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Methodology Series Module 5: Sampling Strategies

Maninder singh setia.

Epidemiologist, MGM Institute of Health Sciences, Navi Mumbai, Maharashtra, India

Once the research question and the research design have been finalised, it is important to select the appropriate sample for the study. The method by which the researcher selects the sample is the ‘ Sampling Method’. There are essentially two types of sampling methods: 1) probability sampling – based on chance events (such as random numbers, flipping a coin etc.); and 2) non-probability sampling – based on researcher's choice, population that accessible & available. Some of the non-probability sampling methods are: purposive sampling, convenience sampling, or quota sampling. Random sampling method (such as simple random sample or stratified random sample) is a form of probability sampling. It is important to understand the different sampling methods used in clinical studies and mention this method clearly in the manuscript. The researcher should not misrepresent the sampling method in the manuscript (such as using the term ‘ random sample’ when the researcher has used convenience sample). The sampling method will depend on the research question. For instance, the researcher may want to understand an issue in greater detail for one particular population rather than worry about the ‘ generalizability’ of these results. In such a scenario, the researcher may want to use ‘ purposive sampling’ for the study.

Introduction

The purpose of this section is to discuss various sampling methods used in research. After finalizing the research question and the research design, it is important to select the appropriate sample for the study. The method by which the researcher selects the sample is the “Sampling Method” [ Figure 1 ].

Flowchart from “Universe” to “Sampling Method”

Why do we need to sample?

Let us answer this research question: What is the prevalence of HIV in the adult Indian population?

The best response to this question will be obtained when we test every adult Indian for HIV. However, this is logistically difficult, time consuming, expensive, and difficult for a single researcher – do not forget about ethics of conducting such a study. The government usually conducts an exercise regularly to measure certain outcomes in the whole population – ”the census.” However, as researchers, we often have limited time and resources. Hence, we will have to select a few adult Indians who will consent to be a part of the study. We will test them for HIV and present out results (as our estimates of HIV prevalence). These selected individuals are called our “sample.” We hope that we have selected the appropriate sample that is required to answer our research question.

The researcher should clearly and explicitly mention the sampling method in the manuscript. The description of these helps the reviewers and readers assess the validity and generalizability of the results. Furthermore, the authors should also acknowledge the limitations of their sampling method and its effects on estimated obtained in the study.

Types of Methods

We will try to understand some of these sampling methods that are commonly used in clinical research. There are essentially two types of sampling methods: (1) Probability sampling – based on chance events (such as random numbers, flipping a coin, etc.) and (2) nonprobability sampling – based on researcher's choice, population that accessible and available.

What is a “convenience sample?”

Research question: How many patients with psoriasis also have high cholesterol levels (according to our definition)?

We plan to conduct the study in the outpatient department of our hospital.

This is a common scenario for clinical studies. The researcher recruits the participants who are easily accessible in a clinical setting – this type of sample is called a “convenience sample.” Furthermore, in such a clinic-based setting, the researcher will approach all the psoriasis patients that he/she comes across. They are informed about the study, and all those who consent to be the study are evaluated for eligibility. If they meet the inclusion criteria (and need not be excluded as per the criteria), they are recruited for the study. Thus, this will be “consecutive consenting sample.”

This method is relatively easy and is one of the common types of sampling methods used (particularly in postgraduate dissertations).

Since this is clinic-based sample, the estimates from such a study may not necessarily be generalizable to the larger population. To begin with, the patients who access healthcare potentially have a different “health-seeking behavior” compared with those who do not access health in these settings. Furthermore, many of the clinical cases in tertiary care centers may be severe, complicated, or recalcitrant. Thus, the estimates of biological parameters or outcomes may be different in these compared with the general population. The researcher should clearly discuss in the manuscript/report as to how the convenience sample may have biased the estimates (for example: Overestimated or underestimated the outcome in the population studied).

What is a “random sample?”

A “random sample” is a probability sample where every individual has an equal and independent probability of being selected in the sample.

Please note that “random sample” does not mean arbitrary sample. For example, if the researcher selects 10–12 individuals from the waiting area (without any structure), it is not a random sample. Randomization is a specific process, and only samples that are recruited using this process is a “random sample.”

What is a “simple random sample?”

