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What are sampling methods and how do you choose the best one?

Posted on 18th November 2020 by Mohamed Khalifa

This tutorial will introduce sampling methods and potential sampling errors to avoid when conducting medical research.

Introduction to sampling methods

Examples of different sampling methods, choosing the best sampling method.

It is important to understand why we sample the population; for example, studies are built to investigate the relationships between risk factors and disease. In other words, we want to find out if this is a true association, while still aiming for the minimum risk for errors such as: chance, bias or confounding .

However, it would not be feasible to experiment on the whole population, we would need to take a good sample and aim to reduce the risk of having errors by proper sampling technique.

What is a sampling frame?

A sampling frame is a record of the target population containing all participants of interest. In other words, it is a list from which we can extract a sample.

What makes a good sample?

A good sample should be a representative subset of the population we are interested in studying, therefore, with each participant having equal chance of being randomly selected into the study.

We could choose a sampling method based on whether we want to account for sampling bias; a random sampling method is often preferred over a non-random method for this reason. Random sampling examples include: simple, systematic, stratified, and cluster sampling. Non-random sampling methods are liable to bias, and common examples include: convenience, purposive, snowballing, and quota sampling. For the purposes of this blog we will be focusing on random sampling methods .

Example: We want to conduct an experimental trial in a small population such as: employees in a company, or students in a college. We include everyone in a list and use a random number generator to select the participants

Advantages: Generalisable results possible, random sampling, the sampling frame is the whole population, every participant has an equal probability of being selected

Disadvantages: Less precise than stratified method, less representative than the systematic method

Example: Every nth patient entering the out-patient clinic is selected and included in our sample

Advantages: More feasible than simple or stratified methods, sampling frame is not always required

Disadvantages:  Generalisability may decrease if baseline characteristics repeat across every nth participant

Example: We have a big population (a city) and we want to ensure representativeness of all groups with a pre-determined characteristic such as: age groups, ethnic origin, and gender

Advantages:  Inclusive of strata (subgroups), reliable and generalisable results

Disadvantages: Does not work well with multiple variables

Example: 10 schools have the same number of students across the county. We can randomly select 3 out of 10 schools as our clusters

Advantages: Readily doable with most budgets, does not require a sampling frame

Disadvantages: Results may not be reliable nor generalisable

How can you identify sampling errors?

Non-random selection increases the probability of sampling (selection) bias if the sample does not represent the population we want to study. We could avoid this by random sampling and ensuring representativeness of our sample with regards to sample size.

An inadequate sample size decreases the confidence in our results as we may think there is no significant difference when actually there is. This type two error results from having a small sample size, or from participants dropping out of the sample.

In medical research of disease, if we select people with certain diseases while strictly excluding participants with other co-morbidities, we run the risk of diagnostic purity bias where important sub-groups of the population are not represented.

Furthermore, measurement bias may occur during re-collection of risk factors by participants (recall bias) or assessment of outcome where people who live longer are associated with treatment success, when in fact people who died were not included in the sample or data analysis (survivors bias).

By following the steps below we could choose the best sampling method for our study in an orderly fashion.

Research objectiveness

Firstly, a refined research question and goal would help us define our population of interest. If our calculated sample size is small then it would be easier to get a random sample. If, however, the sample size is large, then we should check if our budget and resources can handle a random sampling method.

Sampling frame availability

Secondly, we need to check for availability of a sampling frame (Simple), if not, could we make a list of our own (Stratified). If neither option is possible, we could still use other random sampling methods, for instance, systematic or cluster sampling.

Study design

Moreover, we could consider the prevalence of the topic (exposure or outcome) in the population, and what would be the suitable study design. In addition, checking if our target population is widely varied in its baseline characteristics. For example, a population with large ethnic subgroups could best be studied using a stratified sampling method.

Random sampling

Finally, the best sampling method is always the one that could best answer our research question while also allowing for others to make use of our results (generalisability of results). When we cannot afford a random sampling method, we can always choose from the non-random sampling methods.

To sum up, we now understand that choosing between random or non-random sampling methods is multifactorial. We might often be tempted to choose a convenience sample from the start, but that would not only decrease precision of our results, and would make us miss out on producing research that is more robust and reliable.

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Mohamed Khalifa

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No Comments on What are sampling methods and how do you choose the best one?

Thank you for this overview. A concise approach for research.

really helps! am an ecology student preparing to write my lab report for sampling.

I learned a lot to the given presentation.. It’s very comprehensive… Thanks for sharing…

Very informative and useful for my study. Thank you

Oversimplified info on sampling methods. Probabilistic of the sampling and sampling of samples by chance does rest solely on the random methods. Factors such as the random visits or presentation of the potential participants at clinics or sites could be sufficiently random in nature and should be used for the sake of efficiency and feasibility. Nevertheless, this approach has to be taken only after careful thoughts. Representativeness of the study samples have to be checked at the end or during reporting by comparing it to the published larger studies or register of some kind in/from the local population.

Thank you so much Mr.mohamed very useful and informative article

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• Sampling Methods | Types, Techniques, & Examples

Sampling Methods | Types, Techniques, & Examples

Published on 3 May 2022 by Shona McCombes . Revised on 10 October 2022.

When you conduct research about a group of people, it’s rarely possible to collect data from every person in that group. Instead, you select a sample. The sample is the group of individuals who will actually participate in the research.

To draw valid conclusions from your results, you have to carefully decide how you will select a sample that is representative of the group as a whole. There are two types of sampling methods:

• Probability sampling involves random selection, allowing you to make strong statistical inferences about the whole group. It minimises the risk of selection bias .
• Non-probability sampling involves non-random selection based on convenience or other criteria, allowing you to easily collect data.

You should clearly explain how you selected your sample in the methodology section of your paper or thesis.

Table of contents

Population vs sample, probability sampling methods, non-probability sampling methods, frequently asked questions about sampling.

First, you need to understand the difference between a population and a sample , and identify the target population of your research.

• The population is the entire group that you want to draw conclusions about.
• The sample is the specific group of individuals that you will collect data from.

The population can be defined in terms of geographical location, age, income, and many other characteristics.

It is important to carefully define your target population according to the purpose and practicalities of your project.

If the population is very large, demographically mixed, and geographically dispersed, it might be difficult to gain access to a representative sample.

Sampling frame

The sampling frame is the actual list of individuals that the sample will be drawn from. Ideally, it should include the entire target population (and nobody who is not part of that population).

You are doing research on working conditions at Company X. Your population is all 1,000 employees of the company. Your sampling frame is the company’s HR database, which lists the names and contact details of every employee.

Sample size

The number of individuals you should include in your sample depends on various factors, including the size and variability of the population and your research design. There are different sample size calculators and formulas depending on what you want to achieve with statistical analysis .

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Probability sampling means that every member of the population has a chance of being selected. It is mainly used in quantitative research . If you want to produce results that are representative of the whole population, probability sampling techniques are the most valid choice.

There are four main types of probability sample.

1. Simple random sampling

In a simple random sample , every member of the population has an equal chance of being selected. Your sampling frame should include the whole population.

To conduct this type of sampling, you can use tools like random number generators or other techniques that are based entirely on chance.

You want to select a simple random sample of 100 employees of Company X. You assign a number to every employee in the company database from 1 to 1000, and use a random number generator to select 100 numbers.

2. Systematic sampling

Systematic sampling is similar to simple random sampling, but it is usually slightly easier to conduct. Every member of the population is listed with a number, but instead of randomly generating numbers, individuals are chosen at regular intervals.

All employees of the company are listed in alphabetical order. From the first 10 numbers, you randomly select a starting point: number 6. From number 6 onwards, every 10th person on the list is selected (6, 16, 26, 36, and so on), and you end up with a sample of 100 people.

If you use this technique, it is important to make sure that there is no hidden pattern in the list that might skew the sample. For example, if the HR database groups employees by team, and team members are listed in order of seniority, there is a risk that your interval might skip over people in junior roles, resulting in a sample that is skewed towards senior employees.

3. Stratified sampling

Stratified sampling involves dividing the population into subpopulations that may differ in important ways. It allows you draw more precise conclusions by ensuring that every subgroup is properly represented in the sample.

To use this sampling method, you divide the population into subgroups (called strata) based on the relevant characteristic (e.g., gender, age range, income bracket, job role).

Based on the overall proportions of the population, you calculate how many people should be sampled from each subgroup. Then you use random or systematic sampling to select a sample from each subgroup.

The company has 800 female employees and 200 male employees. You want to ensure that the sample reflects the gender balance of the company, so you sort the population into two strata based on gender. Then you use random sampling on each group, selecting 80 women and 20 men, which gives you a representative sample of 100 people.

4. Cluster sampling

Cluster sampling also involves dividing the population into subgroups, but each subgroup should have similar characteristics to the whole sample. Instead of sampling individuals from each subgroup, you randomly select entire subgroups.

If it is practically possible, you might include every individual from each sampled cluster. If the clusters themselves are large, you can also sample individuals from within each cluster using one of the techniques above. This is called multistage sampling .

This method is good for dealing with large and dispersed populations, but there is more risk of error in the sample, as there could be substantial differences between clusters. It’s difficult to guarantee that the sampled clusters are really representative of the whole population.

The company has offices in 10 cities across the country (all with roughly the same number of employees in similar roles). You don’t have the capacity to travel to every office to collect your data, so you use random sampling to select 3 offices – these are your clusters.

In a non-probability sample , individuals are selected based on non-random criteria, and not every individual has a chance of being included.

This type of sample is easier and cheaper to access, but it has a higher risk of sampling bias . That means the inferences you can make about the population are weaker than with probability samples, and your conclusions may be more limited. If you use a non-probability sample, you should still aim to make it as representative of the population as possible.

Non-probability sampling techniques are often used in exploratory and qualitative research . In these types of research, the aim is not to test a hypothesis about a broad population, but to develop an initial understanding of a small or under-researched population.

1. Convenience sampling

A convenience sample simply includes the individuals who happen to be most accessible to the researcher.

This is an easy and inexpensive way to gather initial data, but there is no way to tell if the sample is representative of the population, so it can’t produce generalisable results.

You are researching opinions about student support services in your university, so after each of your classes, you ask your fellow students to complete a survey on the topic. This is a convenient way to gather data, but as you only surveyed students taking the same classes as you at the same level, the sample is not representative of all the students at your university.

2. Voluntary response sampling

Similar to a convenience sample, a voluntary response sample is mainly based on ease of access. Instead of the researcher choosing participants and directly contacting them, people volunteer themselves (e.g., by responding to a public online survey).

Voluntary response samples are always at least somewhat biased, as some people will inherently be more likely to volunteer than others.

You send out the survey to all students at your university and many students decide to complete it. This can certainly give you some insight into the topic, but the people who responded are more likely to be those who have strong opinions about the student support services, so you can’t be sure that their opinions are representative of all students.

3. Purposive sampling

Purposive sampling , also known as judgement sampling, involves the researcher using their expertise to select a sample that is most useful to the purposes of the research.

It is often used in qualitative research , where the researcher wants to gain detailed knowledge about a specific phenomenon rather than make statistical inferences, or where the population is very small and specific. An effective purposive sample must have clear criteria and rationale for inclusion.

You want to know more about the opinions and experiences of students with a disability at your university, so you purposely select a number of students with different support needs in order to gather a varied range of data on their experiences with student services.

4. Snowball sampling

If the population is hard to access, snowball sampling can be used to recruit participants via other participants. The number of people you have access to ‘snowballs’ as you get in contact with more people.

You are researching experiences of homelessness in your city. Since there is no list of all homeless people in the city, probability sampling isn’t possible. You meet one person who agrees to participate in the research, and she puts you in contact with other homeless people she knows in the area.

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling , and quota sampling .

Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others.

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Sampling is the statistical process of selecting a subset—called a ‘sample’—of a population of interest for the purpose of making observations and statistical inferences about that population. Social science research is generally about inferring patterns of behaviours within specific populations. We cannot study entire populations because of feasibility and cost constraints, and hence, we must select a representative sample from the population of interest for observation and analysis. It is extremely important to choose a sample that is truly representative of the population so that the inferences derived from the sample can be generalised back to the population of interest. Improper and biased sampling is the primary reason for the often divergent and erroneous inferences reported in opinion polls and exit polls conducted by different polling groups such as CNN/Gallup Poll, ABC, and CBS, prior to every US Presidential election.

The sampling process

As Figure 8.1 shows, the sampling process comprises of several stages. The first stage is defining the target population. A population can be defined as all people or items ( unit of analysis ) with the characteristics that one wishes to study. The unit of analysis may be a person, group, organisation, country, object, or any other entity that you wish to draw scientific inferences about. Sometimes the population is obvious. For example, if a manufacturer wants to determine whether finished goods manufactured at a production line meet certain quality requirements or must be scrapped and reworked, then the population consists of the entire set of finished goods manufactured at that production facility. At other times, the target population may be a little harder to understand. If you wish to identify the primary drivers of academic learning among high school students, then what is your target population: high school students, their teachers, school principals, or parents? The right answer in this case is high school students, because you are interested in their performance, not the performance of their teachers, parents, or schools. Likewise, if you wish to analyse the behaviour of roulette wheels to identify biased wheels, your population of interest is not different observations from a single roulette wheel, but different roulette wheels (i.e., their behaviour over an infinite set of wheels).

The second step in the sampling process is to choose a sampling frame . This is an accessible section of the target population—usually a list with contact information—from where a sample can be drawn. If your target population is professional employees at work, because you cannot access all professional employees around the world, a more realistic sampling frame will be employee lists of one or two local companies that are willing to participate in your study. If your target population is organisations, then the Fortune 500 list of firms or the Standard & Poor’s (S&P) list of firms registered with the New York Stock exchange may be acceptable sampling frames.

Note that sampling frames may not entirely be representative of the population at large, and if so, inferences derived by such a sample may not be generalisable to the population. For instance, if your target population is organisational employees at large (e.g., you wish to study employee self-esteem in this population) and your sampling frame is employees at automotive companies in the American Midwest, findings from such groups may not even be generalisable to the American workforce at large, let alone the global workplace. This is because the American auto industry has been under severe competitive pressures for the last 50 years and has seen numerous episodes of reorganisation and downsizing, possibly resulting in low employee morale and self-esteem. Furthermore, the majority of the American workforce is employed in service industries or in small businesses, and not in automotive industry. Hence, a sample of American auto industry employees is not particularly representative of the American workforce. Likewise, the Fortune 500 list includes the 500 largest American enterprises, which is not representative of all American firms, most of which are medium or small sized firms rather than large firms, and is therefore, a biased sampling frame. In contrast, the S&P list will allow you to select large, medium, and/or small companies, depending on whether you use the S&P LargeCap, MidCap, or SmallCap lists, but includes publicly traded firms (and not private firms) and is hence still biased. Also note that the population from which a sample is drawn may not necessarily be the same as the population about which we actually want information. For example, if a researcher wants to examine the success rate of a new ‘quit smoking’ program, then the target population is the universe of smokers who had access to this program, which may be an unknown population. Hence, the researcher may sample patients arriving at a local medical facility for smoking cessation treatment, some of whom may not have had exposure to this particular ‘quit smoking’ program, in which case, the sampling frame does not correspond to the population of interest.

The last step in sampling is choosing a sample from the sampling frame using a well-defined sampling technique. Sampling techniques can be grouped into two broad categories: probability (random) sampling and non-probability sampling. Probability sampling is ideal if generalisability of results is important for your study, but there may be unique circumstances where non-probability sampling can also be justified. These techniques are discussed in the next two sections.

Probability sampling

Probability sampling is a technique in which every unit in the population has a chance (non-zero probability) of being selected in the sample, and this chance can be accurately determined. Sample statistics thus produced, such as sample mean or standard deviation, are unbiased estimates of population parameters, as long as the sampled units are weighted according to their probability of selection. All probability sampling have two attributes in common: every unit in the population has a known non-zero probability of being sampled, and the sampling procedure involves random selection at some point. The different types of probability sampling techniques include:

Stratified sampling. In stratified sampling, the sampling frame is divided into homogeneous and non-overlapping subgroups (called ‘strata’), and a simple random sample is drawn within each subgroup. In the previous example of selecting 200 firms from a list of 1,000 firms, you can start by categorising the firms based on their size as large (more than 500 employees), medium (between 50 and 500 employees), and small (less than 50 employees). You can then randomly select 67 firms from each subgroup to make up your sample of 200 firms. However, since there are many more small firms in a sampling frame than large firms, having an equal number of small, medium, and large firms will make the sample less representative of the population (i.e., biased in favour of large firms that are fewer in number in the target population). This is called non-proportional stratified sampling because the proportion of the sample within each subgroup does not reflect the proportions in the sampling frame—or the population of interest—and the smaller subgroup (large-sized firms) is oversampled . An alternative technique will be to select subgroup samples in proportion to their size in the population. For instance, if there are 100 large firms, 300 mid-sized firms, and 600 small firms, you can sample 20 firms from the ‘large’ group, 60 from the ‘medium’ group and 120 from the ‘small’ group. In this case, the proportional distribution of firms in the population is retained in the sample, and hence this technique is called proportional stratified sampling. Note that the non-proportional approach is particularly effective in representing small subgroups, such as large-sized firms, and is not necessarily less representative of the population compared to the proportional approach, as long as the findings of the non-proportional approach are weighted in accordance to a subgroup’s proportion in the overall population.

Cluster sampling. If you have a population dispersed over a wide geographic region, it may not be feasible to conduct a simple random sampling of the entire population. In such case, it may be reasonable to divide the population into ‘clusters’—usually along geographic boundaries—randomly sample a few clusters, and measure all units within that cluster. For instance, if you wish to sample city governments in the state of New York, rather than travel all over the state to interview key city officials (as you may have to do with a simple random sample), you can cluster these governments based on their counties, randomly select a set of three counties, and then interview officials from every office in those counties. However, depending on between-cluster differences, the variability of sample estimates in a cluster sample will generally be higher than that of a simple random sample, and hence the results are less generalisable to the population than those obtained from simple random samples.

