Sociology | Statistical Techniques in Sociology I
S554 | 20329-20330 | 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 to
a two hour lab session to work on computing methods and data analysis
techniques, and a third (optional) lab may occasionally be scheduled
on Fridays.  The prerequisite for this course is at least one
statistics course at the level of S250, the undergraduate course
required for 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
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 techniques.