Let us recruit a “simple random sample” in the above example. The center only allows a fixed number of patients every day. All the patients have to confirm the appointment a day in advance and should present in the clinic between 9 and 9:30 a.m. for the appointment. Thus, by 9:30 a.m., you will all have all the individuals who will be examined day.

We wish to select 50% of these patients for posttreatment survey.

• Make a list of all the patients present at 9:30 a.m.
• Give a number to each individual
• Use a “randomization method” to select five of these numbers. Although “random tables” have been used as a method of randomization, currently, many researchers use “computer-generated lists for random selection” of participants. Most of the statistical packages have programs for random selection of population. Please state the method that you have used for random selection in the manuscript
• Recruit the individuals whose numbers have been selected by the randomization method.

The process is described in Figure 2 .

Representation of Simple Random Sample

What is a major issue with this recruitment process?

As you may notice, “only males” have been recruited for the study. This scenario is possible in a simple random sample selection.

This is a limitation of this type of sampling method – population units which are smaller in number in the sampling frame may be underrepresented in this sample.

What is “stratified sample?”

In a stratified sample, the population is divided into two or more similar groups (based on demographic or clinical characteristics). The sample is recruited from each stratum. The researcher may use a simple random sample procedure within each stratum.

Let us address the limitation in the above example (selection of 50% of the participants for postprocedure survey).

• Divide the list into two strata: Males and females
• Use a “randomization method” to select three numbers among males and two numbers among females. As discussed earlier, the researcher may use random tables or computer generated random selection. Please state the method that you have used for random selection in the manuscript

The process is described in Figure 3 .

Representation of Stratified Random Sample

Thus, with this sampling method, we ensure that people from both sexes are included in the sample. This type of sampling method is used for sampling when we want to ensure that minority populations (in number) are adequately represented in the sample.

Kindly note that in this example, we sampled 50% of the population in each stratum. However, the researcher may oversample in one particular stratum and under-sample in the other. For instance, in this example, we may have taken three females and three males (if want to ensure equal representation of both). All this should be discussed explicitly in methods.

What is a “systematic sample?”

Sometimes, the researcher may decide to include study participants using a fixed pattern. For example, the researcher may recruit every second patient, or every patient whose registration ends with an even number or those who are admitted in certain days of the week (Tuesday/Thursday/Saturday). This type of sample is generally easy to implement. However, a lot of the recruitments are based on the researcher and may lead to selection bias. Furthermore, patients who come to the hospital may differ on different days of the week. For example, a higher proportion of working individuals may access the hospital on Saturdays.

This is not a “random sample.” Please do not write that “we selected the participants using a random sample method” if you have selected the sample systematically.

Another type of sampling discussed by some authors is “systematic random sample.” The steps for this method are:

• Make a list of all the potential recruits
• Using a random method (described earlier) to select a starting point (example number 4)
• Select this number and every fifth number from this starting point. Thus, the researcher will select number 9, 14, and so on.

Please note that the “skip” depends on the total number of potential participants and the total sample size. For instance, you have a total of fifty potential participants and you wish to recruit ten participants, do not skip to every 10 th patient.

Aday (1996) states that the skip depends on the total number of participants and the total sample size required.

• Fraction = total number of participants/total sample size
• In the above example, it will be 50/10 = 5
• Thus, using a random table or computer-generated random number selection, the researcher will select a random number from 1 to 5
• The number selected in two
• The researcher selects the second patient
• The next patient will be the fifth patient after patient number two – patient number 7
• The next patient will be patient number 12 and so on.

What is a “cluster sample?”

For some studies, the sample is selected from larger units or “clusters.” This type of method is generally used for “community-based studies.”

Research question: What is the prevalence of dermatological conditions in school children in city XXXXX?

In this study, we will select students from multiple schools. Thus, each school becomes one cluster. Each individual child in the school has much in common with other children in the same school compared with children from other schools (for example, they are more likely to have the same socioeconomic background). Thus, these children are recruited from the same cluster.

If the researcher uses “cluster sample,” he/she also performs “cluster analysis.” The statistical methods for these are different compared with nonclustered analysis (the methods we use commonly).

What is a “multistage sample?”

In many studies, we have to combine multiple methods for the appropriate and required sample.

Let us use a multistage sample to answer this research question.

Research question: What is the prevalence of dermatological conditions in school children in city XXXXX? (Assumption: The city is divided into four zones).

