Matt Lawrence

The Family in Contemporary Society
This course will investigate the social, economic, and political forces that have brought about changes in family life in the beginning of the 21st century. We will begin by looking at various attempts to define "the family," and we will then explore a range of topics, including the webs of family relationships (e.g., mothering, fathering, kin networks), labor and family intersections (e.g., mediating between work and family; the household division of labor), gay and lesbian family life, and domestic violence. Although the focus will be on contemporary United States, we will also examine some cross-cultural and historical material. 3 hrs. lect.

The Logic of Sociological Inquiry
In this course students will be introduced to the basic tools of sociological research including problem formulation, strategies of design and data collection, and analysis and presentation of results. This class will help students formulate a research question and develop a research strategy to best explore that question. Those strategies may include interviews, structured observation, participant observation, content analysis, and surveys. This class, strongly recommended for juniors, will culminate in the submission of a senior project proposal. (SOAN 0105 or SOCI 0105) (formerly SOAN 03010) 3 hrs. lect./disc.

Higher Education and Society
Concerns about quality, value, and cost have raised doubts about whether higher education remains a pathway to opportunity. In this course we will consider these issues by reviewing research on the changing demographics of students, the evolving definition of “merit” in admissions, the challenges of assessing what students learn, and the relationship between student loan debt and economic inequality. We will also examine how college shapes later outcomes such as income, health, and family formation. Finally, we will discuss efforts to reform higher education and the potential for innovations like global expansion to reshape postsecondary schooling in the future. 3 hrs.lct. (Formerly SOCI 0430. Not open to students who have already taken SOCI 0430.)

Social Statistics
In this course we will learn the practical tools social sociologists and other scientists use to analyze data quantitatively. Topics will emphasize applications with statistical software and data from the General Social Survey and other datasets. We will explore methods to describe statistics about samples, apply the principles of probability to make predictions about populations, and estimate the significance of those predictions through inference and hypothesis testing. We will conclude with an introduction to linear regression. (Open only to majors or by Instructor Approval) (formerly SOAN 0385) 3 hrs. lect./disc.

Topics in Sociological Practice
In this project-based course, we will use tools of data analysis and digital methods to explore sociological research topics. We will learn the fundamentals of coding and statistics in order to conduct original data analyses using publicly available data from sites such as the Opportunity Atlas, Social Explorer, the General Social Survey, and the Department of Education. We will reflect on how our computational and research practices relate to data ethics, principles of data visualization, and digital pedagogy. Students will build on their previous coursework to develop interactive exercises to analyze and interpret data which will be integrated throughout the department's curricula. Students will extensively use programs such as R, Shiny, and Datawrapper but no previous experience with these tools is required.

Data Science Across Disciplines
In this course, we will gain exposure to the entire data science pipeline—obtaining and cleaning large and messy data sets, exploring these data and creating engaging visualizations, and communicating insights from the data in a meaningful manner. During morning sessions, we will learn the tools and techniques required to explore new and exciting data sets. During afternoon sessions, students will work in small groups with one of several faculty members on domain-specific research projects in Biology, Geography, History, Mathematics/Statistics and Sociology. This course will use the R programming language. No prior experience with R is necessary.

BIOL 1230: Students enrolled in Professor Casey’s (Biology) afternoon section will use the tools of data science to investigate the drivers of tick abundance and tick-borne disease risk. To do this students will draw from a nation-wide ecological database.

GEOG 1230: In this section, we will investigate human vulnerability to natural hazards in the United States using location-based text data about hurricane and flood disasters from social media. We will analyze data qualitatively, temporally, and spatially to gain insights into the human experience of previous disasters and disaster response. We will present findings using spatial data visualizations with the aim of informing future disaster preparedness and resilience.

HIST 1230: In U.S. history, racial differences and discrimination have powerfully shaped who benefited from land and farm ownership. How can historians use data to understand the history of race and farming? Students will wrangle county- and state-level data from the U.S. Census of Agriculture from 1840-1912 to create visualizations and apps that allow us to find patterns in the history of race and land, to discover new questions we might not know to ask, and to create tools to better reveal connections between race, land, and farming for a general audience.

MATH/STAT 1230: Students will explore pediatric healthcare data to better understand the risks correlated with various childhood illnesses through an emphasis on the intuition behind statistical and machine learning techniques. We will practice making informed decisions from noisy data and the steps to go from messy data to a final report. Students will become proficient in R and gain an understanding of various statistical techniques.

SOCI 1230: Do sports fans care about climate change? Can sports communication be used to engage audiences on environmental sustainability? In this section of the course, students will use the tools of data science to examine whether interest in sports is associated with climate change knowledge, attitudes and behaviors, as well as other political opinions. Participants will use survey data to produce visualizations and exploratory analyses about the relationship between sports fandom and attitudes about environmental sustainability.