Introduction to Statistics for the Social Sciences SBS200 - Lecture Section 001, Fall 2016 Room 150 Harvill Building 10:00 - 10:50 Mondays, Wednesdays & Fridays. Welcome http://www.youtube.com/watch?v=oSQJP40PcGI

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

Schedule of readings Before next exam (September 23rd) 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

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

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

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

“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

Final results might look like this Predicting One positive correlation 15 12 9 6 3 “Passion for Gaming” Score Time Studying 0 3 6 9 12 15 20

Final results might look like this Predicting One positive correlation 15 12 9 6 3 “Passion for Gaming” Score Time Studying 0 3 6 9 12 15 20

Final results might look like this Predicting One negative correlation 15 12 9 6 3 “Passion for Gaming” Score Age 0 16 18 20 22 24 26

Final results might look like this Predicting One negative correlation 15 12 9 6 3 “Passion for Gaming” Score Age 0 16 18 20 22 24 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

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

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

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

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

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

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

“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

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

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

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

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

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

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

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

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

Please note : page 29 in text

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?

Thank you! See you next time!!