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A Flexible Comparison Process As A Critical Mechanism For Context Effects, Andrea M. Cataldo
A Flexible Comparison Process As A Critical Mechanism For Context Effects, Andrea M. Cataldo
Doctoral Dissertations
Context effects such as the attraction, compromise, and similarity effects demonstrate that a comparison process, i.e., a method of comparing dimension values, plays an important role in choice behavior. Recent research suggests that this same comparison process, made more flexible by allowing for a variety of comparisons, may provide an elegant account of observed correlations between context effects by differentially highlighting dimension-level and alternative-level stimulus characteristics. Thus, the present experiments test the comparison process as a critical mechanism underlying context-dependent choice behavior. Experiment 1 provides evidence that increasing a dimension-level property, spread, promotes the attraction and compromise effects and reduces …
Estimating Age-Specific Contraceptive Use For Spacing Of Childbirth For All Countries In Sub-Saharan Africa From 1985 To 2030 Using A Bayesian Hierarchical Time Series Model, Gregory Guranich
Masters Theses
Contraceptive usage for spacing of childbirth is an important indicator for understanding family planning practices as well as fertility transitions. Fertility transition are especially important in sub-Saharan Africa where fertility remains high in many countries. However, estimates and short-term projections are generally not available for countries in this region. We developed a Bayesian hierarchical time series model to estimate and project usage of contraceptives for spacing by 5-year age groups for all countries in sub-Saharan Africa for the years 1985-2030. Estimating country-age-year specific usage is challenging due to limited data availability. We use Bayesian hierarchical models to share information across …
Allocative Poisson Factorization For Computational Social Science, Aaron Schein
Allocative Poisson Factorization For Computational Social Science, Aaron Schein
Doctoral Dissertations
Social science data often comes in the form of high-dimensional discrete data such as categorical survey responses, social interaction records, or text. These data sets exhibit high degrees of sparsity, missingness, overdispersion, and burstiness, all of which present challenges to traditional statistical modeling techniques. The framework of Poisson factorization (PF) has emerged in recent years as a natural way to model high-dimensional discrete data sets. This framework assumes that each observed count in a data set is a Poisson random variable $y ~ Pois(\mu)$ whose rate parameter $\mu$ is a function of shared model parameters. This thesis examines a specific …