Psychology 225: Statistical
Techniques
Fall 2018
Course Syllabus
TR 9:00-10:15PM, KUY 306
Description: We will cover central tendency, variability, probability, random variables,
the normal distribution, sampling distribution, the central limit theorem,
confidence intervals/estimation, hypothesis testing (t-tests, correlation and
regression, ANOVA, chi-square, etc.), effect size, and Bayesian inference.
Student learning outcomes: This course provides an overview of statistics,
focusing on their application to psychology. The focus is on how sample
statistics support inferences about population parameters. By the end of the
course:
1.
Students will be
able to describe key statistical
concepts (central tendency, variability, law of large numbers, random variable,
normal curve, sampling distributions, central limit theorem, estimation,
confidence interval, effect size, bayes factor, etc.) and develop a working
knowledge of frequentist and Bayesian statistical inference in psychology (Psychological
Knowledge)
2.
Students will be
able to distinguish strong from weak evidence for (or against) a claim using
scientific reasoning; design, conduct,
and interpret a replication study. (Scientific inquiry and critical thinking)
3.
Students will be able to apply ethical standards to evaluate
psychological science and practice. (Ethical
responsibility)
4.
Students will be able to demonstrate effective writing and to interact
effectively with others. (Communication)
Instructor: Adam S. Cohen
Office: Sakamaki C405
Office hours: by
appointment (Tuesday after class is usually best)
Email: If x = cohen9,
then x@hawaii.edu (put “psych 225” in the subject line)
*Unless it is a private matter, please include both me
and the TA on email.
Teaching Assistants: Heewon Kwon
Office: Sakamaki 400-2
Office hours: by
appointment
Email: If x = hkwon7,
then x@hawaii.edu (put “psych 225” in the subject line)
Required reading: Good news: your textbook for this class is available
for free online! Go here: www.openstax.org/details/introductory-statistics
If you prefer, you can
also get a print version at a very low cost. Your book is available in web view
and PDF for free. You can also choose to purchase on iBooks or get a print
version from OpenStax on Amazon.com. You can use whichever formats you want.
Web view is recommended -- the responsive design works seamlessly on any
device. If you buy on Amazon, make sure you use the link on your book page on
openstax.org so you get the official OpenStax print version. (Simple printouts
sold by third parties on Amazon are not verifiable and not as high-quality.)
Introductory Statistics
from OpenStax, Print ISBN 1938168208, Digital ISBN 1947172050.
With the exception of the first lecture, readings
should be completed ahead of time. It is critical that you read ahead of
lecture to do well in this course.
Additional
resources:
JASP: https://jasp-stats.org/
JASP is a statistics
program. Please download it before the
second class.
Seeing
Theory: http://students.brown.edu/seeing-theory/
“A
visual introduction to probability and statistics.” Some of these
visualizations will be used in class.
R Psychologist: http://rpsychologist.com/tag/visualization
More useful visualizations we will explore during the
course.
Laptop
Policy: Except for
in-class demonstrations with JASP, you are encouraged not to use a laptop in
class. The reasons for this will be discussed during week 1.
Grading:
|
Assignment |
Weight |
|
Homework |
30% |
|
Exam 1 |
10% |
|
Exam 2 |
10% |
|
Exam 3 |
10% |
|
Exam 4 |
10% |
|
Exam 5 |
10% |
|
Exam 6 |
10% |
|
Exam 7 |
10% |
Homework:
There are 15 homework assignments. They are due on Tuesdays at the start of class
(9:00AM sharp!) with two exceptions noted in the schedule below. Only hard
copies will be accepted. Make sure to include first and last name, student
number, and chapter # at the top. Important:
·
Illegible
homework will receive 0 points.
·
Failure to show
work will receive 0 points.
·
A 10% penalty
applies each day HW is late. See the FAQ and Advice section for full details
about the late penalty.
HW labeled as “optional”
will not be graded. But the more you practice the better you will do on the HW,
if not the course.
