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Harvill 150
renumbered

2. Even if you have not yet registered your clicker you can still participate..
The
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3. Introduction to Statistics for the Social Sciences SBS200 - Lecture Section 001, Fall 2018 Room 150 Harvill Building 10: :50 Mondays, Wednesdays & Fridays.
Welcome

4. Lab sessions Labs Next week Everyone will want to be enrolled
in one of the lab sessions
Labs
Next week

5. Schedule of readings Before next exam (September 21)
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

9. Random sampling vs Random assignment
Already have participants
Random assignment of participants into groups:
Any subject had an equal chance of getting assigned to
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
Recruiting
participants
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

10. Pick 64th name on the list (64 is just an example here)
Simple random sampling: each person from the
population has an equal probability of being included
Sample frame = how you define population
Question: Average weight of U of A football player
Sample frame population of the U of A football team
=RANDBETWEEN(1,83)
Pick 64th name on the list (64 is just an example here) 64
Let’s take a sample
…a random sample
2018

11. 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
3) Sample every 100th shipping crate

12. 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

13. 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

14. 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

15. IV:
Nominal
IV: Nominal Ordinal Interval or Ratio?
Independent
Variable?
Type of
Cow Chow
Mariska works at a cattle ranch, and wants cattle to gain as much weight as possible. Mariska wants to know if the new feed makes a difference in how much weight the cattle gain.
She gathers the first 100 cows that she finds in the meadow, and then randomly assigns those 100 cows into two groups (50 each group)
One group gets the new feed for 6 months, while the other group of cattle gets the old feed. She is not looking for any trends over time, but is just looking for a difference between the two types of cow chow (feed).
IV: Continuous or discrete?
IV:
Discrete
Random
Sampling?
No, only convenience sampling
Random
Assignment?
Random Assignment
(True Expt)
DV:
Ratio
DV: Nominal Ordinal Interval or Ratio?
Dependent Variable?
Weight
DV: Continuous or discrete?
DV: Continuous
Between or within?
Between Participant Design
Cross
Sectional
Cross Sectional or Time Series

17. What is the independent variable? Amount of sleep
Does amount of sleep (4 vs 8 hours) affect class attendance? Selected 350 students who happened to be walking along the mall 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?
Note: Parameter would be what we are guessing for the whole school based on these 350 students
Population: whole school
Sample: group of 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?
No, not all students equally likely to be on mall

18. 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 then just asked them to report the gender of their teacher.
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)?
No, no random assignment, just used current teacher
Random sample?
No – Random sample would require that everyone in
California be equally likely to be chosen.

19. Scatterplot displays relationships between two continuous variables
Correlation: Measure of how two variables co-occur and
also can be used for prediction
Range between -1 and +1
The closer to zero the weaker the relationship
and the worse the prediction
Positive or negative

20. Correlation
Range between -1 and +1
+1.00 perfect relationship = perfect predictor
+0.80 strong relationship = good predictor
+0.20 weak relationship = poor predictor
0 no relationship = very poor predictor
-0.20 weak relationship = poor predictor
-0.80 strong relationship = good predictor
-1.00 perfect relationship = perfect predictor

21. Positive correlation: as values on one variable go up, so do values
Positive correlation: as values on one variable go up, so do values for the other variable
Negative correlation: as values on one variable go up, the values for the other variable go down
Height of Mothers
by Height of Daughters
Height of Mothers
Positive
Correlation
Height of
Daughters

22. Positive correlation: as values on one variable go up, so do values
Positive correlation: as values on one variable go up, so do values for the other variable
Negative correlation: as values on one variable go up, the values for the other variable go down
Brushing teeth
by number cavities
Brushing Teeth
Negative
Correlation
Number Cavities

23. Perfect correlation = +1.00 or -1.00
One variable perfectly predicts the other
Height in inches
and height in feet
Speed (mph)
and time to finish race
Positive
correlation
Negative
correlation

24. Correlation
The more closely the dots approximate a straight line, (the less spread out they are) the stronger the relationship is.
Perfect correlation = or -1.00
One variable perfectly predicts the other
No variability in the scatterplot
The dots approximate a straight line

