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.