1. Introduction to Statistics for the Social Sciences SBS200, COMM200, GEOG200, PA200, POL200, or SOC200 Lecture Section 001, Spring 2016 Room 150 Harvill Building 9:00 - 9:50 Mondays, Wednesdays & Fridays.
Welcome

2. Lab sessions Everyone will want to be enrolled
in one of the lab sessions
Labs start
this week

3. Schedule of readings Before next exam (February 12th)
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. By the end of lecture today 1/25/16
Use this as your
study guide
By the end of lecture today 1/25/16
Introduction to Project 1
Review of Methodologies

5. writing assignment forms notebook and clickers to each lecture
Remember bring your
writing assignment forms
notebook and clickers to each lecture
Register your clicker by February 1st
and receive extra credit!
student.turningtechnologies.com
(Please note there is no “www”)

6. Project 1 Likert Scale (summated scale) Correlation (scatterplots) Comparing two means (bar graph)
Review

7. Likert Scale is always a “summated scale” with multiple items.
All items are measuring the same construct. The score reflects the sum of responses on a series of items.
Review

8. Project 1 Likert Scale (summated scale) Correlation (scatterplots) Comparing two means (bar graph)
Review

9. 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
Review

10. 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
Review

11. 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
Review

12. 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

13. 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

14. Correlation

15. 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

16. Project 1 Likert Scale (summated scale) Correlation (scatterplots) Comparing two means (bar graph)

20. Final results might look like this
Predicting One
positive correlation 15 12 9 6 3
“Passion for Gaming” Score
Time Studying

23. Final results might look like this
Predicting One
positive correlation 15 12 9 6 3
“Passion for Gaming” Score
Time Studying

25. Final results might look like this
Predicting One
negative correlation 15 12 9 6 3
“Passion for Gaming” Score
Age

28. Final results might look like this
Predicting One
negative correlation 15 12 9 6 3
“Passion for Gaming” Score
Age

30. Final results might look like this
Predicting One
Group has bigger mean 15 12 9 6 3
“Passion for Gaming” Score
Gender
Female Male

31. Final results might look like this
Predicting One
Group has bigger mean 15 12 9 6 3
“Passion for Gaming” Score
Gender
Female Male

32. Final results might look like this
Average of three scores for males
Final results might look like this 10
Predicting One
Group has bigger mean 15 12 9 6 3
“Passion for Gaming” Score
Gender
Female Male

33. Final results might look like this
Average of three scores for females
Final results might look like this 12
Predicting One
Group has bigger mean 15 12 9 6 3
“Passion for Gaming” Score
Gender
Female Male

34. Final results might look like this
Predicting One
Group has bigger mean 15 12 9 6 3
“Passion for Gaming” Score
Gender
Female Male

35. Final results might look like this
Predicting One
Group has bigger mean 15 12 9 6 3
“Passion for Gaming” Score
Gender
Female Male

37. Final results might look like this
Predicting One
Group has bigger mean 15 12 9 6 3
“Passion for Gaming” Score
Gender
Female Male

38. “Serious Gamer” Score “Serious Gamer” Score “Serious Gamer” Score Time
One positive
correlation
One negative
correlation
Comparing
Two means
“Serious Gamer” Score
“Serious Gamer” Score
“Serious Gamer” Score
Time
Studying
Age
Gender

43. Project 1 - Likert Scale - Correlations - Comparing two means (bar graph)
Questions?

44. 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

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

46. 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”

47. 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.

48. 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

49. 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

50. 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

51. Let’s try one
When Martiza was preparing her experiment, she knew it was important that the participants not know which condition they were in, to avoid bias from the subjects.
This is called a _____ study.
She also was careful that the experimenters who were interacting with the participants did not know which condition those participants were in.
This is called a ____ study.
a. between participant; within participant
b. within participant; between participant
c. double blind design; single blind
d. single blind; double blind design

52. Let’s try one A measurement that has high validity is one that
a. measures what it intends to measure
b. will give you similar results with each replication
c. will compare the performance of the same subjects
in each experimental condition
d. will compare the performance of different subjects

53. Thank you!
See you next time!!