Research Methods and Statistics . . . sample syllabus
SR 111. Research Methods and Statistics Fall, 1997
Dr. John B. Gatewood (Instructor) John R. Palaia (A.T.)
10C Price Hall 16 Price Hall
758-3814 / JBG1 758-3810 / JRP3
Overview
This course is an introduction to social science research methods and
to elementary statistics. Methods are explicit ways of gathering
information. Statistics are numerical procedures for manipulating,
describing, and making inferences from existing information. Thus, methods
and statistics are very interrelated subjects (which is why we teach both
subjects in the same four-hour course).
Used in concert, explicit methods and subsequent statistical analyses
distinguish social science propositions from mere opinions. Whereas all of
us have opinions, science requires that propositions be evaluated with
respect to publicly available evidence (data). Further, scientific
propositions must be testable (capable of being shown false) with respect
to explicitly defined and replicable data: "This is what I did, this is
what I found, this is my interpretation of the findings. I'm not asking you
just to believe me -- you can collect your own data and draw your own
conclusions." The point is that simply asserting such-and-such is true will
not convince skeptics. They require supporting evidence and argumentation,
and science is a particular way of arguing knowledge claims.
For the first third of the semester, we will focus on social science
research methods, concluding the section with an hour exam. I've chosen to
cover these topics first because methods for obtaining data temporally
precede data analysis in the daily business of social research. However, we
will devote only a third of the semester to these important matters because
(a) Babbie's book is exceedingly clear and thorough, and (b) methods are
less technical ("easier" to understand) than statistics for most social
science majors.
With the rapid overview of research methods behind us, we will proceed
somewhat more leisurely through Runyon and Haber's elementary statistics
text. Some topics occupy only a single class period, but others -- ones I
feel are especially important and/or difficult -- are scheduled over
several days. All of the statistics covered in this course require only
high school mathematics (i.e., arithmetic and algebra), but do not fall
behind. Like any mathematics (or foreign language) course, statistics
involve cumulative knowledge. So, if you should have trouble understanding
something, come see me and/or the teaching assistant without delay. We're
available, ready, and willing to help. If you work hard and I don't get
diverted, we may actually get through all the statistics I've scheduled!
(If not, then I'll have to adjust the schedule and assignments as the
semester progresses.)
One final thought. Some of you are taking this course because you want
to do social research yourself, whether writing a senior thesis, going on
to graduate school, or working in careers like marketing, personnel, social
services, etc. That goal is consistent with the course objectives, and I
hope the course will meet your needs. Others of you are taking the course
simply because it is required in your major, i.e., you have no desire to
conduct social research yourself. That's okay, too. Your goal for the
course should be learning enough about methods and statistics to understand
published social science literature, as well as news stories. As H.G. Wells
said about century ago, "Statistical thinking will one day be as necessary
for efficient citizenship as the ability to read and write." That day is
nigh upon us.
Materials
There are three required books for the course, two dealing with
research methods and one on elementary statistics. These are available in
the bookstore and should be purchased, underlined, and kept for future
reference.
Babbie, Earl (1995) Research Methods for Social Science, 7th Edition.
Belmont, CA: Wadsworth Publishing.
Weller, Susan C. and A. Kimball Romney (1988) Systematic Data
Collection. Newbury Park, CA: Sage Publications.
Runyon, Richard P., Audrey Haber, David J. Pittenger, and Kay A.
Coleman (1996) Fundamentals of Behavioral Statistics, 8th
Edition. New York: McGraw-Hill.
Requirements
The formal requirements for the course are three hour exams, homework
assignments, a term paper, and class participation. The relative weighting
of these is as follows:
First exam (methods) 100 pts.
Second exam (descriptive statistics) 100 pts.
Third exam (inferential statistics) 150 pts.
Homework * 100 pts.
Research proposal * 100 pts.
Class participation 50 pts.
(* Assignments/Instructions will be distributed separately.)
