Introduction to Statistics for the Social Sciences SBS200, COMM200, GEOG200, PA200, POL200, or SOC200 Lecture Section 001, Spring 2016 Room 150 Harvill Building 9:00 - 9:50 Mondays, Wednesdays & Fridays. Welcome http://www.youtube.com/watch?v=oSQJP40PcGI

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

Schedule of readings Before next exam (February 12th) 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 1/25/16 Use this as your study guide By the end of lecture today 1/25/16 Introduction to Project 1 Review of Methodologies

writing assignment forms notebook and clickers to each lecture Remember bring your writing assignment forms notebook and clickers to each lecture Register your clicker by February 1st and receive extra credit! student.turningtechnologies.com (Please note there is no “www”)

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

Scatterplot displays relationships between two continuous variables Correlation: Measure of how two variables co-occur and also can be used for prediction Range between -1 and +1 The closer to zero the weaker the relationship and the worse the prediction Positive or negative Review

Positive correlation: as values on one variable go up, so do values Positive correlation: as values on one variable go up, so do values for the other variable Negative correlation: as values on one variable go up, the values for the other variable go down Height of Mothers by Height of Daughters Height of Mothers Positive Correlation Height of Daughters Review

Positive correlation: as values on one variable go up, so do values Positive correlation: as values on one variable go up, so do values for the other variable Negative correlation: as values on one variable go up, the values for the other variable go down Brushing teeth by number cavities Brushing Teeth Negative Correlation Number Cavities Review

Perfect correlation = +1.00 or -1.00 One variable perfectly predicts the other Height in inches and height in feet Speed (mph) and time to finish race Positive correlation Negative correlation

Correlation Range between -1 and +1 +1.00 perfect relationship = perfect predictor +0.80 strong relationship = good predictor +0.20 weak relationship = poor predictor 0 no relationship = very poor predictor -0.20 weak relationship = poor predictor -0.80 strong relationship = good predictor -1.00 perfect relationship = perfect predictor

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

“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?

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

What is the independent variable? Amount of sleep Does amount of sleep (4 vs 8 hours) affect class attendance? Selected 350 students from 38,000 undergraduates at U of Washington and randomly assigned students into two groups. What is the independent variable? Amount of sleep How many levels are there of the IV? 2 levels (4 hours vs 8 hours) What is the dependent variable? Group 1 gets 4 hours sleep Class attendance What is population and sample? Population: whole school Sample: group of 350 students Note: Parameter would be what we are guessing for the whole school based on these 350 students What is statistic ? Group 2 gets 8 hours sleep Average class attendance for 350 students Quasi versus true experiment (random assignment)? True Random sample? Doesn’t say in the problem, so we have to assume “no”

What is the independent variable? Gender of teacher Does gender of the teacher affect test scores for the students in California? Selected 150 students from Santa Monica and created two groups. What is the independent variable? Gender of teacher How many levels are there of the IV? 2 levels (male vs female teacher) What is the dependent variable? Group 1 gets a female teacher Test Scores What is population and sample? Population: California Sample: group of 150 students from Santa Monica What is statistic ? Group 2 gets a male teacher Average test score for 150 students Quasi versus true experiment (random assignment)? Doesn’t say in the problem, so we have to assume “no” Random sample? No – Random sample would require that everyone in California be equally likely to be chosen.

Let’s try one A study explored whether eating carrots really improves vision. Half of the subjects ate a package of carrots everyday for 3 months while the other group did not. Then, they tested the vision for all of the subjects. The independent variable in this study was a. the performance of the subjects on the vision exam b. the subjects who ate the carrots c. whether or not the subjects ate the carrots d. whether or not the subjects had their vision tested

Let’s try one A study explored whether eating carrots really improves vision. Half of the subjects ate a package of carrots everyday for 3 months while the other group did not. Then, they tested the vision for all of the subjects. The dependent variable in this study was a. the performance of the subjects on the vision exam b. the subjects who ate the carrots c. whether or not the subjects ate the carrots d. whether or not the subjects had their vision tested

Let’s try one A study explored whether eating carrots really improves vision. Half of the subjects ate a package of carrots everyday for 3 months while the other group did not. Then, they tested the vision for all of the subjects. This experiment was a a. within participant experiment b. between participant experiment c. mixed participant experiment d. non-participant experiment

Let’s try one When Martiza was preparing her experiment, she knew it was important that the participants not know which condition they were in, to avoid bias from the subjects. This is called a _____ study. She also was careful that the experimenters who were interacting with the participants did not know which condition those participants were in. This is called a ____ study. a. between participant; within participant b. within participant; between participant c. double blind design; single blind d. single blind; double blind design

Let’s try one A measurement that has high validity is one that a. measures what it intends to measure b. will give you similar results with each replication c. will compare the performance of the same subjects in each experimental condition d. will compare the performance of different subjects

Thank you! See you next time!!