Soc. 920:502, Sociology of Research Methods
Patricia A. Roos
Rm. A342, Lucy Stone Hall
Phone: (732) 4455848
Email: roos@rutgers.edu
Office Hours: Thursdays 12:301:30 p.m. (or by appointment)
It's an experience like no other experience I can describe, the best
thing that can happen to a scientist, realizing that something that's happened
in his or her mind exactly corresponds to something that happens in nature.
It's startling every time it occurs. One is surprised that a construct of
one's own mind can actually be realized in the honesttogoodness world
out there. A great shock, and a great, great joy. Leo Kadanoff, Chaos
I. Goals: This is a course on doing
theoretically informed quantitative social research. The emphasis is on
data analysis, interpreting, presenting, and writing up the analytic results. This course is not a statistics course per se, although I will review a number of statistical techniques, including tabular
analysis; regression analysis in its various forms, including dummy variable
regression analysis, and decomposition of Rsquare into component parts; factor
analysis (for scale construction); logistic regression; and multinomial logit.
A graduatelevel statistics course through multiple regression is a prerequisite. Soc. 502 satisfies the department's "second methods" requirement.
I'm trying something new this year. In the past I've spent quite a bit of time going over the various statistical techniques. This year, I plan to reduce the presentation of techniques by about half, and spend more time in a "quantitative writing seminar" going over your developing work. Here is the basic idea: I'll do some initial presentation/review of the "statistical technique of the day" for the "lecture" portion of the class, and then in the "seminar" portion of the class we'll turn to commenting and discussing your written work/programs/output using the technique we discussed in the previous class. This will provide you the opportunity to practice what you've learned and to learn from each other, all the while honing your writing and analytic skills.
I assume that you already have a semideveloped paper (talk with me if you don't). At the minimum you need a data set up and running by week 1. What you get out of this course depends entirely on what you put into it. By the end of the semester, you should be able to successfully transform a class paper into a qualifying paper, a qualifying paper into a publishable paper, or a quantitative paper into a chapter of your dissertation. In that sense, this class is like the writing seminar.
II. Books: There is one required book for the course (available at the Livingston bookstore):
Jane E. Miller. 2005. The Chicago Guide to Writing about Multivariate Analysis. Chicago: University of Chicago Press.
This book provides many important "tools of the trade" for those who want to think, write, and speak about quantitative data. You'll want to read it through once and then actively use it when writing, especially when you get stuck on how to present or phrase your writing.
I will assume that you have a statistics text to which you can refer. If you don't, I'd recommend Agresti and Finlay, which is more accessible to social scientists than many statistics books:
Agresti, Alan, and Barbara Finlay. 1997. Statistical Methods for the Social Sciences. Third edition. New Jersey: Prentice Hall.
Re: SAS or SPSS or STATA: I use SAS as my statistical package. If you are already familiar with SPSS or STATA feel free to use either (although my help in deciphering your computing errors will be somewhat more limited). Each of you will have access to Sociology's Computing Lab, which has relevant documentation for whichever package program you use. If you don't have a userid, contact Shan Harewood at the Lab, and let him know you're in my class. If you want to order SAS, SPSS, or STATA for your home computer, see Shan as well. The department has a site license for all three packages, which makes them relatively inexpensive.
There is little required reading for this course because most of your reading will be on your paper topic. I do, however, provide a bibliography (see Sakai, under "Readings/Slides") of readings for the course, which I'll continue to update as I find new references. I include these so that you will have background reading on the techniques we discuss, and examples of how they are used in practice. A number of the illustrative articles are written by our own faculty. I'd advise you to read a number of applications of the technique(s) you use. The emphasis will be on learning by doing. This means lots of computer work and lots of writing up results.
Other articles noted on the syllabus are available through Sakai.
Choosing a data set: In choosing a data set, feel free to use your own data (either data you collected, or have access to from other sources, or ICPSR data, or whatever). The only criterion for using your own data is that their quality must be sufficient to meet the requirements of the multivariate techniques we use. Check with me if you have any questions. If you do not have your own data, you can always use the General Social Survey (available from 1972 to 2006). Check out the GSS website.
III. Course Requirements: The core of the course is a set of six assignments (see Sakai), worth 60 percent of the grade. Each of the assignments, due every other week, are steps to your final paper (see attached calendar). You must post each assignment to Sakai the day it is due (Ass. 4 and Ass. 5 should be posted by 12 noon the day before they are due). We will then review these in "seminar" the following week (or for Ass. 4 and 5, the day they are due). Feel free to post other things to Sakai, including output, questions about analyses, questions re wording, the best way to present data, etc. As long as these are posted by Wed. 12 p.m. the day before Thursday's class, we'll try to get to them in "seminar" that week. If not, we'll do it the following week.
The final paper (on a topic of your choice) will constitute 40 percent of your grade. In this paper you'll carry out a quantitative analysis of some substantive issue using the technical and analytical skills you're working on in the course. The final paper proposal is due March 13th (as Assignment 4), the literature review is due April 3rd (as Assignment 5), and the final paper is due May 5th. I don't like incompletes, and you shouldn't either, so plan to get the paper (and those assignments) in on time!
I expect you to support each other as well. Please come with comments on each other's work and/or responses to people's questions/suggestions as posted on Sakai (we'll discuss these in "seminar").
IV. Miscellaneous:
1) It is your responsibility to determine if you need to undergo IRB review. Read through the IRB annual memo to understand the rules and to find the appropriate forms if you do need IRB review.
2) All assignments and the final paper must be typed. Use Word or Excel to prepare tables.
3) We have only 14 meetings, three of which are given over to student presentations. Attendance and participation are critical. The norm for graduate courses is: thou shalt not miss class! If you do, you'd better have an excellent excuse, and let me know ahead of time.
4) For Assignment 4 you will write a brief proposal of your final paper, and present the proposal to the class on March 13th. These should be posted to Sakai by 12 noon the day before (March 12th). Similarly, for Assignment 5 you will write a literature review for your final paper topic, due April 3rd (posted to Sakai by 12 noon April 2nd. During the last two weeks of class you will present your final paper to the class. As with the proposal and literature review, you will post your preliminary tables and writing to Sakai by 12 noon the day before you present. The class discussion will focus on comments and suggestions for revisions.
V. Course Outline (see attached tentative schedule):
Week 1 (January 24): Lecture: Crosstabs, computer info/Seminar: Discussion of readings
Week 2 (January 31): Lecture: Simple regression and correlation/Seminar: crosstabs or regression questions
Week 3 (February 7): Lecture: Multiple regression and correlation/Seminar: Assignment 1 discussion (crosstabs)
Week 4 (February 14): Lecture: Dummy variable regression I/Seminar: Multiple regression output and tables/ Example of multivariate analysis (Gatta and Roos, 2005)
Week 5 (February 21): Lecture: Dummy variable regression II/Seminar: Assignment 2 discussion (regression)
Week 6 (February 28): Lecture: Related topics in multivariate analysis/Seminar: Review of electronic literature searches/Dummy variable output and tables
Week 7 (March 6): Lecture: Decomposition of means/Seminar: Assignment 3 discussion (dummy variables)
Week 8 (March 13): Proposal presentations (Assignment 4 discussion)
Spring Break: No class March 20th!
Week 9 (March 27): Lecture: Factor analysis for scale creation/Seminar: Factor analysis output and questions
Week 10 (April 3): Lecture: Logistic regression I/Seminar: Assignment 5 discussion
Week 11 (April 10: Lecture: Logistic regression II/Seminar: Logistic regression output and questions
Week 12 (April 17): Lecture: Multinomial logit/Seminar: Multinomial logit output and questions
Week 13 (April 24): Final paper presentations
Week 14 (May 1): Final paper presentations
FINAL PAPERS DUE: Monday, May 5th
Week

Readings

Assignments 
Week 1 (January 24) 
Babbie, Notes on Percentaging (Sakai) 

Week 2 (January 31)
Simple regression and correlation 
Miller, Chs. 58, 13, Appendix A

Ass. 1: Crosstabulation

Week 3 (February 7)
Multiple regression and correlation 
Miller, Chs. 910


Week 4 (February 14) 
Miller, Ch. 14 (skim Chs. 1516) 
Ass. 2: Regression and correlation

Week 5 (February 21)
Dummy variable regression II 
Miller, Appendix C


Week 6 (February 28)
Related topics in multivariate analysis 

Ass. 3: Dummy variables

Week 7 (March 6)
Decomposition of means 


Week 8 (March 13)
Proposal presentations 
Miller, Chs. 1112

Ass. 4: Proposal

Week 9 (March 27)
Factor analysis for scale creation 


Week 10 (April 3)
Logistic regression I 
Miller, Appendix B and C

Ass. 5: Literature review

Week 11 (April 10)
Logistic regression II 


Week 12 (April 17)
Multinomial logit 

Ass. 6: Factor analysis, decomposition, or logistic regression, or 
Week 13 (April 24)
Final paper presentations 

Ass. 6: multinomial logit or other advanced technique

Week 14 (May 1)
Final paper presentations 


Monday, May 5


Final paper due

VI. Research and Writing Citations (for your writing
pleasure):
Alford, Robert T. 1998. The Craft of Inquiry: Theories, Methods, Evidence. New York: Oxford University Press.
Becker, Howard S. 1998. Tricks of the Trade: How to Think About Your Research While You're Doing It. Chicago: University of Chicago Press.
Becker, Howard S. 1986. Writing for Social Scientists: How to Start and Finish Your Thesis, Book, or Article. Chicago: University of Chicago Press.
Lee Clarke, Lee. "Notes on Proposing" and "On Writing and Criticism" [Sakai]
Germano, William. 2005. "Passive is Spoken Here." Chronicle of Higher Education, April 22, 2005. [Sakai]
Jasper, James. "Why So Many Academics are Lousy Writers" [Sakai]
Miller, Jane E. 2005. The Chicago Guide to Writing About Multivariate Analysis. Chicago: University of Chicago Press.
Peters, Mark. "Like a Bowl in a China Shop." Chronicle of Higher Education, August 9, 2006. [Sakai]
Rosenfield, Sarah. "Some Things To Think About While Reading Papers" [Sakai]
Strunk, William Jr., and E.B. White. 2000. The Elements of Style, Fourth Edition. New York: Allyn & Bacon.
American Sociological Association, "Writing an Informative Abstract" [Sakai]
And, for some humor: "How to Write Good" [Sakai]