methods of sampling in quantitative research

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3.4 Sampling Techniques in Quantitative Research

Target population.

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

Sampling techniques

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

Sample size calculation

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

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

p = Expected prevalence (of health condition of interest)

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

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

z = 1.96 (95% confidence)

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

p = 50% = 50/ 100 = 0.5

Now we can enter the values into the formula

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

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

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.

Psst... there’s more!

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 “..😉

Excellent and helpful for junior researcher!

Grad Coach tutorials are excellent – I recommend them to everyone doing research. I will be working with a sample of imprisoned women and now have a much clearer idea concerning sampling. Thank you to all at Grad Coach for generously sharing your expertise with students.

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Statistics and probability

Course: statistics and probability   >   unit 6.

• Picking fairly
• Using probability to make fair decisions
• Techniques for generating a simple random sample
• Simple random samples
• Techniques for random sampling and avoiding bias
• Sampling methods

Sampling methods review

• Samples and surveys

What are sampling methods?

Bad ways to sample.

• (Choice A)   Convenience sampling A Convenience sampling
• (Choice B)   Voluntary response sampling B Voluntary response sampling

Good ways to sample

• (Choice A)   Simple random sampling A Simple random sampling
• (Choice B)   Stratified random sampling B Stratified random sampling
• (Choice C)   Cluster random sampling C Cluster random sampling
• (Choice D)   Systematic random sampling D Systematic random sampling

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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.

References (pdf)

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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An overview of sampling methods

Last updated

27 February 2023

Reviewed by

Cathy Heath

When researching perceptions or attributes of a product, service, or people, you have two options:

Survey every person in your chosen group (the target market, or population), collate your responses, and reach your conclusions.

Select a smaller group from within your target market and use their answers to represent everyone. This option is sampling .

Sampling saves you time and money. When you use the sampling method, the whole population being studied is called the sampling frame .

The sample you choose should represent your target market, or the sampling frame, well enough to do one of the following:

Generalize your findings across the sampling frame and use them as though you had surveyed everyone

Use the findings to decide on your next step, which might involve more in-depth sampling

Make research less tedious

Dovetail streamlines research to help you uncover and share actionable insights

How was sampling developed?

Valery Glivenko and Francesco Cantelli, two mathematicians studying probability theory in the early 1900s, devised the sampling method. Their research showed that a properly chosen sample of people would reflect the larger group’s status, opinions, decisions, and decision-making steps.

They proved you don't need to survey the entire target market, thereby saving the rest of us a lot of time and money.

• Why is sampling important?

We’ve already touched on the fact that sampling saves you time and money. When you get reliable results quickly, you can act on them sooner. And the money you save can pay for something else.

It’s often easier to survey a sample than a whole population. Sample inferences can be more reliable than those you get from a very large group because you can choose your samples carefully and scientifically.

Sampling is also useful because it is often impossible to survey the entire population. You probably have no choice but to collect only a sample in the first place.

Because you’re working with fewer people, you can collect richer data, which makes your research more accurate. You can:

Ask more questions

Go into more detail

Seek opinions instead of just collecting facts

Observe user behaviors

Double-check your findings if you need to

In short, sampling works! Let's take a look at the most common sampling methods.

• Types of sampling methods

There are two main sampling methods: probability sampling and non-probability sampling. These can be further refined, which we'll cover shortly. You can then decide which approach best suits your research project.

Probability sampling method

Probability sampling is used in quantitative research , so it provides data on the survey topic in terms of numbers. Probability relates to mathematics, hence the name ‘quantitative research’. Subjects are asked questions like:

How many boxes of candy do you buy at one time?

How often do you shop for candy?

How much would you pay for a box of candy?

This method is also called random sampling because everyone in the target market has an equal chance of being chosen for the survey. It is designed to reduce sampling error for the most important variables. You should, therefore, get results that fairly reflect the larger population.

Non-probability sampling method

In this method, not everyone has an equal chance of being part of the sample. It's usually easier (and cheaper) to select people for the sample group. You choose people who are more likely to be involved in or know more about the topic you’re researching.

Non-probability sampling is used for qualitative research. Qualitative data is generated by questions like:

Where do you usually shop for candy (supermarket, gas station, etc.?)

Which candy brand do you usually buy?

Why do you like that brand?

• Probability sampling methods

Here are five ways of doing probability sampling:

Simple random sampling (basic probability sampling)

Systematic sampling

Stratified sampling.

Cluster sampling

Multi-stage sampling

Simple random sampling.

There are three basic steps to simple random sampling:

Choose your sampling frame.

Decide on your sample size. Make sure it is large enough to give you reliable data.

Randomly choose your sample participants.

You could put all their names in a hat, shake the hat to mix the names, and pull out however many names you want in your sample (without looking!)

You could be more scientific by giving each participant a number and then using a random number generator program to choose the numbers.

Instead of choosing names or numbers, you decide beforehand on a selection method. For example, collect all the names in your sampling frame and start at, for example, the fifth person on the list, then choose every fourth name or every tenth name. Alternatively, you could choose everyone whose last name begins with randomly-selected initials, such as A, G, or W.

Choose your system of selecting names, and away you go.

This is a more sophisticated way to choose your sample. You break the sampling frame down into important subgroups or strata . Then, decide how many you want in your sample, and choose an equal number (or a proportionate number) from each subgroup.

For example, you want to survey how many people in a geographic area buy candy, so you compile a list of everyone in that area. You then break that list down into, for example, males and females, then into pre-teens, teenagers, young adults, senior citizens, etc. who are male or female.

So, if there are 1,000 young male adults and 2,000 young female adults in the whole sampling frame, you may want to choose 100 males and 200 females to keep the proportions balanced. You then choose the individual survey participants through the systematic sampling method.

Clustered sampling

This method is used when you want to subdivide a sample into smaller groups or clusters that are geographically or organizationally related.

Let’s say you’re doing quantitative research into candy sales. You could choose your sample participants from urban, suburban, or rural populations. This would give you three geographic clusters from which to select your participants.

This is a more refined way of doing cluster sampling. Let’s say you have your urban cluster, which is your primary sampling unit. You can subdivide this into a secondary sampling unit, say, participants who typically buy their candy in supermarkets. You could then further subdivide this group into your ultimate sampling unit. Finally, you select the actual survey participants from this unit.

• Uses of probability sampling

Probability sampling has three main advantages:

It helps minimizes the likelihood of sampling bias. How you choose your sample determines the quality of your results. Probability sampling gives you an unbiased, randomly selected sample of your target market.

It allows you to create representative samples and subgroups within a sample out of a large or diverse target market.

It lets you use sophisticated statistical methods to select as close to perfect samples as possible.

• Non-probability sampling methods

To recap, with non-probability sampling, you choose people for your sample in a non-random way, so not everyone in your sampling frame has an equal chance of being chosen. Your research findings, therefore, may not be as representative overall as probability sampling, but you may not want them to be.

Sampling bias is not a concern if all potential survey participants share similar traits. For example, you may want to specifically focus on young male adults who spend more than others on candy. In addition, it is usually a cheaper and quicker method because you don't have to work out a complex selection system that represents the entire population in that community.

Researchers do need to be mindful of carefully considering the strengths and limitations of each method before selecting a sampling technique.

Non-probability sampling is best for exploratory research , such as at the beginning of a research project.

There are five main types of non-probability sampling methods:

Convenience sampling

Purposive sampling, voluntary response sampling, snowball sampling, quota sampling.

The strategy of convenience sampling is to choose your sample quickly and efficiently, using the least effort, usually to save money.

Let's say you want to survey the opinions of 100 millennials about a particular topic. You could send out a questionnaire over the social media platforms millennials use. Ask respondents to confirm their birth year at the top of their response sheet and, when you have your 100 responses, begin your analysis. Or you could visit restaurants and bars where millennials spend their evenings and sign people up.

A drawback of convenience sampling is that it may not yield results that apply to a broader population.

This method relies on your judgment to choose the most likely sample to deliver the most useful results. You must know enough about the survey goals and the sampling frame to choose the most appropriate sample respondents.

Your knowledge and experience save you time because you know your ideal sample candidates, so you should get high-quality results.

This method is similar to convenience sampling, but it is based on potential sample members volunteering rather than you looking for people.

You make it known you want to do a survey on a particular topic for a particular reason and wait until enough people volunteer. Then you give them the questionnaire or arrange interviews to ask your questions directly.

Snowball sampling involves asking selected participants to refer others who may qualify for the survey. This method is best used when there is no sampling frame available. It is also useful when the researcher doesn’t know much about the target population.

Let's say you want to research a niche topic that involves people who may be difficult to locate. For our candy example, this could be young males who buy a lot of candy, go rock climbing during the day, and watch adventure movies at night. You ask each participant to name others they know who do the same things, so you can contact them. As you make contact with more people, your sample 'snowballs' until you have all the names you need.

This sampling method involves collecting the specific number of units (quotas) from your predetermined subpopulations. Quota sampling is a way of ensuring that your sample accurately represents the sampling frame.

• Uses of non-probability sampling

You can use non-probability sampling when you:

Want to do a quick test to see if a more detailed and sophisticated survey may be worthwhile

Want to explore an idea to see if it 'has legs'

Launch a pilot study

Do some initial qualitative research

Have little time or money available (half a loaf is better than no bread at all)

Want to see if the initial results will help you justify a longer, more detailed, and more expensive research project

• The main types of sampling bias, and how to avoid them

Sampling bias can fog or limit your research results. This will have an impact when you generalize your results across the whole target market. The two main causes of sampling bias are faulty research design and poor data collection or recording. They can affect probability and non-probability sampling.

Faulty research

If a surveyor chooses participants inappropriately, the results will not reflect the population as a whole.

A famous example is the 1948 presidential race. A telephone survey was conducted to see which candidate had more support. The problem with the research design was that, in 1948, most people with telephones were wealthy, and their opinions were very different from voters as a whole. The research implied Dewey would win, but it was Truman who became president.

Poor data collection or recording

This problem speaks for itself. The survey may be well structured, the sample groups appropriate, the questions clear and easy to understand, and the cluster sizes appropriate. But if surveyors check the wrong boxes when they get an answer or if the entire subgroup results are lost, the survey results will be biased.

How do you minimize bias in sampling?

 To get results you can rely on, you must:

Know enough about your target market

Choose one or more sample surveys to cover the whole target market properly

Choose enough people in each sample so your results mirror your target market

Have content validity . This means the content of your questions must be direct and efficiently worded. If it isn’t, the viability of your survey could be questioned. That would also be a waste of time and money, so make the wording of your questions your top focus.