Your course grade will be determined by how many points you earn
divided by the total possible. A score of 90% or higher is an A, 80% to 89%
is an B, ... below 60% is an F. In short, I do not grade on a "curve" -- I
do not pre-determine the percentages of A, B, C, D, and F's to be given.
Everyone can earn an A or everyone fail; you are not in direct competition
with each other.
Finally, and before anyone has sinned, please pay special attention to
the following "ground rules" for this course:
Rule #1. Helping one another understand general concepts and
procedures is encouraged. But, helping one another with the
specifics of a take-home assignment constitutes academic
dishonesty and will be punished accordingly.
Rule #2. Attendance is required.
Rule #3. Failure to take an exam as scheduled will result in a "zero"
for that assignment.
Rule #4. The penalty for failing to turn in homework assignments or
the term paper when they are due is 10% of the assignment's total
point-value for each 24-hour period (or portion thereof) that the
assignment is late, weekends and holidays included.
Rule #5. Any student having a legitimate reason for missing an exam or
turning in work late must obtain MY approval (John Gatewood's) in
advance, i.e., simply failing to show up, calling the Department
office and leaving a message, or talking with the Teaching
Assistant is not sufficient.
Class Periods by Calendar Days
Monday Tuesday Wednesday Thursday Friday *
--- --- Aug 27 --- Aug 29
Sept 1 --- Sept 3 --- Sept 5
Sept 8 --- Sept 10 --- Sept 12
Sept 15 --- Sept 17 --- Sept 19
Sept 22 --- Sept 24 --- Sept 26
Sept 29 --- Oct 1 --- ---
Oct 6 --- Oct 8 ("Fri") --- Oct 10
Oct 13 --- Oct 15 --- Oct 17
Oct 20 --- Oct 22 --- Oct 24
Oct 27 --- Oct 29 --- Oct 31
Nov 3 --- Nov 5 --- Nov 7
Nov 10 --- Nov 12 --- Nov 14
Nov 17 --- Nov 19 --- Nov 21
Nov 24 --- Nov 26 --- ---
Dec 1 --- Dec 3 --- Dec 5
* Remember that all our "Friday" classes meet for 2 hours.
Schedule of Topics and Assignments
RESEARCH METHODS . . .
1. Aug 27 (W) General Introduction
"Science" as a Way of Knowing
Readings: Babbie, Overview & Chpt. 1 (pp. 1-38)
2. Aug 29 (F) Theory and Research
Readings: Babbie, Chpt. 2 (pp. 39-62)
3. Sept 1 (M) The Nature of Causation
Readings: Babbie, Chpt. 3 (pp. 63-79)
4. Sept 3 (W) Research Design
Readings: Babbie, Chpt. 4 (pp. 80-108)
5. Sept 5 (F) Conceptualization and Measurement
Readings: Babbie, Chpt. 5 (pp. 109-130)
6. Sept 8 (M) Operationalization
Readings: Babbie, Chpt. 6 (pp. 131-159)
7. Sept 10 (W) Indexes, Scales, and Typologies
Readings: Babbie, Chpt. 7 (pp. 160-185)
8. Sept 12 (F) The Logic of Sampling
Readings: Babbie, Chpt. 8 (pp. 186-229)
9. Sept 15 (M) Experiments
Readings: Babbie, Chpt. 9 (pp. 232-254)
10. Sept 17 (W) Survey Research
Readings: Babbie, Chpt. 10 (pp. 255-278)
11. Sept 19 (F) Field Research
Readings: Babbie, Chpt. 11 (pp. 279-304)
12. Sept 22 (M) Unobtrusive Research
Readings: Babbie, Chpt. 12 (pp. 305-336)
13. Sept 24 (W) Evaluation Research
Readings: Babbie, Chpt. 13 (pp. 337-359)
14. Sept 26 (F) ----- FIRST HOUR EXAM -----
STATISTICS . . .
