1. 1
Welcome to Stat 101:
Introduction to Quantitative Methods for
Psychology and the Behavioral Sciences
Kevin Rader, krader@fas.harvard.edu

2. 2
Unit Outline
• Course Logistics and Details
• The What and Why of the Field of Statistics?
• Much needed terminology
• Snapshot of what’s to come in the course

3. 3
Stat 100 vs. 101 vs. 102 vs. 104
• Each course assumes no prior knowledge of statistics
• They all cover the same basic statistical concepts (about 3/4 of
the course)s, though they each emphasize different topics
throughout
• Stat 104 will cover more material and is more mathematically
rigorous, others are similar level.
• Stat 101 will use mostly examples from psychology, general
social/behavioral sciences, and public health.
• Stat 102 emphasizes medical and lab science examples
• Stat 104 emphasizes examples from economics/finance
• Stat 100 is a more general course with a wide-range of examples
• More questions: ask after class.

4. 4
Kevin’s Contact Info
• My office: Science Center, Room SC-105 (likely to change)
• Office Hours (stop in unannounced):
• Tues 12:30-1:30pm and Thurs 11:30am-12:30pm
• Also by appointment (via email)
• Phone numbers:
• Statistics Department: (617) 495-5496
• My office (SC-105): NA
• Email: krader@fas.harvard.edu (preferred over phone)

5. 5
Teaching Staff
• Teaching Fellows:
Joseph Lee: lee26@fas.harvard.edu
Lazhi Wang: wang75@fas.harvard.edu
• Teaching assistants will be teaching sections, holding office
hours, answering questions via email, and grading
assignments and exams.

6. 6
Course Website
Course website:
http://isites.harvard.edu/icb/icb.do?keyword=k97307
• There you will find (eventually):
• Syllabus
• Administrative Announcements
• Lecture Notes
• SPSS Tutorial (including download and install instructions)
• Assigned Homeworks
• HW #1 will be posted soon: Due Fri, Sept 13th
• Other Study Material (practice exams, web links, etc...)

7. 7
Lecture Notes
• Paper copies will NOT be handed out at the beginning of
lecture after this week (we will provide copies on Thursday).
• They’re organized in Units: which follow chapters in the
textbook (will diverge a bit at end of semester)
• Lecture notes will be posted at least 24 hours in advance
• Notes are somewhat concise – you are encouraged to add
your own annotations and develop your own notes
• Occasionally mistakes appear in lecture notes; corrected
versions will be posted after class

8. 8
Class Meetings
• Lectures:
• Tues & Thurs, 10–11:30am, Science Center SC-Hall A
• Sections
• Optional (but strongly recommended) weekly section to discuss
homework, do extra problems, and review difficult concepts.
• No section this week (begin week of Sept 9).
• Look for announcement on the course website for permanent times
(OH’s too).
• SPSS Tutorials
• To be held in SC-B09
• One on Thursday afternoon and one on Monday afternoon. Times TBD.

9. 9
Textbook
• Statistical Methods for the Social Sciences, Agresti & Finlay,
4th edition. Amazon Link:
www.amazon.com/Statistical-Methods-Social-Sciences-Edition/dp/0205646417/
• Text’s website - http://bcs.whfreeman.com/ips7e/
(6th edition will work fine, 5th ed is prob OK too)
• About half of the assigned homework problems will be
assigned from the text, so it’s a good idea to have a copy.
• It’s a great reference for more details on what is seen in the
lectures. Fairly straightforward explanations.

10. 10
Computing and Calculations
• For all exams (and some homework), you will need a
calculator with log, exponential, square-root functions.
• Statistical Computing Package: SPSS
• Can be downloaded from:
http://downloads.fas.harvard.edu/download
• Tutorial Document on website:
http://isites.harvard.edu/icb/icb.do?keyword=k97307&pag
eid=icb.page624278
• HW #1 will include an introduction to the software. There
are also the SPSS Tutorial Sessions…

11. 11
Exams
• Tues, October 10th: Midterm I, 10-11:30am (in class)
• Tues, Nov 12th: Midterm II, 10-11:30am (in class)
• Tues, Dec 12-20th: Final Exam, Date and Time TBD
• You will be allowed 1 “cheat sheet” for Midterm 1, 2 sheets
for midterm 2, and 3 sheets for the Final Exam (front and
back OK).

12. 12
Homeworks
• Posted to course website on Fridays:
http://isites.harvard.edu/icb/icb.do?keyword=k97307&pageid=icb.page6
24266
• Hard Copies must be handed in to the 3rd floor HW
boxes.
• Late homeworks will only be accepted with an official
University excuse (either from UHS or from your
resident dean’s office). NO HW Scores will be
dropped!

13. 13
HW Collaboration
• You are encouraged to discuss homework with other
students (and with the instructor and TFs, of course), but
you must write your final answers yourself, in your own
words.
• Solutions prepared “in committee” or by copying or
paraphrasing someone else’s work are not acceptable; your
handed-in assignment must represent your own thoughts.
All computer output you submit must come from work that
you have done yourself.
• Please indicate on your problem sets the names of the
students with whom you worked.

