SOCI 730: Analytic Techniques of Social Research

SOCI 730-001: Analytic Techniques Soci Rsrch
(Spring 2019)

07:20 PM to 10:00 PM R

Innovation Hall 317

Section Information for Spring 2019

This is a graduate level seminar on analytical techniques for sociological research.   The catalog description reads:

Introduces multiple regression and causal analysis to sociological researchers, with a focus on obtaining and disseminating results. Moves from linear regression to the general linear model with several variables, its extensions, assumptions, and regression diagnostics. Examines the use of dummy variables and the analysis of interaction effects. Considers systems of equations and nonlinear outcomes.

The emphasis will be on quantitative techniques, though at times during the semester we may spend some time looking at techniques for analyzing qualitative data, particularly those that use the tools of computational social science and network analysis.  The two approaches, qualitative and quantitative, are equally valuable.  At times one approach or the other will lend itself better to a specific research question, but in general I see the two as complementary, sharing considerable common ground.  The most important element of this common ground is an emphasis on technique.  Good qualitative researchers don’t simply wander around watching people or casually skimming through online or archival materials.  And, good quantitative researchers don’t simply troll through a sea of numbers fishing for a significant relationship.  In both approaches there is a method to the madness.   My overall goal for the semester is to sharpen our appreciation for the techniques employed in a quantitative approach.  More specifically, students should get hands on experience in the application of common quantitative modeling techniques and become familiar with new developments in quantitative modeling.  The hands-on lab component of the course focuses on running statistical procedures, as well as the management and manipulation of data using SPSS and other packages.

Throughout the course I will emphasize the basics of inferential statistics, i.e., those statistics we use to determine if the relationship we observe in a sample is unlikely to occur, if there were no relationship in the population from which the sample was drawn.   Even for those with a strong statistics background, I expect this review will be of value, as we will emphasize the logic of hypothesis testing and not simply the arithmetic that leads us to reject or fail to reject a particular null hypothesis.

Course Information from the University Catalog

Credits: 3

Introduces multiple regression and causal analysis to sociological researchers, with a focus on obtaining and disseminating results. Moves from linear regression to the general linear model with several variables, its extensions, assumptions, and regression diagnostics. Examines the use of dummy variable and the analysis of interaction effects. Considers systems of equations and nonlinear outcomes. May not be repeated for credit.
Recommended Prerequisite: Undergraduate statistics and research methodology, or permission of instructor.
Registration Restrictions:

Enrollment is limited to Graduate or Non-Degree level students.

Students in a Non-Degree Undergraduate degree may not enroll.

Schedule Type: Lecture
Grading:
This course is graded on the Graduate Regular scale.

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