2. Introduction to Statistics for the Social Sciences SBS200, COMM200, GEOG200, PA200, POL200, or SOC200 Lecture Section 001, Fall 2015 Room 150 Harvill Building 10:00 - 10:50 Mondays, Wednesdays & Fridays. http://www.youtube.com/watch?v=oSQJP40PcGI

3. Everyone will want to be enrolled in one of the lab sessions Labs continue this week with Project 1

4. Project 1 - Likert Scale - Correlations - Comparing two means (bar graph)

7. Questions?

8. Homework Assignment 1 due Monday, August 31 st Go to D2L - Click on “Content” Click on “Interactive Online Homework Assignments” Complete Assignment 3: HW3-Part1-Sampling Techniques HW3-Part2-Integrating Methodologies Due: Friday, September 4 th

9. More information on registering clickers coming soon

10. Schedule of readings Before next exam (September 25 th ) Please read chapters 1 - 5 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

11. By the end of lecture today 9/2/15 Use this as your study guide Review Project 1 Placebo (single blind) versus double blind procedure Continuous versus discrete Levels of Measurement Nominal, Ordinal, Interval and Ratio

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

13. Placebo (single blind) versus double blind procedure Single blind procedure (example: use of placebo) Double blind procedure What about experimenter bias?

14. Duration Distance to the moon Number of kids in classroom Height Number of eggs in a carton Number of textbooks required for class Amount of sand Grains of sand Number of cookies on a plate Amount of milk in a glass 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

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

16. Gender - male or female Handedness - right handed or left handed Family size Ethnic group Temperature (Fahrenheit) Age (Time since birth) Yearly salary Hair color GPA Breed of dog 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 Please note this is a binary variable Temperature (Kelvin)

17. On a the top half of a writing assignment form please generate two examples of categorical data and two examples of 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 Please note we’ll use the bottom half for something else

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

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

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

21. Please note : page 29 in text

23. What are the four “levels of measurement”? Interval data - measurable differences in amount, equal intervals Ordinal data - order, rankings, differences in degree 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? Nominal data - classification, differences in kind, names of categories Categorical data Numerical data