Sampling in Quantitative, Qualitative, and Mixed Research
The purpose of Chapter 9 is to help you learn about sampling in quantitative, qualitative, and mixed research. In other words, you will learn how participants are selected to be part of empirical research studies.
refers to drawing a sample (the subset) from a population (the full set).
The usual goal in sampling is to produce a (i.e., a sample that is similar to the population on all characteristics, except that it includes fewer people because it is a sample rather than the complete population).
Metaphorically, a perfectly representative sample would be a "mirror image" of the population from which it was selected (again, except that it would include fewer people).
Terminology Used in Sampling
Here are some important terms used in sampling:
A is a set of elements taken from a larger population.
The sample is a subset of the which is the full set of elements or people from which you are sampling.
A is a numerical characteristic of a sample; a is a numerical characteristic of population.
refers to the difference between the value of a sample statistic (such as the sample mean) and the true value of the population parameter (such as the population mean). Note: some error is always present in sampling. With random sampling methods, however, the error is random rather than systematically wrong.
The is the percentage of people in the sample selected for the study who actually participate in the study.
A is a list of all the people that are in the population. An example of a sampling frame is shown in Figure 9.2 in your textbook (which is a list of all the names in the population, and they are numbered). Note we have also included information on age and gender in our sampling frame.