1. PS 366 4

2. Measurement Related to reliability, validity: Bias and error – Is something wrong with the instrument? – Is something up with the thing being measured?

3. Measurement Bias & error with the instrument – Random? – Systematic?

4. Measurement Bias & error with the thing being measured – Random? failure to understand a survey question – Systematic? does person have something to hide?

5. Measurement Example: – Reliability, validity, error & bias in measuring unemployment – Census survey [also hiring reports, claims filed w/ government, state data to feds...] – What sources of bias?

6. Measurement Unemployment [employment status]: – Fully employed – Part time – looking for work, + part time – looking for work, no job – lost job, not looking for work – retired

7. Measurement Example: – Reliability, validity, error & bias in measuring victims of violent crime – Census surveys, police records, FBI UCR – What sources of bias?

8. Measurement How do we ask people questions about attitudes, behavior that isn’t socially accepted? – prejudice – Racism – Feelings toward gays & lesbians – shoplifting

9. Measurement: Item Count Technique Here are 3 things that sometimes make people angry or upset. After reading these, record how many of them upset you. Not which ones, just how many? federal govt increasing the gas tax professional athletes getting million dollar salaries large corporations polluting the environment

10. Measurement: Item Count Technique federal govt increasing the gas tax professional athletes getting million dollar salaries large corporations polluting the environment federal govt increasing the gas tax professional athletes getting million dollar salaries large corporations polluting the environment a black family moving next door

11. Measurement: Item Count Technique Randomly assign ½ of subjects to the 3 item list Randomly assign ½ subjects to the 4 item list Difference in mean # of responses between groups = % upset by sensitive item – (mean 1 – mean 2) *100 = %

12. Item Count ControlTREATMENT % upset Non South2.282.24 0 South1.952.37 42 2.37 – 1.95 = 0.42 *100 = 42%

13. Item Count – Using poll information 1) The candidate graduated from a prestigious college 2) The candidate ran a business 3) The candidate’s family background 1) The candidate graduated from a prestigious college 2) The candidate ran a business 3) The candidate’s family background 4) The candidate is ahead in polls

14. Use poll info ControlTREATMENT % use poll All2.281.36 1.393.2 Young1.041.46 41 Is it significant? – Depends....how much does mean reflects the group? How much variation around the mean?

15. Central Tendency: Chapter 4 Statistics that describe the ‘average’ or ‘typical’ value of a variable – Mean – Median – Mode

16. Central Tendency Why median vs. mean? – Household income – Home prices

17. Central Tendency Mean 125 92 72 126 120 99 130 100 sum=864 mean = sum X/ N = 864 / 8 mean = 108 Is this representative?

18. Central Tendency Mean 125 92 300 126 120 99 130 100 sum=1092 mean = sum X/ N = 1092 / 8 mean = 136.5 Is this representative?

19. Central Tendency Mean 130 126 125 120 100 99 92 median = (N +1) /2 – (8+1)/2 – 9/2 – 4.5 th – (120, 125) Is this representative?

20. Central Tendency Example $120,00 $60,000 $40,000 $30,000 Mean = $50,000 Mdn = $40,000 Mo = $30,000 Which is most representative?

21. Mean vs Median

22. Mean vs. Median median = ½ point. 12.5 = 32,000 mean = (Sum of X’s) / n = 816,000 / 25 = 32,640 Why is mean higher than median?

23. Normal vs. Skew Distributions

24. Which direction is skew here?

25. Frequency Distribution

26. Median vs Mean Price Seattle, 98121 – Median $375,900 – Mean$434,612 Seattle, 98112 – Median $790,000 – Mean $955,750

27. The Distribution Where is mean, median, mode if – Normal – Left / negative skew – Right / positive skew

28. Normal vs. Skew Distributions

29. The Distribution Where is mean, median, mode if – Normal ALL THE SAME – Left / negative skew Mean less than median – Right / positive skewMean greater than median

30. Chapter 4 Practice # 6 # 7 #8 #15