Matched-pairs sampling. Sometimes, researchers may want to compare two subgroups within one population based on a specific criterion. For instance, why are some firms consistently more profitable than other firms? To conduct such a study, you would have to categorise a sampling frame of firms into ‘high profitable’ firms and ‘low profitable firms’ based on gross margins, earnings per share, or some other measure of profitability. You would then select a simple random sample of firms in one subgroup, and match each firm in this group with a firm in the second subgroup, based on its size, industry segment, and/or other matching criteria. Now, you have two matched samples of high-profitability and low-profitability firms that you can study in greater detail. Matched-pairs sampling techniques are often an ideal way of understanding bipolar differences between different subgroups within a given population.

Multi-stage sampling. The probability sampling techniques described previously are all examples of single-stage sampling techniques. Depending on your sampling needs, you may combine these single-stage techniques to conduct multi-stage sampling. For instance, you can stratify a list of businesses based on firm size, and then conduct systematic sampling within each stratum. This is a two-stage combination of stratified and systematic sampling. Likewise, you can start with a cluster of school districts in the state of New York, and within each cluster, select a simple random sample of schools. Within each school, you can select a simple random sample of grade levels, and within each grade level, you can select a simple random sample of students for study. In this case, you have a four-stage sampling process consisting of cluster and simple random sampling.

Non-probability sampling

Non-probability sampling is a sampling technique in which some units of the population have zero chance of selection or where the probability of selection cannot be accurately determined. Typically, units are selected based on certain non-random criteria, such as quota or convenience. Because selection is non-random, non-probability sampling does not allow the estimation of sampling errors, and may be subjected to a sampling bias. Therefore, information from a sample cannot be generalised back to the population. Types of non-probability sampling techniques include:

Convenience sampling. Also called accidental or opportunity sampling, this is a technique in which a sample is drawn from that part of the population that is close to hand, readily available, or convenient. For instance, if you stand outside a shopping centre and hand out questionnaire surveys to people or interview them as they walk in, the sample of respondents you will obtain will be a convenience sample. This is a non-probability sample because you are systematically excluding all people who shop at other shopping centres. The opinions that you would get from your chosen sample may reflect the unique characteristics of this shopping centre such as the nature of its stores (e.g., high end-stores will attract a more affluent demographic), the demographic profile of its patrons, or its location (e.g., a shopping centre close to a university will attract primarily university students with unique purchasing habits), and therefore may not be representative of the opinions of the shopper population at large. Hence, the scientific generalisability of such observations will be very limited. Other examples of convenience sampling are sampling students registered in a certain class or sampling patients arriving at a certain medical clinic. This type of sampling is most useful for pilot testing, where the goal is instrument testing or measurement validation rather than obtaining generalisable inferences.

Quota sampling. In this technique, the population is segmented into mutually exclusive subgroups (just as in stratified sampling), and then a non-random set of observations is chosen from each subgroup to meet a predefined quota. In proportional quota sampling , the proportion of respondents in each subgroup should match that of the population. For instance, if the American population consists of 70 per cent Caucasians, 15 per cent Hispanic-Americans, and 13 per cent African-Americans, and you wish to understand their voting preferences in an sample of 98 people, you can stand outside a shopping centre and ask people their voting preferences. But you will have to stop asking Hispanic-looking people when you have 15 responses from that subgroup (or African-Americans when you have 13 responses) even as you continue sampling other ethnic groups, so that the ethnic composition of your sample matches that of the general American population.

Non-proportional quota sampling is less restrictive in that you do not have to achieve a proportional representation, but perhaps meet a minimum size in each subgroup. In this case, you may decide to have 50 respondents from each of the three ethnic subgroups (Caucasians, Hispanic-Americans, and African-Americans), and stop when your quota for each subgroup is reached. Neither type of quota sampling will be representative of the American population, since depending on whether your study was conducted in a shopping centre in New York or Kansas, your results may be entirely different. The non-proportional technique is even less representative of the population, but may be useful in that it allows capturing the opinions of small and under-represented groups through oversampling.

Expert sampling. This is a technique where respondents are chosen in a non-random manner based on their expertise on the phenomenon being studied. For instance, in order to understand the impacts of a new governmental policy such as the Sarbanes-Oxley Act, you can sample a group of corporate accountants who are familiar with this Act. The advantage of this approach is that since experts tend to be more familiar with the subject matter than non-experts, opinions from a sample of experts are more credible than a sample that includes both experts and non-experts, although the findings are still not generalisable to the overall population at large.

Snowball sampling. In snowball sampling, you start by identifying a few respondents that match the criteria for inclusion in your study, and then ask them to recommend others they know who also meet your selection criteria. For instance, if you wish to survey computer network administrators and you know of only one or two such people, you can start with them and ask them to recommend others who also work in network administration. Although this method hardly leads to representative samples, it may sometimes be the only way to reach hard-to-reach populations or when no sampling frame is available.

Statistics of sampling

In the preceding sections, we introduced terms such as population parameter, sample statistic, and sampling bias. In this section, we will try to understand what these terms mean and how they are related to each other.

When you measure a certain observation from a given unit, such as a person’s response to a Likert-scaled item, that observation is called a response (see Figure 8.2). In other words, a response is a measurement value provided by a sampled unit. Each respondent will give you different responses to different items in an instrument. Responses from different respondents to the same item or observation can be graphed into a frequency distribution based on their frequency of occurrences. For a large number of responses in a sample, this frequency distribution tends to resemble a bell-shaped curve called a normal distribution , which can be used to estimate overall characteristics of the entire sample, such as sample mean (average of all observations in a sample) or standard deviation (variability or spread of observations in a sample). These sample estimates are called sample statistics (a ‘statistic’ is a value that is estimated from observed data). Populations also have means and standard deviations that could be obtained if we could sample the entire population. However, since the entire population can never be sampled, population characteristics are always unknown, and are called population parameters (and not ‘statistic’ because they are not statistically estimated from data). Sample statistics may differ from population parameters if the sample is not perfectly representative of the population. The difference between the two is called sampling error . Theoretically, if we could gradually increase the sample size so that the sample approaches closer and closer to the population, then sampling error will decrease and a sample statistic will increasingly approximate the corresponding population parameter.

If a sample is truly representative of the population, then the estimated sample statistics should be identical to the corresponding theoretical population parameters. How do we know if the sample statistics are at least reasonably close to the population parameters? Here, we need to understand the concept of a sampling distribution . Imagine that you took three different random samples from a given population, as shown in Figure 8.3, and for each sample, you derived sample statistics such as sample mean and standard deviation. If each random sample was truly representative of the population, then your three sample means from the three random samples will be identical—and equal to the population parameter—and the variability in sample means will be zero. But this is extremely unlikely, given that each random sample will likely constitute a different subset of the population, and hence, their means may be slightly different from each other. However, you can take these three sample means and plot a frequency histogram of sample means. If the number of such samples increases from three to 10 to 100, the frequency histogram becomes a sampling distribution. Hence, a sampling distribution is a frequency distribution of a sample statistic (like sample mean) from a set of samples , while the commonly referenced frequency distribution is the distribution of a response (observation) from a single sample . Just like a frequency distribution, the sampling distribution will also tend to have more sample statistics clustered around the mean (which presumably is an estimate of a population parameter), with fewer values scattered around the mean. With an infinitely large number of samples, this distribution will approach a normal distribution. The variability or spread of a sample statistic in a sampling distribution (i.e., the standard deviation of a sampling statistic) is called its standard error . In contrast, the term standard deviation is reserved for variability of an observed response from a single sample.

Social Science Research: Principles, Methods and Practices (Revised edition) Copyright © 2019 by Anol Bhattacherjee is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Sampling Methods & Strategies 101

Everything you need to know (including examples)

By: Derek Jansen (MBA) | Expert Reviewed By: Kerryn Warren (PhD) | January 2023

If you’re new to research, sooner or later you’re bound to wander into the intimidating world of sampling methods and strategies. If you find yourself on this page, chances are you’re feeling a little overwhelmed or confused. Fear not – in this post we’ll unpack sampling in straightforward language , along with loads of examples .

Overview: Sampling Methods & Strategies

• What is sampling in a research context?
• The two overarching approaches

Simple random sampling

Stratified random sampling, cluster sampling, systematic sampling, purposive sampling, convenience sampling, snowball sampling.

• How to choose the right sampling method

What (exactly) is sampling?

At the simplest level, sampling (within a research context) is the process of selecting a subset of participants from a larger group . For example, if your research involved assessing US consumers’ perceptions about a particular brand of laundry detergent, you wouldn’t be able to collect data from every single person that uses laundry detergent (good luck with that!) – but you could potentially collect data from a smaller subset of this group.

In technical terms, the larger group is referred to as the population , and the subset (the group you’ll actually engage with in your research) is called the sample . Put another way, you can look at the population as a full cake and the sample as a single slice of that cake. In an ideal world, you’d want your sample to be perfectly representative of the population, as that would allow you to generalise your findings to the entire population. In other words, you’d want to cut a perfect cross-sectional slice of cake, such that the slice reflects every layer of the cake in perfect proportion.

Achieving a truly representative sample is, unfortunately, a little trickier than slicing a cake, as there are many practical challenges and obstacles to achieving this in a real-world setting. Thankfully though, you don’t always need to have a perfectly representative sample – it all depends on the specific research aims of each study – so don’t stress yourself out about that just yet!

With the concept of sampling broadly defined, let’s look at the different approaches to sampling to get a better understanding of what it all looks like in practice.

The two overarching sampling approaches

At the highest level, there are two approaches to sampling: probability sampling and non-probability sampling . Within each of these, there are a variety of sampling methods , which we’ll explore a little later.

Probability sampling involves selecting participants (or any unit of interest) on a statistically random basis , which is why it’s also called “random sampling”. In other words, the selection of each individual participant is based on a pre-determined process (not the discretion of the researcher). As a result, this approach achieves a random sample.

Probability-based sampling methods are most commonly used in quantitative research , especially when it’s important to achieve a representative sample that allows the researcher to generalise their findings.

Non-probability sampling , on the other hand, refers to sampling methods in which the selection of participants is not statistically random . In other words, the selection of individual participants is based on the discretion and judgment of the researcher, rather than on a pre-determined process.

Non-probability sampling methods are commonly used in qualitative research , where the richness and depth of the data are more important than the generalisability of the findings.

If that all sounds a little too conceptual and fluffy, don’t worry. Let’s take a look at some actual sampling methods to make it more tangible.

Need a helping hand?

Probability-based sampling methods

First, we’ll look at four common probability-based (random) sampling methods:

Importantly, this is not a comprehensive list of all the probability sampling methods – these are just four of the most common ones. So, if you’re interested in adopting a probability-based sampling approach, be sure to explore all the options.

Simple random sampling involves selecting participants in a completely random fashion , where each participant has an equal chance of being selected. Basically, this sampling method is the equivalent of pulling names out of a hat , except that you can do it digitally. For example, if you had a list of 500 people, you could use a random number generator to draw a list of 50 numbers (each number, reflecting a participant) and then use that dataset as your sample.

Thanks to its simplicity, simple random sampling is easy to implement , and as a consequence, is typically quite cheap and efficient . Given that the selection process is completely random, the results can be generalised fairly reliably. However, this also means it can hide the impact of large subgroups within the data, which can result in minority subgroups having little representation in the results – if any at all. To address this, one needs to take a slightly different approach, which we’ll look at next.

Stratified random sampling is similar to simple random sampling, but it kicks things up a notch. As the name suggests, stratified sampling involves selecting participants randomly , but from within certain pre-defined subgroups (i.e., strata) that share a common trait . For example, you might divide the population into strata based on gender, ethnicity, age range or level of education, and then select randomly from each group.

The benefit of this sampling method is that it gives you more control over the impact of large subgroups (strata) within the population. For example, if a population comprises 80% males and 20% females, you may want to “balance” this skew out by selecting a random sample from an equal number of males and females. This would, of course, reduce the representativeness of the sample, but it would allow you to identify differences between subgroups. So, depending on your research aims, the stratified approach could work well.

Next on the list is cluster sampling. As the name suggests, this sampling method involves sampling from naturally occurring, mutually exclusive clusters within a population – for example, area codes within a city or cities within a country. Once the clusters are defined, a set of clusters are randomly selected and then a set of participants are randomly selected from each cluster.

Now, you’re probably wondering, “how is cluster sampling different from stratified random sampling?”. Well, let’s look at the previous example where each cluster reflects an area code in a given city.

With cluster sampling, you would collect data from clusters of participants in a handful of area codes (let’s say 5 neighbourhoods). Conversely, with stratified random sampling, you would need to collect data from all over the city (i.e., many more neighbourhoods). You’d still achieve the same sample size either way (let’s say 200 people, for example), but with stratified sampling, you’d need to do a lot more running around, as participants would be scattered across a vast geographic area. As a result, cluster sampling is often the more practical and economical option.

If that all sounds a little mind-bending, you can use the following general rule of thumb. If a population is relatively homogeneous , cluster sampling will often be adequate. Conversely, if a population is quite heterogeneous (i.e., diverse), stratified sampling will generally be more appropriate.

The last probability sampling method we’ll look at is systematic sampling. This method simply involves selecting participants at a set interval , starting from a random point .

For example, if you have a list of students that reflects the population of a university, you could systematically sample that population by selecting participants at an interval of 8 . In other words, you would randomly select a starting point – let’s say student number 40 – followed by student 48, 56, 64, etc.

What’s important with systematic sampling is that the population list you select from needs to be randomly ordered . If there are underlying patterns in the list (for example, if the list is ordered by gender, IQ, age, etc.), this will result in a non-random sample, which would defeat the purpose of adopting this sampling method. Of course, you could safeguard against this by “shuffling” your population list using a random number generator or similar tool.

Non-probability-based sampling methods

Right, now that we’ve looked at a few probability-based sampling methods, let’s look at three non-probability methods :

Again, this is not an exhaustive list of all possible sampling methods, so be sure to explore further if you’re interested in adopting a non-probability sampling approach.

First up, we’ve got purposive sampling – also known as judgment , selective or subjective sampling. Again, the name provides some clues, as this method involves the researcher selecting participants using his or her own judgement , based on the purpose of the study (i.e., the research aims).

For example, suppose your research aims were to understand the perceptions of hyper-loyal customers of a particular retail store. In that case, you could use your judgement to engage with frequent shoppers, as well as rare or occasional shoppers, to understand what judgements drive the two behavioural extremes .

Purposive sampling is often used in studies where the aim is to gather information from a small population (especially rare or hard-to-find populations), as it allows the researcher to target specific individuals who have unique knowledge or experience . Naturally, this sampling method is quite prone to researcher bias and judgement error, and it’s unlikely to produce generalisable results, so it’s best suited to studies where the aim is to go deep rather than broad .

Next up, we have convenience sampling. As the name suggests, with this method, participants are selected based on their availability or accessibility . In other words, the sample is selected based on how convenient it is for the researcher to access it, as opposed to using a defined and objective process.

Naturally, convenience sampling provides a quick and easy way to gather data, as the sample is selected based on the individuals who are readily available or willing to participate. This makes it an attractive option if you’re particularly tight on resources and/or time. However, as you’d expect, this sampling method is unlikely to produce a representative sample and will of course be vulnerable to researcher bias , so it’s important to approach it with caution.

Last but not least, we have the snowball sampling method. This method relies on referrals from initial participants to recruit additional participants. In other words, the initial subjects form the first (small) snowball and each additional subject recruited through referral is added to the snowball, making it larger as it rolls along .

Snowball sampling is often used in research contexts where it’s difficult to identify and access a particular population. For example, people with a rare medical condition or members of an exclusive group. It can also be useful in cases where the research topic is sensitive or taboo and people are unlikely to open up unless they’re referred by someone they trust.

Simply put, snowball sampling is ideal for research that involves reaching hard-to-access populations . But, keep in mind that, once again, it’s a sampling method that’s highly prone to researcher bias and is unlikely to produce a representative sample. So, make sure that it aligns with your research aims and questions before adopting this method.

How to choose a sampling method

Now that we’ve looked at a few popular sampling methods (both probability and non-probability based), the obvious question is, “ how do I choose the right sampling method for my study?”. When selecting a sampling method for your research project, you’ll need to consider two important factors: your research aims and your resources .

As with all research design and methodology choices, your sampling approach needs to be guided by and aligned with your research aims, objectives and research questions – in other words, your golden thread. Specifically, you need to consider whether your research aims are primarily concerned with producing generalisable findings (in which case, you’ll likely opt for a probability-based sampling method) or with achieving rich , deep insights (in which case, a non-probability-based approach could be more practical). Typically, quantitative studies lean toward the former, while qualitative studies aim for the latter, so be sure to consider your broader methodology as well.

The second factor you need to consider is your resources and, more generally, the practical constraints at play. If, for example, you have easy, free access to a large sample at your workplace or university and a healthy budget to help you attract participants, that will open up multiple options in terms of sampling methods. Conversely, if you’re cash-strapped, short on time and don’t have unfettered access to your population of interest, you may be restricted to convenience or referral-based methods.

In short, be ready for trade-offs – you won’t always be able to utilise the “perfect” sampling method for your study, and that’s okay. Much like all the other methodological choices you’ll make as part of your study, you’ll often need to compromise and accept practical trade-offs when it comes to sampling. Don’t let this get you down though – as long as your sampling choice is well explained and justified, and the limitations of your approach are clearly articulated, you’ll be on the right track.

Let’s recap…

In this post, we’ve covered the basics of sampling within the context of a typical research project.

• Sampling refers to the process of defining a subgroup (sample) from the larger group of interest (population).
• The two overarching approaches to sampling are probability sampling (random) and non-probability sampling .
• Common probability-based sampling methods include simple random sampling, stratified random sampling, cluster sampling and systematic sampling.
• Common non-probability-based sampling methods include purposive sampling, convenience sampling and snowball sampling.
• When choosing a sampling method, you need to consider your research aims , objectives and questions, as well as your resources and other practical constraints .

If you’d like to see an example of a sampling strategy in action, be sure to check out our research methodology chapter sample .

Last but not least, if you need hands-on help with your sampling (or any other aspect of your research), take a look at our 1-on-1 coaching service , where we guide you through each step of the research process, at your own pace.

