Sociology | Statistical Techniques in Sociology I
S554 | 3800 | McManus

This is the first semester of the two-course sequence in social
statistics required of graduate students in Sociology. The course
takes a systematic approach to the exposition of the general linear
model for continuous 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 course is organized
into four sections. The first section of the course reviews the
fundamental 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 and introduces the
model in matrix form. At the end of the second section you will have
a good mechanical knowledge of regression analysis. The third section
deals with violations of 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 introduces students to the use of structural
equations models in social science research. The purpose of this
brief section is to give you some exposure to these complex models
for continuous dependent variables rather than to ask you to develop
sophistication with these techniques.

In addition to the regularly scheduled class periods, students are
required to attend lab sessions which focus on computing methods and
data analysis techniques. Students who enroll in this course have
taken at least one statistics course at the level of S250, the
undergraduate course required of Sociology majors. Students are not
expected to have a background in calculus, but facility with algebra
and knowledge of the rudiments of statistical distribution theory and
hypothesis testing is a prerequisite.