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 http://www.youtube.com/watch?v=oSQJP40PcGI

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

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

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 ..

Schedule of readings Before next exam (February 9) 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

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

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

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

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

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: +1.00 or -1.00 zero correlation: 0.0 Review

Correlation

Correlation - How do numerical values change? http://neyman.stat.uiuc.edu/~stat100/cuwu/Games.html http://argyll.epsb.ca/jreed/math9/strand4/scatterPlot.htm 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

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

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

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

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

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

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

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”

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”

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

Thank you! See you next time!!