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

3. writing assignment forms notebook and clickers to each lecture
Remember bring your
writing assignment forms
notebook and clickers to each lecture ..

4. In nearly every class we will use clickers to
answer questions in class and participate in
interactive class demonstrations
Even if you have not yet registered your clicker you can still participate ..

5. 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

7. By the end of lecture today 1/22/18
Use this as your
study guide
By the end of lecture today 1/22/18
Project 1
Levels of Measurement
Nominal, Ordinal, Interval and Ratio
Categorical vs Numeric

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

9. Review 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

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

11. Scatterplot displays relationships between two numeric variables
Correlations are either positive or negative
Correlations have a low value (scattered dots)
or high value (dots fall on a straight line)
Perfect correlation: or -1.00
zero correlation:
0.0
Review

12. Correlation

13. 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

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

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

21. 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
negative correlation 15 12 9 6 3
“Passion for Gaming” Score
Age

26. 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
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
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

31. 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

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. 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

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

42. 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

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

44. Sample versus population (census)
How is a census different from a sample?
Census measures each person in the specific population
Sample measures a subset of the population and infers
about the population – representative sample is good
What’s better?
Use of existing survey data
U.S. Census
Family size, fertility, occupation
The General Social Survey
Surveys sample of US citizens over 1,000 items
Same questions asked each year

45. Population (census) versus sample Parameter versus statistic
Parameter – Measurement or characteristic of the population
Usually unknown (only estimated)
Usually represented by Greek letters (µ)
pronounced “mew”
pronounced “mu”
Statistic – Numerical value calculated from a sample
Usually represented by Roman letters (x)
pronounced “x bar”

46. Descriptive or inferential?
To determine this we have to consider the
methodologies used in collecting the data
Descriptive or inferential?
Descriptive statistics - organizing and summarizing data
Inferential statistics - generalizing beyond actual observations
making “inferences” based on data collected
What is the average height of the basketball team?
Measured all of the players and reported the average height
Measured only a sample of the players and reported the average height for team
In this class, percentage of students who support the death penalty?
Measured all of the students in class and reported percentage who said “yes”
Measured only a sample of the students in class and reported percentage who said “yes”
Based on the data collected from the students in this class we can
conclude that 60% of the students at this university support the death penalty
Measured all of the students in class and reported percentage who said “yes”

47. Descriptive or inferential?
Descriptive statistics - organizing and summarizing data
Inferential statistics - generalizing beyond actual observations
making “inferences” based on data collected
Men are in general taller than women
Measured all of the citizens of Arizona and reported heights
Shoe size is not a good predictor of intelligence
Measured all of the shoe sizes and IQ of students of 20 universities
Blondes have more fun
Asked 500 actresses to complete a happiness survey
The average age of students at the U of A is 21
Asked all students in the fraternities and sororities their age

48. Thank you!
See you next time!!