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Just Plain Data Analysis:

Just Plain Data Analysis:. Compiling, evaluating, and presenting numerical evidence to support and illustrate arguments about politics and public affairs. Numerical evidence: social, political and economic indicators

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Just Plain Data Analysis:

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  1. Just Plain Data Analysis: Compiling, evaluating, and presenting numerical evidence to support and illustrate arguments about politics and public affairs. • Numerical evidence: social, political and economic indicators • Arguments: “informal” arguments consisting of one or more premise and a conclusions.

  2. Common Statistical Fallacies in the Interpretation of Social Indicators in Data-based Public Affairs Writing

  3. A Statistical Fallacy: • A form of Inductive or Informal Argument Logical Fallacy: • When premises do not provide enough support for an argument’s conclusions. • Premises consist of time series, cross sectional and demographic numerical comparisons. • Note: Traditional hypothesis-testing research methods design defines a“deductive” argument structure, but only to establish a very limited conclusion.

  4. The Ask a Stupic Question Fallacy: A new national survey by the Pew Research Center finds that nearly one-in-five Americans (18%) now say Obama is a Muslim, CNN/Opinion Research Corporation survey, more than a quarter of the public have doubts about Obama's citizenship

  5. The Ask a Stupic Question Fallacy: More than half (51%) believe it is very likely or somewhat likely that government officials were "directly responsible for the assassination of President Kennedy.“ More than half (52%) believe it is likely that the CIA allowed drug dealers from Central America to sell crack cocaine to African-Americans in US inner cities.

  6. The Ask a Stupic Question Fallacy: Nearly half (47%) believe it is very likely or somewhat likely that "The U.S. Air Force is withholding proof of the existence of intelligent life from other planets.”

  7. Global Warming • Data in text file • Excel file: • http://lilt.ilstu.edu/gmklass/pos138/cherry2.xlsx

  8. Two measures of voter turnout

  9. Graphical distortion

  10. Graphical distortion

  11. Pass Rate

  12. Failure Rate

  13. Other Denominator Misinterpretations • CPI adjustments for inflation overestimate inflation by %1 per year. Effect:: • Underestimates of income and price growth • Overestimates of poverty rates • “Percentage of median family income,” minimizes price increases, most commonly: • University tuition and fees as a percentage of median family income.

  14. Threats to External Validity: • Treatment or Setting does not correspond to future policy • Hawthorne Effect • Multiple experimental treatments • Example: Third (rear window) brake light experiment • Example: Rats and cancer experiments

  15. Rear window brake light experiment (1974): • 343 San Francisco taxicabs with CHMSL(Center High Mounted Stop Lamps) • 160 taxis with no additional light (random assignment) • Findings: CHMSL taxis:: 61% fewer rear end crashes, 61% fewer driver injuries, 62% lower repair costs • On all cars since 1986 • Later Finding: from 1989-95 CHMSLs reduced rear-end crashes by only 4.3%

  16. Threats to Internal Validity All threats to internal validity are due to the lack of an equivalent or randomly assigned control group • History – something else happened at the same time to produce the effect • Maturation: long term processes affecting the results • Testing: the first test affects the scores on the second • Instrumentation: unreliable measures of effect

  17. Threats to Internal Validity All threats to internal validity are due to the lack of an equivalent or randomly assigned control group • Instability: another form of unreliable measures • Regression artifact: Policy was conducted on a group, a place, or at a time chosen for its high or low scores on the test.Example: Murder rates are higher in states with the death penalty

  18. Regression artifact: example • Students who do the best on the first exam usually do worse on the second • Students who do the worst on the first exam usually do better on the second

  19. Examples • Rudi Giuliani and New York City’s Crime RateJPDA, pp 24-29 • Election Day RegistrationJPDA, pp 91-95

  20. Election Day Registration

  21. Election Day Registration Issue: Why do 6 states with EDR average about 10% higher voter turnout Possible Reasons • Reverse causation: states where civic participation is valued are more likely to enact EDR • History: • Those states may have had very closely contested elections • Those states may have other policies that encourage turnout

  22. Election Day Registration Figure 4.7: State Voter Turnout* and Social Capital

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