Courses

Course Offerings Summer 2020

Session 1 • 3 Weeks

100 units
June 22 – July 10, 2020 (No classes July 3)
Introductory Statistical Methods and Applications

SOSC 20112

Instructor: Yanyan Sheng, Committee on Quantitative Methods in Social, Behavioral, and Health Sciences

M-T-W-Th-F 9:30-11:30am


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. This course is equivalent to SOSC 20004/30004, CHDV 20101/3010, PSYCH 20100, SOSC 26009/36009, and other introductory level applied statistics courses. This course is not STATS 22000.

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.

 

Session 1 • 5 Weeks

100 units
June 22 – July 24, 2020 (No classes July 3)
Computing for Social Sciences

MACS 20500/30500 (SOCI 20278;40176; ENST 20550; PLSC 30235; MAPS 30500; CHDV 30511)

Instructor: Benjamin Soltoff, Computational Social Science

M-T-W-Th 9:30-11:30am

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

Additional Course Notes

  • Approved elective course for the ENST major and minor requirements
  • Satisfies the methods requirement in the Public Policy Studies major

 

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 instruction will be in the classroom via lecture and live-coding exercises in the main lecture and lab sessions. The course implements a flipped classroom design, enabling students to receive direct feedback and instruction when solving problems and debugging code.

 

 

Introduction to GIS & Spatial Analysis for Social Scientists

GEOG 28702/38702 (SOCI 20283/30283; ENST 28702)

Instructor: Marynia Kolak, Center for Spatial Data Science

M-T-W-Th 1:30-3:00pm


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 in 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.

Course Notes

 

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. We’ll also visit the Oriental Institute to see how ancient cartographic techniques still remain fresh and modern today.

 

Econometrics

ECON 21020

Christopher Roark,  Department of Economics


Required of students who are majoring in economics; those students are encouraged to meet this requirement by the end of their third year. 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. Prerequisite: ECON 20100, ECON 21010, or STAT 23400 and MATH 19620 

Ethnographic Research Methods

SOSC 20223/30223 (ANTH 21422)

Instructor: Benjamin Fogarty-Valenzuela, Department of Sociology


This course is designed to provide students in the social sciences with a review of ethnographic research methods, exposure to writings from the field, opportunities to try their hand at practicing fieldwork, and feedback on a proposed study that employs ethnographic methods.   This seminar is a practicum in theoretically grounded and critically reflexive qualitative methods of research.

Course Notes

  • Satisfies the methodology course requirement for the Sociology major
  • Approved elective course for the ENST major and minor requirements
  • Approved as an ENST elective conditional on petition and relevance to course of study
  • Satisfies the methods requirement for the Comparative Human Development major
  • Satisfies one of the general course requirements for the Education and Society minor
  • Satisfies the methodology course requirement for the Anthropology major

September Session • 1 Week Offerings

50 units

Geovisualization and Cartography

Instructor: Marynia Kolak, Center for Spatial Data Science

Geovisualization integrates approaches in scientific computing, cartography, image analysis, information visualization, exploratory data analysis, and geographic information systems to facilitate new spatial thinking, understanding, and knowledge construction. In this course we introduce geovisual analytics by reviewing the basics of geographic data representation, symbolization, and map design. Students will review cartographic design principles, layout, typography, and color theory. We will explore the basics of making both static and dynamic maps, and focus on the task, interaction, and user unique to every cartographic process. The course also reviews statistical graphics, scientific visualizations, and analytic cartography approaches with applications from political science to epidemiology. A range of software and tools are reviewed to expand the visual analytic toolbox for cartographic design.

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How to Register

If you are a
  • UChicago College Student, visit the summer session website, scroll down to “How to Register,” and follow instructions for requesting access to register for courses during the summer;
  • UChicago Graduate Student, Complete the SSD Dean of Students form for Summer Quarter registration;
  • Visiting Undergraduate Student, please apply through UChicago Summer Session .

The Summer Institute in Social Research Methods has really shifted and transformed my notion of what research can be. To take these learned skills and apply them to a 21st century understanding is exciting. I think this has helped open more doors for me.

Ciara C.

2019 SISRM RA

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