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This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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Excellent and helpful. Best site to get a full understanding of Research methodology. I’m nolonger as “clueless “..😉

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Sampling Methods In Reseach: Types, Techniques, & Examples

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

Sampling methods in psychology refer to strategies used to select a subset of individuals (a sample) from a larger population, to study and draw inferences about the entire population. Common methods include random sampling, stratified sampling, cluster sampling, and convenience sampling. Proper sampling ensures representative, generalizable, and valid research results.

• Sampling : the process of selecting a representative group from the population under study.
• Target population : the total group of individuals from which the sample might be drawn.
• Sample: a subset of individuals selected from a larger population for study or investigation. Those included in the sample are termed “participants.”
• Generalizability : the ability to apply research findings from a sample to the broader target population, contingent on the sample being representative of that population.

For instance, if the advert for volunteers is published in the New York Times, this limits how much the study’s findings can be generalized to the whole population, because NYT readers may not represent the entire population in certain respects (e.g., politically, socio-economically).

The Purpose of Sampling

We are interested in learning about large groups of people with something in common in psychological research. We call the group interested in studying our “target population.”

In some types of research, the target population might be as broad as all humans. Still, in other types of research, the target population might be a smaller group, such as teenagers, preschool children, or people who misuse drugs.

Studying every person in a target population is more or less impossible. Hence, psychologists select a sample or sub-group of the population that is likely to be representative of the target population we are interested in.

This is important because we want to generalize from the sample to the target population. The more representative the sample, the more confident the researcher can be that the results can be generalized to the target population.

One of the problems that can occur when selecting a sample from a target population is sampling bias. Sampling bias refers to situations where the sample does not reflect the characteristics of the target population.

Many psychology studies have a biased sample because they have used an opportunity sample that comprises university students as their participants (e.g., Asch ).

OK, so you’ve thought up this brilliant psychological study and designed it perfectly. But who will you try it out on, and how will you select your participants?

There are various sampling methods. The one chosen will depend on a number of factors (such as time, money, etc.).

Random Sampling

Random sampling is a type of probability sampling where everyone in the entire target population has an equal chance of being selected.

This is similar to the national lottery. If the “population” is everyone who bought a lottery ticket, then everyone has an equal chance of winning the lottery (assuming they all have one ticket each).

Random samples require naming or numbering the target population and then using some raffle method to choose those to make up the sample. Random samples are the best method of selecting your sample from the population of interest.

• The advantages are that your sample should represent the target population and eliminate sampling bias.
• The disadvantage is that it is very difficult to achieve (i.e., time, effort, and money).

Stratified Sampling

During stratified sampling , the researcher identifies the different types of people that make up the target population and works out the proportions needed for the sample to be representative.

A list is made of each variable (e.g., IQ, gender, etc.) that might have an effect on the research. For example, if we are interested in the money spent on books by undergraduates, then the main subject studied may be an important variable.

For example, students studying English Literature may spend more money on books than engineering students, so if we use a large percentage of English students or engineering students, our results will not be accurate.

We have to determine the relative percentage of each group at a university, e.g., Engineering 10%, Social Sciences 15%, English 20%, Sciences 25%, Languages 10%, Law 5%, and Medicine 15%. The sample must then contain all these groups in the same proportion as the target population (university students).

• The disadvantage of stratified sampling is that gathering such a sample would be extremely time-consuming and difficult to do. This method is rarely used in Psychology.
• However, the advantage is that the sample should be highly representative of the target population, and therefore we can generalize from the results obtained.

Opportunity Sampling

Opportunity sampling is a method in which participants are chosen based on their ease of availability and proximity to the researcher, rather than using random or systematic criteria. It’s a type of convenience sampling .

An opportunity sample is obtained by asking members of the population of interest if they would participate in your research. An example would be selecting a sample of students from those coming out of the library.

• This is a quick and easy way of choosing participants (advantage)
• It may not provide a representative sample and could be biased (disadvantage).

Systematic Sampling

Systematic sampling is a method where every nth individual is selected from a list or sequence to form a sample, ensuring even and regular intervals between chosen subjects.

Participants are systematically selected (i.e., orderly/logical) from the target population, like every nth participant on a list of names.

To take a systematic sample, you list all the population members and then decide upon a sample you would like. By dividing the number of people in the population by the number of people you want in your sample, you get a number we will call n.

If you take every nth name, you will get a systematic sample of the correct size. If, for example, you wanted to sample 150 children from a school of 1,500, you would take every 10th name.

• The advantage of this method is that it should provide a representative sample.

Sample size

The sample size is a critical factor in determining the reliability and validity of a study’s findings. While increasing the sample size can enhance the generalizability of results, it’s also essential to balance practical considerations, such as resource constraints and diminishing returns from ever-larger samples.

Reliability and Validity

Reliability refers to the consistency and reproducibility of research findings across different occasions, researchers, or instruments. A small sample size may lead to inconsistent results due to increased susceptibility to random error or the influence of outliers. In contrast, a larger sample minimizes these errors, promoting more reliable results.

Validity pertains to the accuracy and truthfulness of research findings. For a study to be valid, it should accurately measure what it intends to do. A small, unrepresentative sample can compromise external validity, meaning the results don’t generalize well to the larger population. A larger sample captures more variability, ensuring that specific subgroups or anomalies don’t overly influence results.

Practical Considerations

Resource Constraints : Larger samples demand more time, money, and resources. Data collection becomes more extensive, data analysis more complex, and logistics more challenging.

Diminishing Returns : While increasing the sample size generally leads to improved accuracy and precision, there’s a point where adding more participants yields only marginal benefits. For instance, going from 50 to 500 participants might significantly boost a study’s robustness, but jumping from 10,000 to 10,500 might not offer a comparable advantage, especially considering the added costs.

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• The Online Researcher’s Guide To Sampling

What Is the Purpose of Sampling in Research?

Quick Navigation:

Defining random vs. non-random sampling.

• Why is Sampling Important for Researchers?

Collect Richer Data

The importance of knowing where to sample.

• Different Use Cases for Online Sampling

Academic Research

Market research, public polling, user testing.

By Aaron Moss, PhD, Cheskie Rosenzweig, PhD, & Leib Litman, PhD

Online Researcher’s Sampling Guide, Part 1: What Is the Purpose of Sampling in Research?

Every ten years, the U.S. government conducts a census—a count of every person living in the country—as required by the constitution. It’s a massive undertaking.

The Census Bureau sends a letter or a worker to every U.S. household and tries to gather data that will allow each person to be counted. After the data are gathered, they have to be processed, tabulated and reported. The entire operation takes years of planning and billions of dollars, which begs the question: Is there a better way?

As it turns out, there is.

Instead of contacting every person in the population, researchers can answer most questions by sampling people. In fact, sampling is what the Census Bureau does in order to gather detailed information about the population such as the average household income, the level of education people have, and the kind of work people do for a living. But what, exactly, is sampling, and how does it work?

At its core, a research sample is like any other sample: It’s a small piece or part of something that represents a larger whole.

So, just like the sample of glazed salmon you eat at Costco or the double chocolate brownie ice cream you taste at the ice cream shop, behavioral scientists often gather data from a small group (a sample) as a way to understand a larger whole (a population). Even when the population being studied is as large as the U.S.—about 330 million people—researchers often need to sample just a few thousand people in order to understand everyone.

Now, you may be asking yourself how that works. How can researchers accurately understand hundreds of millions of people by gathering data from just a few thousand of them? Your answer comes from Valery Ivanovich Glivenko and Francesco Paolo Cantelli.

Glivenko and Cantelli were mathematicians who studied probability. At some point during the early 1900s, they discovered that several observations randomly drawn from a population will naturally take on the shape of the population distribution. What this means in plain English is that, as long as researchers randomly sample from a population and obtain a sufficiently sized sample, then the sample will contain characteristics that roughly mirror those of the population.

“Ok. That’s great,” you say. But what does it mean to randomly sample people, and how does a researcher do that?

Random sampling occurs when a researcher ensures every member of the population being studied has an equal chance of being selected to participate in the study. Importantly, ‘the population being studied’ is not necessarily all the inhabitants of a country or a region. Instead, a population can refer to people who share a common quality or characteristic. So, everyone who has purchased a Ford in the last five years can be a population and so can registered voters within a state or college students at a city university. A population is the group that researchers want to understand.

In order to understand a population using random sampling, researchers begin by identifying a sampling frame —a list of all the people in the population the researchers want to study. For example, a database of all landline and cell phone numbers in the U.S. is a sampling frame. Once the researcher has a sampling frame, he or she can randomly select people from the list to participate in the study.

However, as you might imagine, it is not always practical or even possible to gather a sampling frame. There is not, for example, a master list of all the people who use the internet, purchase coffee at Dunkin’, have grieved the death of a parent in the last year, or consider themselves fans of the New York Yankees. Nevertheless, there are very good reasons why researchers may want to study people in each of these groups.

When it isn’t possible or practical to gather a random sample, researchers often gather a non-random sample. A non-random sample is one in which every member of the population being studied does not have an equal chance of being selected into the study.

Because non-random samples do not select participants based on probability, it is often difficult to know how well the sample represents the population of interest. Despite this limitation, a wide range of behavioral science studies conducted within academia, industry and government rely on non-random samples. When researchers use non-random samples, it is common to control for any known sources of sampling bias during data collection. By controlling for possible sources of bias, researchers can maximize the usefulness and generalizability of their data.

Why Is Sampling Important for Researchers?

Everyone who has ever worked on a research project knows that resources are limited; time, money and people never come in an unlimited supply. For that reason, most research projects aim to gather data from a sample of people, rather than from the entire population (the census being one of the few exceptions). This is because sampling allows researchers to:

Contacting everyone in a population takes time. And, invariably, some people will not respond to the first effort at contacting them, meaning researchers have to invest more time for follow-up. Random sampling is much faster than surveying everyone in a population, and obtaining a non-random sample is almost always faster than random sampling. Thus, sampling saves researchers lots of time.

The number of people a researcher contacts is directly related to the cost of a study. Sampling saves money by allowing researchers to gather the same answers from a sample that they would receive from the population.

Non-random sampling is significantly cheaper than random sampling, because it lowers the cost associated with finding people and collecting data from them. Because all research is conducted on a budget, saving money is important.

Sometimes, the goal of research is to collect a little bit of data from a lot of people (e.g., an opinion poll). At other times, the goal is to collect a lot of information from just a few people (e.g., a user study or ethnographic interview). Either way, sampling allows researchers to ask participants more questions and to gather richer data than does contacting everyone in a population.

Efficient sampling has a number of benefits for researchers. But just as important as knowing how to sample is knowing where to sample . Some research participants are better suited for the purposes of a project than others. Finding participants that are fit for the purpose of a project is crucial, because it allows researchers to gather high-quality data.

For example, consider an online research project. A team of researchers who decides to conduct a study online has several different sources of participants to choose from. Some sources provide a random sample, and many more provide a non-random sample. When selecting a non-random sample, researchers have several options to consider. Some studies are especially well-suited to an online panel that offers access to millions of different participants worldwide. Other studies, meanwhile, are better suited to a crowdsourced site that generally has fewer participants overall but more flexibility for fostering participant engagement.

To make these options more tangible, let’s look at examples of when researchers might use different kinds of online samples.

Different Use Cases of Online Sampling

Academic researchers gather all kinds of samples online. Some projects require random samples based on probability sampling methods. Most other projects rely on non-random samples. In these non-random samples, researchers may sample a general audience from crowdsourcing websites or selectively target members of specific groups using online panels . The variety of research projects conducted within academia lends itself to many different types of online samples.

Market researchers often want to understand the thoughts, feelings and purchasing decisions of customers or potential customers. For that reason, most online market research is conducted in online panels that provide access to tens of millions of people and allow for complex demographic targeting. For some projects, crowdsourcing sites, such as Amazon Mechanical Turk, allow researchers to get more participant engagement than is typically available in online panels, because they allow researchers to select participants based on experience and to award bonuses.

Public polling is most accurate when it is conducted on a random sample of the population. Hence, lots of public polling is conducted with nationally representative samples. There are, however, an increasing number of opinion polls conducted with non-random samples. When researchers poll people using non-random methods, it is common to adjust for known sources of bias after the data are gathered.

User testing requires people to engage with a website or product. For this reason, user testing is best done on platforms that allow researchers to get participants to engage deeply with their study. Crowdsourcing platforms are ideal for user testing studies, because researchers can often control participant compensation and reward people who are willing to make the effort in a longer study.

Online research is big business. There are hundreds of companies that provide researchers with access to online participants, but only a few facilitate research across different types of online panels or direct you to the right panel for your project. At CloudResearch, we are behavioral and computer science experts with the knowledge to connect you with the right participants for your study and provide expert advice to ensure your project’s successful conclusion. Learn more by contacting us today.

Continue Reading: The Online Researcher’s Guide to Sampling

Part 2: How to Reduce Sampling Bias in Research

Part 3: How to Build a Sampling Process for Marketing Research

Part 4: Pros and Cons of Different Sampling Methods

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Home » Research Methodology – Types, Examples and writing Guide

Research Methodology – Types, Examples and writing Guide

Table of Contents

Research Methodology

Definition:

Research Methodology refers to the systematic and scientific approach used to conduct research, investigate problems, and gather data and information for a specific purpose. It involves the techniques and procedures used to identify, collect , analyze , and interpret data to answer research questions or solve research problems . Moreover, They are philosophical and theoretical frameworks that guide the research process.

Structure of Research Methodology

Research methodology formats can vary depending on the specific requirements of the research project, but the following is a basic example of a structure for a research methodology section:

I. Introduction

• Provide an overview of the research problem and the need for a research methodology section
• Outline the main research questions and objectives

II. Research Design

• Explain the research design chosen and why it is appropriate for the research question(s) and objectives
• Discuss any alternative research designs considered and why they were not chosen
• Describe the research setting and participants (if applicable)

III. Data Collection Methods

• Describe the methods used to collect data (e.g., surveys, interviews, observations)
• Explain how the data collection methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or instruments used for data collection

IV. Data Analysis Methods

• Describe the methods used to analyze the data (e.g., statistical analysis, content analysis )
• Explain how the data analysis methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or software used for data analysis

V. Ethical Considerations

• Discuss any ethical issues that may arise from the research and how they were addressed
• Explain how informed consent was obtained (if applicable)
• Detail any measures taken to ensure confidentiality and anonymity

VI. Limitations

• Identify any potential limitations of the research methodology and how they may impact the results and conclusions

VII. Conclusion

• Summarize the key aspects of the research methodology section
• Explain how the research methodology addresses the research question(s) and objectives

Research Methodology Types

Types of Research Methodology are as follows:

Quantitative Research Methodology

This is a research methodology that involves the collection and analysis of numerical data using statistical methods. This type of research is often used to study cause-and-effect relationships and to make predictions.

Qualitative Research Methodology

This is a research methodology that involves the collection and analysis of non-numerical data such as words, images, and observations. This type of research is often used to explore complex phenomena, to gain an in-depth understanding of a particular topic, and to generate hypotheses.

Mixed-Methods Research Methodology

This is a research methodology that combines elements of both quantitative and qualitative research. This approach can be particularly useful for studies that aim to explore complex phenomena and to provide a more comprehensive understanding of a particular topic.

Case Study Research Methodology

This is a research methodology that involves in-depth examination of a single case or a small number of cases. Case studies are often used in psychology, sociology, and anthropology to gain a detailed understanding of a particular individual or group.

Action Research Methodology

This is a research methodology that involves a collaborative process between researchers and practitioners to identify and solve real-world problems. Action research is often used in education, healthcare, and social work.

Experimental Research Methodology

This is a research methodology that involves the manipulation of one or more independent variables to observe their effects on a dependent variable. Experimental research is often used to study cause-and-effect relationships and to make predictions.

Survey Research Methodology

This is a research methodology that involves the collection of data from a sample of individuals using questionnaires or interviews. Survey research is often used to study attitudes, opinions, and behaviors.

Grounded Theory Research Methodology

This is a research methodology that involves the development of theories based on the data collected during the research process. Grounded theory is often used in sociology and anthropology to generate theories about social phenomena.

Research Methodology Example

An Example of Research Methodology could be the following:

Research Methodology for Investigating the Effectiveness of Cognitive Behavioral Therapy in Reducing Symptoms of Depression in Adults

Introduction:

The aim of this research is to investigate the effectiveness of cognitive-behavioral therapy (CBT) in reducing symptoms of depression in adults. To achieve this objective, a randomized controlled trial (RCT) will be conducted using a mixed-methods approach.

Research Design:

The study will follow a pre-test and post-test design with two groups: an experimental group receiving CBT and a control group receiving no intervention. The study will also include a qualitative component, in which semi-structured interviews will be conducted with a subset of participants to explore their experiences of receiving CBT.

Participants:

Participants will be recruited from community mental health clinics in the local area. The sample will consist of 100 adults aged 18-65 years old who meet the diagnostic criteria for major depressive disorder. Participants will be randomly assigned to either the experimental group or the control group.

Intervention :

The experimental group will receive 12 weekly sessions of CBT, each lasting 60 minutes. The intervention will be delivered by licensed mental health professionals who have been trained in CBT. The control group will receive no intervention during the study period.

Data Collection:

Quantitative data will be collected through the use of standardized measures such as the Beck Depression Inventory-II (BDI-II) and the Generalized Anxiety Disorder-7 (GAD-7). Data will be collected at baseline, immediately after the intervention, and at a 3-month follow-up. Qualitative data will be collected through semi-structured interviews with a subset of participants from the experimental group. The interviews will be conducted at the end of the intervention period, and will explore participants’ experiences of receiving CBT.

Data Analysis:

Quantitative data will be analyzed using descriptive statistics, t-tests, and mixed-model analyses of variance (ANOVA) to assess the effectiveness of the intervention. Qualitative data will be analyzed using thematic analysis to identify common themes and patterns in participants’ experiences of receiving CBT.

Ethical Considerations:

This study will comply with ethical guidelines for research involving human subjects. Participants will provide informed consent before participating in the study, and their privacy and confidentiality will be protected throughout the study. Any adverse events or reactions will be reported and managed appropriately.