If using probability sampling, make sure your sampling frame includes everyone it should and that your random sampling selection process includes the right proportion of the subgroups

If using non-probability sampling, focus on fairness, equality, and completeness in identifying your samples and subgroups. Then balance those criteria against simple convenience or other relevant factors.

What are the five types of sampling bias?

Self-selection bias. If you mass-mail questionnaires to everyone in the sample, you’re more likely to get results from people with extrovert or activist personalities and not from introverts or pragmatists. So if your convenience sampling focuses on getting your quota responses quickly, it may be skewed.

Non-response bias. Unhappy customers, stressed-out employees, or other sub-groups may not want to cooperate or they may pull out early.

Undercoverage bias. If your survey is done, say, via email or social media platforms, it will miss people without internet access, such as those living in rural areas, the elderly, or lower-income groups.

Survivorship bias. Unsuccessful people are less likely to take part. Another example may be a researcher excluding results that don’t support the overall goal. If the CEO wants to tell the shareholders about a successful product or project at the AGM, some less positive survey results may go “missing” (to take an extreme example.) The result is that your data will reflect an overly optimistic representation of the truth.

Pre-screening bias. If the researcher, whose experience and knowledge are being used to pre-select respondents in a judgmental sampling, focuses more on convenience than judgment, the results may be compromised.

How do you minimize sampling bias?

Focus on the bullet points in the next section and:

Make survey questionnaires as direct, easy, short, and available as possible, so participants are more likely to complete them accurately and send them back

Follow up with the people who have been selected but have not returned their responses

Ignore any pressure that may produce bias

• How do you decide on the type of sampling to use?

Use the ideas you've gleaned from this article to give yourself a platform, then choose the best method to meet your goals while staying within your time and cost limits.

If it isn't obvious which method you should choose, use this strategy:

Clarify your research goals

Clarify how accurate your research results must be to reach your goals

Evaluate your goals against time and budget

List the two or three most obvious sampling methods that will work for you

Confirm the availability of your resources (researchers, computer time, etc.)

Compare each of the possible methods with your goals, accuracy, precision, resource, time, and cost constraints

Make your decision

• The takeaway

Effective market research is the basis of successful marketing, advertising, and future productivity. By selecting the most appropriate sampling methods, you will collect the most useful market data and make the most effective decisions.

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Sampling methods in Clinical Research; an Educational Review

Mohamed elfil.

1 Faculty of Medicine, Alexandria University, Egypt.

Ahmed Negida

2 Faculty of Medicine, Zagazig University, Egypt.

Clinical research usually involves patients with a certain disease or a condition. The generalizability of clinical research findings is based on multiple factors related to the internal and external validity of the research methods. The main methodological issue that influences the generalizability of clinical research findings is the sampling method. In this educational article, we are explaining the different sampling methods in clinical research.

Introduction

In clinical research, we define the population as a group of people who share a common character or a condition, usually the disease. If we are conducting a study on patients with ischemic stroke, it will be difficult to include the whole population of ischemic stroke all over the world. It is difficult to locate the whole population everywhere and to have access to all the population. Therefore, the practical approach in clinical research is to include a part of this population, called “sample population”. The whole population is sometimes called “target population” while the sample population is called “study population. When doing a research study, we should consider the sample to be representative to the target population, as much as possible, with the least possible error and without substitution or incompleteness. The process of selecting a sample population from the target population is called the “sampling method”.

Sampling types

There are two major categories of sampling methods ( figure 1 ): 1; probability sampling methods where all subjects in the target population have equal chances to be selected in the sample [ 1 , 2 ] and 2; non-probability sampling methods where the sample population is selected in a non-systematic process that does not guarantee equal chances for each subject in the target population [ 2 , 3 ]. Samples which were selected using probability sampling methods are more representatives of the target population.

Sampling methods.

Probability sampling method

Simple random sampling

This method is used when the whole population is accessible and the investigators have a list of all subjects in this target population. The list of all subjects in this population is called the “sampling frame”. From this list, we draw a random sample using lottery method or using a computer generated random list [ 4 ].

Stratified random sampling

This method is a modification of the simple random sampling therefore, it requires the condition of sampling frame being available, as well. However, in this method, the whole population is divided into homogeneous strata or subgroups according a demographic factor (e.g. gender, age, religion, socio-economic level, education, or diagnosis etc.). Then, the researchers select draw a random sample from the different strata [ 3 , 4 ]. The advantages of this method are: (1) it allows researchers to obtain an effect size from each strata separately, as if it was a different study. Therefore, the between group differences become apparent, and (2) it allows obtaining samples from minority/under-represented populations. If the researchers used the simple random sampling, the minority population will remain underrepresented in the sample, as well. Simply, because the simple random method usually represents the whole target population. In such case, investigators can better use the stratified random sample to obtain adequate samples from all strata in the population.

Systematic random sampling (Interval sampling)

In this method, the investigators select subjects to be included in the sample based on a systematic rule, using a fixed interval. For example: If the rule is to include the last patient from every 5 patients. We will include patients with these numbers (5, 10, 15, 20, 25, ...etc.). In some situations, it is not necessary to have the sampling frame if there is a specific hospital or center which the patients are visiting regularly. In this case, the researcher can start randomly and then systemically chooses next patients using a fixed interval [ 4 ].

Cluster sampling (Multistage sampling)

It is used when creating a sampling frame is nearly impossible due to the large size of the population. In this method, the population is divided by geographic location into clusters. A list of all clusters is made and investigators draw a random number of clusters to be included. Then, they list all individuals within these clusters, and run another turn of random selection to get a final random sample exactly as simple random sampling. This method is called multistage because the selection passed with two stages: firstly, the selection of eligible clusters, then, the selection of sample from individuals of these clusters. An example for this, if we are conducting a research project on primary school students from Iran. It will be very difficult to get a list of all primary school students all over the country. In this case, a list of primary schools is made and the researcher randomly picks up a number of schools, then pick a random sample from the eligible schools [ 3 ].

Non-probability sampling method

Convenience sampling

Although it is a non-probability sampling method, it is the most applicable and widely used method in clinical research. In this method, the investigators enroll subjects according to their availability and accessibility. Therefore, this method is quick, inexpensive, and convenient. It is called convenient sampling as the researcher selects the sample elements according to their convenient accessibility and proximity [ 3 , 6 ]. For example: assume that we will perform a cohort study on Egyptian patients with Hepatitis C (HCV) virus. The convenience sample here will be confined to the accessible population for the research team. Accessible population are HCV patients attending in Zagazig University Hospital and Cairo University Hospitals. Therefore, within the study period, all patients attending these two hospitals and meet the eligibility criteria will be included in this study.

Judgmental sampling

In this method, the subjects are selected by the choice of the investigators. The researcher assumes specific characteristics for the sample (e.g. male/female ratio = 2/1) and therefore, they judge the sample to be suitable for representing the population. This method is widely criticized due to the likelihood of bias by investigator judgement [ 5 ].

Snow-ball sampling

This method is used when the population cannot be located in a specific place and therefore, it is different to access this population. In this method, the investigator asks each subject to give him access to his colleagues from the same population. This situation is common in social science research, for example, if we running a survey on street children, there will be no list with the homeless children and it will be difficult to locate this population in one place e.g. a school/hospital. Here, the investigators will deliver the survey to one child then, ask him to take them to his colleagues or deliver the surveys to them.

Conflict of interest:

Part I: Sampling, Data Collection, & Analysis in Quantitative Research

In this module, we will focus on how quantitative research collects and analyzes data, as well as methods for obtaining sample population.

• Levels of Measurement
• Reliability and Validity
• Population and Samples
• Common Data Collection Methods
• Data Analysis
• Statistical Significance versus Clinical Significance

Objectives:

• Describe levels of measurement
• Describe reliability and validity as applied to critical appraisal of research
• Differentiate methods of obtaining samples for population generalizability
• Describe common data collection methods in quantitative research
• Describe various data analysis methods in quantitative research
• Differentiate statistical significance versus clinical significance

Levels of measurement

Once researchers have collected their data (we will talk about data collection later in this module), they need methods to organize the data before they even start to think about statistical analyses. Statistical operations depend on a variable’s level of measurement. Think about this similarly to shuffling all of your bills in some type of organization before you pay them. With levels of measurement, we are precisely recording variables in a method to help organize them.

There are four levels of measurement:

Nominal:  The data can only be categorized

Ordinal:  The data can be categorized and ranked

Interval:   The data can be categorized, ranked, and evenly spaced

Ratio:   The data can be categorized, ranked, even spaced, and has a natural zero

Going from lowest to highest, the 4 levels of measurement are cumulative. This means that they each take on the properties of lower levels and add new properties.

• A variable is nominal  if the values could be interchanged (e.g. 1 = male, 2 = female OR 1 = female, 2 = male).
• A variable is ordinal  if there is a quantitative ordering of values AND if there are a small number of values (e.g. excellent, good, fair, poor).
• A variable is usually considered interval  if it is measured with a composite scale or test.
• A variable is ratio level if it makes sense to say that one value is twice as much as another (e.g. 100 mg is twice as much as 50 mg) (Polit & Beck, 2021).

Reliability and Validity as Applied to Critical Appraisal of Research

Reliability measures the ability of a measure to consistently measure the same way. Validity measures what it is supposed to  measure. Do we have the need for both in research? Yes! If a variable is measured inaccurately, the data is useless. Let’s talk about why.

For example, let’s set out to measure blood glucose for our study. The validity  is how well the measure can determine the blood glucose. If we used a blood pressure cuff to measure blood glucose, this would not be a valid measure. If we used a blood glucose meter, it would be a more valid measure. It does not stop there, however. What about the meter itself? Has it been calibrated? Are the correct sticks for the meter available? Are they expired? Does the meter have fresh batteries? Are the patient’s hands clean?

Reliability  wants to know: Is the blood glucose meter measuring the same way, every time?

Validity   is asking, “Does the meter measure what it is supposed to measure?” Construct validity: Does the test measure the concept that it’s intended to measure? Content validity: Is the test fully representative of what it aims to measure? Face validity: Does the content of the test appear to be suitable to its aims?

Leibold, 2020

Obtaining Samples for Population Generalizability

In quantitative research, a population is the entire group that the researcher wants to draw conclusions about.

A sample is the specific group that the researcher will actually collect data from. A sample is always a much smaller group of people than the total size of the population. For example, if we wanted to investigate heart failure, there would be no possible way to measure every single human with heart failure. Therefore, researchers will attempt to select a sample of that large population which would most likely reflect (AKA: be a representative sample) the larger population of those with heart failure. Remember, in quantitative research, the results should be generalizable to the population studied.

