Methods of sampling
There are a variety of different sampling methods available to researchers to select individuals for a study. Sampling method fall into two categories:
1.Probability sampling: Every individual in the population is known and each has a certain probability of being selected. A random process decides the sample based on each individual’s probability.
2.Nonprobability sampling: The population is not entirely known, thus individual probabilities cannot be known. Common sense or ease is used to choose the sample, but efforts are made to avoid bias and keep the sample representative.
To ensure reliable and valid inferences from a sample, probability sampling technique is used to obtain unbiased results. The four most commonly used probability sampling methods in medicine are simple random sampling, systematic sampling, stratified sampling and cluster sampling.
In simple random sampling, every subject has an equal chance of being selected for the study. The most recommended way to select a simple random sample is to use a table of random numbers or a computer-generated list of random numbers. Consider the study by Kamal et al. that aimed to assess the burden of stroke and transient ischemic attack in Pakistan. In this study, the investigators used a household list from census data and picked a random set of households from this list. They subsequently interviewed the members of the randomly chosen households and used this data to estimate cerebrovascular disease prevalence in a particular region of Pakistan. Prevalence studies such as this are often conducted by using random sampling to generate a sampling frame from preexisting lists (such as census lists, hospital discharge lists, etc.).
A systematic random sample is one in which every ‘kth’ item is selected. ‘k’ is determined by dividing the number of items in the sampling frame by sample size.
A stratified random sample is one in which the population is first divided into relevant strata or subgroups and then, using the simple random sample method, a sample is drawn from each strata.
A cluster sample results from a two-stage process in which the population is divided into clusters, and a subset of the clusters is randomly selected. Clusters are commonly based on geographic areas or districts and, therefore, this approach is used more often in epidemiologic research than in clinical studies.
The most important question that a researcher should ask when planning a study is “How large a sample do I need?” If the sample size is too small, even a well-conducted study may fail to answer its research question, may fail to detect important effects or associations, or may estimate those effects or associations too imprecisely. Similarly, if the sample size is too large, the study will be more difficult and costly, and may even lead to a loss in accuracy. Hence, optimum sample size is an essential component of any research.