Courses

SOCY 2061 - Introduction to Social Statistics
A significant share of sociological research relies almost exclusively on quantitative methods (e.g., statistical analyses) to investigate social phenomena. These researchers use large national surveys, public opinion polls, and census data to document, describe, and explain a wide range of sociologically motivated research questions. As a result, students of this body of research need to have a basic understanding of statistics if they are to be active participants in the local, regional, national, and international dialog within the sociological community. The primary goal of this class is to provide each student with the requisite skills to not only understand the mainstream sociological research but also to be critical consumers of statistical information that is often presented as “factual”. Although the primary emphasis is on social research, the information and skills that you will learn in this class will be applicable to most academic and non-academic careers. The course is divided into four sections that focus on descriptive Statistics and inferential Statistics and various applied statistical techniques. Descriptive statistics are methods that allow you to present a set of scores in a parsimonious summary form that measure individual and social characteristics (e.g., socioeconomic status, self-esteem, residential segregation). The primary concepts that we emphasize are central tendency (e.g., mean, mode, median) and dispersion (e.g., standard deviation, variance, inter-quartile range). The second section, Inferential Statistics, is the backbone of statistical reasoning and it involves making estimates about a population (e.g., this entire class) based on a sample (e.g., 10 or 12 students in the class). This process necessarily involves the invocation of the basic rules of probability and it will introduce you to hypothesis testing which is used throughout the physical, behavioral, and social sciences. In the third section of the course, we will review five important applications of statistics (e.g., Cross-Tabs, Correlation, Simple Regression, and Multivariate Regression).  ~  Syllabus (pdf)
SOCY 3015 - Sociology of Race and Ethnicity
The primary goal of this course is to introduce students to research within sociological and social demographic research on race and ethnicity. Specific areas will include the following: conceptual/measurement issues; population size, growth, and migration; health and mortality; marriage, family, and fertility; socioeconomic context; and policy considerations. The reading materials in this class, for the most part, will be structured around current empirical pieces in the sociology of race and ethnicity.  ~  Syllabus (pdf)
SOCY 5021 - Data Analysis
This course will introduce students to multivariate statistical analysis and it will stress the application of these methods. I will assume that all students have had an elementary statistics course at the undergraduate level. Thus, I will review basic descriptive statistics and inferential statistics but we will transition into regression techniques by the third week of the course. The goal of the course is to teach students the skills and concepts that are necessary to perform and interpret elementary, intermediate, and some advanced statistical analyses in the Social Sciences. Accordingly, the primary project of the class is to write an original research paper using any of the techniques that we discuss in the class. We will work on these papers throughout the course.  ~  Syllabus (pdf)
SOCY 5161 - Topics in Stratification: Social Demography of Race and Ethncitiy
The purposes of this class are two-fold. The primary goal of this course is to introduce students to relevant and timely research within sociological and social demographic research on race and ethnicity. Specific areas will include the following: conceptual/measurement issues; population size, growth, and migration; health and mortality; marriage, family, and fertility; socioeconomic context; and policy considerations. The reading materials in this class, for the most part, will be structured around current empirical pieces in the sociology literature with an emphasis on the methodological approach in the analyses. It my hope that this course will provide important opportunities for students’ professional development as researchers. Unfortunately, methods, theory, and substance are often taught as distinct areas. In this class I hope to place equal emphasis on the ideas presented in a particular paper and the analytic strategy employed by the researcher(s). Accordingly, a core component of the class will be the research papers that each of you produce. These papers should be considered professional grade with an eye on future conference presentations or publication in particular scholarly journals.  ~  Syllabus (pdf)
SOCY 5601 - Advanced Data Analysis: Generalized Linear and Mixed Modeling Techniques
The Department of Sociology offers several graduate seminars on Advanced Data Analyses that emphasize the application of statistical analyses (e.g., using statistics on real data). Each course is intended to describe a group of multivariate techniques for different types of outcome variable (e.g., continuous, categorical, count, etc.). Students bring their own quantitative data set to these classes and analyze it using the techniques covered during the semester and for those without their own data, the Social Science Data lab provides access to the cumulative files of the General Social Survey (GSS). Whereas previous courses have relied on STATA and SPSS, this course will use the SAS System on the UNIX machines in 117 Ketchum Hall to run all analyses. After reviewing traditional multivariate techniques we will emphasize mixed modeling techniques (e.g., multilevel modeling) that are used to analyze data obtained from multiple levels such as neighborhoods, schools, and families. These models can be extended to repeated measures analyses on data from longitudinal studies (e.g., growth models). We will then discuss mixed-modeling techniques for categorical outcome variables.  ~  Syllabus (pdf)