Sampling: How to Select a Few to Represent the Many Chapter 4

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.

How and Why Do Samples Work?

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.

Focusing On At A Specific Group: Four Types Of Non-Random Samples

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.

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.

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.

Coming to Conclusions about Large Populations Sampling ratio = the ratio of the sample size to the size of the target population.

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.

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.

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.

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.

Coming to Conclusions about Large Populations

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.

Coming to Conclusions about Large Populations

Coming to Conclusions about Large Populations

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.

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.

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. Confidence Interval with sample size of 100, 99% confidence 48.4 55.6 52% estimate Confidence Interval with sample size of 100, 99% confidence 50.5 53.5 52% estimate