About
Welcome! In this website you find my most recent peer-reviewed publications, the courses I’ve taught, and the projects I’m currently working on.
If you are interested in collaborating with me, obtaining one of my papers, or in telling me about your research, please get in touch via e-mail to rodriguezs[@]uni-potsdam.de
Here two samples of my academic work:
PhD Dissertation
Family behavior and children’s wellbeing: statistical modeling and measurement issues
Abstract: In this dissertation, I consider various statistical modeling and measurement issues that complicate the causal attributions made about those associations in the literature in family sociology and social inequality. First, life course informed research suggests that the problem of selection bias in the father absence literature may be more complex than currently thought. After adjusting for dynamic biases, estimates of father absence’s effect on children’s wellbeing are reduced. Second, family instability experienced during childhood is said to negatively affect children’s wellbeing. However, time-dependent confounders affected by past episodes of family instability and affecting future family stability might explain away part of the negative impact. I show that a dynamic version of the selection hypothesis counters the family instability hypothesis, and the effects of cumulative family instability are small and not consistent with the family instability hypothesis. Third, research suggest that socioeconomic status gaps in language skills among preschoolers could be substantially reduced by intervening on the parenting styles, practices, and parental investments of low-resource parents. Employing interventional causal mediation analysis, however, I show parenting mediates around one third of the total effect of SES on early language skills. Fourth, the measurement of cognitive abilities is complicated by various features of standardized assessments. Those problems have important implications for the quantification of social inequality in unobservable variables and for causal inference research because test scores capture non-random noise. The dissertation concludes by making a plea for furthering causal inference thinking in family sociology, social inequality, social mobility, and family demography research.
MSc. Statistics Thesis
Dimenstionality reduction methods for high-dimensional prediction tasks: Overview, simulation study, and prediction in the social sciences
Abstract: An increasing set of model selection methods for high-dimensional data have been developed over the last decades. When the number of parameters to be estimated is much larger than the number of observations available, the model selection theory and the traditional methods that inform the selection of variables fail. The problems associated to developing a predictive model in a high-dimensional setting, as they occur in many applications, can be overcome by regularization. In this paper I provide a comparison of the performance of five methods (namely PCR, PLSR, Lasso, AdaLasso and Boosting) that propose different forms of regularization as a solution to the “ill-posedness” of the problem of model selection or optimization of a risk function. This thesis surveys the main statistical properties of these methods and evaluates their respective performances in terms of common metrics using a simulation study. Finally, as an illustration of an application, the methods are applied to the problem of predicting the GPA of a cohort of children when age 15 using all the information captured by the Future of Families study from birth up to 9 years of age.