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Edoardo Costantini; Kyle M. Lang; Tim Reeskens; Klaas Sijtsma – Sociological Methods & Research, 2025
Including a large number of predictors in the imputation model underlying a multiple imputation (MI) procedure is one of the most challenging tasks imputers face. A variety of high-dimensional MI techniques can help, but there has been limited research on their relative performance. In this study, we investigated a wide range of extant…
Descriptors: Statistical Analysis, Social Science Research, Predictor Variables, Sociology
Gibson, C. Ben; Sutton, Jeannette; Vos, Sarah K.; Butts, Carter T. – Sociological Methods & Research, 2023
Microblogging sites have become important data sources for studying network dynamics and information transmission. Both areas of study, however, require accurate counts of indegree, or follower counts; unfortunately, collection of complete time series on follower counts can be limited by application programming interface constraints, system…
Descriptors: Social Networks, Network Analysis, Social Media, Mathematics
Xu, Jun; Bauldry, Shawn G.; Fullerton, Andrew S. – Sociological Methods & Research, 2022
We first review existing literature on cumulative logit models along with various ways to test the parallel lines assumption. Building on the traditional frequentist framework, we introduce a method of Bayesian assessment of null values to provide an alternative way to examine the parallel lines assumption using highest density intervals and…
Descriptors: Bayesian Statistics, Evaluation Methods, Models, Intervals
Bloome, Deirdre; Schrage, Daniel – Sociological Methods & Research, 2021
Causal analyses typically focus on average treatment effects. Yet for substantive research on topics like inequality, interest extends to treatments' distributional consequences. When individuals differ in their responses to treatment, three types of inequality may result. Treatment may shape inequalities between subgroups defined by pretreatment…
Descriptors: Regression (Statistics), Outcomes of Treatment, Statistical Analysis, Correlation
Feng, Xiang-Nan; Wu, Hao-Tian; Song, Xin-Yuan – Sociological Methods & Research, 2017
We consider an ordinal regression model with latent variables to investigate the effects of observable and latent explanatory variables on the ordinal responses of interest. Each latent variable is characterized by correlated observed variables through a confirmatory factor analysis model. We develop a Bayesian adaptive lasso procedure to conduct…
Descriptors: Bayesian Statistics, Regression (Statistics), Models, Observation
Stamey, James D.; Beavers, Daniel P.; Sherr, Michael E. – Sociological Methods & Research, 2017
Survey data are often subject to various types of errors such as misclassification. In this article, we consider a model where interest is simultaneously in two correlated response variables and one is potentially subject to misclassification. A motivating example of a recent study of the impact of a sexual education course for adolescents is…
Descriptors: Bayesian Statistics, Classification, Models, Correlation