University of Oregon, Fall 2005
Psychology 302: STATISTICAL METHODS
221 McKenzie Hall, MW 14:00-15:20 (2-3:20 pm)
Professor: Gerard Saucier, Ph.D.
Office: 312 Straub
E-mail: gsaucier@darkwing.uoregon.edu Phone: 346-4927 with voice mail
Office Hours: Mondays 10 am till noon, Weds. 3:40-4:40 pm, or by appointment
Teaching Assistants: Bridget Klest – 353 Straub, 346-4966, office hours Fridays 10 am - noon
Jessica Tipsord – 398 Straub, 346-4947, office hours Tuesdays 3-4 pm and Thursdays 12-1 pm
Text: Gravetter, F. J., & Wallnau, L. B. (2005). Essentials of statistics for the behavioral sciences.
Belmont, CA: Thomson/Wadsworth.
Course web page: http://darkwing.uoregon.edu/~gsaucier/Psych_302_fall2005.htm
Course Objectives (or, what's the purpose of this course?)
Welcome to Psychology 302. Statistical methods are a crucial part of research in many sciences,
including psychology. Statistical analyses help scientists discern patterns in phenomena, and determine
the relative generalizability of these patterns. Everyday people increasingly use statistics for the same
ends. And statistics is an important conceptual structure for thinking in a rational and scientific way
about phenomena. Statistics does much to help people make good sense of the world. This course is
designed to help you gain the following:
1. The ability to understand and explain to others the statistical analyses and statistical concepts in
reports of social and behavioral science research.
2. Preparation for learning about research methods, and about more advanced statistical methods.
3. The ability to identify the appropriate statistical procedure for many basic research situations and to
carry out the necessary computations, by hand (for simple computations) or by computer (for
more complex ones).
4. Further development of your quantitative and analytic thinking skills and reasoning ability.
The ability to reason in a logical manner is more important to successful understanding of
psychological statistics than is the ability to manipulate complex equations. Inescapably (!), the course
involves numbers, but if you have basic arithmetic and algebra skills, the mathematical part of the
course is straightforward. This course concerns important statistics and how to calculate them, but just
as much (maybe more!) about conceptual approaches for thinking about observations (data).
What Methods Are Used for Learning?
1. Reading the assigned material. That includes following the numeric examples closely and writing
down questions about anything not entirely clear. You are expected to read the text, in full. In
this course, the first reading assignments are long, but their pace slows down especially in the
last part of the course when the material becomes more advanced.
2. Completing the assigned homework practice problems (and turning them in on time). Statistics
involves learned SKILLS, so it necessary to do statistics, not just read and understand.
3. Attending the class sessions, listening closely, asking questions -- be sure to have done the reading
first. Do not fall behind!
4. Studying for, taking, and reviewing answers for quizzes.
5. Attending your lab section. Be sure to bring questions from the reading with you. This is a great
chance to get real help with what is not completely clear and to pursue deeply whatever has
excited you (yes, there can be exciting things in statistics!). Lab sections will also be the place
to develop some computer data-analysis experience.
The class format is mainly prepared presentations (i.e., lecture) with response to questions, but there
will be some in-class exercises and student participation in work teams. Whereas lab sessions are
especially oriented toward homework problems that emphasize calculations, class sessions have more
emphasis on a conceptual comprehension of statistical methods and are more directly oriented toward
the content of quizzes. This is an important difference in emphasis (on two complementary aspects of
the course), but there is crossover between lab and class.
Summary of Basis for Evaluation
Your final course grade is based on the following components:
40% Score on the homework assignments (problem sets)
Note: 35% based on average score on problem sets, 5% is bonus for turning all of them in
10% First midterm-quiz score
15% Second midterm-quiz score
25% Score on the final quiz (exam)
5% Sufficient participation in in-class exercises (groups and EFOs, described below)
5% Responses to reading (you need to turn in three, including at least one before midterm 2)
This final percentage is then converted into a grade. A range is 90% to 100%, B range is 80% to
90%, C range 70% to 80%, D range 60% to 70%, with '+' and '-' being assigned if the percentage is
within the top or bottom 1/3, respectively, of each of these ranges. F is 59.99% or lower (there is, of
course, no F+ or F-). Note that there is no grade for class attendance per se, but if you miss most of the
class sessions that obstructs your "sufficient participation" credit.
