Sociology | Statistical Techniques in Sociology I
S554 | 10301-10302 | McManus

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 exposition of 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.  The primary emphasis is on
applied methods.  Students are encouraged to think creatively about
how to use statistical methods in their own research.  Students meet
twice a week for a 75 minute lecture on statistical fundamentals,
theory, applications, and topics.  After each lecture, students
reconvene for a two-hour lab session to work on computing methods
and data analysis techniques, and a third (optional) lab may
occasionally be scheduled for Fridays.  The prerequisite for this
course is at least one statistics course at the level of S250, the
undergraduate course required of Sociology majors.  There are no
mathematical prerequisites.  Students are not expected to have a
background in calculus, but facility with algebra and exposure to
the rudiments of statistics distribution theory and hypothesis
testing is expected.

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 assumptions 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.  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