SISRM Courses

Course Offerings Summer 2024

Students participating in the 10-week Summer Institute RA program must enroll in a summer methodologies course. Each of the SISRM courses satisfies major or minor requirements in a number of programs. Information about the courses and their relationships with different majors or minors follows. 

Unless otherwise noted, course instruction will take place June 10 – July 12, 2024.

Course Credits

Summer Quarter courses carries the equivalent credit of one full-length (quarter-long) course in the College at the University of Chicago. Unless otherwise noted, each course offered at the University during the Summer Quarter is the equivalent of 5 quarter hours (100 credits per course).

The following course information is tentative and subject to change. Final course offerings will be posted in December. 

In-Person Courses Summer 2024

All courses are offered in a synchronous format.

Introduction to GIS and Spatial Analysis

GISC 28702/38702 (ARCH 28702; CEGU 28702; ENST 28702; PPHA 38712; SOCI 20283; SOCI 30283)

M T W Th: 10–11:30 A.M.

Undergraduate; Graduate
Format: In-person

Instructor: Crystal Bae, Assistant Instructional Professor of GIScience

Introduction to GIS & Spatial Analysis Course Description:

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.

 

Curricular Connections:

Learn more about the course from the instructor:

Archival Methods and Historical Thinking

HIST 29806

M W F: 9–11 A.M.

Undergraduate
Format: In-Person

Instructor: Alexander Hofmann, Earl S. Johnson Instructor in History

Archival Methods and Historical Thinking Course Description:

In Archival Methods and Historical Thinking, 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.

 

Curricular Connections:

Archival Methods and Historical Thinking:

Learn more about the course from the instructor:

Psychological Research Methods

PSYC 20200

M T Th F: 9:30–11:30 A.M.

Undergraduate
Format: In-person

Kerry Ledoux, Associate Instructional Professor in Psychology

Psychological Research Methods Course Description:

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.

 

 

Curricular Connections:

Learn more about the course from the instructor:

Virtual Ethnographic Field Research Methods

SOSC 20224 (SOSC 30224; ANTH 21432; ANTH 31432; ENST 20224; GLST 26220; SOCI 20515)

M W F: 9:30–11:30 A.M.

Undergraduate; Graduate
Format: In-Person

Instructor: Cate Fugazzola, Assistant Senior Instructional Professor, Global Studies

Virtual Ethnographic Field Research Methods Course Description:

“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 in an online environment, exposure to major debates on virtual ethnographic research, and opportunities to try their hand at practicing fieldwork virtually. We will analyze and problematize enduring oppositions associated with ethnographic fieldwork – field/home, insider/outsider, researcher/research subject, expert/novice, ‘being there’/removal—and we will debate epistemological, ethical, and practical matters in online ethnographic research. Mirroring the complexities and opportunities of research in virtual worlds, this course will alternate between in-person and online instruction, and will combine synchronous and asynchronous opportunities for conversation, work, and play.

Curricular Connections

Cross-listings: ANTH 21432; ANTH 31432; ENST 20224; GLST 26220; SOCI 20515; SOSC 30224

Learn more about the course from the instructor:

Approaches to Social Science Research Design

SOSC 26035 (GLST 26035; SOCI 28099; PLSC 26035)

T W Th: 1:30–3:30 P.M.

Format: In-person
Undergraduate

Instructors: Andrew Proctor, Assistant Instructional Professor in Political Science

Approaches to Social Science Research Design Course Description:

This course explores critical foundations of social science research design. The course will place emphasis on how social scientists identify and create data to empirically examine social phenomena through a variety of different theoretical and methodological approaches. The course will cover the relationship between research questions, design, and generating data across different methodological and epistemological approaches in the social sciences.

 

Curricular Connections:

Cross-listings: GLST 26035, SOCI 28099; PLSC 26035.

Learn more about the course from the instructor:

Remote Courses Summer 2024

All courses are offered in a synchronous format.

Econometrics

ECON 21020

M W F: 9:30–11:40AM

Undergraduate
Format: Remote

Instructor: Murilo Ramos, Assistant Instructional Professor in Economics and the College

Econometrics 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 2023

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.

Curricular Connections:

Learn more about the course from the instructor:

Computing for the Social Sciences

MACS 20500 (MACS 30500; CHDV 30511; ENST 20550; MAPS 30500; PLSC 30235; PSYC 30510; SOCI 20278; SOCI 40176; SOSC 26032)

M T W Th: 9:30–11:30 A.M.

Undergraduate; Graduate
Format: Remote

Instructor: Sabrina Nardin, Assistant Instructional Professor Masters in Computational Social Science

Computing for the Social Sciences Course Description:

Computing for the Social Sciences 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 analyzing data and generating reproducible research through the use of the programming language R and version control software. Topics include coding concepts (e.g., data structures, control structures, functions, etc.), data visualization, data wrangling, exploratory data analysis, etc. 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; 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. The course will be taught in R.

 

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)
  • Identify and use external libraries to expand on base functions
  • Apply Git and GitHub workflows for version control
  • Implement best practices for reproducible research
  • Understand how to debug programs for errors
  • Import data from files or the internet
  • Transform, visualize, and descriptively interpret data
  • Munge raw data into a tidy format
  • Scrape websites to collect data for analysis
  • Parse and analyze text documents

Curricular Connections:

Cross-listings: SOCI 20278; SOCI 40176; ENST 20550; PLSC 30235; MAPS 30500; CHDV 30511; MACS 30500

Learn more about the course from the instructor:

Introduction to Spatial Data Science

SOCI 20253 (SOCI 30253; CEGU 20253; ENST 20253; GISC 20500; GISC 30500; MACS 54000)

M T W: 9–11:00 A.M.

Undergraduate; Graduate
Format: Virtual

Instructor: Yue Lin, Assistant Instructional Professor of GIScience

Introduction to Spatial Data Science Course Description:

Spatial data science consists of a collection of concepts and methods drawn from both statistics and computer science that deal with accessing, manipulating, visualizing, exploring and reasoning about geographical data. The course introduces the types of spatial data relevant in social science inquiry and reviews a range of methods to explore these data. Topics covered include formal spatial data structures, geovisualization and visual analytics, rate smoothing, spatial autocorrelation, cluster detection and spatial data mining. An important aspect of the course is to learn and apply open source GeoDa software.

STAT 22000 (or equivalent), familiarity with GIS is helpful, but not necessary.

Curricular Connections:

 

Learn more about the course from the instructor:

Introductory Statistical Methods and Applications for the Social Sciences

SOSC 20112/30112

M T W Th F: 9:30–11:50 A.M.
June 10 – June 28, 2024

Pre-College; Undergraduate; Graduate
Format: Remote

Instructor: Yanyan Sheng, Senior Instructional Professor and Associate Director, Committee on Quantitative Methods in Social, Behavioral, and Health Sciences

Introductory Statistical Methods and Applications for the Social Sciences Course Description:

The primary goal of the course is to assist the student in learning to perform descriptive and inferential statistics 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 for data analysis, (5) select statistical procedures appropriate for 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 of statistical power and the influence of sample size on inference, and (8) summarize and write up the results that can be 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 Note: 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.

Curricular Connections:
Learn more about the course from the instructor:

I’m more aware of what it takes to be a social scientist, and of the various fields of social science I could go into. Specifically, I know more about how to conduct future research properly, both technically and personally.

Cooper K.
SISRM 2021