Lectures and Laboratories
At the end of the syllabus is a list of lecture topics and reading assignments. Please read the relevant
section of the text before the lecture to which it corresponds. Note also that lecture notes will be
available on the Blackboard web site (see below) by 10 am, 4 hours prior to each class. To avoid
copying down the content on class slides, you can bring these notes to class. In addition to attending
lectures, you must also enroll in and attend one of the 4 weekly statistical laboratories run by the class
TAs. The labs will be held in 180 Straub, the Psychology Department''s computer lab (open 8am-9pm
Monday through Thursday, and 8-5 Friday). The labs will provide an opportunity to gain hands-on
computing experience relevant to concepts discussed in lectures. The statistical software for this course
is a recent version of SPSS for Windows. It is installed on the computers in 180 Straub. The labs will
also involve discussion of the weekly problem sets, going over quizzes, as well as allowing you the
chance to raise any questions you have concerning lectures or the textbook. Labs begin in Week 1 with
an introduction to the SPSS computer package. The follow-up course to 302 is 303 (Research Methods
in Psychology), and computer stats are usually a major part of 303. They are also, of course, useful in
the world of work beyond the University.
Components of Your Performance in Psychology 302
In order to give ongoing performance feedback, and help students keep focus on the important
subject matter of this course (a prerequisite to upper division courses in psychology), the course does
have quizzes. The quizzes include two midterm quizzes and a final quiz (i.e., exam). These quizzes
consist of a combination of "problem" items, multiple-choice, fill-in-the-blank, and mini-essay items.
Compared to homework, quizzes place far more emphasis on conceptual understanding and less on
calculations. The midterms will begin approximately 15 minutes into the class session (i.e., at 2:15
pm) on the day scheduled for each quiz; the first 15 minutes of these midterm class-sessions will be
devoted to presentation or review of material that will make up part of the quiz, so it makes sense to
come to class on time that day (like every day). If you must miss a quiz, talk to the instructor, as it may
be possible (e.g., with a signed medical excuse) to arrange a make-up quiz (different version than the
one given earlier in class) on the first day of the final exam period; there will be no make-up quizzes
prior to the final-exam period.
All quizzes are cumulative, but all have an emphasis on more recent material, and are closed-book.
Because comprehension rather than memorization is the goal, we will provide a list of mathematical
formulas; your job will be to know what formula is relevant to a particular problem and how to use it
correctly. It will be helpful to have a calculator for the quizzes but to receive credit for calculation
problems you will need to show each step of your calculations; do not rely on advanced calculators that
directly compute complex formulas. Individuals may submit written challenges to their quiz grade
immediately after quizzes are administered. Grades will be adjusted only if the challenge is successful
and ONLY for the individual that submitted the challenge.
Sufficient participation credit is gained from in-class exercises, which are of two major kinds. First,
we will have in-class groups to carry out learning-focused exercises during class sessions. These
groups will often be responsible for producing a written product/report when they meet, and your credit
for "sufficient participation" will be based on how often you are around to sign these products/reports,
and on your being reasonably cooperative with other group members. EFO (early feedback
opportunity) exercises are essentially "one-minute tests" designed not so much to evaluate your
performance as to enable you to check how well you understand key course material, providing
valuable performance feedback. Credit is based not at all on whether you got the right answer, but only
on whether you put in effort to see how well you could do.
Three responses to readings are due overall. They can be submitted prior to any class session except
those with quizzes or exams or those with no new reading assignment (i.e., Sept. 26, Oct. 24, Nov. 23,
Nov. 30). At least one must be submitted by November 2, the other two can be at any point in the term.
You are assigned to send by e-mail to the instructor (with a cc to your lab instructor) by 10 am (four
hours before class) a response to the assigned chapter(s) for that day. To get credit, responses must be
on time and do one of the following: (1) state one specific question you would like answer, or (2)
describe one topic or specific point about which you are confused and would like to get some
clarification, or (3) give a summary of what you think are the three important points in the reading for
that session (use this option if you can't think of a question or unclear point. Refer to specific page
numbers. Keep responses short, no longer than 1 page if it were printed. To get credit, an RTR cannot
be late (after 10 am on day of assignment)! Example of a good specific RTR question: "On page 357,
it says that the Scheffe test is extremely cautious and safe. Does this mean it is better than the Tukey
test on page 356? If not, how do we choose?" Example of a vague non-question "I don't understand
chapter 13." Always specify WHAT you don't understand. Note: Questions about reading material are
welcome any time, by any communication medium, not just via the RTR assignment for credit.
