Research

Articles:

Dodson, Kyle. (Forthcoming) “Economic Threat and Protest Behavior in Comparative Perspective.” Sociological Perspectives. 

Dodson, Kyle. (Forthcoming) “Gendered Activism: A Cross-National View on Gender Differences in Protest Activity.” Social Currents. 

Dodson, Kyle. (2015) “Globalization and Protest Expansion.” Social Problems 62: 15-39.

Dodson, Kyle. (2011) “The Movement Society in Comparative Perspective.” Mobilization 16: 475-494.

Dodson, Kyle. (2010) “The Return of the American Voter?  Party Polarization and Voting Behavior, 1988 to 2004.” Sociological Perspectives. 53: 443-449.

Brooks, Clem, Kyle Dodson, and Nikole Hotchkiss. (2010) “National Security Issues and U.S. Presidential Elections, 1992-2008.” Social Science Research 39: 518-526.

Frisco, Michelle, Chandra Muller and Kyle Dodson. (2004). “Participation in Youth-Serving Associations and Early Adult Voting Behavior.” Social Science Quarterly 85: 660-676.

Book Chapters:

Dodson, Kyle. (2014) “The Effect of College on Social and Political Attitudes and Civic Participation.” In Professors and Their Politics: The View from Social Science, edited by Neil Gross and Solon Simmons. Baltimore, MD: Johns Hopkins University Press.

Teaching

Introduction to SociologySyllabus

Description: The “sociological imagination” provides a useful framework for exploring the causes and consequences of social behavior.   During the semester, we will take this framework and apply it to a variety of topics, including racial inequality, education, and social change.  In so doing, you will become acquainted with the terms and concepts that are central to sociology as well as some of the unique insights provided by the discipline.  Ultimately, I hope this class equips you with some of the tools that sociologists use in understanding our society.

Undergraduate StatisticsSyllabus

Description: This course introduces statistical techniques appropriate for answering social science questions. We will cover both descriptive and inferential statistics. Descriptive statistics describe or summarize sets of numbers. Inferential statistics use sample data to make estimates about the wider population of interest (for example, using surveys to find out which candidate will win an election, whether or not voters will recall a governor, or what’s the most popular TV show in America). This course will cover statistics that describe a single variable (e.g. what is the average income of Americans?) as well as statistics that describe relationships between multiple variables (e.g. what is the difference in income between men and women?).

Research MethodsSyllabus

Description: This is a research methods course required for undergraduate majors in Sociology. It provides a broad introduction to the sociological research process, focusing on how a variety of methods can be used to study sociological phenomena. It is, essentially, a course on how to do sociology. We will address both general issues in research design, such as measurement and sampling, as well as various data collection techniques and approaches, including ethnography, interviewing, content analysis, and survey research. We will discuss the advantages and disadvantages of various research methods and how sociologists choose the most appropriate method for their research. These discussions will provide information regarding how to conduct a study and a basis for informed evaluation of other researchers’ claims.

Graduate Statistics I: Linear RegressionSyllabus

Description: This is the first semester of the two-course sequence in social statistics required of graduate students in Sociology. This course takes a systematic approach to the general linear model for continuous dependent variables; the second semester course covers nonlinear regression models for categorical and limited dependent variables. In addition to laying the theoretical foundations for future social science research, this course introduces students to the use of computerized statistical analysis using the software program Stata. Students are encouraged to think creatively about how to use statistical methods in their own research.

 The course is organized into four sections. The first section of the course covers the fundamental mathematical and statistical concepts that are the building blocks for regression analysis. The purpose of this section is both to refresh your memory and to provide a deeper, more formal presentation of familiar concepts. The second section focuses on the assumption and mechanics of the classical linear regression model. At the end of the second section you will have a good mechanical knowledge of regression analysis. The third section includes a practical exposition of the general linear model as we begin to relax the assumptions of the classical linear regression model. At the end of the third section you will have a deeper theoretical and applied understanding of the flexibility and limitations of the general linear regression model for social science data. The final section presents an overview of topics in estimation for common problems in social science research, including an introduction to structural equation models. The purpose of this brief section is to give you some exposure to more complex models for continuous dependent variables rather than to ask you to develop sophistication with these techniques.