In this course, students will be introduced to archival research methods and to the ways in which historians work with and interpret the sources they use in constructing historical narratives and arguments. We will visit Special Collections, explore digital archives, and consider the range of possible sources and archives, from texts held in national government archives to material objects, maps, audio or video recordings, and everything in between. We will also engage with the work of historians as they seek to make sense of the material they find in archives, considering questions of interpretation, narrative, and holes–that is, what is missing from archives. Students will gain an understanding of the mechanics of archival work and an appreciation for the complexity of historical thinking.

This is an applied course for social scientists with little-to-no programming experience who wish to harness growing digital and computational resources. The focus of the course is on generating reproducible research through the use of programming languages and version control software. Major emphasis is placed on a pragmatic understanding of core principles of programming and packaged implementations of methods. Students will leave the course with basic computational skills implemented through many computational methods and approaches to social science; while students will not become expert programmers, they will gain the knowledge of how to adapt and expand these skills as they are presented with new questions, methods, and data. More information can be found at https://cfss.uchicago.edu

Goals and Objectives: By the end of the course, students will:

• Construct and execute basic programs in R using elementary programming techniques and tidyverse packages (e.g. loops, conditional statements, user-defined functions)
• Apply stylistic principles of coding to generate reusable, interpretable code
• Debug programs for errors
• Identify and use external libraries to expand on base functions
• Generate reproducible research with R Markdown
• Implement statistical learning algorithms
• Utilize cross validation methods
• Visualize information and data using appropriate graphical techniques
• Import data from files or the internet
• Munge raw data into a tidy format
• Scrape websites to collect data for analysis
• Parse and analyze text documents
• Implement programs via distributed computing platforms
• Create interactive web pages using flexdashboard and Shiny

Course Description: This course covers the single and multiple linear regression model, the associated distribution theory, and testing procedures; corrections for heteroskedasticity, autocorrelation, and simultaneous equations; and other extensions as time permits. Students also apply the techniques to a variety of data sets using PCs.

Course Objectives: The purpose of this course is to give a fundamental understanding of the liner regression model used on a variety of economic analysis. It also stresses the many issues that students may encounter when doing their own empirical analysis using the linear regression model as a tool. Prerequisite: ECON 20100, ECON 21010, or STAT 23400 and MATH 19620.

Plan Ahead for Summer 2022

Economics majors who want to take ECON 21020 Econometrics through SISRM can meet the course pre-requisites in one of two ways:

1. Take ECON 21010 Statistical Methods in Economics in Spring Quarter; or
2. Take ECON 20100 The Elements of Economic Analysis II in Winter Quarter and STAT 23400 Statistical Models and Methods in Spring Quarter.

If you’ve ever been interested in learning more about spatial analysis or getting a geographic twist in computational thinking, this is the course to take. You may be interested in working with new types of spatial data to enhance a project in social science, economics, public health, crime, etc. You may be interested in learning some applied coding or extending the programming or statistical skills you already have. Or, you may just be curious about thinking about the world in a different way. The spatial perspective is a powerful conceptual and technical-scientific approach that facilitates new ways of viewing the world.

This course provides an overview of how spatial thinking is translated into specific methods to handle geographic information and statistical analysis, with a focus on research questions relevant in the social sciences. Basics of cartography, spatial data wrangling, and the essential elements of spatial analysis are introduced within this context. Examples include spatial data integration (spatial join), transformations between different spatial scales (overlay), the computation of “spatial” variables (distance, buffer, shortest path), geovisualization, visual analytics, and the assessment of spatial autocorrelation (the lack of independence among spatial variables). The methods will be illustrated by means of open-source software such as QGIS and R; this course does not teach a specific GIS software program.

Goals and Objectives: We’ll be using the R programming language and additional open-source software packages to learn and practice spatial analysis, and use various (old and new) types of data in applied labs to put newly learned concepts to the test. Favorite labs include working with raw crime data from multiple U.S. cities; learning about how coal mining impacts West Virginian towns across time; and developing and visualizing a bike network using millions of Divvy data points.

This course introduces and applies fundamental statistical concepts, principles, and procedures to the analysis of data in the social and behavioral sciences. Students will learn computation, interpretation, and application of commonly used descriptive and inferential statistical procedures as they relate to social and behavioral research. These include z-test, t-test, bivariate correlation and simple linear regression with an introduction to analysis of variance and multiple regression. The course emphasizes on understanding normal distributions, sampling distribution, hypothesis testing, and the relationship among the various techniques covered, and will integrate the use of SPSS as a software tool for these techniques.

Goals and Objectives: This course introduces descriptive statistics and basic inferential statistics that can be pre-required for more advanced applied statistics classes, such as multiple regression, experimental design, multivariate statistics.

The primary goal of the course is to assist the student in learning to perform descriptive and inferential analyses of data from single and multi-factor experiments. After completion of the course, the student will be able to (1) differentiate, utilize and apply statistical description and inference to applied research in behavioral sciences or other disciplines, (2) understand and be able to utilize various forms of charts and plots useful for statistical description, (3) understand and utilize the concept of statistical error and sampling distribution, (4) use a statistical program (e.g., SPSS) for data analysis, (5) select statistical procedures appropriate to the type of data collected and the research questions hypothesized, (6) distinguish between Type I and Type II errors in statistical hypothesis testing, (7) understand the concepts statistical power and the influence of sample size on inference, and (8) interpret SPSS output so that it can be written up and understood by a non-statistician. These specific goals and objectives will be reached through lab sessions, assigned homework problems, in-class quizzes and exams.

Course Notes: This course is equivalent to SOCI 20004/30004 (Statistical Methods of Research), CHDV 20101/3010 (Applied Statistics in Human Development Research), PSYC 20100 (Psychological Statistics), SOSC 26009/36009 (Introductory Statistical Methods), and other introductory level applied statistics courses.

This course introduces concepts and methods used in behavioral research. Topics include the nature of behavioral research, testing of research ideas, quantitative and qualitative techniques of data collection, artifacts in behavioral research, analyzing and interpreting research data, and ethical considerations in research.

This course overviews the way scientific surveys are conducted, the survey data structure, and common techniques to analyze survey data. Students will explore the actual survey data (using major surveys such as the General Social Survey) and look for answers to their research question. Students will learn where to find information about survey data sources and how to conduct analyses for their research project. The course also introduces some online tools and statistical software.

Pre-requisites: Some knowledge of statistical analysis and familiarity with statistics software are helpful but not required.

This course is limited to rising second-, third-, and fourth-year undergraduate students.

“Virtual worlds are places of imagination that encompass practices of play, performance, creativity and ritual.” – Tom Boellstorff, from Ethnography and Virtual Worlds: A Handbook of Method

This course is designed to provide students in the social sciences with a review of ethnographic research methods, exposure to major debates on ethnographic research, opportunities to try their hand at practicing fieldwork virtually, and feedback on a proposed study that employs ethnographic methods. By way of analyzing and problematizing enduring oppositions associated with ethnographic fieldwork – field/home, insider/outsider, researcher/research subject, expert/novice, ‘being there’/removal – this seminar is a practicum in theoretically grounded and critically reflexive qualitative methods of research. By introducing students to participant observation and interviews in virtual worlds, ethics, data analysis and writing up, the course offers an opportunity to make sense of the current pandemic we’re all experiencing in real time. An emphasis will be placed on multimedia, digital, and virtual ethnography.