Exams: There
are seven exams. Sounds like a lot but research shows that testing improves
learning (why might this be?). This is known as the testing effect; on the
virtues of repeated testing, see the FAQ and Advice section below. Exams are
cumulative and cover lecture and reading material. You do not want to be late!
Once the first exam is turned in, the exam is closed – no new exams will be
given out.
Make-up exams
will not be granted without prior approval from the instructor. No make-up
exam is possible for Exam 7 (the “final” exam): If you cannot take Exam
7 when scheduled, you must drop the course.
Grade lines
90% ≤ A ≤ 100%
80% ≤ B < 90%
70% ≤ C < 80%
60% ≤ D < 70%
+/- Grades: Will be given within
3% of the grade cutoff, lower bound inclusive, upper bound exclusive. For
example, an 86.9% is a B, an 87.0% - 89.9% is a B+, a 90.0% - 92.9% is an A-,
and a 93.0% is an A.
Requests for accommodation: Any student who feels s/he may need an accommodation
based on the impact of a disability is invited to contact me privately. Please
come to office hours or make an appointment with me outside of class to discuss
potential requests before the first exam. I would be happy to work with you,
and the KOKUA Program (Office for Students with Disabilities) to ensure
reasonable accommodations in my course. KOKUA can be reached at (808) 956-7511
or (808) 956-7612 (voice/text) in room 013 of the Queen Lili'uokalani Center.
Course Schedule
|
Lecture |
Date |
Topic |
Readings
(complete before class) |
HW (due in class) |
|
|
week 1 |
T |
8/21 |
Sampling and Data; Data collection activity |
Chapter 1 |
|
|
R |
8/23 |
Sampling and Data; Intro to JASP |
|
|
|
|
week 2 |
T |
8/28 |
Descriptive statistics: Shape and central tendency |
Chapter 2 |
Ch 1: 42-89 (even required; odd optional) |
|
R |
8/30 |
Descriptive statistics: Variability |
|
|
|
|
week 3 |
T |
9/4 |
Probability |
Chapter 3 |
Ch 2: 74-107 (even required; odd optional) |
|
R |
9/6 |
Exam
1 (Ch 1-2) |
|
|
|
|
week 4 |
T |
9/11 |
Discrete random variables |
Chapter 4 |
Ch 3: 66-116 (even required; odd optional) |
|
R |
9/13 |
Discrete random variables |
|
|
|
|
week 5 |
T |
9/18 |
Continuous random variables |
Chapter 5 |
Ch 4: 69-111 (even required; odd optional) |
|
R |
9/20 |
Exam
2 (Ch 1-4) |
|
|
|
|
week 6 |
T |
9/25 |
Normal distribution |
Chapter 6 |
Ch 5: 72-101 |
|
R |
9/27 |
Normal distribution |
|
|
|
|
week 7 |
T |
10/2 |
The central limit theorem |
Chapter 7 |
Ch 6: 60-88 |
|
R |
10/4 |
Exam
3 (Ch 1-6) |
|
|
|
|
week 8 |
T |
10/9 |
Confidence intervals/estimation |
Chapter 8 |
Ch 7: 61-97 (even required; odd optional) |
|
R |
10/11 |
Confidence intervals/estimation |
|
|
|
|
week 9 |
T |
10/16 |
Hypothesis testing: One sample comparisons |
Chapter 9 |
|
|
R |
10/18 |
Exam 4(Ch 1-8) |
|
Ch 8: 95-134 (even required; odd optional) |
|
|
week 10 |
T |
10/23 |
Hypothesis testing:
One-sample comparisons |
|
|
|
R |
10/25 |
Hypothesis testing:
Two-sample comparisons |
Chapter 10 |
|
|
|
week 11 |
T |
10/30 |
Hypothesis testing: Two-sample comparisons |
|
Ch 9: 62-116 (odd required; even
optional) |
|
R |
11/1 |
Exam
5 (Ch 1-9) |
|
|
|
|
week 12 |
T |
11/6 |
Election Day |
|
Ch
10: 78, 80, 82, 84, 86, 87, 89, 91, 115-123 due Monday 11/05 |
|
R |
11/8 |
Hypothesis testing: Multiple-group
comparisons |
Chapter 13 |
|
|
|
week 13 |
T |
11/13 |
Hypothesis testing:
Regression |
Chapter 12 |
Ch 13: 59-74 |
|
R |
11/15 |
Hypothesis testing: Correlation |
Chapter 12 |
|
|
|
week 14 |
T |
11/20 |
Exam
6 (Ch 1-10, 13) |
|
|
|
R |
11/22 |
Thanksgiving |
|
|
|
|
week 15 |
T |
11/27 |
Criticisms of NHST and
p-values Effect size and Confidence
intervals (revisited) |
Ch 12: 57-79 |
|
|
R |
11/29 |
Bayesian statistics: A gentle primer |
Excerpt
from Brewer |
|
|
|
week 16 |
T |
12/4 |
Strengthening