A researcher will specify population characteristics through eligibility criteria. This means that they consider which characteristics to include ( inclusion criteria ) and which characteristics to exclude ( exclusion criteria ).

For example, if we were studying chemotherapy in breast cancer subjects, we might specify:

• Inclusion Criteria: Postmenopausal women between the ages of 45 and 75 who have been diagnosed with Stage II breast cancer.
• Exclusion Criteria: Abnormal renal function tests since we are studying a combination of drugs that may be nephrotoxic. Renal function tests are to be performed to evaluate renal function and the threshold values that would disqualify the prospective subject is serum creatinine above 1.9 mg/dl.

Sampling Designs:

There are two broad classes of sampling in quantitative research: Probability and nonprobability sampling.

Probability sampling : As the name implies, probability sampling means that each eligible individual has a random chance (same probability) of being selected to participate in the study.

There are three types of probability sampling:

Simple random sampling :  Every eligible participant is randomly selected (e.g. drawing from a hat).

Stratified random sampling : Eligible population is first divided into two or more strata (categories) from which randomization occurs (e.g. pollution levels selected from restaurants, bars with ordinances of state laws, and bars with no ordinances).

Systematic sampling : Involves the selection of every __ th eligible participant from a list (e.g. every 9 th  person).

Nonprobability sampling : In nonprobability sampling, eligible participants are selected using a subjective (non-random) method.

There are four types of nonprobability sampling:

Convenience sampling : Participants are selected for inclusion in the sample because they are the easiest for the researcher to access. This can be due to geographical proximity, availability at a given time, or willingness to participate in the research.

Quota sampling : Participants are from a very tailored sample that’s in proportion to some characteristic or trait of a population. For example, the researcher could divide a population by the state they live in, income or education level, or sex. The population is divided into groups (also called strata) and samples are taken from each group to meet a quota.

Consecutive sampling : A sampling technique in which every subject meeting the criteria of inclusion is selected until the required sample size is achieved. Consecutive sampling is defined as a nonprobability technique where samples are picked at the ease of a researcher more like convenience sampling, only with a slight variation. Here, the researcher selects a sample or group of people, conducts research over a period, collects results, and then moves on to another sample.

Purposive sampling : A group of non-probability sampling techniques in which units are selected because they have characteristics that the researcher needs in their sample. In other words, units are selected “on purpose” in purposive sampling.

Common Data Collection Methods in Quantitative Research

There are various methods that researchers use to collect data for their studies. For nurse researchers, existing records are an important data source. Researchers need to decide if they will collect new data or use existing data. There is also a wealth of clinical data that can be used for non-research purposed to help answer clinical questions.

Let’s look at some general data collection methods and data sources in quantitative research.

Existing data  could include medical records, school records, corporate diaries, letters, meeting minutes, and photographs. These are easy to obtain do not require participation from those being studied.

Collecting new data:

Let’s go over a few methods in which researcher can collect new data. These usually requires participation from those being studied.

Self-reports can be obtained via interviews or questionnaires . Closed-ended questions can be asked (“Within the past 6 months, were you ever a member of a fitness gym?” Yes/No) or open-ended questions such as “Why did you decide to join a fitness gym?” Important to remember (this sometimes throws students off) is that conducting interviews and questionnaires does not mean it is qualitative in nature! Do not let that throw you off in assessing whether a published article is quantitative or qualitative. The nature of the questions, however, may help to determine the type of research (quantitative or qualitative), as qualitative questions deal with ascertaining a very organic collection of people’s experiences in open-ended questions. 

Advantages of questionnaires (compared to interviews):

• Questionnaires are less costly and are advantageous for geographically dispersed samples.
• Questionnaires offer the possibility of anonymity, which may be crucial in obtaining information about certain opinions or traits.

Advances of interviews (compared to questionnaires):

• Higher response rates
• Some people cannot fill out a questionnaire.
• Opportunities to clarify questions or to determine comprehension
• Opportunity to collect supplementary data through observation

Psychosocial scales are often utilized within questionnaires or interviews. These can help to obtain attitudes, perceptions, and psychological traits. 

Likert Scales :

• Consist of several declarative statements ( items ) expressing viewpoints
• Responses are on an agree/disagree continuum (usually five or seven response options).
• Responses to items are summed to compute a total scale score.

Visual Analog Scale:

• Used to measure subjective experiences (e.g., pain, nausea)
• Measurements are on a straight line measuring 100 mm.
• End points labeled as extreme limits of sensation

Observational Methods include the observation method of data collection involves seeing people in a certain setting or place at a specific time and day. Essentially, researchers study the behavior of the individuals or surroundings in which they are analyzing. This can be controlled, spontaneous, or participant-based research .

When a researcher utilizes a defined procedure for observing individuals or the environment, this is known as structured observation. When individuals are observed in their natural environment, this is known as naturalistic observation.  In participant observation, the researcher immerses himself or herself in the environment and becomes a member of the group being observed.

Biophysiologic Measures are defined as ‘those physiological and physical variables that require specialized technical instruments and equipment for their measurement’. Biophysiological measures are the most common instruments for collecting data in medical science studies. To collect valid and reliable data, it is critical to apply these measures appropriately.

• In vivo  refers to when research or work is done with or within an entire, living organism. Examples can include studies in animal models or human clinical trials.
• In vitro is used to describe work that’s performed outside of a living organism. This usually involves isolated tissues, organs, or cells.

Let’s watch a video about Sampling and Data Collection that I made a couple of years ago.

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 .

Prevent plagiarism, run a free check.

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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Home » Sampling Methods – Types, Techniques and Examples

Sampling Methods – Types, Techniques and Examples

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Sampling refers to the process of selecting a subset of data from a larger population or dataset in order to analyze or make inferences about the whole population.

In other words, sampling involves taking a representative sample of data from a larger group or dataset in order to gain insights or draw conclusions about the entire group.

Sampling Methods

Sampling methods refer to the techniques used to select a subset of individuals or units from a larger population for the purpose of conducting statistical analysis or research.

Sampling is an essential part of the Research because it allows researchers to draw conclusions about a population without having to collect data from every member of that population, which can be time-consuming, expensive, or even impossible.

Types of Sampling Methods

Sampling can be broadly categorized into two main categories:

Probability Sampling

This type of sampling is based on the principles of random selection, and it involves selecting samples in a way that every member of the population has an equal chance of being included in the sample.. Probability sampling is commonly used in scientific research and statistical analysis, as it provides a representative sample that can be generalized to the larger population.

Type of Probability Sampling :

• Simple Random Sampling: In this method, every member of the population has an equal chance of being selected for the sample. This can be done using a random number generator or by drawing names out of a hat, for example.
• Systematic Sampling: In this method, the population is first divided into a list or sequence, and then every nth member is selected for the sample. For example, if every 10th person is selected from a list of 100 people, the sample would include 10 people.
• Stratified Sampling: In this method, the population is divided into subgroups or strata based on certain characteristics, and then a random sample is taken from each stratum. This is often used to ensure that the sample is representative of the population as a whole.
• Cluster Sampling: In this method, the population is divided into clusters or groups, and then a random sample of clusters is selected. Then, all members of the selected clusters are included in the sample.
• Multi-Stage Sampling : This method combines two or more sampling techniques. For example, a researcher may use stratified sampling to select clusters, and then use simple random sampling to select members within each cluster.

Non-probability Sampling

This type of sampling does not rely on random selection, and it involves selecting samples in a way that does not give every member of the population an equal chance of being included in the sample. Non-probability sampling is often used in qualitative research, where the aim is not to generalize findings to a larger population, but to gain an in-depth understanding of a particular phenomenon or group. Non-probability sampling methods can be quicker and more cost-effective than probability sampling methods, but they may also be subject to bias and may not be representative of the larger population.

Types of Non-probability Sampling :

• Convenience Sampling: In this method, participants are chosen based on their availability or willingness to participate. This method is easy and convenient but may not be representative of the population.
• Purposive Sampling: In this method, participants are selected based on specific criteria, such as their expertise or knowledge on a particular topic. This method is often used in qualitative research, but may not be representative of the population.
• Snowball Sampling: In this method, participants are recruited through referrals from other participants. This method is often used when the population is hard to reach, but may not be representative of the population.
• Quota Sampling: In this method, a predetermined number of participants are selected based on specific criteria, such as age or gender. This method is often used in market research, but may not be representative of the population.
• Volunteer Sampling: In this method, participants volunteer to participate in the study. This method is often used in research where participants are motivated by personal interest or altruism, but may not be representative of the population.

Applications of Sampling Methods

Applications of Sampling Methods from different fields:

• Psychology : Sampling methods are used in psychology research to study various aspects of human behavior and mental processes. For example, researchers may use stratified sampling to select a sample of participants that is representative of the population based on factors such as age, gender, and ethnicity. Random sampling may also be used to select participants for experimental studies.
• Sociology : Sampling methods are commonly used in sociological research to study social phenomena and relationships between individuals and groups. For example, researchers may use cluster sampling to select a sample of neighborhoods to study the effects of economic inequality on health outcomes. Stratified sampling may also be used to select a sample of participants that is representative of the population based on factors such as income, education, and occupation.
• Social sciences: Sampling methods are commonly used in social sciences to study human behavior and attitudes. For example, researchers may use stratified sampling to select a sample of participants that is representative of the population based on factors such as age, gender, and income.
• Marketing : Sampling methods are used in marketing research to collect data on consumer preferences, behavior, and attitudes. For example, researchers may use random sampling to select a sample of consumers to participate in a survey about a new product.
• Healthcare : Sampling methods are used in healthcare research to study the prevalence of diseases and risk factors, and to evaluate interventions. For example, researchers may use cluster sampling to select a sample of health clinics to participate in a study of the effectiveness of a new treatment.
• Environmental science: Sampling methods are used in environmental science to collect data on environmental variables such as water quality, air pollution, and soil composition. For example, researchers may use systematic sampling to collect soil samples at regular intervals across a field.
• Education : Sampling methods are used in education research to study student learning and achievement. For example, researchers may use stratified sampling to select a sample of schools that is representative of the population based on factors such as demographics and academic performance.

Examples of Sampling Methods

Probability Sampling Methods Examples:

• Simple random sampling Example : A researcher randomly selects participants from the population using a random number generator or drawing names from a hat.
• Stratified random sampling Example : A researcher divides the population into subgroups (strata) based on a characteristic of interest (e.g. age or income) and then randomly selects participants from each subgroup.
• Systematic sampling Example : A researcher selects participants at regular intervals from a list of the population.

