1. Introduction to Statistics for the Social Sciences SBS200 - Lecture Section 001, Fall 2016 Room 150 Harvill Building 10: :50 Mondays, Wednesdays & Fridays.
Welcome

2. Lab sessions Everyone will want to be enrolled
in one of the lab sessions
No class
On Monday
No Labs next week

3. Schedule of readings Before next exam (September 23rd)
Please read chapters in OpenStax 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

4. Homework Assignment 3 due Wednesday, Sept
Homework Assignment 3 due Wednesday, Sept. 7 Go to D2L - Click on “Content”
Click on “Interactive Online Homework Assignments”
Complete the next two modules:
HW3-Part1-Sampling Techniques
HW3-Part2-Integrating Methodologies
Due: Wednesday, September 7, 2016

5. By the end of lecture today 9/2/16
Use this as your
study guide
By the end of lecture today 9/2/16
Population (census) versus sample
Descriptive or inferential Parameter versus statistic
Random sampling vs Random assignment
Random versus non-random sampling techniques
Simple random sampling
Systematic random sampling
Stratified sampling
Cluster sampling
Convenience sampling
Snowball sampling
Judgment sampling

6. So far,
Measurement: observable actions
Theoretical constructs: concepts (like “humor” or “satisfaction”)
Operational definitions
Validity and reliability
Independent and dependent variable
Random assignment and Random sampling
Within-participant and between-participant design
Single blind (placebo) and double blind procedures

7. So far,
Continuous vs Discrete variables
Quantitative vs qualitative variables
Levels of measurement:
Nominal, Ordinal, Interval and Ratio

8. Time series versus cross-sectional comparisons:
Trends over time versus a snapshot comparison
Time series design: Each observation represents a measurement
at some point in time. Repeated measurements allow us to see
trends.
Cross-sectional design: Each observation represents a
measurement at some point in time. Comparing across groups
allows us to see differences.
Traffic accidents
Please note: Any one piece of data can often (not always) be used in either a time series comparison or a cross-sectional comparison. It depends how you set up your question.
Does Tucson or Albuquerque have more traffic accidents (they have similar population sizes)?
Does Tucson have more traffic accidents as the year
ends and winter approaches?

9. Time series versus cross-sectional comparisons:
Trends over time versus a snapshot comparison
Time series design: Each observation represents a measurement
at some point in time. Repeated measurements allow us to see
trends.
Cross-sectional design: Each observation represents a measurement at
some point in time. Comparing across groups allows us to see differences.
Unemployment rate
Is there an increase in workers calling in sick as the summer months approach?
Do more young workers call in sick than older workers?
Grade point average (GPA)
Does GPA tend to go up or down as students move from freshman to sophomores to juniors to seniors?
Does GPA tend to go up or down when you compare Mr. Chen’s class with Mr. Frank’s Freshman English classes?

10. Random sampling vs Random assignment
Random assignment of participants into groups:
Any subject had an equal chance of getting assigned to
either condition (related to quasi versus true experiment)
We know this one
Let’s explore
this one
Random sampling of participants into experiment:
Each person in the population has an equal chance
of being selected to be in the sample
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

11. Sample versus population (census)
How is a census different from a sample?
Census measures each person in the specific population
Sample measures a subset of the population and infers
about the population – representative sample is good
What’s better?
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

12. 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 “mew”
pronounced “mu”
Statistic – Numerical value calculated from a sample
Usually represented by Roman letters (x)
pronounced “x bar”

13. Descriptive or inferential?
To determine this we have to consider the
methodologies used in collecting the data
Descriptive or inferential?
Descriptive statistics - organizing and summarizing data
Inferential statistics - generalizing beyond actual observations
making “inferences” based on data collected
What is the average height of the basketball team?
Measured all of the players and reported the average height
Measured only a sample of the players and reported the average height for team
In this class, percentage of students who support the death penalty?
Measured all of the students in class and reported percentage who said “yes”
Measured only a sample of the students in class and reported percentage who said “yes”
Based on the data collected from the students in this class we can
conclude that 60% of the students at this university support the death penalty
Measured all of the students in class and reported percentage who said “yes”

14. Descriptive or inferential?
Descriptive statistics - organizing and summarizing data
Inferential statistics - generalizing beyond actual observations
making “inferences” based on data collected
Men are in general taller than women
Measured all of the citizens of Arizona and reported heights
Shoe size is not a good predictor of intelligence
Measured all of the shoe sizes and IQ of students of 20 universities
Blondes have more fun
Asked 500 actresses to complete a happiness survey
The average age of students at the U of A is 21
Asked all students in the fraternities and sororities their age

15. 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
…a random sample
Question: Average weight of U of A football player
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)
2016
Pick 64th name on the list (64 is just an example here) 64

16. 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

17. 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
4% from Illinois
4% from Ohio
4% from Pennsylvania
3% from Michigan
etc
Average cost for text books for a semester
17.7% of sample are Pre-business majors
4.6% of sample are Psychology majors
2.8% of sample are Biology majors
2.4% of sample are Architecture majors
etc

18. 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

19. 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

20. What is the independent variable? Amount of sleep
Does amount of sleep (4 vs 8 hours) affect class attendance? Selected 350 students from 38,000 undergraduates at U of Washington and randomly assigned students into two groups.
What is the independent variable?
Amount of sleep
How many levels are there of the IV?
2 levels (4 hours vs 8 hours)
What is the dependent variable?
Group 1 gets 4 hours sleep
Class attendance
What is population and sample?
Population: whole school
Sample: group of 350 students
Note: Parameter would be what we are guessing for the whole school based on these 350 students
What is statistic ?
Group 2 gets 8 hours sleep
Average class attendance for 350 students
Quasi versus true experiment (random assignment)?
True
Random sample?
Doesn’t say in the problem, so we have to assume “no”

21. What is the independent variable? Gender of teacher
Does gender of the teacher affect test scores for the students in California? Selected 150 students from Santa Monica and created two groups.
What is the independent variable?
Gender of teacher
How many levels are there of the IV?
2 levels (male vs female teacher)
What is the dependent variable?
Group 1
gets a female teacher
Test Scores
What is population and sample?
Population: California
Sample: group of 150 students from Santa Monica
What is statistic ?
Group 2
gets a male teacher
Average test score for 150 students
Quasi versus true experiment (random assignment)?
Doesn’t say in the problem, so we have to assume “no”
Random sample?
No – Random sample would require that everyone in
California be equally likely to be chosen.

22. Let’s try one
A study explored whether eating carrots really improves vision. Half of the subjects ate a package of carrots everyday for 3 months while the other group did not. Then, they tested the vision for all of the subjects. The independent variable in this study was
a. the performance of the subjects on the vision exam
b. the subjects who ate the carrots
c. whether or not the subjects ate the carrots
d. whether or not the subjects had their vision tested

23. Let’s try one
A study explored whether eating carrots really improves vision. Half of the subjects ate a package of carrots everyday for 3 months while the other group did not. Then, they tested the vision for all of the subjects. The dependent variable in this study was
a. the performance of the subjects on the vision exam
b. the subjects who ate the carrots
c. whether or not the subjects ate the carrots
d. whether or not the subjects had their vision tested

24. Let’s try one
A study explored whether eating carrots really improves vision. Half of the subjects ate a package of carrots everyday for 3 months while the other group did not. Then, they tested the vision for all of the subjects. This experiment was a
a. within participant experiment
b. between participant experiment
c. mixed participant experiment
d. non-participant experiment

25. Thank you!
See you next time!!