Introduction to Statistics for the Social Sciences
SBS200, COMM200, GEOG200, PA200, POL200, or SOC200
Lecture Section 001, Fall, 2014
Room 120 Integrated Learning Center (ILC)
10:00 - 10:50 Mondays, Wednesdays & Fridays.
http://www.youtube.com/watch?v=oSQJP40PcGI
Everyone will want to be enrolled
in one of the lab sessions
Labs will
Continue today
Labs
next week
Everyone will want to be enrolled
in one of the lab sessions
Remember:
•
•
•
Bring electronic copy of your data
(flash drive or email it to yourself)
Your data should have correct formatting
See Lab Materials link on class website to doublecheck formatting of excel is exactly consistent
Schedule of readings
Before next exam (September 26th)
Please read chapters 1 - 4 in Ha & Ha textbook
Please read Appendix D, E & F online
On syllabus this is referred to as online readings 1, 2 & 3
Please read Chapters 1, 5, 6 and 13 in Plous
Chapter 1: Selective Perception
Chapter 5: Plasticity
Chapter 6: Effects of Question Wording and Framing
Chapter 13: Anchoring and Adjustment
Reminder
Use this as your
study guide
By the end of lecture today
9/10/14
Questionnaire design and evaluation
Surveys and questionnaire design
Random versus Non-random sampling
Simple versus systematic random sampling
Sample frame and randomization
Homework due – Friday (September 12th)
Make improvements and corrections for project
(homework assignments #3 & 4)
Please note: This assignment allows us to take advantage of the
review process to make the project better – iterative design process
Preview of Questionnaire Homework
There are four parts:
• Statement of Objectives
• Questionnaire itself
(which is the operational definitions of the objectives)
• Data collection and creation of database
• Creation of graphs representing results
Peer review
Please exchange questionnaires with someone
(who has same TA as you)
and complete the peer review handed out in class
You have 10 minutes
Peer review is an important skill in nearly all
areas of business and science. Please strive
to provide productive, useful and kind
feedback as you complete your peer review
Review of
Homework Worksheet
Make notes
about suggested
improvements
Review of
Homework Worksheet
Hand in the peer review
writing assignment
(be sure Lab Sections are at top)
Random sampling vs Random assignment
Random assignment of participants into groups:
We know
Any subject had an equal chance of getting assigned to
this one
either condition (related to quasi versus true experiment)
Random sampling of participants into experiment:
Each person in the population has an equal chance
of being selected to be in the sample
Let’s explore
this one
Population: The entire group of people about
whom a researcher wants to learn
Sample: The subgroup of people who
actually participate in a research study
Sample versus population (census)
How is a census different from a sample?
Census measures each person in the specific population
What’s
better?
Sample measures a subset of the population and infers
about the population – representative sample is good
Use of existing survey data
U.S. Census
Family size, fertility, occupation
The General Social Survey
Surveys sample of US citizens over 1,000 items
Same questions asked each year
Population (census) versus sample
Parameter versus statistic
Parameter – Measurement or characteristic of the population
Usually unknown (only estimated)
Usually represented by Greek letters (µ)
pronounced
“mu”
“mew”
Statistic – Numerical value calculated from a sample
Usually represented by Roman letters (x)
pronounced
“x bar”
Simple random sampling: each person from the
population has an equal probability of being included
Sample frame = how you define population
Let’s take a sample
Question: Average weight of U of A football player
…a random sample
Sample frame population of the U of A football team
Random number table – List of random numbers
Pick 24th
name on
the list
Or, you can use excel to provide
number for random sample
=RANDBETWEEN(1,115)
64
Pick 64th name on the list
(64 is just an example here)
Systematic random sampling: A probability sampling
technique that involves selecting every
kth person from a sampling frame
You pick the
number
Other examples of systematic random sampling
1) check every 2000th light bulb
2) survey every 10th voter
Stratified sampling: sampling technique that involves
dividing a sample into subgroups (or strata) and then
selecting samples from each of these groups
- sampling technique can maintain ratios for the different groups
Average number of speeding tickets
12% of sample is from California
7% of sample is from Texas
6% of sample is from Florida
6% from New York
Average cost for text books for a semester
4% from Illinois
4% from Ohio
17.7% of sample are Pre-business majors
4% from Pennsylvania
4.6% of sample are Psychology majors
3% from Michigan
2.8% of sample are Biology majors
etc
2.4% of sample are Architecture majors
etc
Cluster sampling: sampling technique divides a population
sample into subgroups (or clusters) by region or physical space.
Can either measure everyone or select samples for each cluster
Textbook prices
Southwest schools
Midwest schools
Northwest schools
etc
Average student income, survey by
Old main area
Near McClelland
Around Main Gate
etc
Patient satisfaction for hospital
7th floor (near maternity ward)
5th floor (near physical rehab)
2nd floor (near trauma center)
etc
Non-random sampling is vulnerable to bias
Convenience sampling: sampling technique that involves
sampling people nearby.
A non-random sample and vulnerable to bias
Snowball sampling: a non-random technique in which one or more
members of a population are located and used to lead the researcher
to other members of the population
Used when we don’t have any other way
of finding them - also vulnerable to biases
Judgment sampling: sampling technique that involves
sampling people who an expert says would be useful.
A non-random sample and vulnerable to bias