Non-probability Sampling Methods Examples:

• Convenience sampling Example: A researcher selects participants who are conveniently available, such as students in a particular class or visitors to a shopping mall.
• Purposive sampling Example : A researcher selects participants who meet specific criteria, such as individuals who have been diagnosed with a particular medical condition.
• Snowball sampling Example : A researcher selects participants who are referred to them by other participants, such as friends or acquaintances.

How to Conduct Sampling Methods

some general steps to conduct sampling methods:

• Define the population: Identify the population of interest and clearly define its boundaries.
• Choose the sampling method: Select an appropriate sampling method based on the research question, characteristics of the population, and available resources.
• Determine the sample size: Determine the desired sample size based on statistical considerations such as margin of error, confidence level, or power analysis.
• Create a sampling frame: Develop a list of all individuals or elements in the population from which the sample will be drawn. The sampling frame should be comprehensive, accurate, and up-to-date.
• Select the sample: Use the chosen sampling method to select the sample from the sampling frame. The sample should be selected randomly, or if using a non-random method, every effort should be made to minimize bias and ensure that the sample is representative of the population.
• Collect data: Once the sample has been selected, collect data from each member of the sample using appropriate research methods (e.g., surveys, interviews, observations).
• Analyze the data: Analyze the data collected from the sample to draw conclusions about the population of interest.

When to use Sampling Methods

Sampling methods are used in research when it is not feasible or practical to study the entire population of interest. Sampling allows researchers to study a smaller group of individuals, known as a sample, and use the findings from the sample to make inferences about the larger population.

Sampling methods are particularly useful when:

• The population of interest is too large to study in its entirety.
• The cost and time required to study the entire population are prohibitive.
• The population is geographically dispersed or difficult to access.
• The research question requires specialized or hard-to-find individuals.
• The data collected is quantitative and statistical analyses are used to draw conclusions.

Purpose of Sampling Methods

The main purpose of sampling methods in research is to obtain a representative sample of individuals or elements from a larger population of interest, in order to make inferences about the population as a whole. By studying a smaller group of individuals, known as a sample, researchers can gather information about the population that would be difficult or impossible to obtain from studying the entire population.

Sampling methods allow researchers to:

• Study a smaller, more manageable group of individuals, which is typically less time-consuming and less expensive than studying the entire population.
• Reduce the potential for data collection errors and improve the accuracy of the results by minimizing sampling bias.
• Make inferences about the larger population with a certain degree of confidence, using statistical analyses of the data collected from the sample.
• Improve the generalizability and external validity of the findings by ensuring that the sample is representative of the population of interest.

Characteristics of Sampling Methods

Here are some characteristics of sampling methods:

• Randomness : Probability sampling methods are based on random selection, meaning that every member of the population has an equal chance of being selected. This helps to minimize bias and ensure that the sample is representative of the population.
• Representativeness : The goal of sampling is to obtain a sample that is representative of the larger population of interest. This means that the sample should reflect the characteristics of the population in terms of key demographic, behavioral, or other relevant variables.
• Size : The size of the sample should be large enough to provide sufficient statistical power for the research question at hand. The sample size should also be appropriate for the chosen sampling method and the level of precision desired.
• Efficiency : Sampling methods should be efficient in terms of time, cost, and resources required. The method chosen should be feasible given the available resources and time constraints.
• Bias : Sampling methods should aim to minimize bias and ensure that the sample is representative of the population of interest. Bias can be introduced through non-random selection or non-response, and can affect the validity and generalizability of the findings.
• Precision : Sampling methods should be precise in terms of providing estimates of the population parameters of interest. Precision is influenced by sample size, sampling method, and level of variability in the population.
• Validity : The validity of the sampling method is important for ensuring that the results obtained from the sample are accurate and can be generalized to the population of interest. Validity can be affected by sampling method, sample size, and the representativeness of the sample.

Advantages of Sampling Methods

Sampling methods have several advantages, including:

• Cost-Effective : Sampling methods are often much cheaper and less time-consuming than studying an entire population. By studying only a small subset of the population, researchers can gather valuable data without incurring the costs associated with studying the entire population.
• Convenience : Sampling methods are often more convenient than studying an entire population. For example, if a researcher wants to study the eating habits of people in a city, it would be very difficult and time-consuming to study every single person in the city. By using sampling methods, the researcher can obtain data from a smaller subset of people, making the study more feasible.
• Accuracy: When done correctly, sampling methods can be very accurate. By using appropriate sampling techniques, researchers can obtain a sample that is representative of the entire population. This allows them to make accurate generalizations about the population as a whole based on the data collected from the sample.
• Time-Saving: Sampling methods can save a lot of time compared to studying the entire population. By studying a smaller sample, researchers can collect data much more quickly than they could if they studied every single person in the population.
• Less Bias : Sampling methods can reduce bias in a study. If a researcher were to study the entire population, it would be very difficult to eliminate all sources of bias. However, by using appropriate sampling techniques, researchers can reduce bias and obtain a sample that is more representative of the entire population.

Limitations of Sampling Methods

• Sampling Error : Sampling error is the difference between the sample statistic and the population parameter. It is the result of selecting a sample rather than the entire population. The larger the sample, the lower the sampling error. However, no matter how large the sample size, there will always be some degree of sampling error.
• Selection Bias: Selection bias occurs when the sample is not representative of the population. This can happen if the sample is not selected randomly or if some groups are underrepresented in the sample. Selection bias can lead to inaccurate conclusions about the population.
• Non-response Bias : Non-response bias occurs when some members of the sample do not respond to the survey or study. This can result in a biased sample if the non-respondents differ from the respondents in important ways.
• Time and Cost : While sampling can be cost-effective, it can still be expensive and time-consuming to select a sample that is representative of the population. Depending on the sampling method used, it may take a long time to obtain a sample that is large enough and representative enough to be useful.
• Limited Information : Sampling can only provide information about the variables that are measured. It may not provide information about other variables that are relevant to the research question but were not measured.
• Generalization : The extent to which the findings from a sample can be generalized to the population depends on the representativeness of the sample. If the sample is not representative of the population, it may not be possible to generalize the findings to the population as a whole.

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In This Article Expand or collapse the "in this article" section Qualitative, Quantitative, and Mixed Methods Research Sampling Strategies

Introduction.

• Sampling Strategies
• Sample Size
• Qualitative Design Considerations
• Discipline Specific and Special Considerations
• Sampling Strategies Unique to Mixed Methods Designs

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Qualitative, Quantitative, and Mixed Methods Research Sampling Strategies by Timothy C. Guetterman LAST REVIEWED: 26 February 2020 LAST MODIFIED: 26 February 2020 DOI: 10.1093/obo/9780199756810-0241

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

Sampling in Qualitative Research

Sampling in qualitative research may be divided into two major areas: overall sampling strategies and issues around sample size. Sampling strategies refers to the process of sampling and how to design a sampling. Qualitative sampling typically follows a nonprobability-based approach, such as purposive or purposeful sampling where participants or other units of analysis are selected intentionally for their ability to provide information to address research questions. Sample size refers to how many participants or other units are needed to address research questions. The methodological literature about sampling tends to fall into these two broad categories, though some articles, chapters, and books cover both concepts. Others have connected sampling to the type of qualitative design that is employed. Additionally, researchers might consider discipline specific sampling issues as much research does tend to operate within disciplinary views and constraints. Scholars in many disciplines have examined sampling around specific topics, research problems, or disciplines and provide guidance to making sampling decisions, such as appropriate strategies and sample size.

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7.3: Sampling in Quantitative Research

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Learning Objectives

• Describe how probability sampling differs from nonprobability sampling.
• Define generalizability, and describe how it is achieved in probability samples.
• Identify the various types of probability samples, and provide a brief description of each.

Quantitative researchers are often interested in being able to make generalizations about groups larger than their study samples. While there are certainly instances when quantitative researchers rely on nonprobability samples (e.g., when doing exploratory or evaluation research), quantitative researchers tend to rely on probability sampling techniques. The goals and techniques associated with probability samples differ from those of nonprobability samples. We’ll explore those unique goals and techniques in this section.

Probability Sampling

Unlike nonprobability sampling, probability sampling refers to sampling techniques for which a person’s (or event’s) likelihood of being selected for membership in the sample is known. You might ask yourself why we should care about a study element’s likelihood of being selected for membership in a researcher’s sample. The reason is that, in most cases, researchers who use probability sampling techniques are aiming to identify a representative sample from which to collect data. A representative sample is one that resembles the population from which it was drawn in all the ways that are important for the research being conducted. If, for example, you wish to be able to say something about differences between men and women at the end of your study, you better make sure that your sample doesn’t contain only women. That’s a bit of an oversimplification, but the point with representativeness is that if your population varies in some way that is important to your study, your sample should contain the same sorts of variation.

Obtaining a representative sample is important in probability sampling because a key goal of studies that rely on probability samples is generalizability . In fact, generalizability is perhaps the key feature that distinguishes probability samples from nonprobability samples. Generalizability refers to the idea that a study’s results will tell us something about a group larger than the sample from which the findings were generated. In order to achieve generalizability, a core principle of probability sampling is that all elements in the researcher’s target population have an equal chance of being selected for inclusion in the study. In research, this is the principle of random selection . Random selection is a mathematical process that we won’t go into too much depth about here, but if you have taken or plan to take a statistics course, you’ll learn more about it there. The important thing to remember about random selection here is that, as previously noted, it is a core principal of probability sampling. If a researcher uses random selection techniques to draw a sample, he or she will be able to estimate how closely the sample represents the larger population from which it was drawn by estimating the sampling error. Sampling error is a statistical calculation of the difference between results from a sample and the actual parameters of a population.

Types of Probability Samples

There are a variety of probability samples that researchers may use. These include simple random samples, systematic samples, stratified samples, and cluster samples.

Simple random samples are the most basic type of probability sample, but their use is not particularly common. Part of the reason for this may be the work involved in generating a simple random sample. To draw a simple random sample, a researcher starts with a list of every single member, or element, of his or her population of interest. This list is sometimes referred to as a sampling frame . Once that list has been created, the researcher numbers each element sequentially and then randomly selects the elements from which he or she will collect data. To randomly select elements, researchers use a table of numbers that have been generated randomly. There are several possible sources for obtaining a random number table. Some statistics and research methods textbooks offer such tables as appendices to the text. Perhaps a more accessible source is one of the many free random number generators available on the Internet. A good online source is the website Stat Trek, which contains a random number generator that you can use to create a random number table of whatever size you might need ( stattrek.com/Tables/Random.aspx ). Randomizer.org also offers a useful random number generator ( http://randomizer.org ).

