Chapter 7

Sampling

(Reminder: Don’t forget to utilize the concept maps and study questions as you study this and the other chapters.)

 

The purpose of Chapter 7 it to help you to learn about sampling in quantitative and qualitative research. In other words, you will learn how participants are selected to be part of empirical research studies.


Sampling refers to drawing a sample (a subset) from a population (the full set).

 

Terminology Used in Sampling

Here are some important terms used in sampling:

 

 


 


  Random Sampling Techniques

The two major types of sampling in quantitative research are random sampling and nonrandom sampling.

 

 

Simple Random Sampling

The first type of random sampling is called simple random sampling.

You will see below that, simple random samples are not the only equal probability sampling method (EPSEM). It is the most basic and well know, however.

 

“How do you draw a simple random sample?"

 

Systematic Sampling

Systematic sampling is the second type of random sampling.

 

 

Systematic sampling involves three steps:

·        First, determine the sampling interval, which is symbolized by "k," (it is the population size divided by the desired sample size).

·        Second, randomly select a number between 1 and k, and include that person in your sample.

·        Third, also include each kth element in your sample. For example if k is 10 and your randomly selected number between 1 and 10 was 5, then you will select persons 5, 15, 25, 35, 45, etc.

·        When you get to the end of your sampling frame you will have all the people to be included in your sample.

·        One potential (but rarely occurring) problem is called periodicity (i.e., there is a cyclical pattern in the sampling frame). It could occur when you attach several ordered lists to one another (e.g., if you had took lists from multiple teachers who had all ordered their lists on some variable such as IQ). On the other hand, stratification within one overall list is not a problem at all (e.g., if you have one list and have it ordered by gender, or by IQ). Basically, if you are attaching multiple lists to one another, there could be a problem. It would be better to reorganize the lists into one overall list (i.e., sampling frame).

 

Stratified Random Sampling

The third type of random sampling is called stratified random sampling.

 

There are actually two different types of stratified sampling.

 

The first type of stratified sampling, and most common, is called proportional stratified sampling.

 

 

The second type of stratified sampling is called disproportional stratified sampling.

·        In disproportional stratified sampling, the subsamples are not proportional to their

sizes in the population.

 

Here is an example showing the difference between proportional and disproportional stratified sampling:

 

Cluster Random Sampling

In this type of sampling you randomly select clusters rather than individual type units in the first stage of sampling.

 

We discuss two types of cluster sampling in the chapter, one-stage and two-stage (note that more stages are possible in multistage sampling but are left for books on sampling).
 

The first type of cluster sampling is called one-stage cluster sampling.

 

 

The second type of cluster sampling is called two-stage cluster sampling.

 

 

Important points about cluster sampling:

 

Nonrandom Sampling Techniques

The other major type of sampling used in quantitative research is nonrandom sampling (i.e., when you do not use one of the ransom sampling techniques). There are four main types of nonrandom sampling:

Random Selection and Random Assignment

In random selection (using an equal probability selection method), you select a sample from a population using one of the random sampling techniques discussed earlier.

 

In random assignment, you start with a set of people (you already have a sample, which very well may be a convenience sample), and then you randomly divide that set of people into two or more groups (i.e., you take the full set and randomly divide it into subsets).

 

Determining the Sample Size
When Random Sampling is Used

Would you like to know the answer to the question "How big should my sample be?"

I will start with my four "simple" answers to your question:

 

I want to make a few more points about sample size in this chapter. In particular, note that you will need larger samples under these circumstances:

 

Sampling in Qualitative Research

Sampling in qualitative research is usually purposive (see the above discussion of purposive sampling). The primary goal in qualitative research is to select information rich cases.

 

There are several specific purposive sampling techniques that are used in qualitative research:

 

For a little more information on sampling in qualitative research, click here. (Hit the right arrow key to move from slide to slide.)