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Lecture #7: Types of Bias, Association vs. Causation.doc

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Jeff Racine

Lecture #7- Chapter 3 continued (Part 2) • Probability Sample: Occurs when a sample is taken with the help of a randomizing device that assures each member a known (positive, not necessarily equal) probability of selection • (in other words; a subset of a population that has been taken from a randomizing device) o Simple random sample: obtained if every member of a population has an equal chance of being in the sample (main type) o Systematic random sample: randomly select 1 element, then include every kth element thereafter till sample complete o Stratitified random sample: take random samples from every stratum (clearly distinguishable subgroups) in a population. o Clustered random sample: population naturally subdivided into geographically distinct units in which you will randomly select clusters, and then take a sample form each cluster. Errors in Survey Data • We expect that there will exist random error when we conduct a survey (also known as chance error or sampling error) –byproduct of taking a subset of a population • Arises only in sample surveys (ie: this will be 0 in a census) • Sample error equals difference between: o Value obtained from single random sample o Value obtained by taking a census (true value)what we would get if we actually took a census • Size of error (and its frequency) often estimated and reported with observed data (eg: “A census would reveal a number within 2% of the value”) Bias in Survey Data • Random errors are the least of our problems, and if even if we took a census, we might encounter serious problems • Systematic error (Non-sampling error): equals the difference between o Value obtained by taking a census o The true (but unknown value) • Problem with systematic error is that it is hard to detect • Bias can creep into a survey in a number of ways: o Can be built into the survey’s design o Can occurs during the survey’s execution (during survey) o Can transpire during the final processing (post survey) Selection Bias • Selection Bias: A systematic tendency to choose certain elements from a population and ignore other elements from a population. Ie: if you call someone in 3 in afternoon you are more likely to get someone not in the labour force; than in the labour force • Sources of Selection Bias o Nonrandom samples (eg: surveying all employees who leave work at 5 pm) o Faulty design of random samples th (eg: selecting every 12 month to survey monthly stock prices) o Faulty executions of perfect sampling plans (eg: interviewers substituting other persons for those randomly selected) Nonresponse Bias • Nonresponse Bias: Systematic tendency for some elements of a population in a survey to choose not to respond (not to contribute data). • Sources of Nonresonse Bias o Contact problems (eg: potential respondent tears up
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