Psych. 210: Statistics in the Behavioral Sciences
Fall ’09 3 cr Sections
3 & 4 HYL 102 [5-28-09]
TIME/PLACE: Sect
3 Tu, Th 10-11:15 Sect 4 Tu,
Th 1-2:15
CRN
11258 CRN
11259
INSTRUCTOR: Dr.
Tom Hogan, Professor of Psychology
Office:
AMH 223, Tel: Office 941-4268
e-mail:
Thomas.Hogan@Scranton.edu
Office
hours: Tu, Th 2:30-3:30 PM, Wed. 10:00-11:00 AM
Other
times by arrangement.
REQUIRED
MATERIALS:
Text:
Minium, E.W., Clarke, R. C., & Coladarci, T.
(1999) Elements of statistical
reasoning.
Calculator: Get a good calculator at the
University bookstore or any other store.
You will also need a computer disk for computer analyses.
EVALUATION
PROCEDURES:
There
will be six exams, each equally weighted in determining 85% of the final
grade. Each exam will be a combination
of multiple-choice questions, problems to be worked, and short essays. The approximate schedule for the exams is
given in the course schedule below. The fifth
and sixth exams will be given on the date specified for the final exam. The sixth exam is a “competency test” on
SPSS. Make-up exams will be given only
in documented emergency cases; the nature of the make-up exam is at the
discretion of the instructor.
Basis for final grade: Six exams 85% Homework 15%
ASSIGNMENTS:
"Homework"
assignments will be given in almost every class (25 total). These are
designed to reinforce material covered in class and are reasonable
approximations of problems that will be presented in exams. Assignments missed, turned in late, or
completed unsatisfactorily result in a one-point reduction in the homework grade.
Copying homework will be considered
academic dishonesty by both the student copying and the student supplying the
material. Generally, assignments turned
in at one class will be returned at the next class and corrections will be
discussed in class. The homework must be
completed before it is discussed in class.
ACADEMIC
HONESTY:
See
the University's policy on academic honesty.
A student found cheating or engaging in another form of academic
dishonesty will be given an F for the assignment.
REVISIONS AND ANNOUNCEMENTS:
The syllabus is subject to revision. Any
revisions will be announced in class.
CLASS
ATTENDANCE, PROCEDURES, SUGGESTIONS FOR SUCCESS
Most
of what you need to learn is in the textbook.
However, class attendance is expected and, except for quantitative
geniuses, is normally essential for learning the material. You are responsible
for knowing all announcements made in class, including those related to
any changes in the attached schedule.
Classes
begin and end promptly. Suitable attire
and civil behavior are expected in class.
Turn
off cell phones, pagers, etc. No flash photography.
Following
are basic rules for getting along with the material to be covered:
1. Study the darn stuff. Students sometimes overlook this seemingly
self-evident point. Normally you will
need to study 2-3 hours outside of class for every hour in class.
2. Work the problems at the end of each
chapter. It is very easy to deceive
yourself into thinking you know the material by just reading about it -- it all
seems so simple -- but you don't really know it until you work problems.
3. Do all homework assignments. Work additional problems in the
"Problems" on your own. This
will help things sink in.
4. Isn't all this stuff done by computer
these days? Some of it is, but you won't
know what to ask the computer to do if you don't understand the basic concepts
of statistics. We'll concentrate on
these basic concepts. However, you will
also learn how to apply the procedures using SPSS/PC.
5. Find examples of statistics outside
the text, e.g., in journals in your field and in popular media.
6. Become accustomed to speaking and writing
in appropriate statistical jargon. (This
is part of "eloquentia perfecta.")
7. Don't slide. With few exceptions, each topic and each
class builds on previous topics and classes.
If you get behind, you won't know what's going on.
8. If you're totally lost at any point,
SCREAM! That is, if you become
disoriented or confused, call attention to the problem immediately (assuming
you're diligently keeping up with the material). Everything fits nicely in the course and it
all fits together so if you're lost at some point you'll probably just
continue to be lost if you don't immediately scream.