NHST and improving statistical practice: power
analysis, preregistration, open science, replication |
optional:
Spellman, Gilbert, & Corker
(2018) |
HW
Week 15: Compute confidence intervals for these problems (use the alpha given in the problem; if alpha is not specified,
assume alpha = .05): Ch
9: 75-87,107-115 (odd) Ch
10: 78, 80, 82, 84, 86, 87, 89, 91, 115-123 Ch
12: none Ch
13: none Compute
effect sizes for these problems: Ch
9: 75-87,107-115 (odd) Ch
10: 78, 80, 82, 84, 86, 87, 89, 91, 115-123 Ch
12: 67-71 Ch
13: 64-74 |
|
R |
12/6 |
Review |
|
|
|
|
Exam Period |
R |
12/13 945-1145AM |
Exam
7 (cumulative) |
|
FAQs and Advice:
1.
What should I do
if I miss a class?
3.
What if I need to miss a lecture/exam/lab for an
Official University-Sponsored Event?
4. What happens if I turn in my assignment one minute
past the deadline?
Advice
5.
How to (and how not to) write an e-mail to your
professor and TAs
6.
When to ask
questions over email and when to set up an appointment
7. Questions you probably should avoid asking your professor
8.
Performance enhancing supplements for learning and
remembering
FAQs
1.
What should I do if I miss a class?
You
should find a classmate willing to lend you their notes and review the material
with you. Do this as soon as possible while the material is still fresh in
their mind. After meeting with the classmate, if you have questions, I
encourage you to set up an appointment with me or the TA. Although we want to
help you catch up, please understand that it’s not feasible for us to repeat
the class during office hours, so make sure to have specific questions prepared
before seeing us.
2. What should I
do if I have to miss an exam because of [a family wedding, vacation plans, or
other event scheduled in advance]?
If
you need to miss an exam because of an event scheduled in advance, you should
take the course next semester. Accommodation is provided under extreme and
limited circumstances. Medical and family emergencies
will be accommodated as long as appropriate documentation is provided
(e.g., formal note on letterhead from a physician for medical emergencies or a
university counselor for family emergencies). If you miss an exam because of a
medical or family emergency, you should contact me by e-mail no later than
the day after the exam (for a Thursday exam, notify me on Friday at the
latest) to discuss accommodation.
3.
What if I need to miss a lecture/exam/lab for an
Official University-Sponsored Event?
“For
regularly-scheduled events, students are to notify instructors within the first
two weeks of the semester. For special events or tournaments, students are to
notify their instructors as soon as they learn of the anticipated absence. In
both cases, students who must miss class for such events will be responsible
for completing all assigned work as expeditiously as possible.” http://www.catalog.hawaii.edu/about-uh/campus-policies1.htm#excused
4.
What happens if I turn in my assignment one minute
past the deadline?
Doh!
One of the most painful, spirit-crushing academic experiences a student can
endure! My advice: don’t put yourself in a situation where you could turn in an
assignment 1 minute late. Do whatever you need to do to turn it in with plenty
of time to spare: pretend the deadline is 5 hours earlier; reward yourself with
a cookie for turning it in early; plan something an hour before the paper is
due so that you have to get it done well in advance. A 10% penalty applies for
every day an assignment is late. Even if the assignment is 1 minute late. Even
if it’s for reasons out of your control like the internet going down or your
hard drive crashing. So avoid putting yourself in this situation and submit
early. That way if any unforeseen things happen, the assignment won’t be late.