Data Management:

All data collected will be kept confidential and stored securely using password-protected databases. Identifying information will be removed from qualitative data transcripts to ensure participants’ anonymity.

Limitations:

One potential limitation of this study is that it only focuses on one type of psychotherapy, CBT, and may not generalize to other types of therapy or interventions. Another limitation is that the study will only include participants from community mental health clinics, which may not be representative of the general population.

Conclusion:

This research aims to investigate the effectiveness of CBT in reducing symptoms of depression in adults. By using a randomized controlled trial and a mixed-methods approach, the study will provide valuable insights into the mechanisms underlying the relationship between CBT and depression. The results of this study will have important implications for the development of effective treatments for depression in clinical settings.

How to Write Research Methodology

Writing a research methodology involves explaining the methods and techniques you used to conduct research, collect data, and analyze results. It’s an essential section of any research paper or thesis, as it helps readers understand the validity and reliability of your findings. Here are the steps to write a research methodology:

• Start by explaining your research question: Begin the methodology section by restating your research question and explaining why it’s important. This helps readers understand the purpose of your research and the rationale behind your methods.
• Describe your research design: Explain the overall approach you used to conduct research. This could be a qualitative or quantitative research design, experimental or non-experimental, case study or survey, etc. Discuss the advantages and limitations of the chosen design.
• Discuss your sample: Describe the participants or subjects you included in your study. Include details such as their demographics, sampling method, sample size, and any exclusion criteria used.
• Describe your data collection methods : Explain how you collected data from your participants. This could include surveys, interviews, observations, questionnaires, or experiments. Include details on how you obtained informed consent, how you administered the tools, and how you minimized the risk of bias.
• Explain your data analysis techniques: Describe the methods you used to analyze the data you collected. This could include statistical analysis, content analysis, thematic analysis, or discourse analysis. Explain how you dealt with missing data, outliers, and any other issues that arose during the analysis.
• Discuss the validity and reliability of your research : Explain how you ensured the validity and reliability of your study. This could include measures such as triangulation, member checking, peer review, or inter-coder reliability.
• Acknowledge any limitations of your research: Discuss any limitations of your study, including any potential threats to validity or generalizability. This helps readers understand the scope of your findings and how they might apply to other contexts.
• Provide a summary: End the methodology section by summarizing the methods and techniques you used to conduct your research. This provides a clear overview of your research methodology and helps readers understand the process you followed to arrive at your findings.

When to Write Research Methodology

Research methodology is typically written after the research proposal has been approved and before the actual research is conducted. It should be written prior to data collection and analysis, as it provides a clear roadmap for the research project.

The research methodology is an important section of any research paper or thesis, as it describes the methods and procedures that will be used to conduct the research. It should include details about the research design, data collection methods, data analysis techniques, and any ethical considerations.

The methodology should be written in a clear and concise manner, and it should be based on established research practices and standards. It is important to provide enough detail so that the reader can understand how the research was conducted and evaluate the validity of the results.

Applications of Research Methodology

Here are some of the applications of research methodology:

• To identify the research problem: Research methodology is used to identify the research problem, which is the first step in conducting any research.
• To design the research: Research methodology helps in designing the research by selecting the appropriate research method, research design, and sampling technique.
• To collect data: Research methodology provides a systematic approach to collect data from primary and secondary sources.
• To analyze data: Research methodology helps in analyzing the collected data using various statistical and non-statistical techniques.
• To test hypotheses: Research methodology provides a framework for testing hypotheses and drawing conclusions based on the analysis of data.
• To generalize findings: Research methodology helps in generalizing the findings of the research to the target population.
• To develop theories : Research methodology is used to develop new theories and modify existing theories based on the findings of the research.
• To evaluate programs and policies : Research methodology is used to evaluate the effectiveness of programs and policies by collecting data and analyzing it.
• To improve decision-making: Research methodology helps in making informed decisions by providing reliable and valid data.

Purpose of Research Methodology

Research methodology serves several important purposes, including:

• To guide the research process: Research methodology provides a systematic framework for conducting research. It helps researchers to plan their research, define their research questions, and select appropriate methods and techniques for collecting and analyzing data.
• To ensure research quality: Research methodology helps researchers to ensure that their research is rigorous, reliable, and valid. It provides guidelines for minimizing bias and error in data collection and analysis, and for ensuring that research findings are accurate and trustworthy.
• To replicate research: Research methodology provides a clear and detailed account of the research process, making it possible for other researchers to replicate the study and verify its findings.
• To advance knowledge: Research methodology enables researchers to generate new knowledge and to contribute to the body of knowledge in their field. It provides a means for testing hypotheses, exploring new ideas, and discovering new insights.
• To inform decision-making: Research methodology provides evidence-based information that can inform policy and decision-making in a variety of fields, including medicine, public health, education, and business.

Advantages of Research Methodology

Research methodology has several advantages that make it a valuable tool for conducting research in various fields. Here are some of the key advantages of research methodology:

• Systematic and structured approach : Research methodology provides a systematic and structured approach to conducting research, which ensures that the research is conducted in a rigorous and comprehensive manner.
• Objectivity : Research methodology aims to ensure objectivity in the research process, which means that the research findings are based on evidence and not influenced by personal bias or subjective opinions.
• Replicability : Research methodology ensures that research can be replicated by other researchers, which is essential for validating research findings and ensuring their accuracy.
• Reliability : Research methodology aims to ensure that the research findings are reliable, which means that they are consistent and can be depended upon.
• Validity : Research methodology ensures that the research findings are valid, which means that they accurately reflect the research question or hypothesis being tested.
• Efficiency : Research methodology provides a structured and efficient way of conducting research, which helps to save time and resources.
• Flexibility : Research methodology allows researchers to choose the most appropriate research methods and techniques based on the research question, data availability, and other relevant factors.
• Scope for innovation: Research methodology provides scope for innovation and creativity in designing research studies and developing new research techniques.

Research Methodology Vs Research Methods

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Muhammad Hassan

Researcher, Academic Writer, Web developer

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Business Jargons

A Business Encyclopedia

Sampling Plan

Definition : A sampling plan provides an outline based on which the researcher performs research. Also, it provides a sketch required for ensuring that the data gathered is a representation of the defined target population. It is widely used in research studies. A researcher designs a sampling plan to prove that the data collected is valid and reliable for the concerned population.

It explains which category the researcher chooses for the survey. Also, it states the right sample size. Additionally, it expresses how the researcher has to be selected out of the population.

Issues Addressed by Sampling Plan

A sampling plan is the base from which the research starts. It includes the following three major decisions:

Sampling Unit

The researcher decides what the sampling unit should be. It involves choosing the category of the population to be surveyed. It defines the specific target population.

Example: In the Banking industry, the researcher decides: what should the sampling unit include. It may cover current account holders, saving account holders, or both.

The researcher takes such decisions at the time of designing the sampling frame. They do so to give all the elements of the target population an equal chance of getting included in the sample.

Sampling unit

The researcher has to determine the sample size. This means how many objects in the sample the researcher will survey. Generally, “the larger the sample size, the more is the reliability”. Therefore, researchers try to cover as many samples as possible.

Sampling Procedure

Which method should the researcher use to perform sampling ? For that, he must ensure that all the objects of the population have a fair and equal change of selection. Generally, researchers use probability sampling for determining the objects for selection. This is because probability sampling represents the sample more accurately.

In this regard, we are going to learn the two sampling methods :

Probability Sampling

• Simple Random Sampling : In this, every item of the sample has an equal chance of getting selected.
• Stratified Sampling : Here, the researcher divides the population into mutually exclusive groups, viz., age group. After that, the researcher will choose the elements randomly from each group.
• Cluster Sampling : Another name for cluster sampling is area sampling. In this, the researcher divides the population into existing groups or clusters. After that he chooses a sample of clusters on a random basis from the population.

However, the researcher usually finds probability sampling costly and time-consuming. In such a case, he can make use of non-probability sampling. It is a sampling by means of choice.

Non-Probability Sampling

• Convenience Sampling : Here, the researcher selects the easiest and most accessible population member.
• Judgment Sampling : Here, the researcher selects those members of the population whom he thinks that will contribute accurate information.
• Quota sampling : Here, the researcher interviews the fixed number of members of each category.

Thus, a researcher can select any kind of sample as per his convenience, subject to it fulfilling the purpose for which research takes place.

Steps involving Sampling Plan

An ideal sampling plan covers the following steps:

Define the target population

First of all, the researcher needs to decide and identify the group or batch for the study. The target population must be alloted identity by using descriptors. These descriptors indicate the characteristics of the elements. This will depict the target population frame.

Choose the data collection method

The researcher must choose a method for collecting the necessary data from the target population elements. For this, he uses information problem definition, data requirements and set research objectives.

Find out the sampling frames required

Once the researcher decides whom or what should be evaluated. The next step is to bring together a list of eligible sampling units. This list must have enough information about each prospective sampling unit. This allows the researcher can communicate with them. An incomplete sampling frame decreases the possibility of drawing a representative sample.

Pick the suitable sampling method

The researcher needs to pick any of the two types of sampling methods. The methods are probability and non-probability sampling. Usually, probability sampling yields better results. Also, it provides valid information about the target population’s criteria.

Ascertain necessary sample sizes and contract rates

The researcher must consider how accurate the sample estimates must be. Also, he needs to take into account how much time and money are available to collect data. To decide the right size of the sample, the researcher has to make the following decisions:

• Variability of population characteristics that is undergoing investigation.
• The confidence level is desired in the estimates.
• Degree of precision needed to estimate the population characteristic.

Design an operating plan for choosing the sample units

The researcher will design the actual procedures to use. He must include all the prospective respondents who form part of the sample.

Execute the operational plan

Carrying out data collection activities. This may involve actually talking to the prospective respondents by way of a telephone interview.

A word from Business Jargons

A sampling plan states the procedure for determining when the group under study is to be accepted or rejected. Further, if the sample gets rejected, the researcher must integrate corrective measures. He should do so after the complete inspection. After that, replacement of defective items with good ones takes place. We call this process a rectifying inspection.

Related terms:

• Stratified Sampling
• Sampling Methods
• Systematic Sampling
• Sampling Error
• Sampling Distribution of Proportion

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This might not be a term you are very familiar with, but it is a significant part of marketing. We know how important research is for marketing. We need to know the target audience to plan a successful marketing campaign, and a sampling plan is essential to make it successful. Wondering how? Keep reading to find out!

Sampling Plan Definition

Knowing the target audience is vital to understanding their needs and wants. Researchers need to study the population to draw conclusions. These conclusions will serve as a basis for constructing a suitable marketing campaign. But observing every person in the selected location is impractical and, at times, impossible. Therefore, researchers select a group of individuals representative of the population. A sampling plan is an outline based on which research is conducted.

A sampling plan outlines the individuals chosen to represent the target population under consideration for research purposes.

It is crucial to verify that the sampling plan is representative of all kinds of people to draw accurate conclusions.

Sampling Plan Research

The sampling plan is an essential part of the implementation phase in market research - it is the first step of implementing market research.

Check out our explanation of market research to find out more.

Researchers decide the sampling unit, size, and procedure when creating a sampling plan.

Deciding the sampling unit involves defining the target population. The area of interest for the research may contain people that may be out of the scope of the research. Therefore, the researcher must first identify the type of people within the research's parameters.

The sample size will specify how many people from the sampling unit will be surveyed or studied. Usually, in realistic cases, the target population is colossal. Analyzing every single individual is an arduous task. Therefore, the researcher must decide which individuals should be considered and how many people to survey.

The sampling procedure decides how the sample size is chosen. Researchers can do this based on both probability sampling methods and non-probability sampling methods. We will talk about this in more detail in the following sections.

Sampling Plan Types

The sampling plan mainly consists of two different types of methods - one based on probability methods and the other based on non-probability methods .

In the probability sampling method, the researcher lists a few criteria and then chooses people randomly from the population. In this method, all people of the population have an equal chance to be selected. The probability methods are further classified into:

1. Simple Random Sampling - as the name suggests, this type of sampling picks individuals randomly from the selection.

2. Cluster Sampling - the whole population gets divided into groups or clusters. Researchers then survey people from the selected clusters.

3. Systematic Sampling - researchers select individuals at a regular interval; for example, the researcher will select every 15th person on the list for interviews.

4. Stratified Sampling - researchers divide the group into smaller subgroups called strata based on their characteristics. Researchers then pick individuals at random from the strata.

Difference between cluster sampling and stratified sampling

In cluster sampling, all individuals are put into different groups, and all people in the selected groups are studied.

In stratified sampling, all the individuals are put into different groups, and some people from all groups are surveyed.

A non-probability method involves choosing people at random without any defined criteria. This means that not everybody has an equal chance of being selected for the survey. N on-probability techniques can be further classified into:

1. Convenience Sampling - this depends on the ease of accessing a person of interest.

2. Judgemental Sampling - also known as purposive sampling, includes selecting people with a particular characteristic that supports the scope of the research.

3. Snowball Sampling - used when trying to find people with traits that are difficult to trace. In such cases, the researcher would find one or two people with the traits and then ask them to refer to people with similar characteristics.

4. Quota Sampling - this involves collecting information from a homogenous group.

Steps of a Sample Plan

A sampling plan helps researchers collect data and get results quicker, as only a group of individuals is selected to be studied instead of the whole population. But how is a sampling plan conducted? What are the steps of a sample plan?

A sampling plan study consists of 5 main steps:

1. Sample Definition - this step involves identifying the research goals or what the research is trying to achieve. Defining the sample will help the researcher identify what they have to look for in the sample.

2. Sample Selection - after the sample definition, researchers now have to obtain a sample frame. The sample frame will give the researchers a list of the population from which the researcher chooses people to sample.

3. Sample Size Determination - the sample size is the number of individuals that will be considered while determining the sampling plan. This step defines the number of individuals that the researcher will survey.

4. Sample Design - in this step, the samples are picked from the population. Researchers can select individuals based on probability or non-probability methods.

5. Sample Assessment - this step ensures that the samples chosen are representative enough of the population and ensures quality data collection.

After these processes are finalized, researchers carry forward with the rest of the research, such as drawing conclusions that form a basis for the marketing campaign.

Probability sampling methods are more complex, costly, and time-consuming than non-probability methods.

Sampling Plans Example

Different methods of sampling plans help to yield different types of data. The sampling plan will depend on the company's research goals and limitations. Given below are a few examples of companies that use different types of sampling plans:

1. Simple Random Sampling - A district manager wants to evaluate employee satisfaction at a store. Now, he would go to the store, pick a few employees randomly, and ask them about their satisfaction. Every employee has an equal chance of being selected by the district manager for the survey.

2. Cluster Sampling - A reputed private school is planning to launch in a different city. To gain a better insight into the city, they divided the population based on families with school-aged kids and people with high incomes. These insights will help them decide if starting a branch in that particular city would be worth it or not.

3. Systematic Sampling - A supermarket with many branches decides to reallocate its staff to improve efficiency. The manager decides that every third person, chosen per their employee number, would be transferred to a different location.

4. Stratified Sampling - A research startup is trying to understand people's sleep patterns based on different age groups. Therefore, the whole sampling unit gets divided into different age groups (or strata), such as 0-3 months, 4-12 months, 1-2 years, 3-5 years, 6-12 years, and so on. Some people from all the groups are studied.

5. Convenience Sampling - An NGO is trying to get people to sign up for a "street-clean" program as part of the Earth Day campaign. They have stationed themselves on the sidewalks of a busy shopping street, and are approaching people who pass them by to try and pursue them to join the program.

6. Judgemental Sampling - A real estate company is trying to determine how the rental price hike affects people. To find the answer to this question, they would only have to consider people that live in rented houses, meaning that people who own a home would be excluded from this survey.

7. Snowball Sampling - A pharmaceutical company is trying to get a list of patients with leukemia. As the company cannot go to hospitals to ask for patients' information, they would first find a couple of patients with the illness and then ask them to refer patients with the same illness.

8. Quota Sampling - Recruiters that want to hire employees with a degree from a particular school will group them into a separate subgroup. This type of selection is called quota selection.

Sampling plan - Key takeaways

• During a sampling plan in research, the sampling unit, the sampling size, and the sampling procedure are determined.
• The sample size will specify how many people from the sampling unit will be surveyed or studied.
• The sampling procedure decides how researchers will select the sample size.
• The methods of probability sampling include simple random, cluster, systematic, and stratified sampling.
• The non-probability sampling plan methods include convenience, judgemental, snowball, and quota sampling.
• Sample definition, sample selection, sample size determination, sample design, and sample assessment are the steps of a sample plan.

Frequently Asked Questions about Sampling Plan

--> what is a sample plan in marketing .

Researchers need to study the population to draw conclusions. But observing every person in the selected location is impractical and, at times, impossible. Therefore, researchers select a group of individuals representative of the population. A sampling plan outlines the individuals chosen to represent the target population under consideration for research purposes. 

--> What is a sampling plan and its types? 

The sampling plan mainly consists of two different types of methods - one based on probability methods and the other based on non-probability methods. Probability sampling methods include simple random, cluster, systematic, and stratified sampling. The non-probability sampling methods include convenience, judgemental, snowball, and quota sampling.

--> Why is the sampling plan important? 

The sampling plan is an essential part of the implementation phase in market research - it is the first step of implementing market research. Observing every person in the selected location is impractical. Therefore, researchers select a group of individuals representative of the population called the sampling unit. This is outlined in the sampling plan. 

--> What should a marketing plan include? 

A good marketing plan should include the target market, the unique selling proposition, SWOT analysis, marketing strategies, the budget, and the duration of the research. 

--> What are the components of a sampling plan? 

The sample definition, sample selection, sample size determination, sample design, and sample assessment are the components of a sampling plan. 

Test your knowledge with multiple choice flashcards

The sampling plan is a part of the _________ phase.

The ___________  involves deciding the target population. 

The sample size

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Define sampling plan.

A   sampling   plan   outlines the individuals chosen to represent the target population under consideration for research purposes.

During a sampling plan in research, _____________, ___________, and the sampling procedure are decided. 

During a sampling plan in research, the sampling unit , the sampling size , and the sampling procedure are decided. 

