This course will instruct students in file management and the statistical techniques used for the analysis of types of dataset typically used by sociologists. Students will further develop their skills in computer programming, file handling, data transformation, index creation, and multivariate statistics. Each student will undertake an individual project and will work on every aspect of the research endeavor from identifying a topic for investigation to writing and presenting a final report. The goal of the individual project is for the student to use quantitative research methodologies to develop the core of a publishable paper. For this course, each student will use Statistical Package for Social Science (SPSS) for Windows to analyze her/his choice of either the National Education Longitudinal Study (NELS) or the General Social Survey (GSS).

Prerequisite: Sociological Statistics I at the graduate level (or its equivalent)

Recommended Texts:

From time to time, I’m asked if there is a ‘good’ textbook for this course. Because we cover such a wide range of material, quite frankly, I haven’t found one. If you happen to have any suggestions, please let me know. I can endorse, however, the Sage Publication series. For just about ever topic we’ll cover, there is a Sage publication or monograph. They are typically inexpensive, very focused, and relatively easy to read.

Norusis, M. 1995. The SPSS 6.1 Guide to Data Analysis. Englewood Cliffs, NJ: Prentice Hall. I only suggest this text if you are absolutely clueless about SPSS for Windows.

Lewis-Beck, M. 1990. Applied Regression: An Introduction. CA: Sage Publications.
Berry, W. and S. Feldman. 1985. Multiple Regression in Practice. CA: Sage Publications.
Both of these are good texts to familiarize you with regression.

Other suggested readings on specific topics will be shared during class.

Topics:

Because students enter this course with a wide range of previous statistical experiences and interests, I offer a buffet of statistical procedures. As with any buffet, there are always more types of food than any one restaurant could handle, you don’t have to consume everything; and if the buffet is good and plentiful, you could never digest it all anyway. However, I at least want you to have some idea of what’s available. A brief outline of topics to be discussed in this course is listed below. Note however, this is not the order in which topics will be discussed; nor is this list exhaustive.

NELS & GSS	OLS & Logistic regression
SPSS	Stepwise regression
File creation & merging	Path Analysis
Factor analysis	Log Linear Analysis
Recoding variables	Hierarchical Linear Modeling
Creating flags and weights	Complex Interaction Effects
Creating aggregate variables	What to do with Missing cases
Creating composites	Geographical Information Systems
Creating dummy variables	Network Analysis
Reliability and validity in data transformations and subsequent analysis	Social Science Research and the Information Superhighway