1. Introduction to Statistics for the Social Sciences SBS200 - Lecture Section 001, Spring 2018 Room 150 Harvill Building 9:00 - 9:50 Mondays, Wednesdays & Fridays.
Welcome

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

3. In nearly every class we will use clickers to
answer questions in class and participate in
interactive class demonstrations
Remember bring your
writing assignment forms
notebook and clickers to each lecture

4. Schedule of readings Before next exam (February 9)
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

6. Homework Assignment 3 due Monday, January 22 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

7. By the end of lecture today 1/19/18
Use this as your
study guide
By the end of lecture today 1/19/18
Levels of Measurement
Nominal, Ordinal, Interval and Ratio
Categorical vs Numeric
Project 1

8. 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

9. 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

10. 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

11. 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

12. 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

13. 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

14. Please note :
page 29 in text

16. 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?

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

18. 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.
- Likert Scale (miniquiz like Cosmo example in lecture
- ask several questions then sum responses)
- For example, several questions on political views (coded so that larger numbers mean “more liberal”)
1. Lower taxes and a smaller
government will improve the standard of living for all.
agree disagree
2. Marriage should be between one man and one woman
agree disagree
3. Evolution of species has no place in public education
agree disagree

19. I prefer rap music to classical music
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.
Anchored rating scales: a written description somewhere on the scale
I prefer rap music to classical music
Agree Disagree
Fully anchored rating scales: a written description for each point on the scale
I prefer rap music to classical music
Strongly Agree
Agree
Neutral
Strongly Disagree
Disagree

20. 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.

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

22. 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

23. 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

24. 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

25. Thank you!
See you next time!!