Introduction to Statistics for the Social Sciences SBS200 - Lecture Section 001, Fall 2017 Room 150 Harvill Building 10:00 - 10:50 Mondays, Wednesdays & Fridays. Welcome http://www.youtube.com/watch?v=oSQJP40PcGI
writing assignment forms notebook and clickers to each lecture Remember bring your writing assignment forms notebook and clickers to each lecture
Three tries Will show up in grades within 24 hours
Lab sessions Labs start Today Everyone will want to be enrolled in one of the lab sessions Labs start Today
Project 1
Project 1 Likert Scale (summated scale) Correlation (scatterplots) Comparing two means (bar graph)
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. - miniquiz (like Cosmo - ask several questions then sum responses) - For example, several questions on political views (coded so that larger numbers mean “more liberal”) 1. Lower taxes and a smaller government will improve the standard of living for all. agree 1 --- 2 --- 3 --- 4 --- 5 disagree 2. Marriage should be between one man and one woman agree 1 --- 2 --- 3 --- 4 --- 5 disagree 3. Evolution of species has no place in public education agree 1 --- 2 --- 3 --- 4 --- 5 disagree
I prefer rap music to classical music Agree 1---2---3---4---5 Disagree 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. Anchored rating scales: a written description somewhere on the scale I prefer rap music to classical music Agree 1---2---3---4---5 Disagree Fully anchored rating scales: a written description for each point on the scale I prefer rap music to classical music 1---------2---------3---------4---------5 Strongly Disagree Strongly Agree Agree Neutral Disagree
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.
Project 1 Likert Scale (summated scale) Correlation (scatterplots) Comparing two means (bar graph)
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
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
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
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?
Random assignment Independent and Dependent Variables How do we decide who gets into each condition? Random assignment of subjects into groups: Any subject had an equal chance of getting assigned to either condition Gender and spending If random assignment then you may have a “true” experiment If no random assignment then you have a “quasi” experiment Sleep and memory Music and plants Cell phones and driving Cars and cool
What if we can’t randomly assign people to groups?!?? Random assignment of subjects into groups: Any subject had an equal chance of getting assigned to either condition Comparing heights of 7-year-olds with 17-year-olds Quasi-experiment: comparing “subject variables” - no random assignment - 7 17 Height Quasi-experiment: comparing group means without random assignment Comparing cost of care for men and women Quasi-experiment: Correlation compares relationship between two measures with no random assignment Correlation: relationship between two dependent measures (no random assignment) Men Women Cost of Nursing Care Looking at relationship between height and weight Looking at relationship between age and salary Weight Height Salary Age
Random sampling vs Random assignment Random assignment of participants into groups: Any subject had an equal chance of getting assigned to either condition (related to quasi versus true experiment) We know this one Random sampling of participants into experiment: Each person in the population has an equal chance of being selected to be in the sample Population: The entire group of people about whom a researcher wants to learn Sample: The subgroup of people who actually participate in a research study
Within - participant (same as within - subject) & Between - participant (same as between - subject) Within-participant design: each subject participates in every level of independent variable (aka repeated measures) Between-participant design: each subject participates in only one level of independent variable Within or between participant design? Music make plants grow? Animals make funnier commercials? Effect of sleep on memory ability
Thank you! See you next time!!