Course Description

This course provides an overview of some of the most common concepts, tools, and techniques for applying the sociological imagination to real-world questions and problems. Students will learn to think critically about research design, data collection, data processing, statistical analysis, data visualization, and information communication. Guided by course readings, students will complete a series of four projects which utilize sociological ideas to explore and contextualize social phenomena related to their experiences, communities, work, and/or research interests.

Learning Outcomes

1. Learn to utilize sociological concepts and ideas to investigate and explain social phenomena.


2. Become familiar with common tools and software for applied sociological research.


3. Complete four projects demonstrating an ability to think critically about research design, data collection, data processing, the production of statistics, and information communication.

Course Outline

UNIT 1

Week 1: What is applied sociology?

Readings:

• The Promise (Chapter 1) - C. Wright Mills 1959
  in The Sociological Imagination
• Sociology Engaged in Social Justice - Mary Romero 2020
  https://journals.sagepub.com/doi/full/10.1177/0003122419893677
• Problem-Solving Sociology - Monica Prasad 2018
  https://journals.sagepub.com/doi/abs/10.1177/0094306118779810
• Why Teach Applied Sociology? Four Reasons to Consider - Marv Finkelstein 2010
  https://www.jstor.org/stable/23548929
• An Introduction to Doing Sociology (Chapter 1) - Stephen F. Steele and Jeffrey R. Breese 2009
  in Doing Sociology: Case Studies in Sociological Practice

Week 2: Research design

Topics:

• Research questions
• Research goals
• Data sources
• Systematic analysis

Week 3: Data collection

Topics:

• Quantiative/qualitative data
• Tabular data
• Common data file formats
• Data compilation
• Metadata

UNIT 2

Week 4: What skills are useful for applied sociologists?

Readings:

• From the archives: Ida B. Wells-Barnett confronts "excuses for lynching" in 1901 - Paul H. Stuart 2020
  https://www.tandfonline.com/doi/abs/10.1080/10705422.2020.1805220
• Introduction (Chapter 1) and Observing Behavior (Chapter 2) - Matthew J. Salganik 2017
  in Bit By Bit: Social Research in the Digital Age
  https://www.bitbybitbook.com/en/1st-ed/introduction/
  https://www.bitbybitbook.com/en/1st-ed/observing-behavior/
• Understanding Problems Using Data (Chapter 6) - Beth Simone Noveck 2021
  in Solving Public Problems : A Practical Guide to Fix Our Government and Change Our World
• Record Linkage (Chapter 3) and Databases (Chapter 4) - Ian Foster, Rayid Ghani, Ron S. Jarmin, Frauke Kreuter and Julia Lane 2021
  in Big Data and Social Science
  https://textbook.coleridgeinitiative.org/chap-link.html/
  https://textbook.coleridgeinitiative.org/chap-db.html/

Week 5: Data cleaning

Topics:

• Labeling and formatting
• Data validation
• Filtering and subsetting
• Common transformations and conversions
• Aggregation and formulas

Week 6: Databases

Topics:

• Relational data
• Tables, keys, and relationships
• Database diagrams
• SQL
• Queries, joins, and views

UNIT 3

Week 7: What does applied sociological research look like?

Readings:

• Speaking Sociologically with Big Data - Susan Halford & Mike Savage 2017
  https://journals.sagepub.com/doi/abs/10.1177/0038038517698639
• Computation and the Sociological Imagination - James Evans & Jacob G. Foster Steel 2019
  https://journals.sagepub.com/doi/full/10.1177/1536504219883850
• Data Visualization in Sociology - Kieran Healy and James Moody 2014
  https://www.annualreviews.org/doi/abs/10.1146/annurev-soc-071312-145551
• Look at Data (Chapter 1) - Kieran Healy 2018
  in Data Visualization: A Practical Introduction
  https://socviz.co/lookatdata.html#lookatdata
• Text Analysis (Chapter 8) - Ian Foster, Rayid Ghani, Ron S. Jarmin, Frauke Kreuter and Julia Lane 2021
  in Big Data and Social Science
  https://textbook.coleridgeinitiative.org/chap-text.html/
• Sociology Action Network Profiles - ASA
  https://www.asanet.org/public-engagement/sociology-action-network/profiles

Week 8: Data analysis

Topics:

• Descriptive statistics
• Thematic coding
• Performance metrics
• Basic predictive analytics

Week 9: Data visualization

Topics:

• Choosing a visualization
• Table formatting
• Chart formatting
• Design schemes and style guides
• Interactivity

Week 10: Mapmaking

Topics:

• Types of maps
• Geodata and shapefiles
• ArcGIS and QGIS
• Other mapping tools

UNIT 4

Week 11: How do applied sociologists effect change?

Readings:

• The Relevance of Inequality Research in Sociology for Inequality Reduction - Thomas A. DiPrete and Brittany N. Fox-Williams (2021)
  https://journals.sagepub.com/doi/full/10.1177/23780231211020199
• Remaking Research Tools: Toward Transforming Systems of Inequality through Social Science Research - Elizabeth Birr Moje (2022)
  https://journals.sagepub.com/doi/full/10.1177/23780231221081694
• Introduction (Chapter 1) & Strategies and Tactics (Chapter 2) & Crafting the Case (Chapter 3) - Sanderijn Cels, Jorrit de Jong, and Frans Nauta 2012
  in Agents of Change: Strategy and Tactics for Social Innovation
• Data Quality and Inference Errors (Chapter 10) & Bias and Fairness (Chapter 11) - Ian Foster, Rayid Ghani, Ron S. Jarmin, Frauke Kreuter and Julia Lane 2021
  in Big Data and Social Science
  https://textbook.coleridgeinitiative.org/chap-errors.html/
  https://textbook.coleridgeinitiative.org/chap-bias.html/

Week 12: Data interpretation

Topics:

• Process documentation and labeling
• Performance metrics
• Bias and interpretation errors
• Triangulation

Week 13: Research communication

Topics:

• Reporting methods
• Stakeholders and audiences
• Data norms
• Leveraging evidence