Fall 2020

Emory Sociology provides an extensive curriculum for our graduate students. You will find below the topical courses and individualized programs offered in Fall 2020. Click on each one to see additional information, such as the course description.

You can also click on the links in the sidebar to see our course offerings in other semesters.

Sociology of Health & Illness (SOC 531) Ellen Idler

Tuesday 9:00 am- 12:00 pm

Tarbutton Hall 206

Course Description:

This course will provide graduate students with a survey of research on the social origins of the health, illness, and health care of individuals and populations. Students will be introduced to the process of formulating important social research questions in health and illness, including attention to major theoretical perspectives, measurement of concepts, the merits of various study designs, and both qualitative and quantitative approaches to data collection and analysis.

There are no assigned texts to order.

Racial & Ethnic HLTH Disparities (SOC 585) - Ali Sewell

Tuesday 1:00-4:00 pm

Tarbutton Hall 206

Course Description:

TBA

Required Texts:

TBA

Big/Small Data & Visualization (SOC 585) Roberto Franzosi

Tuesday/Thursday 4:00-5:15 pm

Anthropology 105

Course description:

The course deals with new tools of data analysis and visualization, especially for text data (Natural Language Processing, NLP). It is a very demanding 4-credits course, fulfilling the writing requirement since it requires extensive weekly writing. The course does NOT require any prerequisites or prior knowledge of computational tools. The only requirement is that students come to the class with a corpus of data as txt formatted files (e,g, newspaper articles, books, blogs, websites) that they wish to analyze.

The course is based on a set of specialized NLP tools, written in Java and Python, designed for the analysis of small/large corpora of text.The tools are all wrapped in Python with a convenient Graphical User Interface (GUI) to make things easy for the non expert.

The course relies on the Stanford parser CoreNLP as the main NLP engine(with the option of running co-reference resolution), but a number of other NLP tools will also be usedto investigate the CoNLL table created by the CoreNLP parser for specific relationships between specific words, verb and noun density, “function” words, and automatic extraction of SVOs (Subject, Verb, Objects). Two specific tools for passive/active verb forms and nominalization allow to focus on the “denial of agency” at the linguistic level. Other tools focus on the sentiment and language concreteness of a text. The two tools of N-grams and word co-occurrences viewers mimic the behavior of Google N-Grams Viewer but with a personal corpus. Topic modeling, via Mallet or Gensim, allows users to find the main topics in a large set of documents. Word2Vec (via Gensim), a vector representation of words, can help capture the semantic regularities of a corpus.

The course also embeds easy tools of data visualization for a variety of Excel-type charts, network graphs, and Geographic Information System (GIS) maps. The course focuses on freeware software, from Gephi to Cytoscape, Palladio, Google Earth Pro, QGIS, Carto, TimeMapper.

 

Advanced Network Analysis (SOC 585) Weihua An

Monday/Wednesday 5:30-6:45 pm

Tarbutton Hall 218

Course Description:

Interest in network analysis has EXPLODED in the past few years, partly due to the latest advancements in statistical modeling and the rapid availability of network data and partly due to the recognition that many analytical problems can be re-cast as a network problem. Aiming to examine social connections and interactions quantitatively, network analysis has become an essential method and tool for studying a variety of issues in social and natural sciences. This course covers the major methods to collect, represent, and analyze network data. Selected topics include centrality analysis, positional analysis, clustering analysis, the exponential random graph model for modeling network formations, the stochastic actor-oriented model for dynamic network analysis, meta network analysis, weighted network analysis, text network analysis, causal analysis of network effects, and social network-based predictions and interventions. Examples are drawn from a wide range of disciplines including business, economics, education, political science, public health, and sociology. Students will learn hands-on skills to conduct their own research by using mainstream network packages in R such as “statnet” and “RSiena”. This course requires a basic knowledge of logistic regression and basic programming skills in R.

Recommended (Not Required) Textbooks:

  1. Wasserman, Stanley and Katherine L. Faust. 1994. Social Network Analysis: Methods and Applications. New York: Cambridge University Press. (ISBN- 978-0521387071)
  2. Lusher, D., Koskinen, J. & Robins, G. 2013. Exponential Random Graph Models for Social Networks: Theory, Methods, and Applications. Cambridge University Press. (ISBN- 978-0521141383)

Directed Study (SOC 597R or SOC 797R)

These offer credit for individualized work with a given faculty member.

Please consult with your advisor and / or Dr. Ellen Idler, our Director of Graduate Studies), about enrollment.

MA Research (SOC 599R) or PhD Research (SOC 799R)

These offer credit for ongoing research overseen by a given faculty member.

Please consult with your advisor and / or Dr. Ellen Idler, our Director of Graduate Studies), about enrollment.

Teaching Assistantships (TATT 605SOC & TATT 610SOC)

These offer credit for participation in assistantships (TATT 605C) and for teaching one's own class (TATT 610SOC).

Read more about these credits here.