The ___________    involves deciding the target population.  

sampling unit

The   sample size

will specify how many people from the sampling unit will be surveyed or studied.

What are the two types of sampling plans?

Probability  and  non-probability sampling . 

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Marketing91

What is Sampling plan and its application in Market research?

June 12, 2023 | By Hitesh Bhasin | Filed Under: Marketing

Once you are ready with your market research plan, then comes the implementation part. And the first step of implementation is determining your sampling plan.

A sampling plan basically comprises of different sample units or sample population whom you are going to contact to collect market research data . This sampling unit is a representative of the total population, though it might be a fraction of the total population.

In simple language, if you have 1 lakh customers, you cannot conduct an interview of 1 lakh customers. Instead, you take a sample population of 1000 customers (1 % of your total population). This sample gives you primary data and this is assumed to suit 99% of your customers. Naturally, the 1% whom you have interviewed need to be important to your company . And hence the need of a sampling plan.

There are four steps to making a Sampling plan for Market research .

Table of Contents

1) Define the sample population

More commonly known as the Sample unit, it comprises of the type of customers / people that you want to contact for your market research study. To determine the sample population, first you need to decide what the ideal customer for the firm looks like.

If yours is a restaurant, you will like your sample population to comprise of people who have visited at least 5 times to your restaurant. They will be a fair judge of things you can improve on. Or on the other hand, you can interview people who are just walking in, if you want to improve the ambiance of the restaurant and note down their ideas .

Overall, you need to understand that you cannot interview 100% of your customers. Hence the Sample population will be a small population which will be extrapolated later on. So this step is important and you need to choose your customers with care. They should be a strong representative of the type of business you want to become.

2) Define the size of the population

In the above example of a restaurant, the market research can have 1 of two objectives

• To find out what makes old customers happy
• To improve the ambiance of the restaurant which can be suggested by new people entering the restaurant.

In the above example, in Case A, you need to approach a hand picked customer base who have visited your restaurant time and time again and you need to implement what they say. This customer base can be anywhere between 50 to 200 of your most valuable customers.

In case B, where you want to improve the ambiance of the restaurant, every new comer who is new to the restaurant will have a different suggestion. And the research will be endless, so you can put a questionnaire right at the table for things the customer will like to improve.

Thus as you can see, the sampling size and the size of the population in the sampling plan changes as per the objective of the market research . If the objective is an ongoing objective to make something better all the time, then the population will be large. If it is something you need to determine within a given time period, then the population will be less but it should be important.

Once your Sampling size is decided you can decide on the contact method for your sampling plan.

3) What type of contact options are you using

There are many options in Market research which can be used time and time again to carry out primary data collection . These options include questionnaires, mailers, telephonic interviews and whatnot.

Your contact options depend on your sampling size and your sampling units. If your customers are the busy types and there are a handful of them, then personal interviews with an appointment will serve you perfectly. However, if your customers keep coming and there is a large population of them, then it is manually not possible to touch each and every one personally.

So your sampling plan and its contact method will depend on the size of the population you are going to contact. Mind you, large companies still prefer contacting their customers personally to get personal feedback from them. But smaller companies have various options such as telephonic interview or Forms and questionnaires or mailers to get the work done smoothly .

4) Form a sampling frame

So once you have the sampling units, the sample size and the population you are going to contact, you decide on the contact plan. You need to put it on paper whom you are going to contact when. A market research study might take a single day (in which case you dont need a sampling frame) or it may take months (in which case you definitely need a sampling frame).

Think of a sampling frame as an organizer. If you are too busy, then you are better off with an organizer in your sampling plan. However, if the work is going to get done quickly, then you dont need the sampling frame at all. If you expect facing any problems or questions when the process starts, then you can prepare the sampling frame to answer such questions. It can be the FAQ of your market research plan.

How are you going to analyse the results? – In the sampling plan, you need to decide on the analysis part as well. There are two ways to analyse the results of a Market research study

a) Probabilistic sampling – Most likely to be used for quantitative research, it can pin point sampling errors and therefore gives a correct data. However, it is heavy on time consumed for analysis. It uses mainly an objective questionnaire with proper “Yes / No” type questions and statistical answers.

b) Non probability sampling – It is used mainly for qualitative research wherein you can marketing and customer insights as it is not data based but more based on the quality of the answers. This is more of a subjective questionnaire then objective.

The type of analysis you are going to carry out (probability or non probability) has to be determined in the sampling plan because you need to contact the customers accordingly. Do note, that this has to be incorporated in your market research study so you need to decide your population accordingly as well.

Overall, once you are done with the 5 steps of sampling plan given above, then you are done with the following process

• Who are the customers you are going to contact
• How many customers you are going to contact
• How will you contact the customers
• What is the time frame or the contact frame for getting in touch with customers
• What is the analysis method you are going to use later on.

Once you do the above, you are ready with your sampling plan.

Here is a video by Marketing91 on Sampling.

Liked this post? Check out the complete series on Market research

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About Hitesh Bhasin

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• Published: 29 April 2024

Correction: Impact of sampling and data collection methods on maternity survey response: a randomised controlled trial of paper and push‑to‑web surveys and a concurrent social media survey

• Siân Harrison 1 ,
• Fiona Alderdice 1 &
• Maria A. Quigley 1  

BMC Medical Research Methodology volume  24 , Article number:  100 ( 2024 ) Cite this article

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The Original Article was published on 12 January 2023

Correction: BMC Med Res Methodol 23, 10 (2023)

https://doi.org/10.1186/s12874-023-01833-8

Following publication of the original article [ 1 ], the authors reported an error in the Fig.  4 : the colours in the pie charts in Fig.  4 do not all correspond with the legend. See the Fig.  4 corrected.

Breakdown of total costs across the surveys

The original article [ 1 ] has been updated.

Harrison S, Alderdice F, Quigley MA. Impact of sampling and data collection methods on maternity survey response: a randomised controlled trial of paper and push-to-web surveys and a concurrent social media survey. BMC Med Res Methodol. 2023;23:10. https://doi.org/10.1186/s12874-023-01833-8 .

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Harrison, S., Alderdice, F. & Quigley, M.A. Correction: Impact of sampling and data collection methods on maternity survey response: a randomised controlled trial of paper and push‑to‑web surveys and a concurrent social media survey. BMC Med Res Methodol 24 , 100 (2024). https://doi.org/10.1186/s12874-024-02216-3

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Examining characteristics and sampling methods of phosphor dynamics in lowland catchments

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• Henrike T. Risch   ORCID: orcid.org/0009-0004-8158-004X 1 ,
• Paul D. Wagner 1 ,
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• Nicola Fohrer 1  

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Despite over two decades since the EU Water Framework Directive have passed, achieving the desired water quality in German surface waters remains challenging, regardless of efforts to reduce phosphorus inputs and associated environmental impacts. This study aims at analyzing the characteristics governing the concentrations of four key water quality parameters (total phosphorus, orthophosphate, particulate phosphate, and suspended solids) in two lowland catchments: the 50 km 2 catchment of the Kielstau, Germany, and its 7 km 2 tributary, the Moorau, which are dominated by agricultural land use. To this end, different sampling methods, particularly high-resolution precipitation event-based sampling and daily mixed samples, are conducted and evaluated, and their effectiveness is compared. The identification of sources and characteristics that affect phosphorus and suspended sediment dynamics, both in general and specifically during heavy precipitation events, is one focus of the study. Over a 15-year period, increasing concentrations of these parameters were observed in daily mixed samples, exhibiting distinct seasonal patterns—higher in summer and lower in winter—consistent with lowland catchment behavior. Particularly during heavy precipitation events, the smaller catchment exhibits a more complex and less predictable response to chemical concentrations compared with the dilution effect observed in the larger catchment. The results underline the complexity of phosphorus dynamics in small catchments and emphasize the importance of event-based sampling for capturing short-term concentration peaks for all four parameters, particularly beneficial regarding measuring suspended solids. While daily mixed samples capture average phosphorus concentrations, event-based sampling is crucial for detecting short-term spikes, providing a more comprehensive understanding of phosphorus dynamics.

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Introduction

Although more than two decades have passed since the introduction of the EU Water Framework Directive, the desired “good status” could not be achieved in all German surface waters, despite various implemented measures to reduce agrochemical inputs and mitigate the associated environmental impacts. Studies indicate that the condition of the water network and major rivers is influenced by pollution sources in their immediate vicinity only to a limited extent and the impact of contaminants from smaller tributaries plays a significant role (Rutkowska et al. 2022 ; Steinhoff-Wrześniewska et al. 2022 ). This applies to lowland river systems that are characterized by ponds, lakes, and retention reservoirs.

The demand for agricultural products necessitates fertilization with phosphorus and nitrogen compounds to improve yields, as these are among the main limiting factors in plant growth (Tian et al. 2016 ). This additional nutrient amount on agricultural land also leads to negative impacts on the environment and particularly on water bodies (Addiscott et al. 1991 ; Ansari et al. 2011 ; Cui et al. 2020 ; Qi et al. 2023 ).

Nutrient pollution in water bodies can result from both, diffuse and point sources. The point sources are mainly connected to sewage. In rural areas, the limited cleaning power of the common small sewage treatment plants has a significant influence (Langergraber et al. 2018 ; Schranner 2014 ; UBA 2017 ). Diffuse sources from agriculture, especially inputs through erosion and drainage, make a significant contribution to phosphorus pollution in lowlands (Holsten et al. 2012 ; Trepel 2014 ). In lowland areas, the transport of water and nutrients is heavily influenced by its flat topography and shallow groundwater tables, as well as spatially heterogeneous land use (Krause et al. 2007 ; Lam et al. 2009 ; Lei et al. 2022 ; Schmalz et al. 2009 ).

Agricultural drainage, in otherwise relatively flat areas, greatly accelerates the movement of sediments and nutrients from these fields to receiving surface waters (Blann et al. 2009 ; Brendel et al. 2019 ; Skaggs et al. 1994 ) and plays a significant role in causing algal blooms and eutrophication problems in major surface water bodies worldwide (Bauwe et al. 2019 ; Rabalais and Turner 2019 ; Tiemeyer et al. 2009 ). Due to phosphorus being one of the most immobilized macronutrients in soil and its strong binding to soil particles (Arai and Livi 2013 ; Tian et al. 2016 ), heavy precipitation events have a major impact on the amount of phosphorus introduced into surface waters (Waller et al. 2021 ). Such extreme precipitation events are occurring with increased frequency due to climate change (Myhre et al. 2019 ) and can be observed primarily in the summer months often associated with convective air movements (DWD 2022; LAWA 2018 ; Myhre et al. 2019 ).

Considering the spatial and temporal fluctuations in water quality, ensuring sample representativeness becomes a crucial concern while devising an appropriate monitoring strategy and frequency to assess the long-term water quality trends and the efficacy of mitigation measures. Increasing the precision and accuracy of measurements can be achieved by selecting a suitable sampling time interval tailored to specific parameters (Skeffington et al. 2015 ). Historically, monthly or fortnightly grab sampling has been commonly employed due to its cost-effectiveness (Ferreira et al. 2007 ). However, this approach may overlook short-term, high-concentration peaks that arise during infrequent high-flow events throughout the year, especially with regard to suspended solids (SS) and total phosphorus (TP) and a more frequent sampling is necessary (Bowes et al. 2003 ; Brauer et al. 2009 ; Cassidy and Jordan 2011 ; Horowitz 2008 ; Sun et al. 2022 ; Waller et al. 2021 ).

To capture temporal changes in TP concentrations, especially in smaller agricultural catchments with fast response to rainfall, continuous monitoring at high temporal resolution (e.g., hourly and sub-hourly sampling) is essential (Cassidy and Jordan 2011 ). This is crucial because intense short-duration rainfall events can significantly contribute to the total diffuse transfer of TP from soil to water (Cassidy and Jordan 2011 ).

Another strategy to enhance load estimates is flow proportional sampling, which has been investigated and integrated in agricultural monitoring catchments in Northern Europe. This method ensures that sampling efforts are proportional to the flow rate, enabling a more detailed estimation of pollutant loads during varying flow conditions (Kyllmar et al. 2014 ).

To comprehensively understand and monitor the phosphorus dynamics within a lowland catchment, the following research questions are addressed:

What are the sources and characteristics influencing phosphorus dynamics within a lowland catchment?

How does heavy precipitation influence phosphorus and suspended solids discharge in two different sized lowland catchments?

Could continuous daily sampling benefit from additional precipitation event-based sampling with a high temporal resolution?

Materials and methods

The Kielstau catchment is a rural lowland catchment in northern Germany with a catchment area of about 50 km 2 (Fohrer et al. 2014 ; Wagner et al. 2018 ) (Fig.  1 ). The Kielstau river drains towards the west and into the Treene river. It is a gravel lowland stream and has a total length of about 17 km, flowing through rural areas with a small number of minor communities, detached farms, and no major industry. It originates north of Sörup at an altitude of 45 m above sea level. Water quality measurements are taken in the Kielstau catchment since 2005 by the Department of Hydrology and Water Management at Kiel University (Femeena et al. 2019 ; Sun et al. 2022 ; Wagner et al. 2018 ). Moreover, the area is recognized as a UNESCO ecohydrology reference project since October 2010 (Fohrer and Schmalz 2012 ; UNESCO 2011 ).

Land use map from 2021 of the Kielstau catchment with the sampling locations, the weather station and the wastewater treatment plants

The catchment area is situated in the young moraine landscape of Schleswig–Holstein Morainic Uplands and the topography is comparatively even with heights between 27 and 78 m above sea level (LVERMA 2006 ). The dominant soil types in the area are primarily Luvisols with a distribution of Gleysols and Cambisols in central area of the catchment (BGR 1999 ). The mean annual precipitation of 918.9 mm and a mean annual temperature of 8.2 °C characterize a temperate climate (Flensburg station, period 1961 to 1990, DWD 2021 ). At the Soltfeld gauge, the outlet of the catchment area, an average water level of 50 cm and an average discharge of 0.46 m 3 /s were measured between the years 2007 and 2021 (MELUR 2023 ).

A land use map based on a field survey in summer 2021 shows that agricultural use with arable farming (63.7%) and grassland (20.3%) dominates in the catchment area (Fig.  1 ). Crop rotations are generally used, resulting in frequent changes within the areas of arable farming. Forest (3.3%), orchards and horticulture (0.5%), settlement and traffic areas (10.5%), and water areas (1.6) account for smaller areas. To increase agricultural production, subsurface tile drainages are used and estimated to cover 38% of the entire catchment (Fohrer et al. 2007 ).

The Kielstau river hast two major tributaries, the Moorau and Hennebach rivers (MELUR 2023 ; Schmalz and Fohrer 2010 ). About 5 km downstream from the spring it flows through Lake Winderatt, surrounded by protected areas of the “Winderatter See” foundation that are mainly used for moderate grazing (Fohrer and Schmalz 2012 ; Förderverein Winderatter See – Kielstau e.V. 2017 ).

Six wastewater treatment plants drain into the Kielstau and its tributaries, and two biogas plants are located within the catchment (Lei et al. 2019 ). The treatment plants Ausacker and Freienwill are located on the main stream. Wastewater treatment plant Husby is situated at the beginning of the Moorau tributary, while Hürup Nord, Hürup Weseby, and Hürup Süd are located along the Hennebach tributary (Fig.  1 ).

Precipitation and runoff measurements

Water level and discharge are recorded by the state agency of Schleswig–Holstein at the Soltfeld gauge since 1985 and are available in daily resolution (MELUR 2023 ).

There is a weather station maintained by the Department of Hydrology and Water Resources Management at the Gauge Moorau sampling site since 2010, which records precipitation with a 10-min resolution. The distance from the weather station to the Gauge Soltfeld is 5 km. In line with the German Weather Service classifications for rain intensity, the recorded rainfall can be categorized as follows: light precipitation (in 60 min < 2.5 mm, in 10 min < 0.5 mm), moderate precipitation (in 60 min ≥ 2.5 mm and < 10.0 mm, in 10 min ≥ 0.5 mm and < 1.7 mm), heavy precipitation (in 60 min ≥ 10.0 mm, in 10 min ≥ 1.7 mm), and extreme precipitation (in 60 min ≥ 50.0 mm, in 10 min ≥ 8.3 mm) (DWD 2024 ).

Sampling points

Two points in the Kielstau catchment were selected for sampling. The first sampling point is situated at the outlet of the Moorau tributary upstream of its confluence with the Kielstau River and is referred to as Gauge Moorau in the following. The Moorau tributary flows through a 2-km-long underground pipe system before it emerges south of Husby, about 12 km southeast of Flensburg. After surfacing, it is an approximately 5-km-long stream flows into the Kielstau east of the settlement Ausacker. At the Gauge Moorau sampling location, samples are collected at a bridge, where the river traverses a concrete pipe with a diameter of 100 cm, subsequently transitioning into an open channel. The second sampling point is located at the outlet of the entire Kielstau catchment and is named Gauge Soltfeld. The width of the river at the sampling location measures 310 cm with little variation at the recorded water levels. It is located in Großsoltbrück, which is a district of the municipality of Großsolt in the district of Schleswig-Flensburg in Schleswig–Holstein. It is surrounded by extensively used farmland that is sporadically also used for grazing cattle.

Sampling strategy

Daily mixed samples.

Starting from 2006, a refrigerated auto-sampler (Refrigerated Maxx-Sampler SP 5 S) is installed at the second sampling point (Gauge Soltfeld) at the outlet of the Kielstau catchment. This sampler collects automated mixed samples (100 ml per 70 min over a 24-h period starting at 12:00 a.m.) directly from the river on a daily basis. The samples are stored at a temperature of 4 °C and later transported to and analyzed in the laboratory of the Department of Hydrology and Water Resources Management at Kiel University.

