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Methodological Digression

Methodological Digression. Overview. Clinical Trials Regression Analysis Structure Terminology. Clinical Trials. Gilliam and Iyengar (pp. 127-137) Clinical Trials seek to measure the impact of some variable on a population

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Methodological Digression

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  1. Methodological Digression

  2. Overview • Clinical Trials • Regression Analysis • Structure • Terminology

  3. Clinical Trials • Gilliam and Iyengar (pp. 127-137) • Clinical Trials seek to measure the impact of some variable on a population • To do so, the population is divided into two subgroups who are subject, as nearly as possible, to identical conditions except for the variable under review

  4. Clinical Trials • Then, one group -- the experimental group -- is subject to the variable under review • The other group -- the control group -- is subject to a placebo • The “placebo” is meant to mimic the use of the experimental variable

  5. Clinical Trials • For example, Gilliam and Iyengar are trying to investigate whether news coverage impact public opinion about crime • To do so, they manipulated the lead-ins to the same news story • In one, the lead in was changed to indicate that the crime story to follow related to gang violence

  6. Clinical Trials • In this case, then the control group would be the viewers seeing an ostensibly “objective” version of the story • The experimental group would then be the group watching the contrived “gang story” lead in

  7. Clinical Trials • Ideally, the subjects are randomly assigned to each group, and the experimenter/s (the person/s conducting the experiment) are unaware of which group is which • If that is the case then we characterize that as a double-blind clinical trial.

  8. Clinical Trials • A trial in this sense is an experiment, and a clinical trial means the experiment involves human subjects. • A preclinical trial is an experiment involving non human subjects.

  9. Regression Analysis • Regression Analysis is a statistical tool used to look for relationships between variables • The basic idea (skipping a ton of math) is that we can take a test population and measure the impact of a different variables on another variable

  10. Regression Analysis • Variables come in two varieties: • The dependent variable is the variable the experimenter is attempting to manipulate • Independent variables are variables thought to influence the dependent variable

  11. Regression Analysis • For example, if we’re trying to determine the relationship between television news coverage of crime and attitudes towards crime, which is the dependent and which is the independent variable?

  12. Regression Analysis • Public Opinion (IV) = News Media (Dv) • But there’s likely lots of variables that also might influence PO, so before we can say that news media shapes PO, we’d want to try to measure the impact of these other variables and see if our dependent variable remains the main variable shaping public opinion

  13. Regression Analysis • In which case, our equation would look like: • Dv = ß1 + ß2 + ß3 + ewhere ß = dependent variable e = error • Dv = R ß1 +R ß2 +R ß3 + ewhere R = change (“+” or “-”) and is what we refer to as the correlation coefficient • We can measure not only the direction of change, but also the intensity of the relationship between the variables

  14. Regression Analysis • With Regression Analysis we can mathematically control for all those variables and measure the influence of each on the independent variable • The R will always be a number between -1 and +1. • Influence increases (either positively or negatively) as the variable approaches either pole

  15. Regression Analysis • Note, though, that relationships that get too close to the poles are probably not that important (or spurious) • Also note that because the underlying mathematics is statistically based and therefore probabilistic, we can never be 100% certain of our results

  16. Regression Analysis • Which raises the question of how certain can we be that we just haven’t found a fluke? • The variable used to measure the likelihood of that fluke is traditionally designated by the letter p • Skipping lots of math, we consider something statistically significant (meaning not an accident) when p ≤ .05 • The f-test is another tool to measure the variance of our sample and the likelihood that we have a fluke

  17. Public Opinion • Public Opinion: Definition • Measuring Public Opinion • Survey Design • Scientific vs. “Unscientific” polls • Variables to be measured • Factors Shaping Public Opinion • Importance of Public Opinion

  18. Definition • Polling and Politics • Iraq War

  19. Definition • Public Opinion: Aggregate of individual attitudes or beliefs shared by some portion of the adult population

  20. Measuring Public Opinion • Need to add and combine these individual opinions so that we can then determine what the public as a whole believes • Collect data in a scientifically rigorous fashion

  21. Survey Design • Identify target population • Select Random Sample • Write Questions • Conduct Poll • Analyze Data

  22. Survey Design • Identify target population • Who’s opinion are you interested in measuring? • Select Random Sample • Write Questions • Conduct Poll • Analyze Data

  23. Survey Design • Identify target population • Select Random Sample • every person in the target population has an equal and known probability of being included in the survey • Write Questions • Conduct Poll • Analyze Data

  24. Survey Design • Identify target population • Select Random Sample • Write Questions • ensure that questions are fair, non-leading, and clear • Conduct Poll • Analyze Data

  25. Survey Design • Identify target population • Select Random Sample • Write Questions • Conduct Poll • contact those selected in the random sample • mail in, computer, in person “accidental” contact measures do not “work” • Analyze Data

  26. Survey Design • Identify target population • Select Random Sample • Write Questions • Conduct Poll • Analyze Data • Intepret what the numbers mean

  27. Scientific vs “Unscientific” Polls • Key is in the random sample • “random”: every person in the target population has an equal and known probability of being included in the survey • Allows us to calculate the margin of error and the confidence interval

  28. Scientific vs “Unscientific” Polls • Margin of Error: How much the sample reports differ from the total population • +/- 3.5% to about +/- 6% • 45% with a 4% margin of error 45% 41% 49%

  29. Scientific vs “Unscientific” Polls • Confidence Interval: How sure we are in the results • p should be somewhere in the .01 to .05 range

  30. Scientific vs “Unscientific” Polls • Need to be able to determine how much your sample differs from the total population, and how sure you are in the results • If no random sample, no way to determine that

  31. Variables to Measure • Intensity • Salience • Consensus • Divisiveness • Change StronglyAgree Agree Disagree StronglyDisagree

  32. Variables to Measure • Intensity • Salience • Consensus • Divisiveness • Change Don’t Know Yes No

  33. Variables to Measure • Intensity • Salience • Consensus • Divisiveness • Change Disagree Agree

  34. Variables to Measure • Intensity • Salience • Consensus • Divisiveness • Change Yes No

  35. Variables to Measure • Intensity • Salience • Consensus • Divisiveness • Change % agreeing t1 t2 t3 t4 t5

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