9. Never use the excuse that you're
"no good at math." All you
need to learn (introductory) statistics is proficiency in arithmetic and the
barest elements of algebra.
Side effects:
The Course Objectives identify the intended effects of this treatment.
Suggestions for Success list ordinary dosage levels to help ensure the
treatment works. Minor adjustments may be needed in individual cases. Extensive
observational studies, but not placebo-control studies, have revealed certain
side effects. These include tick-like clicking of the Options button in SPSS to
see what it reveals, aspiration to serve as a tutor for statistics in
subsequent semesters, and precipitous decline in math anxiety. In most cases,
these symptoms are mild and remission occurs within days. If any of these symptoms persist for more
than seven days, consult your professor immediately.
COURSE OBJECTIVES
According
to the catalog, Psych. 210 is "An introduction to the basic statistics
used in the behavioral sciences, including descriptive statistics, correlation,
sampling, hypothesis testing, and inferential statistics."
Following
is a more detailed list of the principal learning objectives for the
course. Note the correspondence with the
topical listing on the course schedule.
Know
the basic terminology of introductory statistics.
Be
able to identify variables in research reports. Define independent and
dependent variables and constants.
Explain
the major divisions of statistics and the problems each attacks.
Recognize
types of scales (nominal, ordinal, interval, ratio) and their key features.
Given
"raw data" be able to organize and summarize it in a frequency
distribution and/or graphic form. Know
standard conventions for preparing these summaries.
Be
able to calculate measures of central tendency (mean, median, mode); and know
their special characteristics.
Be
able to calculate measures of variability (range, standard deviation, variance);
and know their special characteristics.
Be
able to describe shapes of distributions in conventional terms.
Be
able to use z-scores, standard scores, and percentiles to describe the location
of a score within its distribution.
Use
z-scores to determine areas under the normal curve; and use table of areas
under the normal curve proficiently.
Be
able to construct bivariate distributions.
Be
able to calculate (Pearson) correlation coefficients.
Identify
factors affecting the magnitude of r.
Recognize
names of other (zero-order) correlation coefficients, including ICC.
Have
a passing acquaintance with multiple correlation and factor analysis.
Be
able to calculate predicted scores from regression equations. Determine the
standard error of estimate and describe its use.
Be
sensitive to the interpretation of correlations in terms of causality,
heterogeneity, and linearity.
Describe
correlation and regression as a linear model fit on raw data.
Define
and apply the key terms related to probability.
State
and explain the basic steps in testing a hypothesis.
Apply
and explain the z-test for one mean.
Describe
hypothesis testing as use of a probability model.
Explain
the concept of statistical "significance" (or
"significant").
Define
the central limit theorem.
Define,
calculate, and explain "confidence intervals"
(interval
estimates) for a variety of statistics.
Explain
the difference between the t-test and z-test.
Be
able to apply and explain the following t-tests for means:
one sample, two unrelated samples, two
related samples
Be
able to apply and explain significance tests and confidence intervals for r.
Define
type I and type II errors.
Explain
the concept of "power" and identify factors affecting the power of
statistical tests.
Explain
the concept of "effect size" and distinguish between statistical and
practical significance.
Be
able to explain and interpret one-way ANOVA (F-test).
Be
able to explain, interpret 2-way ANOVA and the concept of interaction.
Be
able to apply and explain significance tests variances.
Be
able to calculate and interpret chi-square.
Recognize
non-parametric tests and the problems they address.
Describe
non-parametric procedures in terms of assumptions underlying a model; explain
concept of robustness.
================
Be
able to speak and write about statistical matters in a conventional
scientific manner.
Demonstrate
initial competence in use of SPSS/PC for calculating statistics.
Be
able to give reasonable estimates of statistics based on examination of
raw data.