PSYCHOLOGY 280
Statistical Methods in Psychology
Fall 2015, Sections 1-4
Instructor:
Office:
Telephone/email:
Office Hours:
Kate Hattrup, Ph.D.
LS 275
594-1876 / khattrup@mail.sdsu.edu
Tuesdays and Thursdays, 12:30 – 1:30pm, & by appointment
Class Times and Rooms:
Lecture
Meets in PSFA-350
Section 1
Section 2
Section 3
Section 4
Lab
Meets in GMCS-428
M W 3:30 - 4:45 pm
“ “
“ “
“ “
Section 1
Section 2
Section 3
Section 4
T: 10:00-11:50 am
T: 10:00-11:50 am
W: 12:00 - 1:50 pm
W: 12:00 - 1:50 pm
You must be registered for one PSY 280 Lecture section and its linked Lab section. Students
who are not enrolled in both the Lecture and a Lab will be dropped from the course. Students
must also attend the Lecture and the Lab during the first two weeks of class to avoid being
dropped from the course.
Section 1
Section 2
Section 3
Section 4
TA:
Julia Brown
Ashley Weinberg
Duyen Trang
Rochelle Vertrees
E-mail:
Julia.brown@mail.sdsu.edu
aweinberg@mail.sdsu.edu
dtrang@mail.sdsu.edu
rvertrees@mail.sdsu.edu
GMCS 426A
GMCS 426A
GMCS 426A
Tues. 9-10am
Tues. 3-4pm
Wed. 4-5pm
Th. 3:30-4:30
Tues. 12-2pm
GMCS 426A
Office
Location:
Tues. 9-10am
Office
Tues. 1-2pm
Hours:
Text:
Gravetter, F.J, & Wallnau, L.B. (2013). Statistics for the Behavioral Sciences (9th
Ed). Belmont, CA: Cengage
Website:
blackboard.sdsu.edu
Use the "PSY280-Fall2015: Combined Sections 1-4" site for the Lecture part of
the class. You will find lecture slides there, and additional information about the
class. You will use the Blackboard site for your Lab to retrieve homework grades
and for communicating with your TA.
Objectives: This course is designed to provide students with an introduction to quantitative
methods in Psychology for producing, analyzing, and interpreting data. We will discuss
sampling, probability, describing distributions, correlation, applications of normal probability
curve, and tests of significance. The labs will include discussion and practice with using
statistical, spreadsheet, and word processing software.
By the end of the course, students should be able to:
1. Select appropriate statistical tests for various problems. Understand which types of
circumstances require the use of one technique over another.
2. Conduct analysis by hand-calculation and/or computer software (e.g., SPSS). Learn how
to select proper statistical tests using general statistical software.
3. Interpret statistical results properly.
4. Understand implications of statistical findings and limits of generalizability of results.
5. Apply critical, evidence-based, thinking to everyday life.
In addition, the following goals for the general curriculum in Psychology will be covered.
1. Formulate scientific questions using operational definitions. (SLO 3.1)
2. Generate appropriate methodological techniques for testing specific hypotheses. (SLO
3.2)
3. Identify the factors required to make causal inferences about relationships between
variables. (SLO 3.4)
4. Explain correctly such terms as mean, median, standard deviation, and statistical
significance. (SLO 3.8)
5. Understand and interpret appropriately t, F, r, and r squared. (SLO 3.9)
Class Format: The format of the course is primarily lecture with "hands-on" application of
concepts through exercises and homework. Active participation is encouraged and should
provide a more facilitative and enjoyable learning atmosphere for everyone. Due to the nature of
the course material, it is important that you complete assigned readings before the class sessions.
Also, it is imperative that you attend class every day. This is not a topic that you can teach
yourself, just by reading the book and the lecture slides.
Enforced Prerequisites: Psychology 101; satisfaction of the Entry-Level Mathematics (ELM)
requirement. Proof of satisfying ELM is required (i.e. a copy of ELM score or verification of
exemption.)
Evaluation: There will be three exams, and weekly homework assignments in the Lab sections.
The exams are not cumulative, but later exams will require an understanding of material
presented earlier in the semester. The exams will include multiple choice questions, short
answer, and problems requiring calculations done by hand. See the section below about
calculators. You will not need to memorize formulas (they will be provided to you), but you
must be able to select an appropriate formula to use, and use it correctly. Homework
assignments will be administered and graded by your TA. Final grades in the course will be
based on the total number of points earned during the semester relative to the highest score(s)
received by students in the class. The following is a break-down of how points will be assigned:
Component:
Exams
Homework
# of Points Possible:
300 (100 per exam)
200
% of Total Possible:
60%
40%
Final course grades will be determined based on a curve. The highest obtained score (usually
equal to 100% of possible points) will be calculated by summing the points on all homework
assignments and all exams. Then, students who receive a total that is equal to 92% or more of
the highest obtained score will earn an A; 88-90% of the highest score will earn an A-; 82-88%
of the highest score will earn a B; 80-82% of the highest score will earn a B-; and so on.
Late Paper/Make-Up Exam Policy: Late assignments and make-up exams are not permitted.
Exceptions to this rule will only be made in rare cases in which a student presents a doctor's note
and receives prior approval from the instructor. If you have a religious holiday that requires
accommodation, please make arrangements with the instructor and the TA during the first week
of the semester.
Calculators: You will need a basic calculator that can perform square and square-root functions.
