Selecting representatives of a target population
In this step, we introduce the principles, purpose and types of sampling methods that are used in epidemiological studies in eye care. To obtain correct estimates of prevalence or other epidemiological measures, all the analyses done have to be weighted according to the sampling method used.
As you read through this article, consider the challenges that a researcher may face when trying to conduct a cross-sectional study in a densely populated urban setting versus a sparsely-populated rural setting.
Definition of sampling
Sampling is a procedure by which some members of a given population are selected as representatives of the entire target population. A sample is, therefore, a subset of the target population.
Why do we use samples?
Collecting information from everyone in a large population would be logistically impossible and financially prohibitive. By using a sample from the population of interest we can:
Sampling and representativeness
To ensure that a sample is representative, we must clearly define the target population, sampling frame and the sample selection process. The purpose is to make sure that the study findings (say, for example, on the prevalence of cataract) are similar to those we would find if we conducted a census of the whole population.
In other words, we “trade-off” the above “practicality” factors vs. the “certainty” that we would have by “taking a census” of the entire population.
IMPORTANT: The idea of a sample is to make inferences about the whole population. Key terms in sampling include:
We determine the sample size at the planning stage of any research study. Selecting the sample size is not an exact procedure. It is an exercise in balance between cost (resources needed) and the precision of the prevalence estimates that are to be made.
We should not carry out any study unless we are confident that the sample size is large enough to give the minimum required precision.
A. Probability sampling methods
B. Simple random sampling: Each individual in a target population is enumerated and assigned a number. The sample size required is randomly selected by use of a table of random numbers. Everyone has an equal chance of being selected.
C. Systematic random sampling: We do this by taking every nth person on a list. For example, every 6th person. This method can result in a biased sample. For example, if we use an electoral list, family members may be listed in groups. The sampling interval is made regular in order to select the required sample size. Systematic random sampling can be simpler to administer than random sampling and in some circumstances, such as in a large sampling area, it may guarantee more uniform distribution throughout the survey area.
The challenges in carrying out random or systematic sampling are that, in some settings, population lists may not be available or populations may be scattered, so it would make the process of sampling inefficient. No sample will be exactly the same as the true population, there will always be some effect of sampling, this is known as the sampling error.
Simple random sampling has simple statistical properties, so we can easily measure our likely sampling error and establish the range in which true prevalence is found.
Multi-stage cluster sampling
We use cluster sampling in situations when it’s not possible to carry out the systematic random sampling. For example, creating a list of all households in a large population. Clusters are smaller groups within which we then carry out a random sampling.
Multi-stage cluster sampling is carried out in two stages:
Sampling frames and probability proportional to size
Within a sampling frame, each sampling unit may be variable in size. That is, there may be different numbers of people within each sampling unit and this will affect the odds of selection between large and small units.
Therefore, a technique called probability proportional to size (PPS) is applied to ensure an equal chance of selection across the sampling frame. When we sample through PPS, people in larger population units have the same chance of being included as those in smaller population units.
Advantages of cluster sampling
Disadvantages of cluster sampling
Sampling within clusters
There are various methods that can be used:
Differences between sampling error and no error
Sampling error ALWAYS occurs when samples are examined instead of whole populations. It can be measured (standard error). It cannot be prevented but it can be reduced by increasing the sample size.
Non-sampling error cannot be measured. It can be prevented or minimised by carrying out a well-designed and conducted the study. When considering the results from a prevalence survey, both types of error have to be considered, as both affect the estimate of the prevalence obtained in the sample.
In summary, “True prevalence” in a population of interest is influenced by: