Sampling Theory

Theory of sampling examines the relationship that exist between a population and a sample drawn from it. It applies only to random samples. The aim of sampling theory is to determine statistically the most effective means to obtain a sample that accurately represent the population under investigation (Grove, & Gray, 2019). Sampling decisions affect the meaning and generalization of findings (Grove, & Gray, 2019). Key concepts of sampling theory include representativeness, sampling criteria, populations, elements, sampling errors, sampling plans and sampling frames. Sampling refers to selection of a group of behaviors, events or people with which to carry out a study. This paper describes the sampling theory in the light of its key concepts.

Population and Element

The population refers to a particular group of persons which the researcher is interested in, for example, persons with Covid-19. The target population is the whole set of individuals who satisfy sampling criteria (an example is children, under five years, suffering malnutrition, and receiving nutritional supplements at the primary healthcare setting= target population). The element is the unit of study or basic unit about which data is collected (Grove, & Gray, 2019). A participant, behavior or event are a good example of an element.

Sampling Criteria

Sampling criteria is a list of characteristics important for membership in the target group. Sampling criteria define which elements have to be included or excluded from the study sample.  Thus inclusion criteria entail the characteristics that a study participant must have to be eligible in the target population. By contrast, exclusion criteria involve those characteristics that cause a research participant who is eligible to be removed.


Representativeness implies that the sample is similar to the target population in many respects (Grove, & Gray, 2019). Representativeness is evaluated in terms of participants’ characteristics and setting. A sample should be representative in relation to demographic characteristics and in terms of the setting to allow for generalization of the findings. 

Sampling methods

Sampling methods outline strategies employed to select elements that represent the study population. Sampling methods are developed to improve representativeness and decrease systematic bias. The commonly used sampling methods are probability sampling and nonprobability sampling.

Sampling frame

Sampling frame is the listing of every member of the population so that everyone has equal chance of being selected. Sampling error is the difference between statistic and population parameter.  It results from systematic variation and random variation. Sampling error tend to decrease with increasing sample size. 

Generalizability of Study Results

Generalizability extends the findings from the sample population under investigation to the entire target population (Grove, & Gray, 2019). Study results are generalizable when the sample is a representative of a population. They can be replicated in other settings. Generalizability is affected by research design, representativeness of a sample and subject ascertainment. At the beginning of the study, investigators draw a sample from a target population with the purpose of extending the findings from the sample back to the target population. Findings that First Oral Hormone Therapy is effective in treating men with advanced prostate cancer at Institute of Medical Specialty, for example, can be generalized to the target population of males with advanced prostate cancer. With this kind of information, hospitals can decide whether it is appropriate to apply this evidence in treating similar kind of patients in their care. To generalize findings, target population and accessible population must be defined broadly. In addition to that, biases must be minimized otherwise it would be difficult to defend the findings.     


Muroi, M., Shen, J.J., & Angosta, A. (2017). Association of medication errors with drug classifications, clinical units, and consequence of errors: Are they related? Application Nursing Research, 33:180-185. doi: 10.1016/j.apnr.2016.12.002 

Grove, S. K. & Gray, J. R. (2019). Understanding Nursing Research: building evidence-based practice. Elsevier.

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