When conducting research, one must often use a sample of the population as opposed to using the entire population. Before we go further into the reasons why, let us first discuss what differentiates between a population and a sample.
A population can be defined as any set of persons/subjects having a common observable characteristic. For example, all individuals who reside in the United States make up a population. Also, all pregnant women make up a population. The characteristics of a population are called a parameter. A statistic can be defined as any subset of the population. The characteristics of a sample are called a statistic.
This brings us to the question of why sample. Why should we not use the population as the focus of study. There are a few major reasons to sample.
One of the reasons to sample is that testing the entire population often produces error. Thus, sampling may be more accurate. Perhaps an example will help clarify this point. Say researchers wanted to examine the effectiveness of a new drug on Alzheimer’s disease. One dependent variable that could be used is an Activities of Daily Living Checklist. In other words, it is a measure of functioning o a day to day basis. In this experiment, it would make sense to have as few of people rating the patients as possible. If one individual rates the entire sample, there will be some measure of consistency from one patient to the next. If many raters are used, this introduces a source of error. These raters may all use some slightly different criteria for judging Activities of Daily Living. Thus, as in this example, it would be problematic to study an entire population.
Another reason to sample is that testing may be destructive. It makes no sense to lesion the lateral hypothalamus of all rats to determine if it has an effect on food intake. We can get that information from operating on a small sample of rats. Also, you probably would not want to buy a car that had the door slammed five hundred thousand time or had been crash tested. Rather, you probably would want to purchase the car that did not make it into either of those samples.
Types of Sampling Procedures
As stated above, a sample consists of a subset of the population. Any member of the defined population can be included in a sample. A theoretical list (an actual list may not exist) of individuals or elements who make up a population is called a sampling frame. There are five major sampling procedures.
The first sampling procedure is convenience. Volunteers, members of a class, individuals in the hospital with the specific diagnosis being studied are examples of often used convenience samples. This is by far the most often used sample procedure. It is also by far the most biases sampling procedure as it is not random (not everyone in the population has an equal chance of being selected to participate in the study). Thus, individuals who volunteer to participate in an exersise study may be different that individuals who do not volunteer.
Another form of sampling is the simple random sample. In this method, all subject or elements have an equal probability of being selected. There are two major ways of conducting a random sample. The first is to consult a random number table, and the second is to have the computer select a random sample.
A systematic sample is conducted by randomly selecting a first case on a list of the population and then proceeding every Nth case until your sample is selected. This is particularly useful if your list of the population is long. For example, if your list was the phone book, it would be easiest to start at perhaps the 17th person, and then select every 50th person from that point on.
Stratified sampling makes up the fourth sampling strategy. In a stratified sample, we sample either proportionately or equally to represent various strata or subpopulations. For example if our strata were states we would make sure and sample from each of the fifty states. If our strata were religious affiliation, stratified sampling would ensure sampling from every religious block or grouping. If our strata were gender, we would sample both men and women.
Cluster sampling makes up the final sampling procedure. In cluster sampling we take a random sample of strata and then survey every member of the group. For example, if our strata were individuals schools in the St. Louis Public School System, we would randomly select perhaps 20 schools and then test all of the students within those schools.
There are several potential sampling problems. When designing a study, a sampling procedure is also developed including the potential sampling frame. Several problems may exist within the sampling frame. First, there may be missing elements – individuals who should be on your list but for some reason are not on the list. For example, if my population consists of all individuals living in a particular city and I use the phone directory as my sampling frame or list, I will miss individuals with unlisted numbers or who can not afford a phone.
Foreign elements make up my second sampling problem. Elements which should not be included in my population and sample appear on my sampling list. Thus, if I were to use property records to create my list of individuals living within a particular city, landlords who live elsewhere would be foreign elements. In this case, renters would be missing elements.
Duplicates represent the third sampling problem. These are elements who appear more than once on the sampling frame. For example, if I am a researcher studying patient satisfaction with emergency room care, I may potentially include the same patient more than once in my study. If the patients are completing a patient satisfaction questionnaire, I need to make sure that patients are aware that if they have completed the questionnaire previously, they should not complete it again. If they complete it more that once, their second set of data respresents a duplicate