Advice
5.
How to (and how not to) write an e-mail to your
professor or TAs
When
you email your professors or TAs, take a minute to think about what
you want to say and how you want to say it.
·
Your message
should be clear and grammatical.
·
Stylistically,
there’s no need to be overly formal, but at the other extreme avoid internet slang
(e.g., thx, omg, lol) since some professors care about a minimum level of
formality.
·
Before writing
your message, read the syllabus and other course material carefully to make
sure the information you need isn’t already there.
Here
are some examples of how to and how not to write an email to your professors
and TAs.
Example
#1: Don’t do this
Hi Professor Cohen,
I was wondering for our Methods course if their are weeks posted as to what
chapters in our textbook correlate with the discussions of that weeks lecture.
Thank you so much for your time,
***** *****
What’s the problem with example #1? Besides the sloppy grammar and
spelling and the question not being clear, this information can be located in
the syllabus.
Example
#2: Don’t do this
hey prof, is the paper due tmrw? bc i
dont understand it, thx.
What’s the problem with example #2? Try to avoid internet slang.
But more importantly, ask for support early! We want to help you succeed in
this course!
Example
#3: Do this
Hi Dr. Cohen,
May I look over my exam sometime this
week? I’m available any morning before 12:00 PM. Please let know if any of
those times work for you.
Thanks,
***** *****
Clear and direct. Not too formal, not too informal. Good job!
6.
When to ask a question over email and when to set up
an appointment
Sometimes
we can provide help over e-mail, but often it’s easier to talk in person. If
you have a few questions and they can be answered quickly, e-mail is probably
fine. But if your questions require a longer response, then we recommend you
set up an appointment to meet with me or the TAs. Our top priority is to help
you understand and think about the material, and it’s often more efficient to
do that by talking in real-time during office hours than by the brilliantly
slow process of exchanging e-mails.
Please read the syllabus closely before
asking a question via e-mail. Many of the most common questions are answered in
the syllabus.
7.
Questions you probably should avoid asking your
professor
Q1. Is it still possible for me to get [grade]?
Occasionally, a student will send an email that goes
like this:
Hi Dr. Cohen,
May I please have your honest opinion regarding
whether you think I could end up with a final mark of 75+% in the course if I
work hard on [assignments, exams, etc.]? As well, what do you predict to be the
likelihood that I will be able to get 75+% at the end of the semester? I have
roughly calculated that I would need at least an average grade of 82% on the remaining
60% of the course content.
Thank you very much for your time.
Sincerely,
***** *****
Explanation: Please resist
the urge to contact me or the TAs about whether it’s possible to get a certain
grade. We have too much respect for you to treat you like someone incapable of
doing the math and figuring this out for yourself. Beyond that, there’s not
much we can say because what grade you actually earn depends on how hard you
work, and we can’t predict that. You have control over your performance on the
remaining assignments; the TAs and I do not, although we will do whatever we
can to help you succeed.
Q2. Do we need to know [X] for the exam?
For example:
---start of email---
Hello professor, I have a question from the reader
should I memorize this chart?
Thanks,
****** *****
---end of email---
Explanation: Out of fairness
I can’t provide information to one student that puts them at an advantage
relative to other students. But more importantly, this isn’t the right question
to ask if you’re preparing for an exam. You’re in college to learn, so ask
questions that will help clarify what you don’t know or don’t understand, that
way you can do better on the exam by knowing more, not less. Use questions to
figure out what you don’t know, not what you don’t need to know.
8.
Performance enhancing supplements for learning and
remembering
What
if you could take a pill that would help you learn and remember better? And
what if it were completely safe and legal? Would you be interested in trying it?
It turns out there are performance enhancing supplements that on average give
you an advantage in learning and remembering, but they don’t come in a pill.
They are techniques that cognitive scientists and educational psychologists
have discovered that facilitate remembering and learning, and it’s been shown
that students who use these techniques on average outperform those who don’t.
