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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

Sampling Observations for Study

Political Science 102 - Introduction to Political Inquiry – Lecture 8

Or…Who Cares about Sampling?

populations versus samples
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
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 samples5
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
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 samples7
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 samples8
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 samples9
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
  • 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 samples11
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 samples12
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