2. Introduction to Statistics for the Social Sciences SBS200, COMM200, GEOG200, PA200, POL200, or SOC200 Lecture Section 001, Spring 2015 Room 150 Harvill Building 8:00 - 8:50 Mondays, Wednesdays & Fridays. http://courses.eller.arizona.edu/mgmt/delaney/d15s_database_weekone_screenshot.xlsx

3. Schedule of readings Before next exam (February 13 th ) Please read chapters 1 - 4 in Ha & Ha 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

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

6. Homework due On class website: please print and complete homework worksheet #3 & 4 Monday 2/2/15 Please note: This assignment will require gathering data so plan ahead

7. By the end of lecture today 1/30/15 Use this as your study guide Questionnaire design and evaluation Surveys and questionnaire design Correlational methodology Positive, Negative and Zero correlation Strength and direction

8. Questionnaire is a set of fixed-format, self-report items completed without supervision or time-constraint Response rate and power of random sampling Response rate and power of random sampling Number of responders versus percentage of responders Number of responders versus percentage of responders Wording, order, balance can all affect results Wording, order, balance can all affect results Really important regarding bias ! Questionnaires use self-report items for measuring constructs. Constructs are operationally defined by content of items.

9. Questionnaire is a set of fixed-format, self-report items completed without supervision or time-constraint Response rate and power of random sampling Response rate and power of random sampling Number of responders versus percentage of responders Number of responders versus percentage of responders Wording, order, balance can all affect results Wording, order, balance can all affect results Really important regarding bias ! Questionnaires use self-report items for measuring constructs. Constructs are operationally defined by content of items. Review

10. As “composers” of questionnaire data – how should we ask? - pilot – fix - pilot – analyze – fix - pilot – all the way through your design As “consumers” of questionnaire data – what should we ask? Number of responders versus percentage of responders Operational definitions of constructs Wording Methodology of sampling Questionnaires use self-report items for measuring constructs. Constructs are operationally defined by content of items.

11. The importance of the iterative process in design: Iterative process and peer review is important skill in nearly all areas of business and science. Goal is to provide productive, useful and kind feedback

12. Designed our study / observation / questionnaire Collected our data Organize and present our results

13. 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 Range between -1 and +1 The closer to zero the weaker the relationship The closer to zero the weaker the relationship and the worse the prediction Positive or negative Positive or negative

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

15. Height of Mothers by Height of Daughters Positive Correlation Height of Daughters Height of Mothers 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

16. Brushing teeth by number cavities Negative Correlation Number Cavities Brushing Teeth 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

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

18. Correlation Perfect correlation = +1.00 or -1.00 The more closely the dots approximate a straight line, (the less spread out they are) the stronger the relationship is. One variable perfectly predicts the other No variability in the scatterplot The dots approximate a straight line

19. Correlation

20. Correlation - How do numerical values change? Let’s estimate the correlation coefficient for each of the following r = +1.0r = -1.0 r = +.80 r = -.50r = 0.0 http://neyman.stat.uiuc.edu/~stat100/cuwu/Games.html http://argyll.epsb.ca/jreed/math9/strand4/scatterPlot.htm

21. Number of bathrooms in a city and number of crimes committed Positive correlation Positive correlation: as values on one variable go up, so do values for other variable Negative correlation: as values on one variable go up, Negative correlation: as values on one variable go up, the values for other variable go down

22. Is it possible that they are causally related? Correlation does not imply causation Yes, but the correlational analysis does not answer that question What if it’s a perfect correlation – isn’t that causal? No, it feels more compelling, but is neutral about causality Number of Birthday Cakes Number of Birthdays

23. Linear vs curvilinear relationship Linear relationship is a relationship that can be described best with a straight line Curvilinear relationship is a relationship that can be described best with a curved line

24. r = +0.97 This shows a strong positive relationship (r = 0.97) between the appraised price of the house and its eventual sales price Description includes: Both variables Strength (weak,moderate,strong) Direction (positive, negative) Estimated value (actual number)

25. r = +0.97r = -0.48 This shows a moderate negative relationship (r = -0.48) between the amount of pectin in orange juice and its sweetness Description includes: Both variables Strength (weak,moderate,strong) Direction (positive, negative) Estimated value (actual number)

