How can a nurse researcher tell the difference between an accessible population and target population?

Businesses often perform research to identify consumer buying habits and preferences. It's rarely possible to study all consumers in particular population segment, which means business researchers must find ways to choose the right consumers to study. Done correctly, a statistical study will reveal insights about an entire target population.

Target Population

The target population is the group a researcher hopes to understand, according to Statistics How To. For example, suppose a company is launching a new product for senior citizens. Analyzing the target population – all senior citizens – could uncover insights that allow the company to implement a variety of advertising campaigns suited to different income levels and attitudes within that target population example.

Experimentally Accessible Population

The experimentally accessible population is the group that a researcher actually can measure. Budgetary constraints, for example, often limit the number of consumers a researcher can study, making the experimentally accessible population much smaller than the target population, according to ELITE Institute. Physical limitations also often force a researcher to study groups that are smaller than the target population. For example, interviewing every consumer distributed across a large region often isn't feasible, meaning a researcher must select a smaller group for study.

Statistical Problems

A problem occurs if the experimentally accessible population is not representative of the target population. For example, suppose a researcher wants to study rural consumer attitudes toward a company's product. Surveying all rural consumers isn't possible, so the researcher selects just a few consumers to study. But unless the researcher uses reliable statistical methods for choosing which consumers to study, chances are good the study's results will not be valid for understanding the target population at large. Limiting interviews to only those consumers who are conveniently located might produce biased results that don't hold true for the target population.

Expert Insight

With the right statistical methods, it's possible to select a representative sample from the experimentally accessible population. For example, if a researcher can study a large enough group of consumers while also relying on selection methods that are random, the researcher can be reasonably confident that the study's results will be valid for the target population as a whole.

For most small businesses, the complicated nature of this type of statistical analysis makes hiring expert market researchers an attractive option. Experts understand not only how to identify and access a representative sample of a target population, but also how to extrapolate important patterns and insights from any data that results. Understanding the difference between population and target population will help the business person better understand their consumers and market to their needs.

The pandemic of 2020 created a daily barrage of data released to the consumer for interpretation. Nations and states were compared on new cases, hospitalizations, recoveries, and deaths. National and international data were presented on interactive maps with circle sizes illustrating the spread of the COVID-19 virus. A concept key to understanding how to interpret data is sampling.

Every researcher has a sampling plan, the process of determining who or what will provide data for the study. The target population consists of all the individuals who meet the sampling criteria. For example, a researcher may use a target population of all perianesthesia nurses for a study on resilience during a pandemic. But a study of the target population may not be feasible or affordable. The accessible population is the portion of the target population that the researcher can access, such as membership in the American Society of PeriAnesthesia Nurses (ASPAN). Members who go on to provide data for the study are considered the sample. When reporting the number of subjects participating in a study, the researcher must be careful to provide a denominator.

The denominator in mathematics represents the whole and the number of pieces that the whole has been equally divided by. For example, your mother bakes a cake and cuts it into ten pieces. Two pieces are eaten by your sister and brother; eight of ten pieces remain. Mathematically, ten represents the whole, or the population of cake pieces, the denominator; eight represents the sample of pieces remaining. If the researcher only states that eight pieces of cake are available, the reader does not know how large or small the cake was before it was sampled by your sister and brother.

It is important for the perianesthesia reader to know the size of the target population or, at the least, information about the target population. Such information is frequently provided in the methodology section of a report under a heading of ‘Sampling.’ A target population of patients in the perianesthesia unit could be estimated by the number of patients cared for each year, or the number of surgeries per month, or even the number of admissions per week. In the last issue of the Journal, Andersson et al1 collected anxiety data on 37 postoperative orthopedic patients. The reader can infer that the target population was 120 patients over a 4-week period, and thus, the sample represented 37 of 120 patients (31% of the population). While the authors provided a target population denominator, the small percentage of the population included in the sample limits the applicability of the findings. Repeated studies are needed.

Epidemiologists use the entire population for their studies. Two important calculations, prevalence and incidence, are made using large databases. Prevalence is the number of cases in a particular population at a given time. The denominator is the population. For example, on April 14, there were 1,020 positive cases of COVID-19 in New Hampshire and 752 cases in Vermont. It is tempting to say that the New Hampshire population was more infected. If the denominator is the number of tests performed, the infection rate is 8.7% and 7.1%, respectively. However, when the denominator is the total population, the rate is 0.075 and 0.12, respectively. Thus, from this data snapshot, on April 14, Vermont had a greater disease prevalence than New Hampshire.

Incidence is the number of new cases, with a denominator of the total population. On April 13, there were 56 new cases reported in New Hampshire and 21 new cases in Vermont. The incidence in the respective states was 0.0041 versus 0.0033; thus, there was a lower risk of contracting the disease in Vermont on April 13.

Sample data can be best interpreted if knowledge of the denominator is made available. The perianesthesia reader must carefully examine the research report to determine the target and accessible populations of a study. Without these data, study findings are difficult to translate into clinical practice.

Footnotes

Conflict of interest: None to report.

Reference

1. Andersson V., Bergstrand J., Engstrom A., Gustafsson S. The impact of preoperative patient anxiety on postoperative anxiety and quality of recovery after orthopaedic surgery. J Perianesth Nurs. 2020;35(3):260–264. [PubMed] [Google Scholar]

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