Introductory Statistics for Behavioral Scientists
PSYC W1610
Fall 2014
Syllabus
General Information
Instructor:
Course Hours:
Course Location:
Office Hours:
Office Location:
Phone:
Email:
Greg Jensen
Tuesdays & Thursdays, 4:10pm—5:25pm
Schermerhorn 614
[XX] or by appointment
Schermerhorn 500
[XX]
ggj2102@columbia.edu
Teaching Assistants
Name
Lab Section
Lab Hours
Office Hours
Office
Email
Raphael Gerraty
Judy Xu
rtg2116@columbia.edu
jxu@psych.columbia.edu
Students are welcome to attend all listed office hours, regardless of lab section. If
you would like to make an appointment to meet at another time, you may do so by
email, or by approaching us before or after class.
Course Description
This course provides an introductory overview to the basic statistical concepts and
procedures used in experimental research. The focus is on becoming familiar with
how to interpret and perform statistical tests, in order to design experiments and
interpret their results. It is not a course on mathematical theory. No mathematical
skills beyond arithmetic are required. The content of the course is instead intended
to provide a basic element of scientific literacy, with an emphasis on the
psychological sciences.
In addition to the lectures, students are required to participate in a laboratory
section that meets once a week. All students registered for PSYC W1610 must also
register for one of the PSYCH W1611.00X sections. Lab activities will consists
primarily of hands-on data analysis using Excel and SPSS, applying the concepts
introduced in lecture.
Course Requirements
• Textbook: The course textbook is Introductory Statistics for the Behavioral
Sciences, Seventh Edition (by Joan Welkowitz, Barry H. Cohen, and R. Brooke Lea,
2012). Required reading will consist mainly of chapters from this text. Students are
strongly encouraged to obtain their own copies of the textbook.
In some cases, chapters are listed as required reading more than once.
Students are strongly encouraged to use this as a cue to re-read those chapters prior
to the class in question, either because they are particularly important, or because
they are particularly challenging.
In addition to the required readings, each lecture will also have an
accompanying recommended reading, available electronically. These are strictly
optional and (to the extent possible) non-mathematical. You may find these
supplementary readings to help make the problems of the course a bit less abstruse.
• Lecture Attendance: Because each lecture builds on the material presented in
previous lectures, regular attendance is crucial to success in the class. Thus,
attendance will be collected at the start of every lecture. Please contact Professor
Jensen or one of the TAs before class (the sooner, the better) when you become
aware of an unavoidable conflict. Repeated unexcused absences will impact the class
participation portion of the grade.
• Laboratory Attendance & Assignments: Attendance to and participation in
every lab section is MANDATORY. Each lab consists of a project, to be completed in
class under the supervision of the TA. Foreseeable absences must be approved at
least two weeks in advance. In cases of excused absences, as well as documented
illness or family emergencies, students will have the opportunity to make up the lab
assignments without penalty on their own time. In all other cases, lab assignments
may be made up for partial credit provided they are completed within one week of
the original lab section.
• Homework: Homework assignments consist of problem sets posted to
Courseworks. Typically, homework will be assigned on Tuesdays and be due by the
end of class one week following. Please note the following:
 Homework assignments must be completed individually. Students are not
allowed to share answers or to work together on problems, and doing so may
result in disciplinary action on grounds of academic dishonesty.
 Show all work for every problem. The value, in points, of every problem will
be indicated on the assignment, and points will be deducted for mistakes
made, rather than based on the final answer. If, for example, a small
arithmetic mistake occurs early in a problem, but the procedure is otherwise
followed correctly, you can still get most of the points even though the
answer will be wrong. If, however, you only write the answer, then the TAs
will be unable to follow your reasoning and therefore be unable to give you
partial credit.
 Staple all sheets of your homework together. Unstapled homework will have
points deducted. It is also a good idea to write your name on each sheet.
 Homework not turned in by the end of class will be considered late. Points
will be deducted as a function of how late the homework is, so the sooner you
are able to turn in a late homework, the better.
 Homework must be turned in as a hard copy to be considered for credit.
