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. Labs continue this week
Lab sessions
Everyone will want to be enrolled
in one of the lab sessions
Labs continue this 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. No new homework So there is time to Work on lab projects
Homework Assignment 2 due Wednesday, August 31st Go to D2L - Click on “Content”
Click on “Interactive Online Homework Assignments”
Complete the next two modules:
Independent and dependent variables
Quasi- vs True Experiments
Between vs Within Participant Designs
No new homework
So there is time to
Work on lab projects

5. By the end of lecture today 8/31/16
Use this as your
study guide
By the end of lecture today 8/31/16
Introduction to Project 1
Continuous versus discrete
Levels of Measurement: Nominal, Ordinal, Interval and Ratio

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

7. “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

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

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

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

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

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

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

27. 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

28. 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

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

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

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

33. “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

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

35. Continuous versus discrete
Continuous variable: Variables that can assume any value. There are
(in principle) an infinite number of values between any two numbers
Discrete variable: Variables that can only assume whole numbers.
There are no intermediate values between the whole numbers
Duration
Amount of sand
Number of eggs in a carton
Amount of milk in a glass
Height
Number of cookies on a plate
Distance to the moon
Grains of sand
Number of kids in classroom
Number of textbooks required for class

36. Categorical versus Numerical data
Categorical data (also called qualitative data)
- a set of observations where any single observation is a
word or a number that represents a class or category
Numerical data (also called quantitative data)
- a set of observations where any single observation is a
number that represents an amount or count

37. Handedness - right handed or left handed
Categorical data (also called qualitative data) - a set of observations where any single observation is a word or a number that represents a class or category
Numerical data (also called quantitative data) - a set of observations where any single observation is a number that represents an amount or count
Handedness - right handed or left handed
Family size
Hair color
Ethnic group
GPA
Age (Time since birth)
Temperature
(Kelvin)
Yearly salary
Breed of dog
Gender - male or female
Temperature
(Fahrenheit)
Please note this is a binary variable

38. On a the top half of a writing assignment form
Categorical data (also called qualitative data) - a set of observations where any single observation is a word or a number that represents a class or category
Numerical data (also called quantitative data) - a set of observations where any single observation is a number that represents an amount or count
On a the top half of a writing assignment form
please generate two examples of categorical
data and two examples of numerical data
Please note we’ll use the bottom half for something else

39. What are the four “levels of measurement”?
Ratio
Absolute
zero
Most numeric
Categories
Intrinsic
ordering
Equal sized
intervals
Units
meaningful
Interval
Names
Categories
Intrinsic
ordering
Ordinal
Approaching
Numeric
Categories
Least
numeric
Names
Nominal
Weakest

40. What are the four “levels of measurement”?
Categorical data
Nominal data - classification, differences in kind, names of categories
Ordinal data - order, rankings, differences in degree
Numerical data
Interval data - measurable differences in amount, equal intervals
Ratio data - measurable differences in amount with a “true zero”
Gender - male or female
Family size
Jersey number
Place in a foot race (1st, 2nd, 3rd, etc)
Handedness - right handed or left handed

41. What are the four “levels of measurement”?
Categorical data
Nominal data - classification, differences in kind, names of categories
Ordinal data - order, rankings, differences in degree
Numerical data
Interval data - measurable differences in amount, equal intervals
Ratio data - measurable differences in amount with a “true zero”
Age
Hair color
Telephone number
Ethnic group
Breed of dog
Temperature
Yearly salary

42. Please note :
page 29 in text

44. What are the four “levels of measurement”?
Categorical data
Nominal data - classification, differences in kind, names of categories
Ordinal data - order, rankings, differences in degree
Numerical data
Interval data - measurable differences in amount, equal intervals
Ratio data - measurable differences in amount with a “true zero”
Look at your examples of qualitative and quantitative data.
Which levels of measurement are they?

45. Thank you!
See you next time!!