An example is the Texas Instruments TI-30XA. You may not use a cell phone during exams, and
students may not share calculators during exams. Pre-programmable functions are also not
permitted on exams.
Students with Disabilities: SDSU seeks to provide equal access to its programs, services and
activities for people with disabilities. If you will need accommodations in the class, reasonable
prior notice needs to be given to the Student Disability Services (SDS), 619.594.6473 or online
at http://www.sa.sdsu.edu/sds. SDS will work with you and the instructor to make arrangements
for accommodations. All written information in this course can be made available in alternative
format with prior notification to the SDS.
Academic Integrity: The following is taken from the SDSU Undergraduate Course Catalog:
Plagiarism
Plagiarism is formal work publicly misrepresented as original; it is an activity wherein one
person knowingly, directly, and for lucre, status, recognition, or any public gain resorts to
the published or unpublished work of another in order to represent it as one’s own. Work
shall be deemed plagiarism: (1) when prior work of another has been demonstrated as the
accessible source; (2) when substantial or material parts of the source have been literally or
evasively appropriated (substance denoting quantity; matter denoting qualitative format or
style); and (3) when the work lack sufficient or unequivocal citation so as to indicate or
imply that the work was neither a copy nor an imitation. This definition comprises oral,
written, and crafted pieces. In short, if one purports to present an original piece but copies
ideas word for word or by paraphrase, those ideas should be duly noted.
Source: Lindsay, A. Plagiarism and originality. 1952.
San Diego State University is a publicly assisted institution legislatively empowered to certify
competence and accomplishment in general and discrete categories of knowledge. The
President and faculty of this University are therefore obligated not only to society at large but
to the citizenry of the State of California to guarantee honest and substantive knowledge in
those to whom they assign grades and whom they recommend for degrees. Wittingly or
willfully to ignore or to allow students' ascription of others' work to themselves is to condone
dishonesty, to deny the purpose of formal education, and to fail the public trust.
The objective of university endeavor is to advance humanity by increasing and refining
knowledge and is, therefore, ill served by students who indulge in plagiarism. Accordingly, one
who is suspected or accused of disregarding, concealing, aiding, or committing plagiarism
must, because of the gravity of the offense, be assured of thorough, impartial, and conclusive
investigation of any accusation. Likewise, one must be liable to an appropriate penalty, even
severance from the University and in some cases revocation of an advanced degree, should the
demonstrated plagiarism clearly call into question one's general competence or
accomplishments.
Tentative Schedule and Reading Assignments
Day
Date
Lecture Topic
Skip in Book
RESEARCH METHODS & DESCRIPTIVE STATISTICS
M
W
M
W
M
W
M
8/24
8/26
8/31
9/2
9/7
9/9
9/14
Introduction & Welcome to the Course
Ch 1. Introduction to Statistics
Ch 2. Frequency Distributions
Ch 3. Central Tendency
Labor Day – No Class
Ch 4. Variability
Ch 4. Variability (cont.)
Real limits (throughout book)
Stem-and-leaf plots
Median for continuous variables
"Computational" formulas
(throughout book)
LOGIC BEHIND INFERENTIAL STATISTICS
W
M
W
M
9/16
9/21
9/23
9/28
W
9/30
Ch 5. Z-scores and Score transformations
Ch 6. Probability
Ch 7. Probability and Sample Means
Ch 7. Probability and Sample Means
(cont.)
Exam I (Chapters 1-7)
(Read all)
(Read all)
(Read all)
INFERENTIAL STATISTICS TO TEST ONE OR TWO MEANS
M
W
10/5
10/7
M
10/12
W
M
W
10/14
10/19
10/21
M
10/26
W
10/28
M
W
M
W
11/2
11/4
11/9
11/11
Ch 8. Introduction to Hypothesis Testing
Ch 8. Introduction to Hypothesis Testing
(cont.)
Ch 8. Introduction to Hypothesis Testing
(cont.)
Ch 9. Introduction to the t-Statistic
Ch 9. Introduction to the t-Statistic (cont.)
Ch 10. The t-Test for Two Independent
Samples
Ch 10. The t-Test for Two Independent
Samples (cont.)
Ch 10. The t-Test for Two Independent
Samples (cont.)
Ch 11. The t-Test for Related Samples.
Ch 11. (cont.); Exam Review
Exam 2 (Chapters 8-11)
Veteran’s Day – No Class
Directional 1-tail hypotheses
(throughout book—though will
discuss briefly in class)
1-tailed tests
(Read all)
(Read all)
(Read all)
(Read all)
INFERENTIAL STATISTICS FOR DIFFERENCE BETWEEN SEVERAL MEANS
M
W
M
W
M
W
11/16 Ch 12. Introduction to Analysis of
Variance (ANOVA)
11/18 Ch 12. Introduction to ANOVA (cont.)
11/23 Ch 12. Introduction to ANOVA (cont.)
11/25 Thanksgiving Holiday – No Class
11/30 Ch 14. Two-Factor Analysis of Variance
12/2 Ch 14. Two-Factor Analysis of Variance
(cont.)
INTRODUCTION TO CORRELATION AND REGRESSION
M
W
W
12/7 Ch 15. Introduction to Correlation
12/9 Ch 15. Introduction to Correlation (cont.)
12/16 Exam 3 (chapters 12, 14-15, plus t-test)
3:30pm – 5:30pm
partial correlation