Although electronic submission may be allowed (at the discretion of the TAs)
in order to minimize the lateness of the assignment, it will be counted as a
zero until such time as a hard copy is also provided.
• Exams: There will be a midterm and a final exam. Each will consist of a mixture of
computational problems to be solved by hand and written conceptual problems. As
with the homework, credit will be given for partial work, and for work that displays
knowledge of the correct procedure, so show all work as clearly as you can.
Calculators will be allowed only if they do not include statistical or
programmable functions. Thus, most scientific and graphing calculators are not
suitable. Students should obtain a basic calculator that includes a square root button
(the Canon LS-82Z, for example) as soon as possible.
Students who are unable to attend an exam must make arrangements to take
a make-up exam as far in advance as possible, and must be taken as close to the
exam date as possible (no later than 5 days after the exam date). Absence from an
exam will be excused at the sole discretion of Professor Jensen. Vacation travel
plans, for example, are not grounds for missing an exam. Make-up exams for
unexcused absences will require permission from both the professor and the
student’s dean.
• Grading: Letter grades are defined by the following intervals:
97
93
90
87
83
80
77
73
70
67
63
60
>
>
>
>
>
>
>
>
>
>
>
>
A+
A
AB+
B
BC+
C
CD+
D
DF
≥
≥
≥
≥
≥
≥
≥
≥
≥
≥
≥
≥
97
93
90
87
83
80
77
73
70
67
63
60
The overall class score is based on the following breakdown:
Midterm
Final
Homework
Labs
Participation
30%
35%
25%
5%
5%
Neither exam grades nor overall course grades are “curved” in the traditional sense,
and students will not be competing for a limited pool of each letter grade. If every
student scores a 95 on the final, every final gets an A. However, exam scores and
final letter grades are corrected upward on the basis of the median and interquartile range of class performance. This correction can only ever improve your
score, so the letter grades listed above represent the minimum letter grade that
your raw score can earn, prior to the calculation of the corrected score. The raw
scores for homework and labs are not adjusted.
Participation is partially, but not wholly, a function of class attendance. It
reflects overall student engagement, and every student is assumed from the outset
to have full participation points. Thus, a student who comes to class and does the
work will get the full 5%. Participation credit can be lost in various ways (such as
unexcused absences from lecture or sleeping in class), but extra effort (such as
regularly attending office hours) will also be taken into consideration.
Students with Disabilities
Students with disabilities registered for this course and who require classroom
accommodations should get in touch with me as soon as possible. Additionally, stop
by the Office of Disability Services (ODS), located in Wien Hall, Suite 108A, to
register for support services, if you have not already done so. Students who are
eligible for extra exam time and other accommodations should fill out the
appropriate paperwork at ODS early in the semester, as the professor requires the
confirmation of ODS in order for the appropriate arrangements to be made. Note
that ODS may require up to 2 weeks to process an application, so register with
plenty of time in advance.
Schedule of Classes
Date
9/2/14
9/4/14
Topic
“Why am I here?”
