Shared Flashcard Set


Part IV
Undergraduate 4

Additional Law Flashcards




  • Population and parameter
    • parameter: summary statistic for the population
    • E.g., mean age of the population
  • Sample and statistic
    • Sample is used to make parameter estimates
    • Sample statistic: an estimate of the population parameter
      • E.g., mean age of the sample
Sampling frame
  • List of units from which sample is drawn
    • defines your population
    • E.g., list of memebers of organization or community
  • Ideally you'd like to list all members of your population as your sampling frame
    • Randomly select your sample from the list
  • Telephone book as a sampling frame
    • Very popular in most telephone surveys
    • Misses unlisted numbers
Literary Digest Survey example
  • In 1936, they sent out 10 million post cards
    • names selected from telephone directories and automobile registrations
    • survey results: Landon 57%; Roosevelt 43%
    • election results: Roosevelt in the largest landslide (61% of the vote and 523-8 in Elect. Coll.)
    • Why so inaccurate? Poor sampling frame.
      • Leads to selection of wealthy respondents
Probability vs. Nonprobability Sampling
  • Probability sampling
    • Based on random selection: each unit of the population has an equal chance of being selected into the sample
    • Representativeness as a goal: sample resembles larger population
    • Allows researchers to calculate the amt. of sampling error
  • Nonprobability sampling
    • Based on convenience
    • Sampling frame for randomization does not exist
    • Not able to calculate sampling error

Four types of Nonprobability Samples:

  1. Available Sample
  2. Volunteer Sample 
  3. Purposive Sample
  4. Quota Sample
  • Available Sample
    • People on the street, easily accessible.
    • Notoriously inaccurate -- especially in making inferences about larger population
    • Useful in pretesting questionnaires or other preliminary work (pilot study)
  • Volunteer Sample
    • Sample NOT selected randomly
    • Based on people's willingness to express their views
    • Many quick polls by the media and online websites
  • Purposive Sample
    • Dictated by the purpose of the study
    • Situational judgments about what individuals should be surveyed to make for a useful sample
    • People (or elements) are selected for specific characteristcs or qualities
      • E.g., marketing research for users of a new product; research focusing on Internet users
  • Quota Sample
    • Begins with a table of relevant characteristics of the population
      • Proportions of gender, age, education, ethniciity from census data
      • Selecting  a sample to match those predetermined proportions
    • Problems:
      • Quota frame must be accurate
      • Sample is NOT random

Four types of Probability Sampling:

  1. Simple Random Sample
  2. Systematic Sample
  3. Stratified Sample
  4. Cluster Sample


  • Simple Random Sample
    • A number is assigned to each element
    • Numbers are reandomly selected into the same
      • Random number sample
      • RDD in telephone surveys
        • handling invalid numbers: (A) randomly generate a four-digit phone number, and (B) add a predetermined number
  • Systematic Sampling
    • Select every nth element with random start
    • E.g., 1000 on the population list, choosing ever 10th name yields a sample of 100
    • Sampling interval: standard distance between units on the sampling frame
      • Sampling interval=population size/sample size
    • Sampling ratio: proportion of population that are selected
      • Sampling ratio=sample size/population size.
  •   Stratified Sampling
    • Modification used to reduce potential for sampling error
    • Research ensures that certain groups are represented proportionately in the sample
    • Randomly select within groups in proportion to relative group size
    • E.g., if the population is 60% female, stratified sample selects 60% females into the sample
    • E.g., stratifying by region of the country to make sure that each region is proportionately represented
  • Cluster Sampling
    • Frequently, there is no convenient way of listing the population for sampling purposes
      • E.g., sample of Yolo County or CA: hard to get a list of the population members
    • Cluster samples: Samples of lower-level clusters
      • A) Sample of census blocks
      • B) List of people for selected census blocks
      • C) Select sub-sample or people living on each block

Sampling Error/Standard Error

(Mng., relationship w/ sample size)

  • Sampling error: The degree of error to be expected in probability sampling
  • Every time you draw a sample from the population, the parameter estimate will fluctuate slightly.
    • EX:
      • Population age: 37.0
      • Sample 1: mean age=37.2
      • Sample 2: mean age=36.4
      • Sample 3: mean age: 38.1
  • If you draw lots of samples, you would get a normal curve of values
  • Standard error: The average distance of  parameter estimates from the population parameter
    • As the sample size increases:
      • the standard error decreases
      • in other words, parameter estimate (from sample statistic) is likely to be closer to the population parameter
      • as the sample size increases, we get more confident in our parameter estimate
Confidence Level/Confidence Interval
  • Confidence levels
    • 68% sure estimate is within 1 S.E. of parameter
    • 95% sure estimate is within 2 S.E.
    • 99% sure estimate is within 3 S.E.
  • Confidence interval: interval width at which we are 95% confident contains the population parameter
    • Shrinks as:
      • Standard error is smaller
      • Sample size is larger
Supporting users have an ad free experience!