Event-based samples

The event-campaign ran during the hydrological summer half-year 2021 from May to November. For this campaign, two different samplers were used at the two sites. At the Gauge Soltfeld, the sampling is based on technology from Teledyne ISCO ©, with the ISCO sampler, a signature flow meter and a TIENet Model 350 Area Velocity Sensor. The measurement setup at the Gauge Moorau consists of a combination of a sampler and floating trigger from MAXX GmbH © and a velocity sensor as well as the Avelour 6 software from IJINUS ©. In addition, an SMS modem was connected to the trigger mechanism at Gauge Moorau and informed in real time of the sampling progress so that the pick-up and analysis of the samples in the laboratory were carried out promptly. The flow velocity gauges at both measuring locations took measurements of the water level at 5-min intervals. This makes it possible to automatically trigger a logarithmically structured sampling with high temporal resolution at both locations if the water level rises by at least 2 cm in less than 2 h.

Twelve water samples of about 1800 ml each were taken over the course of 24 h per event using this logarithmic sampling method (Fig.  2 ). The data set per event includes the water level data 24 h before the start of an event, the 24 h of sampling and 1 h after the end of the event. In total, 49-h periods are analyzed for each event.

Distribution of sampling time in minutes after start of sampling event

Water quality analysis

In the laboratory of the Department of Hydrology and Water Resources Management at Kiel University, the daily mixed and event-based samples are assessed using the German standard procedure for water analysis (DEV). Orthophosphate (OP) (DEV D11 and DIN 1189) and total phosphorus (TP) (DEV H36, DEV D11, and DIN 1189) are measured with the ammonium molybdate spectrophotometric method at 880 nm. Particulate phosphate (PP) was calculated as the difference of TP and OP. In addition, the suspended solids (SS) are determined by 45 µm filtration of 1 l sample volume. This relatively small sample volume affects the measured values, especially in SS concentrations. This introduces some uncertainty with regard to the absolute values and their interpretation. However, the setup remains consistent for the daily and the event-based methodology so that the samples are comparable among themselves.

Water quality classification

The Working Group on Water Issues of the Federal States and the Federal Government (LAWA) has developed background and orientation values for water quality parameters in relation to the surface water type groups (Table  1 ) in part B of the Conceptual Framework (RaKon) for the establishment of monitoring programs and the assessment of the state of surface waters. When the orientation value is violated, the phosphorus concentration assumes an order of magnitude that generally no longer permits a good ecological status of the body of water, even if the orientation value of no other parameter is violated (LAWA-RaKon 2015 ). The background value is the threshold for good ecological status. The background and orientation value for the Kielstau, a gravel lowland stream, are provided in Table  1 (LAWA-RaKon 2015 ).

Statistical methods

A comprehensive trend analysis was conducted for the data gathered at the main outlet of daily mixed samples (2007–2021). For a division of the time period in summer and winter season, the months April to September are classified as the summer half-year, and the months from October to March are the winter half-year. To characterize the water and matter balance at both locations, statistical parameters, frequency distributions, correlations, as well as cross-correlations of the parameters are examined. The Pearson’s correlation coefficient is used. The water level is used as the second parameter for the cross-correlation. Linear regression is used to analyze the changes in the water and matter balance at the sampling points. The slope of the regression line reflects the trend in the time series. The significance of the trend is determined at the 5% significance level. In addition to the individual trend analysis for each event, a trend analysis is performed for the entire data set collected at each sampling site. All analyzes were performed with the statistical software R 4.2.3 (R Core Team 2023 ).

Results and discussion

The analysis of the long-term data series in the Kielstau catchment at Gauge Soltfeld from 2007 until 2021 showed an increase in phosphorus concentrations. Over the 15-year period, the linear regressions for PP and TP are statistically significant ( p  < 0.001). However, no significant increase of SS could be determined in the Kielstau at Gauge Soltfeld (Fig.  3 ). The seasonal linear regression lines show an increase in all phosphorous parameters in winter, whereas the summer shows a significant concentration increase only in PP (Fig.  3 ).

Dynamics of discharge and the daily mixed sample data (total phosphorus, orthophosphate, particulate phosphate and suspended solids 2007–2021) with significant linear trend lines, regression equation and p value for the entire period, summer and winter half-year, trend visualization and LAWA-RaKon ( 2015 ) limit values (Table  1 )

The average TP and OP concentrations are higher in summer than in winter. This seasonal pattern, with higher concentrations in summer than in winter, in the Kielstau was also found by Wagner et al. ( 2018 ). Other researchers also documented similar seasonal effects in comparable lowland catchments in north Germany (Bauwe et al. 2019 ; Bitschofsky and Nausch 2019 ) and elsewhere (Ballantine et al. 2008 , May et al. 2001 , Schmalz et al. 2016 2015, Yates and Johnes 2013 , Zieliński and Jekatierynczuk-Rudczyk 2015 ). These effects are frequently linked at least partially to point sources, which experience more dilution during periods of high flow than during periods of low flow (Abbott et al. 2018 ; Bowes et al. 2003 ; Yates and Johnes 2013 ). Diffuse inputs from mineral P fertilization in connection with heavy rainfall can also contribute to these seasonal changes (Wagner et al. 2018 ).

A stronger increase in the concentration of TP can be found from 2020 onwards. Also, in the case of OP, the concentration increased, especially in 2020 and 2021 (Fig.  9 in the Appendix). Among all the calculated regressions of the daily mixed samples, the OP concentrations during the winter period from 2017 to 2021 (Fig.  9 ) exhibit the highest coefficient of determination ( R 2  = 0.26). This indicates that the linear regression model can explain 26% of the variability in OP concentrations. However, it is important to note that while this R 2 value is the highest among the measured datasets, it remains relatively low. The calculated PP also showed an increase in concentration trends over the entire measurement period (Fig.  3 ). One of the impact factors for this increasing trend could be the increase in population in the district Schleswig-Flensburg, in which the Kielstau catchment is located. The population in 2007 was 199.101, which increased to 203.779 by the year 2021 (Keller 2023 ).

As explained in Lei et al. ( 2019 ), six wastewater treatment plants drain into the Kielstau river. One of these plants is the Freienwill wastewater treatment plant which operates under 2700 EWG (population equivalent). The Freienwill wastewater treatment plant is currently being expanded to approximately 6000 EGW until January 2024, due to a merger with the municipality of Hürup with additional wastewater and due to population increase in the Freienwill region (D. Behnemann, personal communication, June 06, 2023), supporting the population increase noted by Keller ( 2023 ).

Between the years 2008 and 2021, the measured annual wastewater volume at Freienwill increased from annually 85,987 m 3 to 114,968 m 3 (Nord 2023b ) and concentrations from 4 mg/l to 0.05 mg/l were released into the Kielstau (Nord 2023b ). A calculated significant linear regression ( p  < 0.001) shows a decrease in released TP concentration over the total summer and winter periods. Even with a concentration decrease, the total of TP drained into the river could have increased and could have contributed to the increase in phosphorus concentrations over the years (Fig.  4 ), but it is unlikely that it is the dominant reason for the increase of phosphorus concentration at Gauge Soltfeld.

Dynamics of total phosphorus concentrations of the Freienwill wastewater treatment plant outlet (2008–2021) (Nord 2023b ) with significant linear trend lines, regression equation and p value for the entire period, summer and winter half-year, trend visualization and LAWA-RaKon ( 2015 ) limit values (Table  1 )

Furthermore, the land use of 2021 (Fig.  1 ) is a factor affecting water quality, because agricultural use with arable farming (63.7%) and grassland (20.3%) dominates in the catchment area. The dissolved form of inorganic phosphorus in the soil, which is readily available for plant uptake, constitutes only a small fraction (Kruse et al. 2015 ). As a result, agricultural production systems usually rely on fertilization to supplement the accessible phosphorus for optimal plant growth.

In the light of the two biogas plants situated in the catchment, the utilization of biogas digestates as fertilizers is probable. Compared with untreated manures, biogas digestates generally have lower dry matter content, influenced by co-substrates used (Kluge et al. 2008 ; Pötsch et al. 2004 ). The TP content in digestates varies (Lfl 2012 ). But it usually ranges from 0.04% to 0.08% of fresh mass, similar to untreated manures (Kluge et al. 2008 ). Phosphates in all types of digestates have long-term effectiveness similar to mineral fertilizers (Bachmann 2013 ; Lfl 2012 ). This and the fact that the biogas plants are not a recent addition (older than 2016) indicate that they are also not the main contributor of increased phosphorus concentrations.

A significant phosphate loss is generally caused by soil erosion, i.e., horizontal displacement of soil particles by water or wind (Christoffels 2013 ; Galler 2008 ; Holsten et al. 2012 ; Trepel 2014 ). Climate change can alter plant growth stages, including crops, impacting ground cover and increasing erosion risks. Drought-induced vegetation gaps and dry soil surfaces worsen erosion, especially in sandy-soil coastal areas, where wind is an additional factor. Escalating spring and summer droughts heighten wind erosion risks (UBA 2019 ). In lowland catchments with little slope, which generally has comparably a low risk of erosion but high levels of agricultural use and drainage density, seepage-related paths via the intermediate runoff and the groundwater can also dominate (Kiesel et al. 2009 , VDLUFA 2001 ). The phosphorus discharge via seepage water increases with the following factors: increasing phosphorus saturation of the topsoil, increasing amount of seepage water, increasing proportion of coarse pores, and decreasing seepage distance (Fischer et al. 2017 ). The amount of seepage water in turn depends on the amount of precipitation and the field capacity. The seepage distance can be shortened by drainage laid close to the surface (Skaggs et al. 1994 ; VDLUFA 2001 ). In 2021, maize, rapeseed, wheat, barley, and rye were primarily cultivated in the catchment. Grain crop rotations are predominant. The most is the alternation of wheat and maize, which occurs on approximately 13% of the area in 2020 and 2021. Maize is also cultivated extensively with barley or rye. If good professional practice and maintenance fertilization (Wiesler et al. 2018 ) is applied, a shift of phosphate to the lower soil layers is low (Galler 2008 ).

From 2017 to 2021, the concentration of only one TP sample was below the background value, and the concentration of only 21 samples were under the LAWA-RaKon ( 2015 ) orientation value. In 47 cases, OP concentrations were below the background and 647 samples were below the orientation value (Table  1 & Fig.  9 ). The ecological status of the Kielstau can only be classified as moderate in this period. The classification of the measured data of 2021 specifically showed an increasing number of violations of the orientation and background value (Figs.  3  and 9 ). Because the classification of the ecological status in the case of non-compliance with an orientation value is an indication of a specific, ecologically effective insufficiency, the Kielstau has to be classified as moderate condition, missing the desired good ecological status (LAWA-RaKon 2015 ).

During the seven-month sampling period from May to November at both stations, event-based samples were collected. At Gauge Moorau, data was collected for eight events and at Gauge Soltfeld for ten events. The events at Gauge Soltfeld range from 21 May until 19 October. The events at Gauge Moorau occurred between 26 May and 28 November with fewer events in the summer months (Fig.  5 ). The time-wise closest events were event 8 at Gauge Soltfeld and within 5 h Event 5 at Gauge Moorau. Events 2 at Gauge Soltfeld and Gauge Moorau were triggered close together (22 h difference). On no occasion was the sampler triggered at the same time at both gauges; therefore, the results for both gauges are presented separately (Figs.  6 and 7 ).

Distribution of events at both gauges over the 7 months sampling period in 2021

Events at Gauge Soltfeld with precipitation, water level, total phosphorus, orthophosphate, particulate phosphate and suspended solids in relation to the event-based sampling start

Events at Gauge Moorau with precipitation, water level, total phosphorus, orthophosphate, particulate phosphate and suspended solids in relation to the event-based sampling start

One impact factor for the difference in reaction at both gauges is the size of the different corresponding catchments. The catchment response time greatly varies for different sized catchments with travel time of the water to the outlet gauge, with usually faster responses and higher impact in small catchments (Black et al. 2021 ; Gericke and Smithers 2014 ). However, the water level increased earlier at Gauge Soltfeld, which has the larger catchment area, as compared with Gauge Moorau. This shorter trigger can be partially attributed to the fact that a 2-cm increase in water level is achieved faster at Gauge Soltfeld due to the larger catchment area. Another contributing reason is that the catchment typically receives precipitation from the prevailing west winds so that the water level also rises earlier in the west, where Gauge Soltfeld is located. Unfortunately, the effect of rainfall distribution cannot be proven with our data, as there is only one precipitation gauge in the entire Kielstau catchment. The weather station is located central near the outlet of the Moorau catchment at Gauge Moorau. A rain event could have already started in parts of the catchment contributing to discharge before being recorded at the measurement station Moorau (Zhang et al. 2021a , 2021b ).

At the start of event 2 in Gauge Soltfeld, east winds were recorded at the weather station Moorau, changing to southeast winds until the start of Event 2 at Gauge Moorau. This would support the earlier statement of wind direction and the corresponding possible precipitation patterns over the catchments influencing the response time until a water level change occurs, starting the sampling process. Rainfall in the eastern part of the catchment contributes to streamflow of the Kielstau, whereas streamflow at the Moorau is generated in the northern part of the catchment. During the start of Event 8 at Gauge Soltfeld, south winds were dominant, changing direction to dominant southwest winds, indicating that big precipitation patterns should hit Kielstau and Moorau patterns in a similar timeframe due to their latitude. Generally, the wind data from this station shows a large variability in wind direction and speed over the seven-month measurement period.

The strongest rise in water level at Gauge Soltfeld was observed during event 8, occurring after approximately 2 h of precipitation. The triggering precipitation events at Gauge Soltfeld were consistently observed to be -180 min bevor trigger of the sampler (within 3 h). The only exception found at event 2 had a delay of approximately 5.5 h, corresponding the rain event visible at approximately -330 min before the trigger. The difference in reaction time can be attributed to the speed and direction of the cloud movement, to rainfall intensities, as well as to different initial hydrologic conditions in the catchment, e.g., in soil moisture. However, the water levels at Gauge Soltfeld begins to rise approximately 2 h before the sampler was triggered, suggesting a consistent pattern in the timing of precipitation and water level changes. Events 1, 2, and 3 at Gauge Soltfeld started with significantly higher water levels 24 h before the trigger of the sampler (Fig.  6 ). This corresponds with what is considered part of the high flow season before the beginning of summer (Abbott et al. 2018 ; Bitschofsky and Nausch 2019 ).

At Gauge Moorau, many discontinuous precipitation events were observed. The strongest rise in water level occurred during event 1, where the water level was increased by 270 mm. This is associated with a rainfall sum of 5.3 mm starting 530 min before the event and continuing until 330 min before the event. From the middle of this strong rainfall period until a noticeable water level change and a trigger of the sampler, 425 min, i.e., approximately 7 h, passed. During the entire scattered rainfall event from minute 530 before and 320 after the start of the event, a total amount of 18 mm rain was recorded, contributing to the extreme rise in water level. After scattered rain events, Gauge Moorau exhibited a slower rise in water levels compared with short and heavy precipitation events. The behavior of chemical concentrations varied significantly between different events at Gauge Moorau (Fig.  7 ). This indicates a multitude of contributing factors. One of these factors, albeit less influential than at the Gauge Soltfeld, the precipitation movement over the Moorau catchment could have some impact. A further factor that was investigated due to its impact on the lag time is the soil moisture content (Haga et al. 2005 ). Using the German drought monitor (GDM) (Boeing et al. 2022 ) as a base, the volumetric data soil water content in the Kielstau area was modeled and used as an indicator of soil moisture before the rain events. However, the soil moisture data could not explain the different dynamics of water level and phosphorus concentrations.

It has to be noted that none of the precipitation events that occurred during the sampling period met the criteria for extreme rainfall (minimum of 50 mm of precipitation per hour or 8.3 mm per 10 min) (DWD 2024 ). The measured rainfall events during the sampling period had a maximum of 9.2 mm per hour, coming short of the 10 mm per hour cutoff (DWD 2024 ) for heavy precipitation. However, several short-term peaks between 1.7 mm and 8.3 mm per 10 min were recorded and can still be classified as heavy precipitation events at Gauge Soltfeld and Gauge Moorau.

Correspondence between the concentration increase of SS and the phosphorus fractions is often visible, but especially dominant in event 2 at Gauge Soltfeld and event 1 at Gauge Moorau (Figs. 6 and 7 ). In terms of phosphorus fractions and SS concentrations, Gauge Soltfeld showed significantly lower levels compared with Gauge Moorau, indicating a dilution effect of phosphorus concentrations and potentially improved water quality. The Gauge Soltfeld exhibited significantly faster and shorter water level changes compared to Gauge Moorau. The triggering rain events at Gauge Soltfeld were consistently within 3 h before the start of the sampler, while Gauge Moorau mostly reacted to discontinuous precipitation events (Figs. 6 and 7 ).

The wastewater treatment plant Ausacker drains into the Moorau close to its source. In the year 2021, a total amount of 11161 m 3 wastewater was cleaned in the facility and three analyses of TP at the outlet into the river were conducted. On 8 April, a concentration of 2.07 mg/l, on 20 July 5.32 mg/l, and on 21 October 6.64 mg/l of TP were measured (Nord 2023a ). A comparison to the larger Freewill treatment plant in the catchment shows that Ausacker has significantly higher concentrations at the outlet. Freienwill’s concentrations of TP are continuously between 0.1 and 1.7 mg/l (Nord 2023a , 2023b ). This indicates that the treatment plant Ausacker with its higher TP concentrations is contributing to the high phosphorus concentrations found at Gauge Moorau.

With regard to the sampling setup, the trigger adequacy of a two-centimeter water level change within 2 h as a suitable approximation for reflecting heavy rain events seems fitting, because the sampling process was triggered on the rising part of the water level curve and the samples were taken during the high water level peaks. However, due to the significant time delay between precipitation events and sampling start, any higher concentration peak that may have occurred without a corresponding water level change was not captured. Generally, the behavior of chemical concentrations varied greater between different events at Gauge Moorau than at Gauge Soltfeld. This suggests a more complex and less predictable system in the smaller upstream catchment compared to the larger catchment, in which different effects may have balanced each other and concentrations were diluted. However, not just both sampling points, but also each individual event greatly differs from the others in terms of behavior and characteristics so that the prediction of future events is difficult.