As you might have guessed, drawing a simple random sample can be quite tedious. Systematic sampling techniques are somewhat less tedious but offer the benefits of a random sample. As with simple random samples, you must be able to produce a list of every one of your population elements. Once you’ve done that, to draw a systematic sample you’d simply select every k th element on your list. But what is k , and where on the list of population elements does one begin the selection process? k is your selection interval or the distance between the elements you select for inclusion in your study. To begin the selection process, you’ll need to figure out how many elements you wish to include in your sample. Let’s say you want to interview 25 fraternity members on your campus, and there are 100 men on campus who are members of fraternities. In this case, your selection interval, or k , is 4. To arrive at 4, simply divide the total number of population elements by your desired sample size. This process is represented in Figure 7.5.

Figure 7.5 Formula for Determining Selection Interval for Systematic Sample

To determine where on your list of population elements to begin selecting the names of the 25 men you will interview, select a random number between 1 and k , and begin there. If we randomly select 3 as our starting point, we’d begin by selecting the third fraternity member on the list and then select every fourth member from there. This might be easier to understand if you can see it visually. Table 7.2 lists the names of our hypothetical 100 fraternity members on campus. You’ll see that the third name on the list has been selected for inclusion in our hypothetical study, as has every fourth name after that. A total of 25 names have been selected.

There is one clear instance in which systematic sampling should not be employed. If your sampling frame has any pattern to it, you could inadvertently introduce bias into your sample by using a systemic sampling strategy. This is sometimes referred to as the problem of periodicity . Periodicity refers to the tendency for a pattern to occur at regular intervals. Let’s say, for example, that you wanted to observe how people use the outdoor public spaces on your campus. Perhaps you need to have your observations completed within 28 days and you wish to conduct four observations on randomly chosen days. Table 7.3 shows a list of the population elements for this example. To determine which days we’ll conduct our observations, we’ll need to determine our selection interval. As you’ll recall from the preceding paragraphs, to do so we must divide our population size, in this case 28 days, by our desired sample size, in this case 4 days. This formula leads us to a selection interval of 7. If we randomly select 2 as our starting point and select every seventh day after that, we’ll wind up with a total of 4 days on which to conduct our observations. You’ll see how that works out in the following table.

Do you notice any problems with our selection of observation days? Apparently we’ll only be observing on Tuesdays. As you have probably figured out, that isn’t such a good plan if we really wish to understand how public spaces on campus are used. My guess is that weekend use probably differs from weekday use, and that use may even vary during the week, just as class schedules do. In cases such as this, where the sampling frame is cyclical, it would be better to use a stratified sampling technique . In stratified sampling, a researcher will divide the study population into relevant subgroups and then draw a sample from each subgroup. In this example, we might wish to first divide our sampling frame into two lists: weekend days and weekdays. Once we have our two lists, we can then apply either simple random or systematic sampling techniques to each subgroup.

Stratified sampling is a good technique to use when, as in our example, a subgroup of interest makes up a relatively small proportion of the overall sample. In our example of a study of use of public space on campus, we want to be sure to include weekdays and weekends in our sample, but because weekends make up less than a third of an entire week, there’s a chance that a simple random or systematic strategy would not yield sufficient weekend observation days. As you might imagine, stratified sampling is even more useful in cases where a subgroup makes up an even smaller proportion of the study population, say, for example, if we want to be sure to include both men’s and women’s perspectives in a study, but men make up only a small percentage of the population. There’s a chance simple random or systematic sampling strategy might not yield any male participants, but by using stratified sampling, we could ensure that our sample contained the proportion of men that is reflective of the larger population.

Up to this point in our discussion of probability samples, we’ve assumed that researchers will be able to access a list of population elements in order to create a sampling frame. This, as you might imagine, is not always the case. Let’s say, for example, that you wish to conduct a study of hairstyle preferences across the United States. Just imagine trying to create a list of every single person with (and without) hair in the country. Basically, we’re talking about a list of every person in the country. Even if you could find a way to generate such a list, attempting to do so might not be the most practical use of your time or resources. When this is the case, researchers turn to cluster sampling. Cluster sampling occurs when a researcher begins by sampling groups (or clusters) of population elements and then selects elements from within those groups.

Let’s take a look at a couple more examples. Perhaps you are interested in the workplace experiences of public librarians. Chances are good that obtaining a list of all librarians that work for public libraries would be rather difficult. But I’ll bet you could come up with a list of all public libraries without too much hassle. Thus you could draw a random sample of libraries (your cluster) and then draw another random sample of elements (in this case, librarians) from within the libraries you initially selected. Cluster sampling works in stages. In this example, we sampled in two stages. As you might have guessed, sampling in multiple stages does introduce the possibility of greater error (each stage is subject to its own sampling error), but it is nevertheless a highly efficient method.

Jessica Holt and Wayne Gillespie (2008)Holt, J. L., & Gillespie, W. (2008). Intergenerational transmission of violence, threatened egoism, and reciprocity: A test of multiple pychosocial factors affecting intimate partner violence. American Journal of Criminal Justice, 33 , 252–266. used cluster sampling in their study of students’ experiences with violence in intimate relationships. Specifically, the researchers randomly selected 14 classes on their campus and then drew a random subsample of students from those classes. But you probably know from your experience with college classes that not all classes are the same size. So if Holt and Gillespie had simply randomly selected 14 classes and then selected the same number of students from each class to complete their survey, then students in the smaller of those classes would have had a greater chance of being selected for the study than students in the larger classes. Keep in mind with random sampling the goal is to make sure that each element has the same chance of being selected. When clusters are of different sizes, as in the example of sampling college classes, researchers often use a method called probability proportionate to size (PPS). This means that they take into account that their clusters are of different sizes. They do this by giving clusters different chances of being selected based on their size so that each element within those clusters winds up having an equal chance of being selected.

KEY TAKEAWAYS

• In probability sampling, the aim is to identify a sample that resembles the population from which it was drawn.
• There are several types of probability samples including simple random samples, systematic samples, stratified samples, and cluster samples.
• Imagine that you are about to conduct a study of people’s use of public parks. Explain how you could employ each of the probability sampling techniques described earlier to recruit a sample for your study.
• Of the four probability sample types described, which seems strongest to you? Which seems weakest? Explain.

7.3 Sampling in Quantitative Research

Learning objectives.

• Describe how probability sampling differs from nonprobability sampling.
• Define generalizability, and describe how it is achieved in probability samples.
• Identify the various types of probability samples, and provide a brief description of each.

Quantitative researchers are often interested in being able to make generalizations about groups larger than their study samples. While there are certainly instances when quantitative researchers rely on nonprobability samples (e.g., when doing exploratory or evaluation research), quantitative researchers tend to rely on probability sampling techniques. The goals and techniques associated with probability samples differ from those of nonprobability samples. We’ll explore those unique goals and techniques in this section.

Probability Sampling

Unlike nonprobability sampling, probability sampling Sampling techniques for which a person’s likelihood of being selected for membership in the sample is known. refers to sampling techniques for which a person’s (or event’s) likelihood of being selected for membership in the sample is known. You might ask yourself why we should care about a study element’s likelihood of being selected for membership in a researcher’s sample. The reason is that, in most cases, researchers who use probability sampling techniques are aiming to identify a representative sample A sample that resembles the population from which it was drawn in all the ways that are important for the research being conducted. from which to collect data. A representative sample is one that resembles the population from which it was drawn in all the ways that are important for the research being conducted. If, for example, you wish to be able to say something about differences between men and women at the end of your study, you better make sure that your sample doesn’t contain only women. That’s a bit of an oversimplification, but the point with representativeness is that if your population varies in some way that is important to your study, your sample should contain the same sorts of variation.

Obtaining a representative sample is important in probability sampling because a key goal of studies that rely on probability samples is generalizability The idea that a study’s results will tell us something about a group larger than the sample from which the findings were generated. . In fact, generalizability is perhaps the key feature that distinguishes probability samples from nonprobability samples. Generalizability refers to the idea that a study’s results will tell us something about a group larger than the sample from which the findings were generated. In order to achieve generalizability, a core principle of probability sampling is that all elements in the researcher’s target population have an equal chance of being selected for inclusion in the study. In research, this is the principle of random selection The principle that all elements in a researcher’s target population have an equal chance of being selected for inclusion in the study. . Random selection is a mathematical process that we won’t go into too much depth about here, but if you have taken or plan to take a statistics course, you’ll learn more about it there. The important thing to remember about random selection here is that, as previously noted, it is a core principal of probability sampling. If a researcher uses random selection techniques to draw a sample, he or she will be able to estimate how closely the sample represents the larger population from which it was drawn by estimating the sampling error. Sampling error The extent to which a sample represents its population on a particular parameter. is a statistical calculation of the difference between results from a sample and the actual parameters The actual characteristics of a population on any given variable; determined by measuring all elements in a population (as opposed to measuring elements from a sample). of a population.

Types of Probability Samples

There are a variety of probability samples that researchers may use. These include simple random samples, systematic samples, stratified samples, and cluster samples.

Simple random samples The most basic type of probability sample; a researcher begins with a list of every member of his or her population of interest, numbers each element sequentially, and then randomly selects the elements from which he or she will collect data. are the most basic type of probability sample, but their use is not particularly common. Part of the reason for this may be the work involved in generating a simple random sample. To draw a simple random sample, a researcher starts with a list of every single member, or element, of his or her population of interest. This list is sometimes referred to as a sampling frame A list of all elements in a population. . Once that list has been created, the researcher numbers each element sequentially and then randomly selects the elements from which he or she will collect data. To randomly select elements, researchers use a table of numbers that have been generated randomly. There are several possible sources for obtaining a random number table. Some statistics and research methods textbooks offer such tables as appendices to the text. Perhaps a more accessible source is one of the many free random number generators available on the Internet. A good online source is the website Stat Trek, which contains a random number generator that you can use to create a random number table of whatever size you might need ( http://stattrek.com/Tables/Random.aspx ). Randomizer.org also offers a useful random number generator ( http://randomizer.org ).

As you might have guessed, drawing a simple random sample can be quite tedious. Systematic sampling A researcher divides a study population into relevant subgroups then draws a sample from each subgroup. techniques are somewhat less tedious but offer the benefits of a random sample. As with simple random samples, you must be able to produce a list of every one of your population elements. Once you’ve done that, to draw a systematic sample you’d simply select every k th element on your list. But what is k , and where on the list of population elements does one begin the selection process? k is your selection interval The distance between elements selected for inclusion in a study. or the distance between the elements you select for inclusion in your study. To begin the selection process, you’ll need to figure out how many elements you wish to include in your sample. Let’s say you want to interview 25 fraternity members on your campus, and there are 100 men on campus who are members of fraternities. In this case, your selection interval, or k , is 4. To arrive at 4, simply divide the total number of population elements by your desired sample size. This process is represented in Figure 7.5 "Formula for Determining Selection Interval for Systematic Sample" .