25. Correlation

26. Correlation does not imply causation
Is it possible that they are causally related?
Yes, but the correlational analysis does not answer that question
What if it’s a perfect correlation – isn’t that causal?
No, it feels more compelling, but is neutral about causality
Number of
Birthdays
Number of
Birthday Cakes

27. Positive correlation: as values on one variable go up,
so do values for other variable
Negative correlation: as values on one variable go up,
the values for other variable go down
Number of bathrooms in a city and number of crimes committed
Positive correlation
Positive correlation

28. Linear vs curvilinear relationship
Linear relationship is a relationship
that can be described best with a straight line
Curvilinear relationship is a relationship that can be described best with a curved line

29. Correlation - How do numerical values change?
Correlation - How do numerical values change?
Let’s estimate the correlation coefficient for each of the following
r = +.80
r = +1.0
r = -1.0
r = -.50
r = 0.0

30. This shows a strong positive relationship (r = 0
This shows a strong positive relationship (r = 0.97) between the price of the house and its eventual sales price
r = +0.97
Description includes:
Both variables
Strength (weak,moderate,strong)
Direction (positive, negative)
Estimated value (actual number)

31. r = +0.97
r = -0.48
This shows a moderate negative relationship (r = -0.48) between the amount of pectin in orange juice and its sweetness
Description includes:
Both variables
Strength (weak,moderate,strong)
Direction (positive, negative)
Estimated value (actual number)

32. r = -0.91 Description includes: Both variables
Strength (weak,moderate,strong)
Direction (positive, negative)
Estimated value (actual number)
This shows a strong negative relationship (r = -0.91) between the distance that a golf ball is hit and the accuracy of the drive
r = -0.91

33. r = -0.91 r = 0.61 Description includes: Both variables
Strength (weak,moderate,strong)
Direction (positive, negative)
Estimated value (actual number)
This shows a moderate positive relationship (r = 0.61) between the price of the length of stay in a hospital and the number of services provided
r = -0.91
r = 0.61

34. r = +0.97
r = -0.48
r = -0.91
r = 0.61

35. Height of Daughters (inches)
Height of Mothers (in)
This shows the strong positive (r = +0.8) relationship between the heights of daughters (in inches) with heights of their mothers (in inches).
Variable name is listed clearly
Description includes:
Both variables
Strength (weak,moderate,strong)
Direction (positive, negative)
Estimated value (actual number)
Both axes have real numbers listed
Both axes and values are labeled
Variable name is listed clearly

36. Height of Daughters (inches)
Height of Mothers (in)
This shows the strong positive (r = +0.8) relationship between the heights of daughters (in inches) with heights of their mothers (in inches).
Variable name is listed clearly
Description includes:
Both variables
Strength (weak,moderate,strong)
Direction (positive, negative)
Estimated value (actual number)
Both axes have real numbers listed
Both axes and values are labeled
Variable name is listed clearly

37. Height of Daughters (inches)
Height of Mothers (in)
This shows the strong positive (r = +0.8) relationship between the heights of daughters (in inches) with heights of their mothers (in inches).
Variable name is listed clearly
Description includes:
Both variables
Strength (weak,moderate,strong)
Direction (positive, negative)
Estimated value (actual number)
Both axes have real numbers listed
Both axes and values are labeled
Variable name is listed clearly

38. Height of Daughters (inches)
Height of Mothers (in)
This shows the strong positive (r = +0.8) relationship between the heights of daughters (in inches) with heights of their mothers (in inches).
Variable name is listed clearly
Description includes:
Both variables
Strength (weak,moderate,strong)
Direction (positive, negative)
Estimated value (actual number)
Both axes have real numbers listed
Both axes and values are labeled
Variable name is listed clearly

39. Height of Daughters (inches)
Height of Mothers (in)
This shows the strong positive (r = +0.8) relationship between the heights of daughters (in inches) with heights of their mothers (in inches).
Variable name is listed clearly
Description includes:
Both variables
Strength (weak,moderate,strong)
Direction (positive, negative)
Estimated value (actual number)
Both axes have real numbers listed
Both axes and values are labeled
Variable name is listed clearly

40. Hand in this Correlation
Worksheet now

41. Thank you!
See you next time!!