Distinct differences in correlations were observed between Gauge Moorau and Gauge Soltfeld. Gauge Moorau exhibited a higher number of significant correlations, suggesting a greater level of interdependence between variables compared to Gauge Soltfeld. At Gauge Moorau, five correlations have an R-value above 0.5, whereas Gauge Soltfeld only achieved correlations above 0.5 in two instances. The largest positive correlation at Gauge Soltfeld was found between PP and TP, with a correlation coefficient of 0.84. At Gauge Moorau, a stronger correlation coefficient of 0.96 was observed between TP and PP (Fig.  10 in the Appendix). Suspended sediments are strongly positively correlated with TP at Gauge Moorau ( r  = 0.798), whereas the correlation is lower at Gauge Soltfeld ( r  = 0.296). Therefore, it can be assumed that the entry path of phosphorus in the Moorau catchment is primarily erosion of sediments. In the larger Kielstau catchment, this effect is not as obvious, possibly due to dilution effects. The very low correlations between precipitation and water level can be attributed to lag time and catchment response time at both sampling points. The cross-correlation value is relatively low (0.32), with peak correlation achieved after a lag of 2 days (Wagner et al. 2018 ). The relatively low correlations between the water level and chemical concentrations, especially at Gauge Soltfeld, are affected by the short water level peaks. Concentration peaks during the events are most common at a rising, but still low water level and after the water level peak. The longer more consistent water level rise at the Gauge Moorau possibly led to higher correlation between water level and the chemical concentrations. The generally lower correlations at the Gauge Soltfeld and larger mixed signal can also at least partially be attributed to the bigger catchment area and the increased distance from the weather station.

Comparison of daily mixed and event-based samples in 2021

The event-based samples are compared to the daily mixed samples at Gauge Soltfeld. To this end, weighted (green) and unweighted (blue) mean values of the event samples are calculated and compared to the dynamics of the daily mixed samples at Gauge Soltfeld (Fig.  8 ). The first data point from event 1 is starting on 21 May, with the beginning of the sampling at 16:40. The 12 samples in the following 24 h are taken following a logarithmically structured sampling scheme so that the time difference between the samples is steadily increasing (Fig.  2 ). A mean value calculation and a weighted mean value with the different times incorporated in the calculation were carried out.

Dynamics of precipitation, discharge and the daily mixed sample data (total phosphorus, orthophosphate, particulate phosphate and suspended solids) over the measurement period with the weighted and unweighted mean values of the event samples (total phosphorus, orthophosphate, particulate phosphate and suspended solids) and the LaWa-RaKon ( 2015 ) limit values (Table  1 ) at Gauge Soltfeld

At Gauge Soltfeld, the concentrations remained consistently above both the background and the orientation value of the LAWA-RaKon ( 2015 ), indicating persistent elevated levels. The lowest value achieved in TP is 0.131 mg/l on 31 May. The lowest value of OP was 1 day later on 1 June with a value of 0.079 mg/l. The event measurements displayed significantly higher concentration peaks in all parameters compared to the daily mixed samples. Notably, in event 6, the concentration of TP exceeded the associated daily mixed sample by up to 0.46 mg/l, while the concentration of OP showed an increase of up to 0.32 mg/l. The largest difference of 0.16 mg/l in PP was observed during event 7. Moreover, the maximum sediment concentration during event 9 exceeded the value of the mixed sample by a factor of 30. This highlights the importance of considering specific events in monitoring and assessing phosphorus levels in water systems (Fig.  8 ). Furthermore, to assess how mean values from the event samples compare to the mean values of the daily mixed samples the weighted daily samples and the weighted event samples were compared. Since the daily mixed samples span exactly 24 h starting from 00:00 until 23:59, they were weighted against the exact measurement period of the event-based results. For example, event 1 started on May 21 at 16:40 and continued until May 22 at 16:40. The daily mixed sample from 21 May was given a weight of 30.5%, and the daily mixed sample of the 22 May was given a 69.5% weight, to represent both days in the comparison with the 24-h period of the event sample. Overall, the results suggest that at Gauge Soltfeld, the event measurements displayed notably higher concentration peaks compared to the daily mixed samples. These findings highlight the importance of considering specific events in monitoring and assessing phosphorus levels in water systems (Fig.  8 ).

A substantial portion of SS and PP transport in catchments occurs during infrequent storm events during small fraction of the entire year. Consequently, an intensive monitoring approach is crucial to accurately capture these transient periods of elevated transport, highlighting the need to monitor them closely (Kronvang et al. 1997 ; Kronvang and Bruhn 1996 ). Employing event-based sampling enhances the precision of TP annual load estimations in some catchments (Lessels and Bishop 2015 ). This however proves to be comparatively subdued in catchments characterized by minor relief and reduced annual rainfall (Lessels and Bishop 2015 ). In lowland catchments with limited slope, the applicability might be constrained, and focus instead on short, transient periods characterized by greater transport (Kronvang et al. 1997 ; Kronvang and Bruhn 1996 ).

Regarding the phosphorus fractions, the 24-h mean values of the event samples and the daily mixed samples demonstrated a significant agreement, as indicated by a significant relationship ( p  < 0.05). However, in terms of the SS samples, the mean values exhibited significant differences and did not show a significant correlation (Fig.  11 in the Appendix). The significant agreement observed in the phosphorus fractions between the event and daily mixed samples further strengthens the reliability of the data and suggests that while the daily mixed samples miss the significantly higher and lower short-term concentrations, the average phosphorus fraction is depicted accurately in most cases. However, the divergent SS mean values indicate the need for further investigation to understand the factors contributing to sediment variations and their implications for phosphorus dynamics in the study area. An additional monitoring to accurately depict the SS concentrations shortly after rain events in the form of event-based sampling would be beneficial.

The analyses of phosphorus dynamics within the lowland catchment Kielstau showed an increase of concentrations during the last 15 and especially during the last 5 years. Seasonal variations in phosphorus concentrations, with peaks occurring during summer months followed by declines in winter, especially notable in TP and PP, were found. The long-term data indicated rising concentrations, notably in TP and PP that were linked to point sources like wastewater treatment plants and diffuse inputs from the rural landscape during heavy rainfall. The quantitative and temporal changes in the phosphorus concentrations in the event-based sampling showed very different responses to precipitation in the different events and sampling points. Heavy precipitation events were observed, leading to short-term concentration peaks above the daily averages. Differences in the reaction time of Gauges Soltfeld and Moorau were identified and may be attributed to catchment size, precipitation distribution, and soil moisture conditions. Catchment area emerged as a determining factor influencing reaction and response times following precipitation events. This highlights the necessity for monitoring approaches with higher resolutions in smaller catchments, with their faster response times compared to bigger catchments. Concentration patterns suggest a more complex, less predictable system in the smaller catchment compared to a dilution effect in the larger catchment. This indicates a greater need for event-based sampling in smaller catchments, where heavy precipitation events have stronger immediate impacts. It is notable that not just both sampling points, but also each individual event greatly differs from the others in terms of behavior and characteristics so that a general prediction about the course of future events is difficult.

With regard to the applied methodology, the event-based sampling could be refined to be dependent on flow velocity instead of water level changes, to capture the small peaks more effectively. Event-based sampling based on water level may otherwise lead to missing a portion of these peaks. A direct comparison of different event-based sampling methods would lead to more definite information. While the logarithmic sampling approach is useful to capture changes at the beginning of the event at a high resolution, a sampling approach using equidistant timing can be recommended for the analysis of auto- and cross-correlations as well as lag times. The precipitation event-based measurements of the phosphorus concentrations with daily mixed samples at the sampling point at Gauge Soltfeld suggest that while the event measurements displayed notably higher short-term concentration peaks, the average concentrations are comparable. This highlights the importance of considering specific events in lowland catchments in monitoring and assessing phosphorus levels in rivers. The data indicates that an additional event-based monitoring is particularly beneficial with regard to measuring SS concentrations. Especially with methodological refinements, event-based sampling enhances the efficacy of capturing short-term concentration peaks.

Given the availability of the unique, detailed and long-term daily data set of water quality for the Kielstau catchment, modeling can help to further analyze factors influencing phosphorus dynamics, migration pathways for phosphorus, and suspended solids. In other lowland catchments where such detailed daily measurements are not available, event-based sampling is more relevant for comprehensive data collection.

Data Availability

The data is available from the corresponding author upon reasonable request.

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Open Access funding enabled and organized by Projekt DEAL. This work was supported by a doctoral study grant from the Federal State of Schleswig–Holstein, Germany, through the Landesgraduiertenstipendium of Kiel University. The authors declare that no other funds, grants, or other support were received during the preparation of this manuscript.

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Figure  9 , 10 and 11

Dynamics of discharge and the daily mixed sample data (total phosphorus, orthophosphate, particulate phosphate and suspended solids 2017–2021) with significant linear trend lines, regression equation and p value for the entire period, summer and winter half-year, trend visualization and LAWA-RaKon ( 2015 ) limit values (Table  1 )

Scatter plot matrix with the density distribution of the precipitation, water level, total phosphorus, orthophosphate, particulate phosphate and suspended solids data in the diagonal, scatter plots in the lower left and the correlations (r) in the top right. The Gauge Soltfeld (G_S) data is represented in red, while the Gauge Moorau (G_M) data is depicted in blue. Grey represents correlations for the entire data set. The precipitation data is inverted

Comparison of the weighted daily mixed samples with the weighted event samples (regression line in blue, 1:1 line in black, grey shows the confidence interval)

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Risch, H.T., Wagner, P.D., Hörmann, G. et al. Examining characteristics and sampling methods of phosphor dynamics in lowland catchments. Environ Sci Pollut Res (2024). https://doi.org/10.1007/s11356-024-33374-y

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The experiences and needs of older adults receiving voluntary services in Chinese nursing home organizations: a qualitative study

• Qin Shen 1 &
• Junxian Wu 1  

BMC Health Services Research volume  24 , Article number:  547 ( 2024 ) Cite this article

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Older adults living in nursing home organizations are eager to get voluntary help, however, their past experiences with voluntary services are not satisfactory enough. To better carry out voluntary services and improve the effectiveness of services, it is necessary to have a deeper understanding of the experiences and needs of older adults for voluntary services.

The purposive sampling method was used to select 14 older adults from two nursing home organizations in Hangzhou and conduct semi-structured interviews, Collaizzi’s seven-step method was used to analyze the data.

Older adults in nursing home organizations have both beneficial experiences and unpleasant service experiences in the process of receiving voluntary services; Beneficial experiences include solving problems meeting needs and feeling warmth and care, while unpleasant service experiences include the formality that makes it difficult to benefit truly, lack of organization, regularity, sustainability, and the mismatch between service provision and actual demands. The needs for voluntary services mainly focuses on emotional comfort, Cultural and recreational, and knowledge acquisition.

Older adults in nursing home organizations have varied voluntary experiences, and their voluntary service needs are diversified. Voluntary service needs of older adults should be accurately assessed, and voluntary service activities should be focused upon.

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Introduction

As a result of advancements in medical technology and improved sanitation conditions, the average life expectancy of Chinese people has increased significantly from 60 years in 1970 to 77.3 years in 2023. However, this has led to a growing number of older adults in China. According to the seventh population census conducted by the National Bureau of Statistics of China, there are now 260 million people over the age of 60 living in the country [ 1 ], The aging population in China is growing, and population balance is becoming a core challenge for the country in the long term. The increasing aging population has posed significant challenges and burdens to the state and society [ 2 ], China’s aging population challenges the current security system, requiring significant efforts from the state and society for improvement [ 3 ].

There are three main modes of old-age care in China: family old-age care, community old-age care, and institutionalized old-age care. Family old-age care is the most traditional form of old-age care in China, due to the reduction in family size and the formation of the “4-2-1” family model - which consists of four older adults, one couple, and one child - the traditional family model is no longer able to meet the growing demand for older adults care [ 4 ]; China’s community old-age care is still in the exploratory stage, facing challenges such as slow construction, insufficient staff, and lack of professional knowledge. As a result, it cannot provide meticulous care services for older adults [ 5 ]. Against this background, institutionalized older adult care has gradually become popular, it refers to older adults in social service organizations such as senior citizen apartments, welfare homes, and homes for older adults to spend their later life [ 6 ]. The challenges of population aging and the inadequacies of family and community support for older adults have resulted in a growing number of older adults opting to reside in nursing home organizations. This has undoubtedly placed additional burdens and challenges on these nursing home organizations. Due to multiple challenges such as late start, low quality, and lack of professional and technical talents, China’s nursing home organizations are still a long way from meeting the comprehensive needs of older adults in terms of health management, skilled nursing care, rehabilitation training, cultural and recreational services, psychological counseling, and social interaction [ 7 ]. To tackle the issue of an aging population in China and ensure that older adults have a high quality of life when choosing nursing home organizations, it is necessary to enhance the quality of older adult care services by engaging social forces, such as volunteer teams [ 8 ]. Voluntary services refer to the voluntary, unpaid public service offered by individuals, organizations, and voluntary service organizations to society or other organizations. The forms of voluntary services are diverse and can be either formal, planned, and long-term, or informal, spontaneous, and intermittent [ 9 ]. At present, volunteer groups in China’s nursing home organizations are mostly informal and consist of university students, healthcare workers, art workers, social workers, and others. These groups are invited by nursing home organizations or come to these institutions on their initiative to provide services for older adults. These services include a wide range of activities such as haircutting, cultural performances, spiritual comfort, hobby learning (e.g., paper-cutting, flower arranging), organizing festive activities (e.g., making rice dumplings on-site at Dragon Boat Festival, making mooncakes at Mid-Autumn Festival, etc.).

Voluntary services are a crucial aspect of long-term care and greatly complement the resources provided by the government,these nursing home organizations welcome volunteers who perform various non-medical activities associated with the daily lives of older adults [ 10 , 11 , 12 ]. Volunteers offer additional assistance and companionship to residents, provide support to employees such as nurses, nutritionists, and physical therapists, and potentially improve the overall quality of care, in China, these services have become increasingly popular and play a crucial role [ 13 , 14 ]. However, some problems have emerged in voluntary services, The voluntary services provided by volunteer organizations for older adults have certain functional defects and efficiency dilemmas, such as an unsound volunteer management system, high mobility of volunteers, and lack of a corresponding volunteer training system, which leads to the inability to provide high-quality services [ 13 ]. The above problems have undermined the effectiveness of voluntary services and affected the regular operation of nursing home organizations [ 15 ].

For effective services for older adults, it’s critical to understand the needs and experiences of older adults in nursing home organizations, there have been limited studies on how older adults feel about receiving voluntary services and if such services are suitable for their actual needs. One qualitative study documented the experiences of older adults who were helped by volunteers, but it was mainly focused on the volunteers themselves [ 16 ]. Another study looked into the benefits and experiences of receiving voluntary services, but it specifically focused on older adults who were confined to their homes [ 17 ]. There is no research available that sheds light on the emotions and requirements of older adults who receive voluntary services in nursing home organizations. To bridge this gap, we conducted interviews with older adults who have been accepted for voluntary services in two nursing home organizations in Hangzhou. The objective of this study is to gain a deeper understanding of the actual needs and experiences of older adults and use this information to guide promoting the effective growth of voluntary services and establishing a voluntary service system that is suitable for older adults in nursing home organizations.

This study adopts a qualitative descriptive approach to examine the experiences and expectations of older adults in nursing home organizations when receiving voluntary services. This study aims to gain a comprehensive understanding of the actual experiences and needs of older adults residing in nursing home organizations regarding receiving voluntary services and explore the types of voluntary services that are most suitable for the needs of older adults. To ensure accuracy and transparency, the authors followed the Consolidated Standards for Reporting Qualitative Research (COREQ) guidelines when reporting their findings [ 18 ].

Participants

During June-August 2023, the authors used purposive sampling to sample older adults residing in two nursing home organizations in Hangzhou, the inclusion criteria for the interview subjects were as follows:

they had to have resided in the nursing home organizations for more than a year;

they had to have received voluntary services;

they had to be conscious and able to express themselves effectively;

they had to have given informed consent and voluntarily agreed to participate in the study.

The number of people participating in the study was decided based on information saturation, this means the interviews were conducted until no new topics emerged and responses were repeated, the data from the twelfth interview indicated that saturation had been reached as confirmed by the other two interviews. This research principle was based on previous qualitative research studies [ 19 ]. A total of 14 older adults, coded N1-N14, were included in this study. All older adults who participated in the study agreed to the interview process, and none withdrew during the study. Detailed information can be found in Table  1 .

Interview outline

We developed an interview outline after thoroughly reviewing the literature sources and consulting with the research group [ 20 , 21 ]. We selected two older adults living in nursing home organizations to conduct pre-interviews, we adjusted the interview outline based on the feedback we received from the pre-interviews.

The interview will cover the following topics:

Please describe the voluntary services you have received in detail. How do you feel about receiving these services?

Are you satisfied with the voluntary service you have received? What aspects of the service make you satisfied?

What are your dissatisfactions with the voluntary service? Why do you feel that way?

What are your expectations and needs for the voluntary service’s content, form, and volunteers?

Is there anything else you would like to add to the discussion?

Data collection

A semi-structured interview method was utilized to gather data for this study. The main researcher, (a master’s degree nursing student) has been trained in qualitative research methods and has mastered the semi-structured interview techniques required to conduct interviews independently. Additionally, the researcher has participated in various volunteer activities in nursing organizations and has established a trustworthy relationship with the interviewees. Before conducting the interviews, the main researcher explained the study’s purpose and methodology to the interviewees and, after acquiring their consent, scheduled an appointment in advance. Face-to-face interviews were conducted with the respondents in a quiet, private, comfortable conference room. During the interview, the researcher recorded the entire process with the respondent’s consent without interrupting the respondent unnecessarily. The researcher confirmed the key concerns and the content that the respondent could not express clearly by repeating, asking follow-up questions, and asking rhetorical questions. The researcher also promptly recorded the respondent’s non-verbal information, such as movements, expressions, and tone of voice. Each interview lasted 30–45 min, and after conducting 14 interviews, no new information was obtained, indicating data saturation and ending the interview process. At the end of the interview, each interviewee was given a small token of appreciation.