We have a list of all the schools in the city. How do we sample them?

Method 1: Select 10% of the schools using “simple random sample” method.

Question: What is the problem with this type of method?

Answer: As discussed earlier, it is possible that we may miss most of the schools from one particular zone.

However, we are interested to ensure that all zones are adequately represented in the sample.

• Stage 1: List all the schools in all zones
• Stage 2: Select 10% of schools from each zone using “random selection method” (first stratum)
• Stage 3: List all the students in Grade VIII, IX, and X(population of interest) in each school (second stratum)
• Stage 4: Create a separate list for males and females in each grade in each school (third stratum)
• Stage 5: Select 10% of males and females in each grade in each school.

Please note that this is just an example. You may have to change the proportion selected from each stratum based on the sample size and the total number of individuals in each stratum.

What are other types of sampling methods?

Although these are the common types of sampling methods that we use in clinical studies, we have also listed some other sampling methods in Table 1 .

Some other types of sampling methods

• It is important to understand the different sampling methods used in clinical studies. As stated earlier, please mention this method clearly in the manuscript
• Do not misrepresent the sampling method. For example, if you have not used “random method” for selection, do not state it in the manuscript
• Sometimes, the researcher may want to understand an issue in greater detail for one particular population rather than worry about the “generalizability” of these results. In such a scenario, the researcher may want to use ‘purposive sampling’.

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Conflicts of interest.

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Article contents

Sampling strategies for quantitative and qualitative business research.

• Vivien Lee Vivien Lee Psychology, University of Minnesota
•  and  Richard N. Landers Richard N. Landers Psychology, University of Minnesota
• https://doi.org/10.1093/acrefore/9780190224851.013.216
• Published online: 23 March 2022

Sampling refers to the process used to identify and select cases for analysis (i.e., a sample) with the goal of drawing meaningful research conclusions. Sampling is integral to the overall research process as it has substantial implications on the quality of research findings. Inappropriate sampling techniques can lead to problems of interpretation, such as drawing invalid conclusions about a population. Whereas sampling in quantitative research focuses on maximizing the statistical representativeness of a population by a chosen sample, sampling in qualitative research generally focuses on the complete representation of a phenomenon of interest. Because of this core difference in purpose, many sampling considerations differ between qualitative and quantitative approaches despite a shared general purpose: careful selection of cases to maximize the validity of conclusions.

Achieving generalizability, the extent to which observed effects from one study can be used to predict the same and similar effects in different contexts, drives most quantitative research. Obtaining a representative sample with characteristics that reflect a targeted population is critical to making accurate statistical inferences, which is core to such research. Such samples can be best acquired through probability sampling, a procedure in which all members of the target population have a known and random chance of being selected. However, probability sampling techniques are uncommon in modern quantitative research because of practical constraints; non-probability sampling, such as by convenience, is now normative. When sampling this way, special attention should be given to statistical implications of issues such as range restriction and omitted variable bias. In either case, careful planning is required to estimate an appropriate sample size before the start of data collection.

In contrast to generalizability, transferability, the degree to which study findings can be applied to other contexts, is the goal of most qualitative research. This approach is more concerned with providing information to readers and less concerned with making generalizable broad claims for readers. Similar to quantitative research, choosing a population and sample are critical for qualitative research, to help readers determine likelihood of transfer, yet representativeness is not as crucial. Sample size determination in qualitative research is drastically different from that of quantitative research, because sample size determination should occur during data collection, in an ongoing process in search of saturation, which focuses on achieving theoretical completeness instead of maximizing the quality of statistical inference.

Theoretically speaking, although quantitative and qualitative research have distinct statistical underpinnings that should drive different sampling requirements, in practice they both heavily rely on non-probability samples, and the implications of non-probability sampling is often not well understood. Although non-probability samples do not automatically generate poor-quality data, incomplete consideration of case selection strategy can harm the validity of research conclusions. The nature and number of cases collected must be determined cautiously to respect research goals and the underlying scientific paradigm employed. Understanding the commonalities and differences in sampling between quantitative and qualitative research can help researchers better identify high-quality research designs across paradigms.

• non-probability sampling
• convenience sampling
• sample size
• quantitative research
• qualitative research

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3.4 Sampling Techniques in Quantitative Research

Target population.