Figure 7.5 Formula for Determining Selection Interval for Systematic Sample

To determine where on your list of population elements to begin selecting the names of the 25 men you will interview, select a random number between 1 and k , and begin there. If we randomly select 3 as our starting point, we’d begin by selecting the third fraternity member on the list and then select every fourth member from there. This might be easier to understand if you can see it visually. Table 7.2 "Systematic Sample of 25 Fraternity Members" lists the names of our hypothetical 100 fraternity members on campus. You’ll see that the third name on the list has been selected for inclusion in our hypothetical study, as has every fourth name after that. A total of 25 names have been selected.

Table 7.2 Systematic Sample of 25 Fraternity Members

There is one clear instance in which systematic sampling should not be employed. If your sampling frame has any pattern to it, you could inadvertently introduce bias into your sample by using a systemic sampling strategy. This is sometimes referred to as the problem of periodicity The tendency for a pattern to occur at regular intervals. . Periodicity refers to the tendency for a pattern to occur at regular intervals. Let’s say, for example, that you wanted to observe how people use the outdoor public spaces on your campus. Perhaps you need to have your observations completed within 28 days and you wish to conduct four observations on randomly chosen days. Table 7.3 "Systematic Sample of Observation Days" shows a list of the population elements for this example. To determine which days we’ll conduct our observations, we’ll need to determine our selection interval. As you’ll recall from the preceding paragraphs, to do so we must divide our population size, in this case 28 days, by our desired sample size, in this case 4 days. This formula leads us to a selection interval of 7. If we randomly select 2 as our starting point and select every seventh day after that, we’ll wind up with a total of 4 days on which to conduct our observations. You’ll see how that works out in the following table.

Table 7.3 Systematic Sample of Observation Days

Do you notice any problems with our selection of observation days? Apparently we’ll only be observing on Tuesdays. As you have probably figured out, that isn’t such a good plan if we really wish to understand how public spaces on campus are used. My guess is that weekend use probably differs from weekday use, and that use may even vary during the week, just as class schedules do. In cases such as this, where the sampling frame is cyclical, it would be better to use a stratified sampling technique A researcher divides the study population into relevant subgroups then draws a sample from within each subgroup. . In stratified sampling, a researcher will divide the study population into relevant subgroups and then draw a sample from each subgroup. In this example, we might wish to first divide our sampling frame into two lists: weekend days and weekdays. Once we have our two lists, we can then apply either simple random or systematic sampling techniques to each subgroup.

Stratified sampling is a good technique to use when, as in our example, a subgroup of interest makes up a relatively small proportion of the overall sample. In our example of a study of use of public space on campus, we want to be sure to include weekdays and weekends in our sample, but because weekends make up less than a third of an entire week, there’s a chance that a simple random or systematic strategy would not yield sufficient weekend observation days. As you might imagine, stratified sampling is even more useful in cases where a subgroup makes up an even smaller proportion of the study population, say, for example, if we want to be sure to include both men’s and women’s perspectives in a study, but men make up only a small percentage of the population. There’s a chance simple random or systematic sampling strategy might not yield any male participants, but by using stratified sampling, we could ensure that our sample contained the proportion of men that is reflective of the larger population.

Up to this point in our discussion of probability samples, we’ve assumed that researchers will be able to access a list of population elements in order to create a sampling frame. This, as you might imagine, is not always the case. Let’s say, for example, that you wish to conduct a study of hairstyle preferences across the United States. Just imagine trying to create a list of every single person with (and without) hair in the country. Basically, we’re talking about a list of every person in the country. Even if you could find a way to generate such a list, attempting to do so might not be the most practical use of your time or resources. When this is the case, researchers turn to cluster sampling. Cluster sampling A researcher begins by sampling groups of population elements and then selects elements from within those groups. occurs when a researcher begins by sampling groups (or clusters) of population elements and then selects elements from within those groups.

Let’s take a look at a couple more examples. Perhaps you are interested in the workplace experiences of public librarians. Chances are good that obtaining a list of all librarians that work for public libraries would be rather difficult. But I’ll bet you could come up with a list of all public libraries without too much hassle. Thus you could draw a random sample of libraries (your cluster) and then draw another random sample of elements (in this case, librarians) from within the libraries you initially selected. Cluster sampling works in stages. In this example, we sampled in two stages. As you might have guessed, sampling in multiple stages does introduce the possibility of greater error (each stage is subject to its own sampling error), but it is nevertheless a highly efficient method.

Jessica Holt and Wayne Gillespie (2008) Holt, J. L., & Gillespie, W. (2008). Intergenerational transmission of violence, threatened egoism, and reciprocity: A test of multiple pychosocial factors affecting intimate partner violence. American Journal of Criminal Justice, 33 , 252–266. used cluster sampling in their study of students’ experiences with violence in intimate relationships. Specifically, the researchers randomly selected 14 classes on their campus and then drew a random subsample of students from those classes. But you probably know from your experience with college classes that not all classes are the same size. So if Holt and Gillespie had simply randomly selected 14 classes and then selected the same number of students from each class to complete their survey, then students in the smaller of those classes would have had a greater chance of being selected for the study than students in the larger classes. Keep in mind with random sampling the goal is to make sure that each element has the same chance of being selected. When clusters are of different sizes, as in the example of sampling college classes, researchers often use a method called probability proportionate to size A cluster sampling technique in which each cluster is given a chance of selection based on its size. (PPS). This means that they take into account that their clusters are of different sizes. They do this by giving clusters different chances of being selected based on their size so that each element within those clusters winds up having an equal chance of being selected.

Table 7.4 Types of Probability Samples

Key Takeaways

• In probability sampling, the aim is to identify a sample that resembles the population from which it was drawn.
• There are several types of probability samples including simple random samples, systematic samples, stratified samples, and cluster samples.
• Imagine that you are about to conduct a study of people’s use of public parks. Explain how you could employ each of the probability sampling techniques described earlier to recruit a sample for your study.
• Of the four probability sample types described, which seems strongest to you? Which seems weakest? Explain.

What are quantitative research sampling methods?

The quantitative research sampling method is the process of selecting representable units from a large population. Quantitative research refers to the analysis wherein mathematical, statistical, or computational method is used for studying the measurable or quantifiable dataset. The core purpose of quantitative research is the generalization of a phenomenon or an opinion. This involves collecting and gathering information from a small group out of a population or universe.

To find out what drives Amazon’s popularity as the most preferred e-commerce company, a small group of Amazon’s customers can be surveyed. It will help arrive at a consensus on the most significant traits that make it successful.

Therefore, an assumption about a population is based on a small or selected dataset. In order to derive accurate results, it is essential to use an appropriate sampling method. The purpose of this article is to review different quantitative sampling methods and their applicability in different types of research.

Quantitative research sampling methods

By examining the nature of the small group, the researcher can deduce the behaviour of the larger population. Quantitative research sampling methods are broadly divided into two categories i.e.

• Probability sampling
• Non-probability sampling

Probability sampling method

In probability sampling, each unit in the population has an equal chance of being selected for the sample. The purpose is to identify those sample sets which majorly represent the characteristics of the population. Herein, all the characteristics of the population are required to be known. This is done through a process known as ‘listing’. This process of listing is called the sampling frame. As probability sampling is a type of random sampling, the generalization is more accurate.

Probability sampling is quite time-consuming and expensive. Hence, this method is only suitable in cases wherein the population are similar in characteristics, and the researcher has time, money, and access to the whole population. Probability sampling is further categorized into 4 types: simple random, systematic, stratified and cluster sampling. The figure below depicts the types of probability sampling.

The difference between and applicability of these sampling methods are depicted in the table below.

Non-probability sampling method

Non-probability-based quantitative research sampling method involves non-random selection of the sample from the entire population. All units of the population do not an equal chance of participating in the survey. Therefore, the results cannot be generalized for the population.

The non-probability technique of sampling is based on the subjective judgement of the researcher. Hence this method can be applied in cases wherein limited information about the population is available. Moreover, it requires less time and money. Non-probability sampling method can be of four types as shown below.

Table 2: Non-probability-based Quantitative research sampling method

The results of the quantitative research are mainly based on the information acquired from the sample. An effective sample yields a representable outcome. To draw valid and reliable conclusions, it is essential to carefully compute the sample size of the study and define the sampling technique of the study.

• McCombes, S. (2019) Understanding different sampling methods . Available at: https://www.scribbr.com/methodology/sampling-methods/ (Accessed: 7 February 2020).
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• Published: 28 May 2024

Relational aggression in romantic relationship: empirical evidence among young female adults in Malaysia

• Mohammad Rahim Kamaluddin 2 ,
• Shalini Munusamy 1 ,
• Chong Sheau Tsuey 2 &
• Hilwa Abdullah & Mohd Nor 2  

BMC Psychology volume  12 , Article number:  305 ( 2024 ) Cite this article

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Aggressive behaviour in romantic relationship is a social problem of great concern. Studies related to the influence of psychosocial factors on relational aggression are still limited. Furthermore, these factors have not been widely studied in the local context, resulting in the issue of relational aggression among young female adults still not being addressed. This study aims to explore whether psychosocial factors such as big five personality traits, adult attachment style and loneliness could predict relational aggression in romantic relationships among young female adults in Malaysia. In addition, this study aims to identify the moderating effect of social support in the relationship between psychosocial factors and relational aggression in romantic relationship.

A quantitative research approach was used with 424 young female adults in Malaysia aged between 18 and 30 years old (mean age = 24.18) were recruited through multistage sampling design by completing a questionnaire consisting of the Big Five Inventory (BFI), Experiences in Close Relationships Scale II (ECRS-II), Revised UCLA Loneliness Scale, Measure of Relational Aggression and Victimization (MRAV) and Multidimensional Scale of Perceived Social Support (MSPSS).

Multiple regression analysis predicted significant relationship between agreeableness personality, loneliness, avoidant attachment style and anxious attachment style with relational aggression in romantic relationships. Hierarchical regression analysis found a significant effect of social support as a moderator between loneliness with relational aggression in romantic relationships.

Conclusions

Thus, the results show that young female adults with low level of agreeableness, high level of loneliness, avoidant attachment style and anxious attachment style are at a higher risk of engaging in relational aggression in romantic relationships. The implication of this study can help in understanding the psychosocial factors that form the basis of relational aggression in romantic relationships. Hence, the gap in knowledge warrants further research.