15. Sept 29 (M) Statistical Analysis and Basic Mathematical Concepts
Readings: Runyon, Haber, et al., Chpts. 1 & 2 (pp. 1-62)
16. Oct 1 (W) Frequency Distributions and Percentiles
Readings: Runyon, Haber, et al., Chpt. 3 (pp. 67-98)
17. Oct 6 (M) Graphs and Tables
Readings: Runyon, Haber, et al., Chapt. 6 (pp. 185-212)
18. Oct 8 (W/"F") Measures of Central Tendency
Readings: Runyon, Haber, et al., Chpt. 4 (pp. 107-136)
19. Oct 10 (F) Measures of Dispersion
Standard Normal Distribution
Readings: Runyon, Haber, et al., Chpt. 5 (pp. 143-179)
20. Oct 13 (M) Correlation
Readings: Runyon, Haber, et al., Chpt. 7 (pp. 219-255)
21. Oct 15 (W) Correlation
Readings: Runyon, Haber, et al., Chpt. 7 (pp. 219-255)
22. Oct 17 (F) Regression and Prediction
Readings: Runyon, Haber, et al., Chpt. 8 (pp. 265-301)
23. Oct 20 (M) REVIEW -- Descriptive Statistics for Interval Variables
Readings: Runyon, Haber, et al., re-read Chpts. 4, 5, 7, 8
24. Oct 22 (W) ----- SECOND HOUR EXAM -----
25. Oct 24 (F) Probability
Readings: Runyon, Haber, et al., Chpt. 9 (pp. 307-348)
26. Oct 27 (M) Introduction to Statistical Inference
Readings: Runyon, Haber, et al., Chpt. 10 (pp. 355-391)
27. Oct 29 (W) Statistical Inference: Single Samples
Readings: Runyon, Haber, et al., Chpt. 11 (pp. 395-433)
28. Oct 31 (F) REVIEW -- Statistical Inference for Single Samples
Readings: Runyon, Haber, et al., review Chpts. 10-11
29. Nov 3 (M) Statistical Inference: Two-Sample Case
Readings: Runyon, Haber, et al., Chpt. 12 (pp. 437-476)
30. Nov 5 (W) Statistical Inference: Two-Sample Case (continued)
Readings: Runyon, Haber, et al., Chpt. 12 (pp. 437-476)
31. Nov 7 (F) REVIEW -- Student's t-test
Readings: Runyon, Haber, et al., re-read Chpt. 12
32. Nov 10 (M) Introduction to Analysis of Variance
Readings: Runyon, Haber, et al., Chpt. 13 (pp. 483-528)
33. Nov 12 (W) Introduction to Analysis of Variance (continued)
Readings: Runyon, Haber, et al., Chpt. 13 (pp. 483-528)
34. Nov 14 (F) REVIEW -- Oneway ANOVA
Readings: Runyon, Haber, et al., re-read Chpt. 13
35. Nov 17 (M) Statistical Inference: Categorical Variables
Readings: Runyon, Haber, et al., Chpt. 15 (pp. 575-601)
36. Nov 19 (W) Statistical Inference: Categorical Variables
Readings: Runyon, Haber, et al., Chpt. 15 (pp. 575-601)
37. Nov 21 (F) ----- THIRD HOUR EXAM -----
38. Nov 24 (M) Statistical Inference: Ordinally Scaled Variables
Readings: Runyon, Haber, et al., Chpt. 16 (pp. 605-616)
39. Nov 26 (W) Statistical Inference: Ordinally Scaled Variables
Readings: Runyon, Haber, et al., Chpt. 16 (pp. 605-616)
THE "BIG PICTURE" . . .
40. Dec 1 (M) The Ethics and Politics of Social Research
Readings: Babbie, Chpt. 18 (pp. 445-466)
41. Dec 3 (W) The Uses of Social Research
Readings: Babbie, Chpt. 19 (pp. 467-476)
42. Dec 5 (F) Course Summary & Student Evaluations