The target population includes the people the researcher is interested in conducting the research and generalizing the findings on. 40 For example, if certain researchers are interested in vaccine-preventable diseases in children five years and younger in Australia. The target population will be all children aged 0–5 years residing in Australia. The actual population is a subset of the target population from which the sample is drawn, e.g. children aged 0–5 years living in the capital cities in Australia. The sample is the people chosen for the study from the actual population (Figure 3.9). The sampling process involves choosing people, and it is distinct from the sample. 40 In quantitative research, the sample must accurately reflect the target population, be free from bias in terms of selection, and be large enough to validate or reject the study hypothesis with statistical confidence and minimise random error. 2

Sampling techniques

Sampling in quantitative research is a critical component that involves selecting a representative subset of individuals or cases from a larger population and often employs sampling techniques based on probability theory. 41 The goal of sampling is to obtain a sample that is large enough and representative of the target population. Examples of probability sampling techniques include simple random sampling, stratified random sampling, systematic random sampling and cluster sampling ( shown below ). 2 The key feature of probability techniques is that they involve randomization. There are two main characteristics of probability sampling. All individuals of a population are accessible to the researcher (theoretically), and there is an equal chance that each person in the population will be chosen to be part of the study sample. 41 While quantitative research often uses sampling techniques based on probability theory, some non-probability techniques may occasionally be utilised in healthcare research. 42 Non-probability sampling methods are commonly used in qualitative research. These include purposive, convenience, theoretical and snowballing and have been discussed in detail in chapter 4.

Sample size calculation

In order to enable comparisons with some level of established statistical confidence, quantitative research needs an acceptable sample size. 2 The sample size is the most crucial factor for reliability (reproducibility) in quantitative research. It is important for a study to be powered – the likelihood of identifying a difference if it exists in reality. 2 Small sample-sized studies are more likely to be underpowered, and results from small samples are more likely to be prone to random error. 2 The formula for sample size calculation varies with the study design and the research hypothesis. 2 There are numerous formulae for sample size calculations, but such details are beyond the scope of this book. For further readings, please consult the biostatistics textbook by Hirsch RP, 2021. 43 However, we will introduce a simple formula for calculating sample size for cross-sectional studies with prevalence as the outcome. 2

z   is the statistical confidence; therefore,  z = 1.96 translates to 95% confidence; z = 1.68 translates to 90% confidence

p = Expected prevalence (of health condition of interest)

d = Describes intended precision; d = 0.1 means that the estimate falls +/-10 percentage points of true prevalence with the considered confidence. (e.g. for a prevalence of 40% (0.4), if d=.1, then the estimate will fall between 30% and 50% (0.3 to 0.5).

Example: A district medical officer seeks to estimate the proportion of children in the district receiving appropriate childhood vaccinations. Assuming a simple random sample of a community is to be selected, how many children must be studied if the resulting estimate is to fall within 10% of the true proportion with 95% confidence? It is expected that approximately 50% of the children receive vaccinations

z = 1.96 (95% confidence)

d = 10% = 10/ 100 = 0.1 (estimate to fall within 10%)

p = 50% = 50/ 100 = 0.5

Now we can enter the values into the formula

Given that people cannot be reported in decimal points, it is important to round up to the nearest whole number.

An Introduction to Research Methods for Undergraduate Health Profession Students Copyright © 2023 by Faith Alele and Bunmi Malau-Aduli is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

Qualitative, Quantitative, and Mixed Methods Research Sampling Strategies

Sampling is a critical, often overlooked aspect of the research process. The importance of sampling extends to the ability to draw accurate inferences, and it is an integral part of qualitative guidelines across research methods. Sampling considerations are important in quantitative and qualitative research when considering a target population and when drawing a sample that will either allow us to generalize (i.e., quantitatively) or go into sufficient depth (i.e., qualitatively). While quantitative research is generally concerned with probability-based approaches, qualitative research typically uses nonprobability purposeful sampling approaches. Scholars generally focus on two major sampling topics: sampling strategies and sample sizes. Or simply, researchers should think about who to include and how many; both of these concerns are key. Mixed methods studies have both qualitative and quantitative sampling considerations. However, mixed methods studies also have unique considerations based on the relationship of quantitative and qualitative research within the study.

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