SOC 206 Quantitative Methods II
Juan Pablo Pardo-Guerra
Office hours: Wednesdays 16:00-17:00
Fortunately for everyone, the world is not a stage that can be modeled through a multiple regression (otherwise, as Prigogine might have argued, we’d be disorganized stardust). Our emphasis in SOC 206 will be to understand how to model processes that cannot be adequately described through a multiple regression. This is a practical course: although we will explore the theoretical foundations of the different approaches, most of our will work will focus on applying concepts to practical examples through the use of STATA.
This course is all about bring-your-own-data-if-you-want/can: if you would like to explore a particular dataset (and potentially work it into a paper), this is a perfect opportunity to get additional feedback and support.
The course is graded through a combination of short quasi-weekly assignments, class participation, a final presentation, and a final paper.
|Short weekly assignments (6)||60||27%|
*Every week, groups of students will be randomly selected to present an article that is relevant to the discussion.
Agresti and Finlay, Statistical Methods for the Social Sciences
Long, JS. 1997. Regression Models for Categorical and Limited Dependent Variables. Sage: Thousand Oaks
Long, JS. and Freese, J. 2006. Regression Models for Categorical Dependent Variables Using Stata. Stata Press.
Additional readings demonstrating the application of relevant approaches will be added to the reading list throughout the course.
SCHEDULE of CLASSES
Week 1 – Review of linear and multiple regressions, model identification, model estimation and diagnostic checking
Week 2 – Path Analysis
Week 3 – Latent Variables
Week 4 – Multilevel Modeling
Luke, D. 2004. Multilevel modeling. Sage: Thousand Oaks. Chapters 1 & 2.
Week 5 – Logit I – Dichotomous and Polychotomous Dependent Variables
Long chapters 3 & 5.
Week 6 – Logit II – Ordered Dependent Variables
Long chapters 6 & 8.
Week 7 – Event History Analysis
Selected chapters from: Yamaguchi, K. 1991. Event History Analysis. Sage: Thousand Oaks.
Week 8 – Basic Topics in Time Series Analysis
Selected chapters from: Box, G., Jenkins, G. and Reinsel, G. 1994. Time Series Analysis: Forecasting and Control, Prentice Hall: Upper Saddle River.
Paper draft due this week.
Week 9 – Select Topics: Monte Carlo Simulation, Cluster Analysis, and the Joy of Lèvy-Stable Distributions
Week 10 – Student Presentations + The Epistemology of Numbers – Challenges, Critiques and Horizons for Quantitative Approaches in the Social Sciences
Some readings to inform discussions:
Ziliak, S.T., and McCloskey, D. 2012. The Cult of Statistical Significance. University of Michigan Press: Ann Arbor.
Cartwright, N. How the Laws of Physics Lie. OUP: Oxford.
Wasserstein, R. and & Lazar, N., 2016. “The ASA’s statement on p-values: context, process, and purpose” The American Statistician