Data analysis

The audio recordings of the interviews were transcribed into text within 24 h of completion, non-verbal information was noted in the transcript at relevant places. The transcribed information was then entered into the NVIVO 11.0 software (QST International, Cambridge, MA, USA) for data extraction, coding, and integration. Two researchers independently analyzed and coded the data, and the results were compared to identify common themes. Any discrepancies were resolved after the research team had discussed them to ensure that the data was complete and the analysis was accurate. Colaizzi’s seven-step analysis method was used to refine the themes from the interviews, which involved the following steps [ 22 ]:

Carefully read all the transcriptions of the interviews.

Analyze the significant statements made by the interviewees.

Code the recurring and meaningful ideas discussed in the interviews.

Gather the coded ideas and form the theme clusters.

Define and describe the themes from the coded ideas.

Identify similar ideas and sublimate the theme concepts.

Return the results to the interviewees for verification, and revision, and add the results based on the feedback from the interviewees. For detailed coding results, please see Table  2 .

After the data analysis was summarized, two main themes were identified: Experiences and Needs for volunteerism.

Theme 1: experiences

Beneficial experiences, solving problems and meeting needs.

Many older adults currently reside in nursing home organizations that are situated far away from their children and friends, they often face difficulties in getting help promptly when they encounter problems, which can affect their daily lives. For instance, in today’s rapidly developing society, many older adults own smartphones but lack the necessary knowledge to use them effectively. This, in turn, reduces their social participation and increases their sense of isolation. However, voluntary services have been instrumental in assisting them in overcoming these hurdles and leading a more fulfilling life.

N11: “When the volunteers come to teach me how to use computers, I ask them something that I don’t understand, and the teacher will explain it to me immediately.” N1: “I don’t know how to buy things online. Volunteers taught me little by little, and after a few teaching sessions, I learned how to do it so I don’t have to bother the caregiver every time. I can also do online shopping by myself, and I feel that life is much more convenient.”

Some respondents stated that volunteers could fulfill their needs. Professional volunteers also taught older adults Chinese medicine and health care and assisted with self-care.

N12: “I’m interested in Chinese medicine health care knowledge, and when students from the University of Traditional Chinese Medicine come over, and I ask them What are the functions of different acupoints, they tell me how to press them to make them work.”

Feel warmth and care

Many older adults live in nursing home organizations, away from their familiar environment and social network. This isolation can generate a sense of loneliness, making them more eager for emotional support. Volunteers provide services to add joy to the lives of older adults so that they feel cared for. Interviewees have mentioned that being taken care of on their initiative makes them feel warm and touched, increasing their overall sense of well-being.

N10: “I am delighted when I participate in volunteering, I feel that I have a group life again, I am pleased, I feel that someone cares about us.” N8: “Volunteers come to serve us, feel that people still care about us older adults, and now the country also cares about us, and society also cares about us, I am thrilled.”

Some respondents said that having someone to talk to and greet them would make them feel happy and that they were willing to communicate with young people and accept their new ideas.

N2: “As soon as I see you young people, I am happy, I feel the atmosphere of youth, my mood is different, I feel less lonely.”

Unpleasant service experiences

A formality that makes it difficult to benefit truly.

According to the interviewees, there are certain formalized phenomena in the domain of volunteering. Some volunteers engage in volunteering activities to obtain a certificate, such certificates can help them get extra points at work. Some volunteers participated in volunteering based on the mentality of the herd under the organizational arrangements of their schools or enterprises. These volunteers lack initiative, violate the principle of voluntarism, and cannot provide services that genuinely benefit older adults due to their single-mindedness and formalism during the service process. As a result, older adults have a poorer sense of experience.

N7: “Some volunteers are asked to serve by their companies, and they have to finish the job; some just go through a process.” N13: “Many volunteers come over to perform a show, then take photos and leave; the service time is very short, just like completing a task.” N5: “Some volunteers are very perfunctory; they come for a while and leave quickly.”

Lack of organization, regularity, sustainability

Many volunteers offer their services without compensation, while they have their formal jobs, which makes it difficult for them to provide services consistently. Additionally, volunteers may be more mobile, which can result in a lack of continuity in the services that are provided and the target groups that are served. However, older adults living in nursing home organizations often have monotonous and lonely lives, and occasional voluntary services may not be enough to meet their needs. As a result, some older adults may feel dissatisfied with the irregular and unsustainable nature of voluntary services.

N12: “Volunteers come on an ad hoc basis; they are not regular. Recently, a school teacher came to teach us how to sing, but unfortunately, they had to leave due to commitments and have not been able to come back.’’ N5: “Volunteers can’t come regularly; they come once in a while or not regularly and don’t have a plan.” N7: “Volunteers come to the nursing home occasionally, so they don’t want to bother them.”

The mismatch between service provision and actual demand

The voluntary services provided to older adults in nursing institutions were not able to match their real needs as the volunteers had no prior knowledge of their needs and did not make any advance preparations.

N4: “Last time, a volunteer came and asked me if I needed help with cleaning. However, I declined their kind offer because caregivers in the nursing home clean rooms every day, and the volunteers could not address the specific things I needed help with.”

The needs of older adults for volunteering can vary significantly based on their experiential backgrounds, and physiological and psychological conditions. Therefore, providing the same services to all older adults can lead to negative feelings towards volunteering among them.

N10: “Some volunteers come just to dance and sing, it feels very noisy. I don’t want to participate, I want the volunteers to talk to me peacefully and quietly.” N14: “I am not very good with my legs, so it is difficult for me to participate in activities organized by the volunteers downstairs. I would like to find activities I can participate in in my room, such as playing games or doing crafts.”

Theme 2 needs for volunteerism

Needs for emotional comfort.

Many older adults live in semi-closed institutions where they lack long-term support from their families and struggle to find someone to talk to. During the epidemic, nursing home organizations prohibited visitors to prevent the spread of the virus, leaving many seniors alone and cut off from the outside world. As a result, many older adults experience feelings of loneliness and depression. To help combat these negative emotions, volunteers can provide companionship and support, which can effectively reduce feelings of loneliness and promote emotional well-being.

N1: “I hope someone will come and chat with us; many older adults have no way to contact the outside world, so they have psychological barriers, they need psychological counseling, they need someone to come and chat with them to relieve their loneliness.” N10: “It’s better to have volunteers to come over to the service, to come and chat with me, to visit me.” N12: “I would like volunteers to communicate with us, tell us what is happening outside, tell us something new.”

Cultural and recreational needs

As people age, their social interactions tend to decrease, and they gradually tend to withdraw from daily life. This results in older adults having more free time after their retirement. Nursing home organizations can provide basic living care and medical assistance for older adults, which relieves them of the burden of cooking, cleaning, and shopping. This also means they have more free time than those who live at home or in the community. Many older adults wish to participate in cultural and recreational activities, such as singing, dancing, sports, and watching performances, to add excitement to their lives. They hope that volunteers can organize such activities to help them reduce their loneliness and spend their time in a meaningful way.

N14: “It’s good for volunteers to come and teach us how to dance, sing, and sing opera, and time passes a little faster when we all get together and learn.” N2: “It is popular for volunteers to bring cultural performances to our nursing home, we love to see young people performing programs, singing some classic old songs or Peking Opera, it is very popular.” N9: “We would like to play tai chi, it is a very suitable sport for us as it strengthens the body and the movements are softer, it would be nice if a teacher could teach us.”

Knowledge acquisition needs

According to Maslow’s Hierarchy of Needs Theory, individuals will naturally shift their focus toward higher-level pursuits once their basic and low-level needs are met. In the case of older adults residing in nursing home organizations, their basic material needs are taken care of, and as a result, their need for knowledge and learning becomes increasingly important. Many older adults require assistance in learning how to use electronic equipment, which can help facilitate their communication with the outside world and reduce feelings of isolation.

N1: “It’s become very convenient to buy things online, but I don’t know how to operate it myself and would like someone to teach me.” N2: “My daughter bought me an expensive Apple phone, but I am unfamiliar with how to use it. It would be great if someone could systematically instruct me on how to use the smartphone.” N8: “I don’t know how to use my smartphone, I don’t understand many functions, so I would benefit from having a teacher to guide me.”

As individuals age, their bodily and cognitive functions may deteriorate, adversely affecting their quality of life. Basic healthcare knowledge can be critical for older adults to maintain good health. Many older adults have a strong desire to learn about nutritional diets, rational exercise, and traditional Chinese medicine physiotherapy as a means of improving their health.

N9: “Volunteers can come and talk to us about medicine and how to predict dementia.” N13: “I have high blood pressure and cholesterol. I need advice on what to eat and what to avoid.”

To prevent any disagreements regarding the distribution of their assets among their heirs after they pass away, older adults seek the help of volunteers to assist them in drafting a will that is by national policies and regulations and has legal validity.

N12: “Volunteers can help us learn how to write a will effectively and can avoid unnecessary trouble and conflicts in the future.”

The current situation of voluntary experiences of older adults in nursing home organizations

Analysis of beneficial experiences.

The study’s findings indicate that individuals residing in nursing home organizations who are of advanced age have mixed experiences when it comes to receiving voluntary services. Most respondents conveyed the warmth and care emanating from the volunteers and the society towards older adults. Furthermore, they shared that volunteering offered them a means to engage in activities actively, create connections with fellow older adults, and foster mutual support and camaraderie. This social participation has the potential to enhance the mental well-being of older adults, thereby decreasing feelings of loneliness and depression [ 17 ]. Voluntary activities like smartphone training can help older adults acquire the necessary needed skills and adapt better to modern technology and life. Competent skills are crucial for older adults, particularly in today’s fast-developing technological society, where electronic devices such as smartphones are becoming increasingly popular. However, many older adults need more skills to operate these devices and thus cannot fully utilize them. Through training, older adults can learn how to use smartphones, including sending text messages, browsing the web, using social media, downloading applications, and more. Learning these skills not only improves the quality of life of older adults but also helps them stay connected with family and friends, thereby reducing loneliness.

Improved skills can assist older adults in accessing and utilizing health information, including online medical advice and health apps. This information can aid in managing their health status, preventing and managing chronic illnesses, and ultimately improving their quality of life. Volunteering is crucial in nursing home organizations. It provides numerous benefits to older adults, including enhancing their mental health and quality of life and receiving the necessary support and care by participating in voluntary activities [ 23 ].

Analysis of unpleasant experiences

During the interviews, some older adults shared negative experiences regarding the content, form, and frequency of voluntary services. They pointed out that volunteers did not understand their needs in advance, focusing too much on material assistance and neglecting their psychological and intellectual needs. Additionally, the service process is often too process-oriented and formalized, with less interaction with older adults, resulting in voluntary services failing to meet their expectations.

Research suggests that negative experiences of receiving voluntary services may impact older adults’ willingness to seek help and the effectiveness of voluntary services. Therefore, when providing voluntary services to older adults, it is essential to take the initiative to understand their experiences and continuously optimize the voluntary program. This approach is crucial to improving the quality of voluntary services [ 24 ].

The current situation of the demands for voluntary services by older adults

The study results show that nursing home organizations can provide comprehensive life care services to older adults, meaning they do not require many voluntary services for life care. However, this does not imply that older adults’ needs are met. Their need for emotional support, cultural recreation, and knowledge-seeking and learning is highly concentrated.

When older adults leave their familiar family environment to move into care institutions, they may experience feelings of loneliness and boredom due to the lack of regular interaction with their children, family members, and friends. This sense of isolation can harm their mental health, and they may seek more opportunities to communicate and interact with younger individuals to gain emotional comfort [ 25 ].

As people age, cultural entertainment and knowledge learning become essential for spiritual growth. After their basic living needs are taken care of, older adults desire more fulfilling recreational activities, such as calligraphy, painting, and singing, these activities enrich the spiritual life of older adults and benefit their physical and mental health [ 26 ].

In today’s rapidly developing society, the widespread use of smartphones and the popularity of online shopping have led to a digital divide among older adults. This phenomenon has, to some extent, hindered their social participation and increased their sense of isolation. Consequently, there is a growing demand for voluntary services that assist with smartphone use and can help them enjoy a convenient and fulfilling digital life.

The need for voluntary services for older adults has changed over time. While they still require help with their daily living, they also need emotional support, cultural engagement, and opportunities to learn new things. We should focus on meeting these needs to ensure our voluntary services are beneficial. By doing so, we can help older adults live fulfilling, healthy, and happy lives in their later years [ 27 ].

Suggestions and strategies for optimizing volunteerism

Accurately assessing older adults’ voluntary service needs.

The study results reveal that some older adults have negative experiences with voluntary services that fail to meet their actual needs, leading to unsatisfactory service outcomes. This highlights the need to accurately identify the real service needs of older adults to improve the quality and effectiveness of voluntary services.

To achieve our goal, we need to take a series of steps. Firstly, we must create appropriate tools for evaluating the needs of older adults for voluntary services. We should also clarify the assessment methods and strategies for assessing these needs, before launching voluntary services, relevant organizations and volunteers must understand older adults’ service experience and needs through qualitative and quantitative assessment methods [ 28 ].

To improve the quality and effectiveness of voluntary services for older adults, we can utilize big data technology to carry out precise reforms. This involves building a unified information platform for voluntary services that enables a quick match between the needs of older adults and the specialties of volunteers through the co-construction, sharing, and everyday use of resource information [ 29 ]. By doing so, we can provide multi-level, multi-category, and personalized voluntary services that cater to the actual needs of older adults, thus achieving the purpose of “precise service.”

In conclusion, we must prioritize the actual needs of older adults and provide them with more personalized and intimate voluntary services by continuously improving the assessment tools and information platforms with the orientation of precise services, the use of big data technology will play a key role in helping us realize the goal of efficient and accurate services.

Improving the quality management system of voluntary services

Volunteering quality refers to the quality of services volunteers provide, as perceived by the direct recipients. Research has shown that low-quality voluntary services fail to achieve their intended goals, moreover, negative experiences of receiving voluntary services may discourage older adults from seeking help in the future. The study highlights a significant gap between older adults’ experience of volunteering quality and their expectations, therefore, it is necessary to strengthen the management of volunteering quality to ensure that expectations are met.

To enhance the quality of volunteering, we need to implement measures. Firstly, we must optimize the recruitment and selection system for volunteers, this entails formulating recruitment plans and selection requirements that align with the voluntary services needs of older adults. We aim to create a stable and committed volunteer team skilled in services knowledge and job skills and willing to participate in voluntary services for an extended period [ 30 ].

To enhance the level and quality of service, it is important to provide regular and standardized training to volunteers. Volunteers should receive professional information support services, such as training on volunteer spirit, etiquette, communication skills, and the physiological and psychological characteristics of older adults. The main forms of training include information consultation, professional knowledge, technology lectures, sharing of previous volunteer experiences, summarizing stage-by-stage voluntary services, and experiential services. Volunteers should be provided with face-to-face or online interaction to help them improve their ability to assist older adults. The training for volunteering encompasses theoretical knowledge about volunteering, including its characteristics and principles, the rights and interests of service users, and respect for them. It also includes basic knowledge of social work, such as interpersonal communication methods and skills, as well as knowledge of health care for older adults. The latter includes the introduction of general knowledge about daily life care for older adults, such as diet, hygiene, and exercise, and the evaluation of the training’s effectiveness. Both voluntary service organizations and nursing home organizations should participate in the training process, only volunteers who have completed the training and assessment can engage in service activities [ 31 ]. It is essential to improve the evaluation mechanism of voluntary service quality. This can be done by creating a scientific evaluation index system involving older adults in evaluating their satisfaction with the voluntary service program and conducting a comprehensive analysis of the evaluation results. This analysis can help to optimize and improve the service program, additionally, tracking and evaluating the effectiveness of optimization measures to continuously enhance service quality is crucial [ 32 ].

Improving the quality of voluntary services is a comprehensive project that enhances various aspects, such as volunteer recruitment, training, and service quality evaluation. This systematic approach can help serve the nursing home organizations better and improve their overall quality of life.

Strengths and limitations

The paper’s strength lies in its focus on the experience of older adults in nursing institutions when receiving voluntary services and their need for such services. This study’s understanding of the real feelings and needs of older adults is beneficial for various organizations in society to provide better services in a targeted manner. However, the study’s limitation is that it mainly focuses on the more developed areas of Hangzhou, which affects the sample’s representativeness and makes it challenging to reflect the general situation of older adults in nursing home organizations. Additionally, the author’s subjective viewpoints may affect the analysis of the material during the data analysis process. Finally, the sample size of this study is relatively small, and there may be individual differences in personality, physical condition, and economic situation, among others. Therefore, expanding the sample size and the region’s scope to carry out more in-depth research is necessary.

This research explored the experiences and requirements of older adults who receive voluntary services in Chinese care homes. The study categorized their experiences into two groups: beneficial experiences and unpleasant service experiences, the needs of older adults who receive voluntary services include emotional comfort, cultural and recreational, and knowledge acquisition. It is crucial to have a timely and comprehensive understanding of the experiences and needs of older adults to create a targeted voluntary service model, standardized management, and training of volunteers in nursing home organizations.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. The datasets are not publicly available due to confidentiality and ethical restrictions.

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Acknowledgements

We want to express our heartfelt appreciation to the 14 older adults who participated in the interview and shared their experiences. We are also grateful to the administrators of nursing home organizations in Hangzhou, Zhejiang Province, for granting us access and allowing us to conduct the interviews at their facility. Their cooperation was invaluable in gaining insights into the needs of older adults.

This study did not receive any form of financial support.

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Li and Wu were responsible for data collection, sorting, and analysis, and Li wrote the paper. Shen directed and revised the article and approved the final version for publication. All authors read and approved the final manuscript.

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The study protocol was approved by the Medical Ethics Committee of Zhejiang Chinese Medical University (approval No. 20230814-2). Before the interviews, the participants were provided with information regarding the study’s purpose and procedures, the voluntary nature of their participation, and the confidentiality of their data. The interview data was stored securely, and only the research team could access it. The Ethics Committee of Zhejiang Chinese Medical University approved this study.

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Li, L., Shen, Q. & Wu, J. The experiences and needs of older adults receiving voluntary services in Chinese nursing home organizations: a qualitative study. BMC Health Serv Res 24 , 547 (2024). https://doi.org/10.1186/s12913-024-11045-5

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