300 W3 Discussion
Tie3D
Neuman_Ch_04.pptThis multimedia product and its contents are protected under copyright law. The following are prohibited by law: any public performance or display, including transmission of any image over a network; preparation of any derivative work, including the extraction, in whole or in part, of any images; any rental, lease or lending of the program.
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