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The development of romantic relationships among early adulthood is crucial in forming views about intimate relationships and exhibiting intimacy, power, and control [ 1 ]. Emerging adulthood is a key developmental stage for creating a healthy romantic relationship. Some romantic relationships involve aggressive behaviour between partners, which can manifest in various forms such as physical, non-physical, direct, or indirect aggression, overt or covert aggression [ 2 ]. Aggressive behaviour is a criminogenic trait linked to various violent crimes including dating violence [ 3 ]. Physical aggression involves intentionally using physical force to hurt the partner, ranging from mild actions like pushing to severe violence like choking, slapping or weapon use [ 4 ]. Emotional abuse is also a common form of abuse in romantic relationships [ 5 ]. The online dating scam is another alarming form of dating violence that can result in financial loss and severe emotional and psychological suffering ( 6 – 7 ). Relational aggression is a form of non-physical and covert aggression, involves threatening others by manipulating and acting to jeopardize romantic relationships [ 8 ]. Unlike physical aggression, relational aggression occurs without any physical force or physically threatening the individual and can be considered a type of psychological aggression, targeting perceptions, feelings, or behaviour in romantic relationship [ 9 ]. Relational aggression can be indirect, such as through negative facial expressions or spreading rumors about a partner. While there has been extensive research on physical aggression and violence in romantic relationships [ 10 , 11 , 12 ], there is relatively less research on relational aggression in romantic relationships.

Relational aggression in romantic relationships might appear as threats to end the relationship if the other person doesn’t cooperate, flirting with other people to make the other person envious, or treating the other person silently while upset [ 9 ]. In terms of relational aggression, females who utilized high levels of relational aggression had a strong tendency to see other people’s acts as hostile and malevolent, whereas males did not [ 13 ]. Examining relational aggression and its relationship with adaptive functioning in females may shed light on the critical mechanisms involved in females’ dating violence. In this study, we hope to study the psychosocial factors most related with relational aggression in females by looking at components known to relate to aggression in females, such as individual characteristics and environmental factors. There is little evidence from research on female gender to differentiate the experience of relational aggression in romantic relationships, female perpetrators will be the greatest risk of this aggressive behaviour and young female adults may experience greater psychological stress than men ( 13 – 14 ). Therefore, this study focuses only on female samples and will be done using Malaysian samples. Despite research, little is known about how relational aggression originate, persist, and have an impact on romantic relationships, including whether men and women experience these issues differently ( 13 – 14 ). Romantic relational aggression has also been linked to relationship quality, violence, psychosocial maladjustment, impulsivity, hostile attribution biases, loneliness, emotional sensitivity to relational incitements, and abuse history [ 13 ].

In addition, this study emphasizes the psychosocial aspect of a person that can cause the tendency to behave aggressively in romantic relationships. It is important to identify the psychosocial aspects of a person who tends to engage in relational aggression in romantic relationships. The link between relational aggression and psychosocial factors such as loneliness, attachment styles, and personality type has been established ( 15 – 16 ). Personality traits of aggressors have been known to be associated with dating violence ( 15 – 16 ). This study used the “Big Five” personality model (extraversion, agreeableness, openness, conscientiousness, and neuroticism) as one of the psychosocial factors. Each main trait from this model can be divided into several aspects to provide a more detailed analysis of a person’s personality. Several theorists argue that personality variable is an important predictor of aggressive behaviour in romantic relationship [ 17 , 18 , 19 ]. Agreeableness dimensions are often associated with aggressive behaviour [ 18 , 20 , 21 ]. Besides that, a study conducted by Ulloa et al. (2016) found individuals with a high neuroticism personality tend to be victims in relational aggression during intimate relationships [ 22 ]. The findings of this study are also supported by other research that neuroticism trait as the main personality trait that gives a strong influence on relational aggression ( 23 – 24 ).

In addition to personality traits, other factors such as the level of loneliness are also considered to be a strong predictive factor of relational aggression especially the tendency to be a victim [ 25 ]. Generally, loneliness can be associated with individuals having a lack of social support as well as showing no interest in social networks [ 25 ]. Many studies have linked aggressive behaviour with loneliness [ 26 , 27 , 28 ]. Loneliness is defined as a negative emotional response to the discrepancy between the desired and achieved quality of one’s social network [ 27 ]. In addition, relational aggression is caused by the loneliness faced by an individual [ 28 ]. Individuals who are lonely describe themselves negatively and have negative ideas about others. As a result, loneliness leads to a bad perception of oneself, such as being unwanted and unaccepted by others, and it leads to aggression, which is a means of using force to influence other people in interpersonal relationships [ 29 ]. Individuals with high level of loneliness are at high risk of engaging in relational aggression in romantic relationship ( 30 – 31 ). Another psychosocial aspect often associated with relational aggression is attachment style. Attachment style is said to be able to shape the probability of an individual being involved in incidents of relational aggression in romantic relationship.

An expanding corpus of research has highlighted attachment theory as a crucial paradigm for comprehending emotional and interpersonal processes that take place across the lifespan [ 32 , 33 , 34 ]. The foundation of attachment theory is the idea of an attachment behavioural system, in which attachment actions are grouped together to strengthen a particular attachment figure. A sense of personal security within the relationship can be established or maintained by intimate partner violence, according to the attachment theory. People feel startled when they sense a threat to their attachment connection, and the ensuing anxiety causes them to act in ways that protect their attachment system [ 35 ]. Individuals with different attachment style also have an influence strongly to the involvement of individuals in the occurrence of aggression ( 36 – 37 ). Besides that, individuals with avoidant attachment shows high relational aggression in romantic relationship ( 38 – 39 ). Besides that, individuals who often exhibit anxious attachment to their partners such as fear of rejection and dependency on their partner are more likely to experience relational aggression in romantic relationships ( 40 – 41 ).

The potentially moderating role of Social Support

In relation to that, social support is used as a moderator based on previous literature studies [ 42 , 43 , 44 ]. Social support is also defined as interpersonal relationships and support provided by social groups that aim to provide well-being to individuals [ 42 ]. Social support from family and friends is important in contributing to positive psychological health among early adulthood and influences the act of aggressive behaviour [ 45 ]. Previous studies have shown that social support has a significant relationship with big personality traits, especially with extraversion and agreeableness [ 45 , 46 , 47 , 48 , 49 ]. In addition, a few studies also found that family members with agreeableness trait also provide more social support [ 46 , 47 , 48 ]. Besides that, people who experience loneliness interact less with friends and family than people who do not feel lonely. In other words, the less social support a person has, the higher the level of loneliness [ 50 ]. According to earlier research, there have been negative association between relational aggression and social support as well positive association between relational aggression and psychosocial maladjustment during major developmental stages including childhood, adolescence, and young adulthood [ 51 , 52 , 53 ].

According to research, individuals with little social support from their parents were more likely to engage in verbal, physical, and relational aggression [ 54 ] whereas individuals who reported high perceived social support from peers were less likely to engage in overt and relational aggression [ 55 ]. Besides that, individuals who have supporting friends and family have lower relational aggression. Family and peer support can help to mitigate the harmful effects from using relational aggression behaviour in their romantic relationship. Adults with high levels of social support outperformed those with low levels of family and peer support in exhibiting relational aggression behaviour in romantic relationships [ 56 ]. Although both relational aggression and social support are empirically connected to maladjustment, research on the interaction effect of psychosocial factors and social support on relational aggression is still limited ( 57 – 58 ).

Besides that, a study done in US had found that there is no evidence of social support act as a moderator between psychosocial factors and dating violence [ 59 ]. Only a small amount is allocated in the extent literature to research the triad of the relationship. In accordance with that, this study will further explore to develop an understanding of the role of social support in the association between psychosocial factors and relational aggression. Among several theories of social behaviour, for this study we have used Albert Bandura (1986) social cognitive theory to help provide researchers with a comprehensive framework to understand the factors that may influence aggressive human behaviour. Although Bowlby (1969) prioritized and focused on understanding the nature of caregiver’s relationship with his infant, at the same time he also believed that bonding features are present in human life experience from “cradle to grave” [ 30 ]. Besides that, attachment style and social support combine the theory-based prediction that people with an insecure attachment style are more likely to evaluate others’ reactions negatively [ 60 ].

This study can give awareness to young female adults about the issue of relational aggression that can happen in a romantic relationship. This is because relational aggression is an issue that is not given attention in romantic relationships by women and only aggressive behaviour such as physical and sexual is considered more harmful in romantic relationships. This study can give awareness to young female adults about the characteristics of an individual who practices relational aggression in a romantic relationship and can help in finding a solution from practicing relational aggression in romantic relationship. This study can also help young adults to identify this issue so that it does not continue and affect romantic relationships in adulthood. Relational aggression is known to be a relevant social problem factor which can be a precursor to abusive romantic relationships in later adulthood [ 61 ].

A conceptual framework in this study was built based on the social cognitive theory introduced by Albert Bandura in 1986, attachment theory developed by John Bowlby (1907–1990) and the big five personality theory developed in 1949 by D. W. Fiske (1949) as well as from the findings of research on previous studies in the field of psychosocial factors and relational aggression in romantic relationship. In general, this study aims to explore whether psychosocial factors could predict relational aggression in romantic relationships. There is not much direct research that examines covert set of manipulative behaviors in romantic relationships such as relational aggression. Besides that, there are only a few studies conducted in Malaysia about relational aggression in romantic relationships compared to studies conducted in Western countries [ 53 , 54 , 55 , 60 , 61 , 62 ]. Therefore, it is important to conduct this study using respondents from Malaysia so that it can help psychologists and other parties involved to identify individuals using relational aggression in romantic relationships and from being involved in psychological problems.

The present study

This study was designed to explore whether psychosocial factors such as big five personality traits, attachment style and loneliness could predict relational aggression in romantic relationship among young female adults in Malaysian context and aims to extend findings from previous studies in this field. The researchers hypothesize that psychosocial factors, such as personality trait, attachment styles, and loneliness, will play a significant role in determining the presence and severity of relational aggression in romantic relationships. In addition, it is believed that social support will act as a moderating factor in the relationship between psychosocial factors and relational aggression. As a result, this study aims to shed light on the drivers behind relational aggression in romantic relationships and to better understand the relationship between psychosocial factors and relational aggression. This study is regarded novel because there are no known studies on relational aggression in romantic relationship in the Malaysian context as this will be the first Malaysian study to define the relational aggression in romantic relationship among the sample of young female adults in Malaysia.