Variables & Measurement
9/9/14
Data Visualization
9/11/14
Central Tendency
9/16/14
Variability
9/18/14
Distributions
9/23/14
Discrete Probability
9/25/14
Probability Density
9/30/14
Statistical Inference
10/2/14
Confidence Intervals
10/7/14
The Small Sample Problem
10/9/14
Testing For A Change
10/14/14
Testing For A Difference
10/16/14
Non-Parametric Tests
10/21/14
MIDTERM
10/23/14
Correlation
10/28/14
Linear Regression
10/30/14
Linear Models
11/6/14
Analysis of Variance
11/11/14
Multiple Comparisons
11/13/14
Factorial ANOVA
11/18/14
Making Sense of ANOVA
11/20/14
12/2/14
Non-Parametric Tests
Redux
Effect Size & Power
Analysis
Bayes’ Rule
12/4/14
TBD
Future Directions
FINAL
11/25/14
Reading (see following page)
Required: Welkowitz Ch. 1
Recommended: Blastland Ch. 1
Required: Welkowitz Ch. 2
Recommended: Tufte Ch. 1
Required: Welkowitz Ch. 3
Recommended: Blastland Ch. 5
Required: Welkowitz Ch. 3 (Again)
Recommended: Gonick Ch. 2
Required: Welkowitz Ch. 4
Recommended: Salsburg Ch. 2
Required: Welkowitz Ch. 16
Recommended: Blastland Ch. 3
Required: Welkowitz Ch. 4 (Again)
Recommended: Salsburg Ch. 9
Required: Welkowitz Ch. 5
Recommended: Salsburg Ch. 11
Required: Welkowitz Ch. 5 (Again)
Recommended: Salsburg Ch. 12
Required: Welkowitz Ch. 6
Recommended: Salsburg Ch. 3
Required: Welkowitz Ch. 6 (Again)
Recommended: Gonick Ch. 8
Required: Welkowitz Ch. 7
Recommended: Gonick Ch. 9
Required: Welkowitz Ch. 8
Recommended: Salsburg Ch. 16
Homework
Homework 1 Due
Homework 2 Due
Homework 3 Due
Homework 4 Due
Homework 5 Due
Homework 6 Due
Homework 7 Due
Required: Welkowitz Ch. 9
Recommended: Blastland Ch. 12
Required: Welkowitz Ch. 10
Recommended: Gonick Ch. 11
Required: Welkowitz Ch. 10 (Again)
Recommended: Dancey Ch. 12
Required: Welkowitz Ch. 12
Recommended: Salsburg Ch. 5
Required: Welkowitz Ch. 13
Recommended: Dancey Ch. 10
Required: Welkowitz Ch. 14
Recommended: Dancey Ch. 11
Required: Welkowitz Ch. 14 (Again)
Required: Welkowitz Ch. 17
Recommended: Salsburg Ch. 10
Required: Welkowitz Ch. 11
Recommended: Wainer Ch. 1
Required: Stone Ch. 1
Recommended: Salsburg Ch. 13
Recommended: Salsburg Ch. 29
Homework 8 Due
Homework 9 Due
Homework 10 Due
Homework 11 Due
Homework 12 Due
Homework 13 Due
List of Readings
Required
Introductory Statistics for the Behavioral Sciences, Seventh Edition (2012), by Joan
Welkowitz, Barry H. Cohen, and R. Brooke Lea. John Wiley & Sons, Inc.
Note: In addition to being available on reserve in the library, students can also
access an electronic version through Columbia’s “Ebrary” collection. Search for the
book in CLIO and click on “Full Text Available” in the right sidebar that appears
under the heading “articles.”
1 chapters in: Bayes’ Rule: A Tutorial Introduction to Bayesian Analysis (2013), by
James Stone. Sebtel Press.
Recommended
Selections from the following books are provided as recommended, non-technical
reading. Students are encouraged to obtain these books on their own if they find the
provided samples helpful.
The Lady Tasting Tea: How Statistics Revolutionized Science in the Twentieth Century
(2001), by David Salsburg. W. H. Freeman and Company.
The Tiger That Isn’t: Seeing Through A World of Numbers (2008), by Michael
Blastland and Andrew Dilnot. Profile Books.
The Cartoon Guide to Statistics (1993), by Larry Gonick and Woollcott Smith. Harper
Perennial.
Statistics Without Maths For Psychology, Fifth Edition (2011), by Christine Dancey
and John Reidy. Prentice Hall.
Picturing the Uncertain World: How to Understand, Communicate, and Control
Uncertainty through Graphical Display (2011), by Howard Wainer. Princeton
University Press.
The Visual Display of Quantitative Information (2001), by Edward Tufte. Graphics
Press.
I also enthusiastically recommend the following book to students seeking to
continue their statistical education after they’ve completed this class:
Serious Stats: A Guide to Advanced Statistics for the Behavioral Sciences (2012), by
Thomas Baguley. Palgrave Macmillan.
Nonparametric Statistics for the Behavioral Sciences, Second Edition (1988), by
Sidney Siegel and N. John Castellan Jr. McGraw-Hill.
Doing Bayesian Data Analysis: A Tutorial with R and BUGS (2010), by John Krushke.
Academic Press.