How to Use Large Samples for Research in Nursing

When you carry out nursing research, carefully select sample populations to avoid wasting research time, patient effort and support costs. Ideally, clinical trials should be large enough to reliably detect the smallest differences in primary outcome among patients. When you work with a large sample, utilize selection criteria that specifically define the population to be studied. As well, utilize exclusion criteria to avoid improperly allocating resources or harming subjects. Finally, particularly large samples can be impossible to study under time and budget constraints; in such a case, you may pick a subset of the sample to work with.

Things You'll Need

• Inclusion criteria
• Exclusion criteria

Show More

Instructions

• 1

  Ensure that your large sample has the clinical characteristics--known as inclusion criteria--you need to complete your study. Age is often a crucial factor. When testing a drug on a population, for instance, you might choose to focus on women in their thirties, reasoning that the benefit-to-harm ratio is highest in women in their thirties and less prominent in other age groups. Alternatively, if your sample size is truly large, you could potentially test a variety of age groups, drawing statistically significant conclusions about each age group.

• 2

  Ensure, as well, that the study results you obtain from your sample can be juxtaposed to the larger population--a second kind of inclusion criteria. Using larger samples may seem to suggest that results are more likely to describe the general population; however, consider how you found your subjects. Patients at your own hospital may be an available and inexpensive source of subjects. Peculiarities of local patients or patients at your hospital, though, might interfere with generalizing results to other populations. No single course of action is clearly right or wrong, and you might have to make choices that involve trade-offs between scientific and practical goals.

• 3

  Establish exclusion criteria. In other words, identify subsets of individuals within your sample who would fit your inclusion criteria, if not for certain characteristics that might interfere with the success of follow-up efforts, data quality, or the acceptability of randomized treatment. For instance, if a patient is alcoholic or planning on moving out of state, carrying out follow-up trials with him might be difficult. In addition, if a patient has a history of stroke, it is likely that she would experience adverse effects as a result of the study.

• 4

  Select a subset of the sample if your sample is too large to study every subject under time and budget constraints. Probability sampling, which involves selecting a subset at random, provides a rigorous basis for generalizing the applicability of study results to the population as a whole. Researchers also often use convenience sampling, which involves using people who meet the entry criteria and are easily accessible to the investigator. Convenience sampling has obvious advantages in terms of costs and logistics. Determine, though, whether you can use convenience sampling and still reliably answer the scientific question at hand.