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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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overview
Overview
  • Clinical Trials
  • Regression Analysis
    • Structure
    • Terminology
clinical trials
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
clinical trials4
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
clinical trials5
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
clinical trials6
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
clinical trials7
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.
clinical trials8
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.
regression analysis
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
regression analysis10
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
regression analysis11
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?
regression analysis12
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
regression analysis13
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
regression analysis14
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
regression analysis15
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
regression analysis16
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
public opinion
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
definition
Definition
  • Polling and Politics
  • Iraq War
definition19
Definition
  • Public Opinion: Aggregate of individual attitudes or beliefs shared by some portion of the adult population
measuring public opinion
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
survey design
Survey Design
  • Identify target population
  • Select Random Sample
  • Write Questions
  • Conduct Poll
  • Analyze Data
survey design22
Survey Design
  • Identify target population
    • Who’s opinion are you interested in measuring?
  • Select Random Sample
  • Write Questions
  • Conduct Poll
  • Analyze Data
survey design23
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
survey design24
Survey Design
  • Identify target population
  • Select Random Sample
  • Write Questions
    • ensure that questions are fair, non-leading, and clear
  • Conduct Poll
  • Analyze Data
survey design25
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
survey design26
Survey Design
  • Identify target population
  • Select Random Sample
  • Write Questions
  • Conduct Poll
  • Analyze Data
    • Intepret what the numbers mean
scientific vs unscientific polls
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
scientific vs unscientific polls28
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%

scientific vs unscientific polls29
Scientific vs “Unscientific” Polls
  • Confidence Interval: How sure we are in the results
  • p should be somewhere in the .01 to .05 range
scientific vs unscientific polls30
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
variables to measure
Variables to Measure
  • Intensity
  • Salience
  • Consensus
  • Divisiveness
  • Change

StronglyAgree

Agree

Disagree

StronglyDisagree

variables to measure32
Variables to Measure
  • Intensity
  • Salience
  • Consensus
  • Divisiveness
  • Change

Don’t Know

Yes

No

variables to measure33
Variables to Measure
  • Intensity
  • Salience
  • Consensus
  • Divisiveness
  • Change

Disagree

Agree

variables to measure34
Variables to Measure
  • Intensity
  • Salience
  • Consensus
  • Divisiveness
  • Change

Yes

No

variables to measure35
Variables to Measure
  • Intensity
  • Salience
  • Consensus
  • Divisiveness
  • Change

% agreeing

t1

t2

t3

t4

t5