This workshop covers advanced statistical methods for analyzing social network data, focusing on testing hypotheses about network structure (e.g. reciprocity, transitivity, and closure), the formation of ties based on attributes (e.g. homophily), and network effects on individual attributes (social influence … Read more
This workshop covers advanced statistical methods for analyzing social network data, focusing on testing hypotheses about network structure (e.g. reciprocity, transitivity, and closure), the formation of ties based on attributes (e.g. homophily), and network effects on individual attributes (social influence … Read more
This workshop covers advanced statistical methods for analyzing social network data, focusing on testing hypotheses about network structure (e.g. reciprocity, transitivity, and closure), the formation of ties based on attributes (e.g. homophily), and network effects on individual attributes (social influence … Read more
Mixed methods research typically refers to research design and implementation that combines qualitative and quantitative data collection and/or analysis strategies. This course examines best practices, through the review of exemplars and discussion of theoretical approaches, in “mixing” methods and integrating … Read more
Mixed methods research typically refers to research design and implementation that combines qualitative and quantitative data collection and/or analysis strategies. This course examines best practices, through the review of exemplars and discussion of theoretical approaches, in “mixing” methods and integrating … Read more
Mixed methods research typically refers to research design and implementation that combines qualitative and quantitative data collection and/or analysis strategies. This course examines best practices, through the review of exemplars and discussion of theoretical approaches, in “mixing” methods and integrating … Read more
Across the social sciences, we encounter multilevel, hierarchical, or clustered data, such as students nested in classrooms or schools or voters nested in electoral districts or states. Often, we have theories about how characteristics at different levels of analysis are … Read more
Across the social sciences, we encounter multilevel, hierarchical, or clustered data, such as students nested in classrooms or schools or voters nested in electoral districts or states. Often, we have theories about how characteristics at different levels of analysis are … Read more
Across the social sciences, we encounter multilevel, hierarchical, or clustered data, such as students nested in classrooms or schools or voters nested in electoral districts or states. Often, we have theories about how characteristics at different levels of analysis are … Read more
Across the social sciences, we encounter multilevel, hierarchical, or clustered data, such as students nested in classrooms or schools or voters nested in electoral districts or states. Often, we have theories about how characteristics at different levels of analysis are … Read more
Across the social sciences, we encounter multilevel, hierarchical, or clustered data, such as students nested in classrooms or schools or voters nested in electoral districts or states. Often, we have theories about how characteristics at different levels of analysis are … Read more
This workshop introduces the Bayesian multilevel model framework. Bayesian methods allow for an extremely flexible approach for estimating hierarchical models with a variety different types of dependent variables. The Bayesian approach simplifies several of the assumptions of the classical techniques … Read more
