PS 366
4
Measurement
• Related to reliability, validity:
• Bias and error– Is something wrong with the instrument?
– Is something up with the thing being measured?
Measurement
• Bias & error with the instrument
– Random?
– Systematic?
Measurement
• Bias & error with the thing being measured
– Random?• failure to understand a survey question
– Systematic?• does person have something to hide?
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?
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
Measurement
• Example: – Reliability, validity, error & bias in measuring
victims of violent crime
– Census surveys, police records, FBI UCR
– What sources of bias?
Measurement
• How do we ask people questions about attitudes, behavior that isn’t socially accepted?– prejudice– Racism– Feelings toward gays & lesbians– shoplifting
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
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
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 = %
Item Count• Control TREATMENT
% upset
• Non South 2.28 2.24 0
• South 1.952.37 42
2.37 – 1.95 = 0.42 *100 = 42%
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
Use poll info• Control TREATMENT % use poll
• All 2.28 1.36 1.39 3.2
• Young 1.04 1.46 41
• Is it significant?– Depends....how much does mean reflects the group?
How much variation around the mean?
Central Tendency: Chapter 4
• Statistics that describe the ‘average’ or ‘typical’ value of a variable
– Mean– Median– Mode
Central Tendency
• Why median vs. mean?
– Household income
– Home prices
Central Tendency
• Mean125 92 72126120 99130100sum=864
• mean = sum X/ N
• = 864 / 8
• mean = 108
• Is this representative?
Central Tendency
• Mean125 92300126120 99130100sum=1092
• mean = sum X/ N
• = 1092 / 8
• mean = 136.5
• Is this representative?
Central Tendency
• Mean130126125120100 99 92
• median = (N +1) /2– (8+1)/2– 9/2– 4.5 th – (120, 125)
• Is this representative?
Central Tendency
• Example$120,00$60,000$40,000$40,000$30,000$30,000$30,000
• Mean = $50,000 Mdn = $40,000 Mo = $30,000
• Which is most representative?
Mean vs Median
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?
Normal vs. Skew Distributions
Which direction is skew here?
Frequency Distribution
Median vs Mean Price
• Seattle, 98121– Median $375,900– Mean $434,612
• Seattle, 98112 – Median $790,000– Mean $955,750
The Distribution
• Where is mean, median, mode if
– Normal
– Left / negative skew
– Right / positive skew
Normal vs. Skew Distributions
The Distribution
• Where is mean, median, mode if
– Normal ALL THE SAME
– Left / negative skew Mean less than median
– Right / positive skew Mean greater than median
Chapter 4 Practice• # 6
• # 7
• #8
• #15
Top Related