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.