SISRM Courses

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.

Archival Methods & Historical Thinking interrogates the concepts, theories, and practices of the archive from a historical perspective. In many ways, history is a discipline defined by a set of questions rather than a singular approach. We will begin by analyzing how historians do the work of interpreting sources to construct historical narratives and arguments. Then, examining archival theory, its lapses, and its possibilities, we will determine what characteristics make an archive and how we can historicize it as an object of inquiry in its own right. We will then tackle a representative sample of the types of sources and archives you are most likely to engage as a social science researcher. Looking at how people have archived written ephemera, material culture, photographs, film, music, urban space, and the internet, we will pair the specific theoretical concerns of a given source type’s archiving with practical examples of how social scientists have explicitly mediated, transcended, or succumbed to the experience of the archive: its structure, its customs, its absences, and more. You will gain an understanding of the mechanics of archival work for a social science researcher as well as an appreciation for the complexity of historical thinking. By the end of the course, you will learn how to reconcile archival theory with the realities and practices of research in order to become better, more ethical, and more rigorous researchers.

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 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.

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.

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.

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 R as a software tool for these techniques.

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.

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.