Statistics is a skill and not a spectator sport -- you must do it to learn it, you must get in the pool to
learn to swim. To help you get yourself into the pool, homework assignments – take-home problem
sets -- will be assigned most weeks. Assignments will be due on Fridays at 3 pm (every Friday except
the day after Thanksgiving). You can hand the assignments in at the Psychology Office (131 Straub
Hall) or in-person (only) to your lab instructor. Be sure to put your name and your lab instructor's
name on it when handing in. The problem sets will be graded on a 10 point scale (0-10); one of them
(that due Dec. 2) will be unusually long and count double. Late problem sets, get ½ of their points if
turned in by Monday 3 pm, but after Monday 3 pm no points can be obtained (we don't grade them,
though we will check them). However, all hope (and credit) is not lost if you fail to get a problem set
in on time – you are required to turn in all homework assignments by the last class session in order to
get bonus credit (5% of the course grade) and even problem sets that were very late and therefore got
no grade count toward this bonus credit.
If you have difficulties with the problems, please consult with the TAs or with the instructor.
Collaborative learning is encouraged: If you want to discuss the problems with other students, feel free
to do so. Homework helps you learn skills by practicing. Talking over the problems and reworking
them when you discover that others got different answers promotes deeper understanding of concepts
and gives you more practice in applying skills. However, each student must submit separate
homework, and you must show your work (no photocopies or word-for-word copying). In other words,
the answers you turn in should be written independently.
You are strongly encouraged to use a calculator for doing your assignments. You are permitted to
use a calculator during tests, though one is not required. A simple calculator that adds, subtracts,
divides, multiplies, and takes square roots should be of great help. Since you must show your work on
all assignments and quizzes (and too fancy a calculator might prevent your doing this), calculators that
also do statistical calculations are not of real help. No pressure to spend a lot of money: less than $10
should do. Solar calculators are environmentally friendly.
Academic Integrity
This instructor takes academic integrity seriously. Insuring the "validity" of grades requires seeing
that they reflect honest work and learning rather than cheating. Cheating is defined as providing or
accepting information on an quiz or exam, plagiarism or copying anyone's written work. Students
caught cheating will be given an "F" for the course, and UO's student conduct coordinator will be
informed. This instructor does have a record of failing students for cheating. The instructor retains the
right to assign seats for tests, to change individual's seating for test security purposes, to require and
check ID for admission to tests.
Top Five Suggestions for Doing Well in This Course
1. Be an active learner, keep a pen/pencil moving, don't become passive, keep trying things...
2. Don't rely on cramming in a stats course, where gradually developed skills are so important
3. Ask for help if you get stuck (as everyone does at some point)
4. Work hard even in the early part of the course – that material is a necessary foundation...
5. Find something to you interesting or fun in statistics (find a bit of intrinsic motivation)
PSYCHOLOGY 302 SCHEDULE: What's Happening When
(Note: the dates on this course outline are subject to change)
Date and Topic and Text Reading
Sept. 26 *
Introduction to course
Sept. 28
Populations and samples; frequency distributions
chs. 1 & 2
Oct. 3
Central tendency and variability
chs. 3 & 4
Oct. 5
Standardized distributions and z-scores
ch. 5
Oct. 10
Probability
ch. 6
Oct. 12 *
Midterm 1 (focuses on chs. 1-5)
Oct. 17
The distribution of sample means
ch. 7
Oct. 19
Hypothesis testing, error, effect size, and power
ch. 8
Oct. 24 *
Hypothesis testing, error, effect size, and power
Oct. 26
The t statistic (as compared to z statistic)
ch. 9
Oct. 31
The t test for two independent samples
ch. 10
Nov. 2
The t test for two related (paired) samples
ch. 11
Nov. 7 *
Midterm 2 (cumulative, but focuses on chs. 6-10)
Nov. 9
Estimation and confidence intervals
ch. 12
Nov. 14
Analysis of variance (ANOVA): simple (one-
way)
ch. 13
Nov. 16
ANOVA: two-factor and repeated measures
ch. 14
Nov. 21
Correlation and regression; correlation as effect
size
ch. 15
Nov. 23 *
Correlation and regression; correlation as effect
size
Nov. 28
Chi-square tests; phi coefficient as correlation
ch. 16
Nov. 30 *
Chi-square tests; phi coefficient; integration
Dec. 5,
3:15 pm *
Final quiz/exam (cumulative, but focuses on chs.
11-16)
* - One of the days for which you cannot submit an RTR (because there's no reading assigned!)