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Sampling Observations for Study Political Science 102 - Introduction to Political Inquiry – Lecture 8 Or…Who Cares about Sampling? Populations versus Samples A population is any well-defined set of units of analysis. The set of cases that we want to understand

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### Sampling Observations for Study

Political Science 102 - Introduction to Political Inquiry – Lecture 8

Or…Who Cares about Sampling?

Populations versus Samples
• A population is any well-defined set of units of analysis.
• The set of cases that we want to understand
• Determined by the research question
• A sample is a subset of a population.
• Selected from population by a systematic procedure: the sampling method.
• Sample statistics measure characteristics of the sample
• We use sample statistics to estimate the characteristics of a population
• Key logical inference is external validity
Some Terms and Definitions
• Population parameter
• Quantifiable characteristic of a population
• Denoted by capital English or Greek letter
• Sampling Frame
• Specific population from which sample is actually drawn
• Sample statistic
• Quantifiable characteristic of a sample
• Denoted with a small letter or a ^
• Estimator
• Sample statistic that estimates a population parameter
Populations and Samples
• We would like to analyze the population but cannot
• Large and impractical data collection
• Desire to generalize across time (and into future)
• Seek to produce a sample that matches population parameters
• Any deviation between sample and population is bias
• Leads to flawed inferences about population
• Two methods of drawing samples:
• Probability and Non-Probability samples
Probability Samples
• Each element in the population has a known probability of inclusion in the sample
• Random selection guards against bias
• Same kind of effect as random assignment in experiments
• Simple random sample
• Each element in a population has an equal chance of selection
• Done by a lottery, a random number generator, dice, etc.
• Example: The Vietnam Draft Lottery
• Be sure that you mix well!
• Computers are useful but dice and tables of random numbers work too!
Probability Samples
• Systematic sample:
• Select elements from a list of the population at a predetermined interval
• Start point for must be random or list must be randomized
• Be aware of cycles in list corresponding to sampling interval
• Stratified sample:
• Population divided into two or more strata based on a criterion
• Elements selected from each strata in proportion to the strata’s representation in the entire population
• Reduces bias if population parameters are known
Probability Samples
• Disproportionate stratified sample:
• Elements are drawn disproportionately from the strata.
• Used to over-represent smaller groups
• Ensures large enough sub-samples for reliable inferences
• Example: 1996 National Black Election Study
• Set of about 1,200 interviews of African American respondents
• Used in combination with ANES for comparisons
• Example: Surveys stratified by State (for Senate elections)
• How can we make unbiased inferences about population parameters?
• Weighted observations!
Probability Samples
• Cluster samples:
• Group elements for an initial sampling frame (e.g. 50 states).
• Random samples drawn from increasingly narrow groups (e.g.) counties, then cities, then blocks)
• Final random sample of elements is drawn from the smallest group (individuals living in each household).
• Almost all face-to-face surveys done this way
• Example: American National Election Study
• Lancet Studies of Iraqi Civilian Casualties
• 2003 ~ 100,000 deaths
• 2006 ~ 650,000 deaths
Nonprobability Samples
• Nonprobability samples: Elements in the population have an unknown probability of inclusion in the sample.
• Used when probability samples are not feasible
• Purposive samples (Case Studies)
• Observations selected because of values on variables
• Select on independent not dependent variables
• Generalizability comes from exogenous knowledge, not probability
• Hard cases vs. easy cases
Nonprobability Samples
• Convenience sample:
• Elements that are convenient for the investigator (e.g. college students)
• Often used in experimental studies
• Quota sample:
• Elements are chosen in proportion to representation in population
• Selection within quotas is nonprobabilistic
• Often used in surveys prior to random sampling, still used in market research (focus groups)
Nonprobability Samples
• Snowball sample:
• Elements in the target population identify other elements
• Useful when studying hard-to-locate or identify populations that have social networks
• Highly subject to selection bias & non-representativeness
• Example: 2004 Study of LGBT environment on college campuses
• A Potential Solution: Matching
• 1996 study of psychiatric disorders & drug abuse
• Match on characteristics of subject AND friends