14. Group Project
• Will be a roughly 3-5 page paper of text (graphs, tables,
etc… are in addition to that) based on a data analysis of
your choosing.
• Groups of size 2 or 3 required. Very helpful to bounce
ideas of each other.
• Due towards end of reading period (Dec. 10).
• More details about the project to come later in the semester
(around Oct 31st).
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15. 15
Course Grading
Component Weighting1 Weighting2 Weighting3
Homeworks 30% 30% 30%
Midterm 1 10% 20% 20%
Midterm 2 20% 10% 20%
Final Exam 30% 30% 20%
Project 10% 10% 10%
Total 100% 100% 100%
Your overall score for the course will be the maximum of the 3 weighting
schemes presented above. Final course letter grades are not assigned
according to a fixed percentages of A's, B's etc (i.e., the course is not `curved').
Letter grades are assigned to the old-fashioned boundaries of A- to A: 90 - 100
final score; B- to B+: 80 - 90, etc. Slight adjustments may be made on the
boundaries of letter grades (boundary moved down a bit).

16. Course Goals for Students
• To learn and understand descriptive statistics and graphical
summaries, basic probability theory, and statistical inference.
• To introduce a range of quantitative tools and methods of
analysis commonly used in the social, psychological and
behavioral sciences with an emphasis on application of methods
to real data.
• To become statistically skilled. At the end of the course, you
should be able to address a research question by choosing a
good data source, be able to figure out and perform what
analysis is most appropriate, and be able to report the findings
that are technically accurate. You should also be able to know
the limitations of your results.
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17. 17
Unit 1: Intro to Statistics
Chapter 1 in the Text

18. 18
So what is statistics?
(and why is it so cool?)
• The study of the methods for obtaining, organizing,
analyzing, and interpreting data.
• Why bother? Principles provide a framework for
• Collecting data and the design of experiments and
observational studies (Design)
• Describing and summarizing data (Description)
• Drawing inferences about populations as a whole and
predicting future events (Inference)
• Short story: Statistics is the science of using data to prove a
point (and hopefully forming a correct conclusion).

19. 19
Proving Points with Statistics

20. 20
Other questions that could be
addressed statistically
Social Sciences
• What are the features of online ads that are more likely to capture
your attention?
• How (if at all) is happiness associated with income, job
satisfaction, social life, religious beliefs, or political ideology?
Health
• Does a smoking ban in bars lower the rate of lung cancer?
• How can we study whether a new therapy is better than a standard
therapy for treating depression?
Sports
• Is David Ortiz truly a clutch hitter? Is Alex Rodriguez anti-clutch?
• Can we predict which teams in the NFL will improve from last
year?

21. 21
In Class Exercise: “Research”
Question about Harvard Students
• Let’s brainstorm! In small groups (2 to 4 students)
discuss something you would like to find out about
your fellow Harvard classmates (the whole student
body or a subgroup).
• Kevin’s Boring Example: Who sends more text
messages: men or women?
• Please think of a more interesting example…
Any group want to share their example with the class?

22. 22
Population vs. Sample
• Population: entire group of individuals on which we
desire information.
• Technicality: actual vs. conceptual populations
• For our Harvard study:
• Sample: a part of the population on which we
actually collect data.
• For our Harvard study:

23. 23
Parameters and Statistics
• Descriptive statistics: summarize the data in the
actual sample of data.
• Inferential statistics: provide predictions or
generalizations about the population based on the
data we collected in the sample.
• Parameter: a numerical summary of the population.
• For our Harvard study:
• Statistic: a numerical summary of the sample data.
• For our Harvard study:

24. How does this apply to the framework
of Statistical studies (3 parts)?
• How should the study
be conducted?
• How should we select
students (subjects) for
the study? How many
should be included?
• What information
(data) do we need to
collect?
Design: Study planning
and implementation
Description: Graphical
and numerical methods
for summarizing data
Inference: predictions
about the population based
on the sample
• What are the
characteristics of the
subjects in our
sample?
• What are the
summary
measurements of the
data we collected?
• How do our
measurements relate?
• How do these
measurements generalize
to all student of interest
(the population)?
• Could we predict how
the measurements would
relate in the larger
population?
• Anything further to
investigate in the future?

25. 25
Software can help…a lot!
Here is just a quick preview
of what a dataset (a
collection of measurements
for the subjects in a
sample) looks like in SPSS:
Each column represents a
variable (a characteristic
measured on the subjects)
Each row represents a
different subject, and
contains the observations
for that subject

26. 26
Take Home Message: 3 Major
Overarching Topics in the Course
1) Design: planning and obtaining data for your research
study.
2) Description: summarizing the data in your sample.
3) Inference: making predictions based on the data and
generalizing the results for the population.

27. Last Word
If you are planning on taking this course, you should…
• Download and install SPSS from FAS IT:
http://downloads.fas.harvard.edu/download
• Go to the course website and follow the SPSS tutorial
document. And/or attend an SPSS tutorial session (the
schedule will be posted on the website later today).
• Read through (or at least browse) chapters 1 and 2 in the
text.
• Be aware that HW #1 will be posted by the end of the
week (it is due next Friday, Sept. 13th).
• Be happy!
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