4/25/18 - Jeff Gill presents "Models for Identifying Substantive Clusters and Fitted Subclusters in Social Science Data"
Publication information:
Abstract
Jeff Gill presents
Models for Identifying Substantive Clusters and Fitted Subclusters in Social Science Data
Unseen grouping, often called latent clustering, is a common feature in social science data. Subjects may intentionally or unintentionially group themselves in ways that complicate the statistical analysis of substantively important relationships. This work introduces a new model-based clustering design which incorporates two sources of heterogeneity. The first source is a random effect that introduces substantively unimportant grouping but must be accounted-for.n The second source is more important and more difficult to handle since it is directly related to the relationships of interest in the data. We develop a model to handle both of these challenges and apply it to data on terrorist groups, which are notoriously hard to model with conventional tools.
Full text
Jeff Gill presents
Models for Identifying Substantive Clusters and Fitted Subclusters in Social Science Data
Unseen grouping, often called latent clustering, is a common feature in social science data. Subjects may intentionally or unintentionially group themselves in ways that complicate the statistical analysis of substantively important relationships. This work introduces a new model-based clustering design which incorporates two sources of heterogeneity. The first source is a random effect that introduces substantively unimportant grouping but must be accounted-for.n The second source is more important and more difficult to handle since it is directly related to the relationships of interest in the data. We develop a model to handle both of these challenges and apply it to data on terrorist groups, which are notoriously hard to model with conventional tools.