Common Sampling Methods in Nursing Research

Nursing research often involves studying a particular population with a predefined condition. Researchers work with samples because it may be impractical to reach all the target population members. Nursing researchers use scientific sampling methods to ensure every member of the population has an equal chance of being selected, thus, obtaining a representative sample.

When selecting the representative sample, the researcher must beware of over-representation or under-representation of the population to avoid exposing the research study to bias and external validity threats. Sampling bias occurs when there is an over or under-representation of the target population in the sample size.

Nurse researchers should select the most appropriate research design and sampling technique to minimize the sampling bias. Sampling methods affect the generalizability of nursing research findings. This article contains a detailed discussion of the common sampling methods in nursing research. In case of any difficulties, one can contact us for the best and most reliable nursing research help at any time, 24/7.

What is Sampling in Statistics?

Sampling is the strategy whereby the researcher selects a subset of the target population or a representative sample and collects data to generalize the findings or make inferences about the whole population.

The sampling theory in nursing research helps to determine the most effective method researchers can use to acquire a study sample that accurately represents the target population using mathematical techniques. There are two categories of sampling methods used in nursing research. These are Probability sampling and non-probability sampling methods.

What is Sampling Theory in Nursing Research?

The sampling theory was established to help find the most suitable method of acquiring a sample that exactly represents the study population using mathematical formulae. The theoretical and mathematical rationale for sampling-related decisions evolved from survey research. The sampling theory assumptions have been incorporated within the nursing research process today. The main concepts identified from the sampling theory include:

• Populations.
• Elements.
• Sampling frames.
• Sampling criteria.
• Randomization.
• Sampling errors.
• Representativeness.
• Sampling plans.

The concepts are used when discussing different sampling methods in nursing and Scientific studies.

What is Population in Sampling?

In sampling, population refers to a particular group of people or elements that are the focus of the nursing study. The target population in a study is the entire group of individuals who meet the sampling or inclusion criteria. An accessible population refers to the group of individuals the researcher can reach.

What is Probability Sampling in Nursing Research?

In probability sampling strategies, each member of the population has an equal chance of being selected. The sampling frame is the entire population. Probability sampling techniques are often used with quantitative research designs in nursing, social sciences, and evidence-based medicine. Probability samples are the best in representing the target population in a nursing research study. The common types of probability sampling methods are:

• Simple random sampling methods.
• Stratified random sampling.
• Systematic random sampling.
• Cluster sampling techniques.

1. Simple random sampling methods in nursing research

In a simple random sampling process, the researcher selects elements or participants randomly from the sampling frame. A random sample can be obtained when the whole population is accessible and researchers can list all the population members.

After listing the subjects, one can select a random sample using random number generators or lottery methods. For example, evaluate 1,000 medical reports using a sample size of 100 to determine the prevalence of malaria in Lake county, the researcher can assign numbers between 1 to 1,000 and use a random sample generator to select the 100 reports for the study.

2. Stratified Sampling techniques

A stratified random sample is obtained by dividing the target population into strata or subgroups based on demographic factors and selecting a random sample from the strata. The sampling method is used when the researcher is aware of the population's characteristics that are fundamental for a representative sample. Stratified sampling helps to reduce sampling bias, data collection duration, and the cost of conducting research projects.

For instance, if in evaluating the medical reports the researcher selects a stratified random sample of 100 children based on age, the sample might include 25 reports in the ages ranging between one and two years, two and six years, six and twelve years, and twelve and eighteen years respectively. In this case, stratified random sampling would ensure that all age levels are sufficiently represented in the nursing research findings report.

3. Systematic random sampling

Systematic samples are selected using regular intervals. If researchers assigned numbers to 1,000 medical reports to select a sample size of 100, they can choose a random starting point like number 10. From number 10 onwards, every 10th report is included in the study sample.

4. Cluster sampling methods in nursing research

Cluster sampling is often used when studying large populations. The population is divided into clusters or subgroups while ensuring that the cluster sample has the same characteristic as the entire population.

Why is Random Sampling Important in Nursing Research?

Random sampling is important in research because the results generated from the study sample are generalizable to the entire population or group from which the sample was drawn. In simple random sampling, all the members of the population have an equal chance of being selected. With stratified random sampling, one can obtain an effect size from each stratum and it is possible to obtain samples from under-represented populations. Generally, random sampling techniques help researchers to reduce sampling bias to draw conclusions or make inferences that are valid and accurate regarding the target population.

Why is Obtaining a Representative Sample Important?

A representative sample is important because it helps researchers to generalize the collected data to a larger population. Researchers ought to ensure their sampling designs are bias-free to obtain valid results. The sample helps save time, money, and other resources that could be used to collect data from all target population members.

Non-Probability Sampling Methods

In non-probability sampling, members of the population do not have an equal chance of being selected for the study sample. There is a greater risk of getting a sampling error and bias because there is no random selection of participants. Although non-probability sampling is a cost-effective and fast method of collecting data from participants, it is hard to establish the sample variability or detect potential bias. The methods are often used in qualitative design and include:

• Convenience sampling.
• Snowball sampling.
• Volunteer sampling.
• Judgmental/purposive sampling.
• Quota sampling.

1. Convenience sampling

In convenience sampling, researchers enroll participants based on their availability and accessibility. The method is widely used in clinical research projects because it is a cheap, fast, and convenient way to collect data from participants. However, there is no guarantee that the selected sample represents the entire population.

2. Snowball sampling

Snowball sampling is used when accessing all the members of the target population is impossible. Snowballing is mainly used on rare and hard-to-find populations like street children, people living with disabilities, or drug users.

The researchers recruit participants through snowballs. For instance, in a study to determine the factors leading to glue sniffing among street children, researchers may access one or two street children who can connect to their friends (other street children like them) for data collection.

3. Volunteer sampling

In volunteer sampling, the participants are volunteers and have to be screened for a set of desirable characteristics that align with the purpose of the study. The method is prone to increased risk of volunteer bias because in a population with the desired study characteristics, some members may be more willing to volunteer for the study than others.

4. Purposive sampling

Purposive sampling involves selecting a representative sample based on previous knowledge of the composition and behavior of the population members. The researcher purposely decides from which population units to obtain the study sample. The method is subject to the researcher's bias and the selected sample may not represent the total population.

5. Quota sampling

Quota sampling entails sampling until one obtains a specific number of units for different subpopulations. The method is ideal for achieving sample size objectives for the target subpopulations. It demands the inclusion of members from different subgroups.

Quota sampling is commonly used by market researchers to collect data from groups or individuals with specified socioeconomic characteristics. It is relatively cheap and easy to administer to participants, with a tendency to meet population proportions.

Non-probability sampling methods are cheap, convenient, and fast to administer compared to probability sampling techniques. However, because participants are not randomly selected, non-probability sampling is prone to selection bias, sampling errors, under-coverage, or non-coverage, and there is no quality guarantee in the findings obtained from a sample selected non-randomly.

Scientific sampling helps nursing researchers to define a particular population's characteristics by observing a portion of the entire population. It is fundamental to select the most appropriate sample size because if research questions are directed to the wrong respondents, the data collected may not represent the population and the findings are most likely biased. To select an ideal representative sample, the researcher must clearly understand:

• The research objectives.
• The target population's characteristics and sampling units.
• The data types and level of precision required to achieve the research objectives.
• Sampling design.
• Sampling frame.
• The required sample size.
• The most appropriate sampling method.

Understanding these elements give researchers ample time when selecting their study samples. We offer professional statistics services for nursing to assist students, scholars, and researchers choose the most appropriate sampling methods to effectively and efficiently achieve research objectives.