Reflection apa format reference less than 5 years

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NGR6812Week6ResearchSampling.ppt
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Qualitative and Quantitative Sampling

Types of Nonprobability Sampling

  • Nonprobability sampling
  • Typically used by qualitative researchers
  • Rarely determine sample size in advance
  • Limited knowledge about larger group or population
  • Types
  • Haphazard
  • Quota
  • Purposive
  • Snowball
  • Deviant Case
  • Sequential

Populations and Samples

  • A population is any well-defined set of units of analysis.
  • The population is determined largely by the research question; the population should be consistent through all parts of a research project.
  • A sample is a subset of a population.
  • Samples are drawn through a systematic procedure called a sampling method.
  • Sample statistics measure characteristics of the sample to estimate the value of population parameters that describe the characteristics of a population.

Populations and Samples

  • A population would be the first choice for analysis.
  • Resources and feasibility usually preclude analysis of population data.
  • Most research uses samples.

Haphazard Sampling

  • Cheap and quick
  • Can produce ineffective, highly unrepresentative samples
  • NOT recommended
  • Person-on-the-street interviews
  • Clip out survey from a newspaper and mail it in

Quota Sampling

  • First you identify relevant categories of people
  • Then you figure out how many to sample from each category
  • Ensures that some differences are in the sample
  • Still haphazard sampling within the category, however

Purposive Sampling

  • Expert uses judgment in selecting cases with a specific purpose in mind
  • Especially informative cases
  • Cultural themed magazines
  • Difficult-to-reach, specialized population
  • Prostitutes
  • Particular types of cases
  • Gamson study in the book

Snowball Sampling

  • Identifying and sampling the cases in a network
  • I find a prostitute to talk to, then ask her for some more prostitutes I could talk to, and it goes on and on and on

Deviant Case Sampling

  • Seeks cases that differ from the dominant pattern or that differ from the predominant characteristics of other cases
  • Selected because they are unusual
  • High school dropouts example

Sequential Sampling

  • Researcher uses purposive sampling until the amount of new information or diversity of cases is filled
  • Gather info until the marginal utility of new information levels off

Probability Sampling

  • Saves time and cost
  • Accuracy
  • Sampling element: unit of analysis or case in a population
  • Population is all of the possible elements, specified for unit, geographical location, and temporal boundaries

Probability Sampling

  • Sampling frame is specific list that closely approximates all of the elements in a population
  • Can be extremely difficult because there just aren’t good lists for some things
  • Frames are almost always inaccurate

Parameter v. Statistic

  • Parameter: characteristic of an entire population
  • Statistic: estimates of population parameters based on sample

Literary Digest Poll Mishap

  • Sampling frame was automobile registrations and telephone directories
  • Accurate predictions in 1920, 24, 28, and 32
  • Send postcard and respondents send back
  • In 1936, sampled 10 million and predicted massive victory for Landon over FDR

Literary Digest Poll Mishap

  • VERY, VERY wrong
  • Frame did NOT represent the target population (all voters)
  • Excluded as much as 65% of voters, including most of FDR’s supporters during the Depression

Why Random Sampling?

  • Each element has an equal probability of selection
  • Can statistically calculate the relationship between sample and the population—sampling error
  • Types:
  • Simple Random
  • Systematic
  • Stratified
  • Cluster

Simple Random Sample

  • Number all of the elements in a sampling frame and use a list of random numbers to select elements (or pull from a hat etc.)
  • Pulling marbles out of a jar
  • Random chance can make it so we’re off on the actual population, but over repeated independent samples, the true number will emerge

Simple Random Sample

  • We will end up with a normal bell curve the more we sample
  • Random sampling does NOT mean that every random sample will perfectly represent the population
  • Confidence intervals are ranges around a specific point used to estimate a parameter
  • I am 95% certain that the population parameter lies between 2,450 and 2,550 red marbles in the jar

Systematic Sampling

  • Simple random sampling with a shortcut for selection
  • Number each element in the sampling frame
  • Calculate a sampling interval—tells researcher how to select elements by skip pattern

Systematic Sampling

  • I want to sample 500 names from a list of 1000
  • Sampling interval is 2
  • I select a random starting point and choose every other name to give me 500
  • Big problem when elements in a sample are organized in some kind of cycle or pattern

Stratified Sampling

  • First divide the population into subpopulations on basis of supplemental info and then do a random sample from each subpopulation
  • Guarantees representation
  • This can allow for oversampling as well for specific research purposes

Cluster Sampling

  • Useful when there is no good sampling frame available
  • All high school basketball players, for example
  • First you random sample clusters of information then draw a random sample of elements from within the clusters you selected

Cluster Sampling

  • Example
  • Want to sample individuals from Cleveland
  • Randomly select city blocks, then households within blocks, then individuals within households
  • Less expensive, but also less precise
  • Error shows up in each sample drawn

How Large Should a Sample Be?

  • It depends
  • Smaller the population, the bigger your sampling ratio will need to be to be accurate
  • < 1,000 = 30%
  • 10,000 = 10%
  • > 150,000 = 1%
  • > 10,000,000 = .025%

How Large Should a Sample Be?

  • For small samples, small increases in sample size produce big gains in accuracy
  • Decision about best sample size depends on:
  • Degree of accuracy required
  • Degree of variability in population
  • Number of variables measured simultaneously

Inference

  • The goal of statistical inference is to make supportable conclusions about the unknown characteristics, or parameters, of a population based on the known characteristics of a sample measured through sample statistics.
  • Any difference between the value of a population parameter and a sample statistic is bias and can be attributed to sampling error.

Inference

  • On average, a sample statistic will equal the value of the population parameter.
  • Any single sample statistic, however, may not equal the value of the population parameter.
  • Consider the sampling distribution: When the means from an infinite number of samples drawn from a population are plotted on a frequency distribution, the mean of the distribution of means will equal the population parameter.

Inference

Inference

  • By calculating the standard error of the estimator (or sample statistic), which indicates the amount of numerical variation in the sample estimate, we can estimate confidence.
  • More variation means less confidence in the estimate.
  • Less variation means more confidence.

Inference

  • One way to increase confidence in an estimate is to collect a larger, rather than a smaller, sample.
  • Measures of variability get smaller with larger samples:
  • But the value of a larger sample may be offset by the increased cost; this is yet another tradeoff in research design.
  • To reduce sampling error by half, a sample must quadruple in size.