26. r = -0.91 This shows a strong negative relationship (r = -0.91) between the distance that a golf ball is hit and the accuracy of the drive Description includes: Both variables Strength (weak,moderate,strong) Direction (positive, negative) Estimated value (actual number)

27. r = -0.91 r = 0.61 This shows a moderate positive relationship (r = 0.61) between the length of stay in a hospital and the number of services provided Description includes: Both variables Strength (weak,moderate,strong) Direction (positive, negative) Estimated value (actual number)

28. r = +0.97r = -0.48 r = -0.91 r = 0.61

29. Height of Daughters (inches) Height of Mothers (in) 48 52 56 60 64 68 72 76 48 52 5660 64 68 72 This shows the strong positive (r = +0.8) relationship between the heights of daughters (in inches) with heights of their mothers (in inches). Both axes and values are labeled Both axes have real numbers listed Variable name is listed clearly Description includes: Both variables Strength (weak,moderate,strong) Direction (positive, negative) Estimated value (actual number)

30. Height of Daughters (inches) Height of Mothers (in) 48 52 56 60 64 68 72 76 48 52 5660 64 68 72 This shows the strong positive (r = +0.8) relationship between the heights of daughters (in inches) with heights of their mothers (in inches). Both axes and values are labeled Both axes have real numbers listed Variable name is listed clearly Description includes: Both variables Strength (weak,moderate,strong) Direction (positive, negative) Estimated value (actual number)

31. Height of Daughters (inches) Height of Mothers (in) 48 52 56 60 64 68 72 76 48 52 5660 64 68 72 This shows the strong positive (r = +0.8) relationship between the heights of daughters (in inches) with heights of their mothers (in inches). Both axes and values are labeled Both axes have real numbers listed Variable name is listed clearly Description includes: Both variables Strength (weak,moderate,strong) Direction (positive, negative) Estimated value (actual number)

32. Height of Daughters (inches) Height of Mothers (in) 48 52 56 60 64 68 72 76 48 52 5660 64 68 72 This shows the strong positive (r = +0.8) relationship between the heights of daughters (in inches) with heights of their mothers (in inches). Both axes and values are labeled Both axes have real numbers listed Variable name is listed clearly Description includes: Both variables Strength (weak,moderate,strong) Direction (positive, negative) Estimated value (actual number)

33. Height of Daughters (inches) Height of Mothers (in) 48 52 56 60 64 68 72 76 48 52 5660 64 68 72 This shows the strong positive (r = +0.8) relationship between the heights of daughters (in inches) with heights of their mothers (in inches). Both axes and values are labeled Both axes have real numbers listed Variable name is listed clearly Description includes: Both variables Strength (weak,moderate,strong) Direction (positive, negative) Estimated value (actual number)

34. 1. Describe one positive correlation Draw a scatterplot (label axes) 2. Describe one negative correlation Draw a scatterplot (label axes) 3. Describe one zero correlation Draw a scatterplot (label axes) Break into groups of 2 or 3 Each person hand in own worksheet. Be sure to list your name and names of all others in your group Use examples that are different from those is lecture 4. Describe one perfect correlation (positive or negative) Draw a scatterplot (label axes) 5. Describe curvilinear relationship Draw a scatterplot (label axes)

35. Height of Daughters (inches) Height of Mothers (in) 48 52 56 60 64 68 72 76 48 52 5660 64 68 72 This shows the strong positive (r = +0.8) relationship between the heights of daughters (in inches) with heights of their mothers (in inches). Both axes and values are labeled Both axes have real numbers listed 1. Describe one positive correlation Draw a scatterplot (label axes) 2. Describe one negative correlation Draw a scatterplot (label axes) 3. Describe one zero correlation Draw a scatterplot (label axes) 4. Describe one perfect correlation (positive or negative) Draw a scatterplot (label axes) 5. Describe curvilinear relationship Draw a scatterplot (label axes) Variable name is listed clearly Description includes: Both variables Strength (weak,moderate,strong) Direction (positive, negative) Estimated value (actual number)