Prospective analyses of a population in transition]] provides worked examples of analyzing fertility and infant mortality data.<br />
}}</div>PaulHooperhttps://centre.santafe.edu/complextime/w/index.php?title=The_Causal_Relationship_between_Fertility_and_Infant_Mortality:_Prospective_analyses_of_a_population_in_transition&diff=2570The Causal Relationship between Fertility and Infant Mortality: Prospective analyses of a population in transition2018-10-14T18:01:59Z<p>PaulHooper: Created page with "{{Reference Material |Meeting=Population and the Environment: Analytical Demography and Applied Population Ethics/Modeling complex populations - statistical inference from dem..."</p>
<hr />
<div>{{Reference Material<br />
|Meeting=Population and the Environment: Analytical Demography and Applied Population Ethics/Modeling complex populations - statistical inference from demographic data<br />
|title=The Causal Relationship between Fertility and Infant Mortality: Prospective analyses of a population in transition<br />
|Mendeley id=e37fb2f4-8dd1-34e9-be6b-b0b8b0f24b5b<br />
}}</div>PaulHooperhttps://centre.santafe.edu/complextime/w/index.php?title=Population_and_the_Environment:_Analytical_Demography_and_Applied_Population_Ethics/Modeling_complex_populations_-_statistical_inference_from_demographic_data&diff=2550Population and the Environment: Analytical Demography and Applied Population Ethics/Modeling complex populations - statistical inference from demographic data2018-10-14T17:52:09Z<p>PaulHooper: </p>
<hr />
<div>{{Agenda item<br />
|Start time=October 13, 2018 02:15:00 PM<br />
|End time=October 13, 2018 02:30:00 PM<br />
|Presenter=PaulHooper<br />
|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. <br />
|Post-meeting notes=It's clear that an extended version of this course should include treatment of inequality (and more generally the distribution of the benefits and costs of environmental impacts within societies) and conflict between and within states. The #1 highlight is of course the group of people assembled here.<br />
}}</div>PaulHooper