For this discussion, section find TWO (2) peer-reviewed academic journal articles that directly relate to your research topic for this class. In detail…
Topic: Sampling
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For this discussion section find TWO (2) peer reviewed academic journal articles that directly relate to your research topic for this class. In detail…
1. Describe their methods of sampling. Be sure to include a description of the sampling procedure.
2. Describe how these two articles help you with developing your own research methods and sampling ideas
3. Describe how these two articles help you apply and develop your understanding of your research project and question in a broader sense.
See the week’s checklist (posted below as a reply) to help organize your response.
In summary, for this assignment,you are going to: 1) Find, 2) Describe, and 3) Apply.
Readings and Materials
1. Read Textbook: Understanding Research: Chapter 4
2. View PowerPoint presentations: Neuman (attached)
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Sampling: How to Select a Few to Represent the Many
Chapter 4
Copyright © Allyn & Bacon 2009
How and Why Do Samples Work?
Sample = a small collection of units taken from a larger collection.
Population = a larger collection of units from which a sample is taken.
Random sample = a sample drawn in which a a random process is used to select units from a population
These are best to get an accurate representation of the population
But are difficult to conduct.
Copyright © Allyn & Bacon 2009
How and Why Do Samples Work?
Copyright © Allyn & Bacon 2009
Focusing On At A Specific Group: Four Types Of Non-Random Samples
Convenience sampling (Accidental or Haphazard) = a non-random sample in which you use an non-systematic selection method that often produces samples very unlike the population.
Quota sample = non-random sample in which you use any means to fill pre-set categories that are characteristics of the population.
Copyright © Allyn & Bacon 2009
Focusing On At A Specific Group: Four Types Of Non-Random Samples
Copyright © Allyn & Bacon 2009
Focusing On At A Specific Group: Four Types Of Non-Random Samples
Purposive (Judgmental) sampling = a non-random sample in which you use many diverse means to select units that fit very specific characteristics.
Snowball (network) sampling = a non-random sample in which selection is based on connections in a pre-existing network.
Copyright © Allyn & Bacon 2009
Coming to Conclusions about Large Populations
Sampling element = a case or unit of analysis of the population that can be selected for a sample.
Universe = the broad group to whom you wish to generalize your theoretical results.
Population = a collection of elements from which you draw a sample.
Copyright © Allyn & Bacon 2009
Coming to Conclusions about Large Populations
Target population = the specific population that you used.
Sampling frame = a specific list of sampling elements in the target population.
Population parameter = any characteristic of the entire population that you estimate from a sample.
Copyright © Allyn & Bacon 2009
Coming to Conclusions about Large Populations
Sampling ratio = the ratio of the sample size to the size of the target population.
Copyright © Allyn & Bacon 2009
Coming to Conclusions about Large Populations
Why Use a Random Sample?
Random samples are most likely to produce a sample that truly represents the population.
They are purely mathematical or mechanical.
Allow calculation of probability of outcomes with great precision.
sampling ratio = the ratio of the sample size to the size of the target population.
Sampling error = the degree to which a sample deviates from a population.
Copyright © Allyn & Bacon 2009
Coming to Conclusions about Large Populations
Types of Random Samples
Simple Random Samples = sample elements selected from the frame based on a mathematically random selection procedure
most times, a proper random sample yields results that are close to the population parameter
Sampling distribution = A plot of many random samples, with a sample characteristic across the bottom and the number of samples indicated along the side.
Copyright © Allyn & Bacon 2009
Coming to Conclusions about Large Populations
Types of Random Samples
Systematic Sampling = An approximation to random sampling in which you select one in a certain number of sample elements, the number is from the sampling interval.
Sampling Interval = the size of the sample frame over the sample size, used in systematic sampling to select units.
Copyright © Allyn & Bacon 2009
Coming to Conclusions about Large Populations
Types of Random Samples
Stratified Sampling = a type of random sampling in which a random sample is draw from multiple sampling frames, each for a part of the population.
Copyright © Allyn & Bacon 2009
Coming to Conclusions about Large Populations
Copyright © Allyn & Bacon 2009
Coming to Conclusions about Large Populations
Types of Random Samples
Cluster (multi-stage) sampling = a multi-stage sampling method, in which clusters are randomly sampled, then a random sample of elements is taken from sampled clusters.
Copyright © Allyn & Bacon 2009
Coming to Conclusions about Large Populations
Copyright © Allyn & Bacon 2009
Coming to Conclusions about Large Populations
Copyright © Allyn & Bacon 2009
Three Specialized Sampling Techniques
Random Digit Dialing = Computer based random sampling of telephone numbers.
Within Household Samples = Random sampling from within households.
Sampling Hidden Populations
Hidden Population = A group that is very difficult to locate and may not want to be found, and therefore, are difficult to sample.
Copyright © Allyn & Bacon 2009
Inferences from A Sample to A Population
How to Reduce Sampling Errors
the larger the sample size, the smaller the sampling error.
the greater the homogeneity (or the less the diversity), the smaller its sampling error.
How Large Should My Sample Be?
the smaller the population, the bigger the sampling ratio must be for an accurate sample.
as populations increase to over 250,000, sample size no longer needs to increase.
Copyright © Allyn & Bacon 2009
Inferences from A Sample to A Population
How to Create a Zone of Confidence
Confidence interval = a zone, above and below the estimate from a sample, within which a population parameter is likely to be.
55.6
48.4
Confidence Interval with sample size of 100, 99% confidence
52% estimate
53.5
50.5
52% estimate
Confidence Interval with sample size of 100, 99% confidence
