1. Introduction to Statistics for the Social Sciences SBS200 - Lecture Section 001, Spring 2017 Room 150 Harvill Building 9:00 - 9:50 Mondays, Wednesdays & Fridays.
Welcome

2. 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

3. Lab sessions Everyone will want to be enrolled
in one of the lab sessions
No Labs
this week

4. Schedule of readings Before next exam (February 10)
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

5. Start by reading Online supplemental reading 1 (Appendix D)
Please read Chapters 1 & 2

6. Homework Assignment 2 Go to D2L - Click on “Content”
Click on “Interactive Online Homework Assignments”
Complete Assignment 2:
HW2-Part1-Quasi-experimental Designs versus True Experiments
HW2-Part2-Between versus Within Participant Design
Due: Friday, January 20th

7. By the end of lecture today 1/18/17
Use this as your
study guide
By the end of lecture today 1/18/17
Independent and dependent variables
Random Assignment
True versus Quasi-Experiments
Within-participant and between-participant design
Single blind (placebo) procedure
Double blind procedure
Continuous versus discrete
Levels of Measurement: Nominal, Ordinal, Interval and Ratio

8. How many levels of the IV are there?
Dependent variable: The behavior or mental process being
measured.
Independent variable: The factor that is being manipulated
by the experimenter. (Must have multiple levels)
Does type of music played to plants make they grow better?
What is the dependent variable?
How many levels of the IV are there?
The weight of plants
What is the independent variable?
Five different types of music
Review

9. Random assignment How do we decide who gets into each condition?
Random assignment of subjects into groups:
Any subject had an equal chance of getting
assigned to either condition
Gender and spending
If random assignment
then you may have a “true” experiment
If no random assignment
then you have a
“quasi” experiment
Sleep
and memory
Music
and plants
Cell phones
and driving
Cars
and cool
Review

10. What if we can’t randomly assign people to groups?!??
Random assignment of subjects into groups: Any subject had an equal chance of getting assigned to either condition
Comparing heights of 7-year-olds with 17-year-olds
Quasi-experiment: comparing “subject variables”
- no random assignment -
Height
Quasi-experiment: comparing group means without random assignment
Comparing cost of care for men and women
Quasi-experiment:
Correlation compares relationship between two measures with no random assignment
Correlation: relationship between two dependent measures (no random assignment)
Men Women
Cost of
Nursing Care
Looking at relationship
between height and weight
Looking at relationship
between age and salary
Weight
Height
Salary
Age

11. 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
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

12. Within - participant (same as within - subject) & Between - participant (same as between - subject)
Within-participant design: each subject participates in every
level of independent variable (aka repeated measures)
Between-participant design: each subject participates in only
one level of independent variable
Within or between participant design?
Music make plants grow?
Animals make funnier commercials?
Effect of sleep on memory ability

13. Break up group of kids into two groups
If you want to know if studying improves test performance in young children
Group 1 - studies & tested
Break up group of kids into two groups
What is the independent variable?
What is the dependent variable?
How many levels are there of the IV?
Is this a “quasi” or “true” experiment?
“Between” or “within” participant design?
Group 2 - does not study & tested

14. What is the independent variable?
If you want to know if “Ginseng drink” is associated with feelings of satisfaction
First test group with placebo drink (sugar pill)
What is the independent variable?
What is the dependent variable?
How many levels are there of the IV?
“Between” or “within” participant design?
Placebo
Then test same group with “Ginseng drink”

20. 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

21. 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

22. 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

23. 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

24. 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

25. 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

26. 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

27. Please note :
page 29 in text

29. 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?

30. Thank you!
See you next time!!