Here are a few of the ingredients for enhancing academic performance:
a.
The spacing
effect: Students who space their
studying across multiple sessions outperform students who spend the same amount
of time studying in a single session. This isn’t just the obvious
recommendation to avoid procrastinating, which has to do with the size of the
gap between studying and testing (study -> gap -> test). This is about
creating a gap between studying and re-studying (study -> gap -> re-study
-> gap -> test). The size of the study-restudy gap, like the size of
study-test gap, matters and there’s interesting evidence that a longer
study-restudy gap is better than a shorter one (see the papers below for
details).
b.
The
alternating practice effect: If
you’re learning to hit a baseball, is it better to practice hitting a 100
fastballs in a row, a 100 curveballs in a row, and a 100 sliders in a row, or
is it better to practice hitting 300 pitches in which the types of pitches are
mixed together? There’s evidence that learners who use the second “alternating”
or “interleaving” or “mixed” approach, outperform those using the first
“blocking” approach. For psychology, if you have a set of practice problems for
chapter 1, chapter 2, and chapter 3, it’s better to alternate the problems than
block them by chapter.
c.
The testing
effect: This one’s on the house.
Students who study and take a test score higher on a final test than students
who spend the same amount of time just studying before the final test. This is
why we have 3 exams throughout the course.
d.
Taking notes
with pen and paper vs a laptop:
There is evidence that students who take notes with pen and paper show better
recall and test performance than students who take notes on their laptop. Students who use pen and paper are more likely to write down
notes in their own words and actively process the material, whereas students
using laptops are more likely to copy down what the teacher says word-for-word
without actively processing the content. See Mueller and Oppenheimer (2014) below
for more details and an explanation.
References:
Mueller, P. A., & Oppenheimer, D. M. (2014). The Pen Is Mightier Than the Keyboard:
Advantages of Longhand Over Laptop Note Taking. Psychological science, v,
pp-pp.
This paper reports a set of studies
showing that students learn better when taking notes with pen and paper than
with a laptop.
Rohrer, D., & Pashler, H. (2010). Recent research on human learning challenges
conventional instructional strategies. Educational
Researcher, 39 , 406–412.
This paper reviews the spacing effect,
the alternating practice effect, and the testing effect.
http://www.nytimes.com/2014/07/20/opinion/sunday/how-tests-make-us-smarter.html
An article in
the NYTimes reviewing the virtues of repeated testing (the testing effect).
http://www.nytimes.com/2014/09/07/magazine/why-flunking-exams-is-actually-a-good-thing.html
Another article in the NYTimes on the
testing effect.
http://www.nytimes.com/2014/11/23/sunday-review/studying-for-the-test-by-taking-it.html
And another article in the NYTimes on
the testing effect and mixed practice effect.
Other
papers of interest on the science of learning:
Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K.,
Okoroafor, N., Jordt, H., & Wenderoth, M. P. (2014). Active learning increases student performance in
science, engineering, and mathematics. Proceedings
of the National Academy of Sciences, v,
pp-pp.
This paper reports a meta-analysis
demonstrating the virtues of being an active learner rather than a passive
listener and shows that active learning is more effective than traditional
lecturing.
Kozhevnikov, M., Evans, C., & Kosslyn, S. M.
(2014). Cognitive Style as Environmentally
Sensitive Individual Differences in Cognition A Modern Synthesis and
Applications in Education, Business, and Management. Psychological Science in the Public Interest, 15, 3-33.
This paper has a section on the
“matching hypothesis” (p. 11), the idea that students learn more efficiently
when the teaching method matches their learning style (whether you’re a visual
learner, auditory learner, etc.). It comes to the surprising conclusion that
the evidence for the matching hypothesis is weak. What seems to matter more is
a) whether the teaching method matches the material and b) style flexibility,
the ability of students to switch between learning styles.
Writing
is hard. And writing clearly is even harder. Here is a paper I recommend that
will help you as an academic writer.
Plaxco,
K. W. (2010). The art of writing science. Protein Science, 19, 2261-2266.