Participants

An online survey was conducted with a total of 424 females from early adulthood stage, aged between 18 and 30 years old in Malaysia. According to DOSM (2021), the total population of women in early adulthood in Malaysia is 15,758.2(‘000). From the entire population in each state, the respondents aged between 18 and 30 were selected in this study using Raosoft formula. Proportionate stratified random sampling was used to recruit respondents from 13 states in Malaysia to get sufficient sample size from each state through Raosoft formula calculation in July 2022. Then, convenience sampling was used to select a study sample from the population to get a sufficient sample from each state where an advertisement was posted in social media. Inclusion criteria: [ 1 ] participants must be Malaysian; [ 2 ] female participants aged between 18 to 30 years old only; [ 3 ] currently in a romantic relationship for more than 3 months; [ 4 ] must answer all questions in relation to the most recent partner or romantic relationship; [ 5 ] informed and voluntary participation in the study. The study sample for this research consists of different races, occupation, and education background so that they will have equal opportunity to be selected as a respondent.

Instruments

Big five inventory (bfi).

The Malay version of Big Five Inventory (BFI; 63) which was developed by Muhammad et al., [ 63 ] was used to measure the five basic personality dimensions, namely extraversion, agreeableness, conscientiousness, openness, and neuroticism. The 44-item BFI is rated on a 5-point Likert Scale from 1 (strongly disagree) to 5 (strongly agree). After reverse scoring, the mean score of each subscale is obtained. The Malay version of the BFI shows good internal consistency, convergent and discriminant validity [ 63 ]. The internal reliability of this scale in the current study was high, with a Cronbach’s alpha calculation of 0.78 to 0.88 with a mean of 0.81.

UCLA loneliness scale-3

The Malay version of the Rusell’s [ 64 ] UCLA Loneliness Scale [ 65 ] was used to measure loneliness. This tool consists of 20 items and is rated on a 4-point Likert scale from 1 (Never) to 4 (Always). Loneliness was assessed by averaging the scores of all items with higher scores indicating higher levels of loneliness. The internal reliability of this scale in the current study reported with a Cronbach’s alpha of 0.83.

Experiences in close relationships– II (ECR-II)

The Experiences in Close Relationships Scale II (ECRS-II; 67) assessed individual differences in anxious attachment style (i.e., the extent to which individuals feel secure versus insecure about romantic partner relationships and reactions) and avoidant attachment style (i.e., the extent to which individuals feel uncomfortable with having close relationships with others versus feel safe to rely on others). The Malay version of the ECR-II [ 66 ] was used in this study. The internal reliability of this scale in the current study was high, with a Cronbach’s alpha calculation of 0.82 to 0.83 with a mean of 0.83.

Measure of relational aggression and victimization (MRAV)

This instrument was developed by Linder et al. [ 67 ]. This 56-item instrument consists of six subscales that measure six dimensions of aggression, namely relational aggression, physical aggression, relational victimization, physical sacrifice, exclusivity, and prosocial behaviour. For this study, only the subscales of relational aggression (5 items) were used. Items in this tool are rated on a 7-point Likert-type scale from 1 (Not at all True) to 7 (Very True). This questionnaire was translated into Malay language using Forward-Backward translation method and followed by content validation. CVR technique was used to measure the content validity of this questionnaire. The CVR was in the range 0.7-1 for all items and the overall mean CVR values were 0.83. According to Rahim et al. [ 68 ], in the context of measuring psychological test, tools which are available in their own native language will be more appropriate and measurement will be more accurate compared to other languages. The internal reliability of this scale in the current study was high with a Cronbach’s alpha calculation of 0.88 with a mean of 0.89.

Multidimensional scale of perceived social support (MSPSS)

This questionnaire was developed by Zimet et al. [ 69 ] and was used to measure social support of an individual. The MSPSS consists of 12 items assessing three specific sources of social support namely family, friends, and others. This test tool uses a 7-point Likert scale where (1 = strongly disagree, 7 = strongly agree). In this study, the Malay version of the MSPSS tool was used which was translated and validated by Ng et al., [ 70 ]. The internal reliability of this scale in the current study was high, with a Cronbach’s alpha calculation of 0.93.

The survey was conducted from July 1 to July 26, 2022. According to Connelly [ 71 ], previous studies suggest that the sample size of the pilot study should be 10% of the sample size used for the actual study. Therefore, a pilot study was carried out before the real study with 44 respondents in the state of Selangor. The researcher chose Selangor because it is the state where the researcher is currently living, and this will make it easier to carry out the study. In the actual study, 424 participants were recruited based on Table  2 . This study was approved by the Research Ethics Committee of The National University of Malaysia (No: 2022 − 549). All participants were informed of the research objectives and their rights on the first screen (voluntary participation, the right to withdraw at any time and anonymity). This study was not conducted with any minors. At the start of the test, informed permission was acquired, this study only moved forward if the subject ticked the box that said, “Yes, I offer my consent to participate.” The participants’ privacy was guaranteed by the test’s anonymity and the numerical coding of their replies.

Data analysis

Descriptive statistics and inferential statistics were calculated using SPSS 26.0. For inferential statistics multiple regression and hierarchical regression has been used in this study. Multiple regression was used to explore whether psychosocial factors such as big five personality traits, attachment style and loneliness could predict relational aggression in romantic relationship. A single dependent variable and numerous independent variables can be analysed using the statistical method known as multiple regression. The value of R, the multiple correlation coefficient, is shown in the “R” column. The “R Square” column displays the R 2 value, also known as the coefficient of determination, which is the percentage of the dependent variable’s variance that can be explained by the independent variables. R can be thought of as one indicator of the accuracy of the dependent variable’s prediction [ 72 ]. It is the proportion of variation accounted for by the regression model above and beyond the mean model. Hierarchical regression was used to study the effect of social support as a moderator in the relationship between psychosocial factors (personality trait, attachment style and loneliness) with relational aggression in romantic relationship. The moderation effect analysis was carried out using SPSS hierarchical regression. The hierarchical regression is a more appropriate method for determining whether a quantitative variable has a moderating effect on the relationship between two other quantitative variables [ 72 ]. If the moderation test result fell within the 95% confidence interval and contained 0, it meant that the moderation impact of social support was not significant; if it did not, it meant that the moderation effect of social support was substantial. In this study, p  <.05 was regarded as statistically significant. In this study, SPSS 26.0 software were used to analyse the data.

Descriptive statistics

A total of 500 participants have completed the online survey but only 424 (M ± SD = 24.18 ± 3.21 years) participants’ responses were included after 76 questionnaires were rejected from this study as it did not meet the inclusion criteria. The highest level of education obtained by the participants is degree education. 18.2% of participants had engaged in aggression towards their romantic partner.

Inferential statistics

Table  2 shows the results of a multiple regression analysis in predicting relational aggression based on big five personality traits, attachment styles, and loneliness among young female adults in Malaysia. Among the five subscales of personality trait, agreeableness showed a significant predictor. In addition, loneliness, avoidant attachment style, and anxious-attachment style also showed significant prediction with relational aggression. Overall, the results of the regression analysis showed that agreeableness, loneliness, avoidant attachment style, and anxious attachment style together can predict 30.3% of the variance in relational aggression (R²=0.303), where [F (3,269) = 22.561, p  < 0 0.05]. The subscale of agreeableness showed negative prediction (β=-0.305, p  <.05) with relational aggression whereas loneliness (β = 0.364, p  <.05), avoidant attachment style (β = 0.420, p  <.05), and anxious attachment style (β = 0.321, p  <.05) showed positive prediction with relational aggression. These findings showed that higher level of agreeableness trait contributes to lower level of relational aggression in romantic relationships. Besides that, high levels of loneliness, avoidant attachment style, and anxious attachment style contribute to higher level of relational aggression in romantic relationship.

For hierarchical regression analysis, only those variables that were significant in the multiple regression analyses were entered into hierarchical regression models which are agreeableness trait, loneliness, avoidant attachment style, and anxious attachment style. Table  3 shows the hierarchical regression analysis where R² value for Model 1 is 0.097, F (25.735) = 22.545, p  <.05. This means that the agreeableness dimension accounts for 9.7% of the variance in relational aggression. While the R² value obtained for Model 2 is 0.098, F (17.410) = 15.240, p  <.05. This means that social support and agreeableness dimensions contribute as much as 9.8% of the variance to relational aggression in romantic relationships. These results showed that the percentage of variance only increases by 0.1% (9.8%– 9.7%) with the presence of a moderator in this model. The results in Table  3 showed that the dimension of agreeableness as a predictor is significant with a value of β =-0.296, t = -6.333, p  <.05. While social support as a predictor is not significant with β value = -0.062, t = -1.331, p  >.05. After entering the moderator, the interaction term of social support and agreeableness is not significant with a value of β = -0.406, t = − 0.816 and p  >.05. The agreeableness subscale was a significant predictor in the first block ( p  <.05) but did not reach significance in the second block ( p  =.415).

Table  4 shows the hierarchical regression analysis where R² value for Model 1 is 0.135, F (35.826) = 32.761, p  <.05. This means that the loneliness level dimension accounts for 13.5% of the variance in relational aggression. While the R² value obtained for Model 2 is 0.146, F (25.874) = 23.915, p  <.05. This means that social support and loneliness level dimensions contribute as much as 14.6% of the variance to relational aggression in romantic relationships. These results show that the percentage of variance only increases by 1.1% (14.6%– 13.5%) with the presence of a moderator in this model. The results in Table  4 show that the dimension of loneliness as a predictor is significant with a value of β = 0.383, t = 7.767, p  <.05. While social support as a predictor is not significant with a value of β = 0.048, t = 0.964, p  >.05. After entering the moderator, the interaction term of social support and loneliness is significant with a value of β = 0.550, t = 2.349 and p  <.05. The loneliness subscale was a significant predictor in all blocks ( p  <.05), with p  =.019 in the second block.

Table  5 shows the hierarchical regression analysis where R² value for Model 1 is 0.231, F (40.936) = 42.014, p  <.05. This means that the attachment style dimension accounts for 23.1% of the variance in relational aggression. While the R² value obtained for Model 2 is 0.237, F (25.225) = 25.976, p  <.05. This means that social support and attachment style dimensions account for 23.7% of the variance in relational aggression in romantic relationships. These results show that the percentage of variance only increases by 0.6% (23.7%– 23.1%) with the presence of a moderator in this model. The results in Table  5 show that the dimension of avoidant attachment style as a predictor is significant with a value of β = 0.368, t = 8.345, p  <.05 and the dimension of anxious attachment style as a predictor is significant with a value of β = 0.244, t = 5.364, p  <.05. While social support as a predictor is.

not significant with a value of β = 0.20, t = 0.447, p  >.05. After entering the moderator, the interaction term of social support and attachment style was not significant on the relational aggression with values ​​of β = 0.155, t = 0.676, p  >.05 and β = 0.520, t = 2.925, p  >.05. The ECR’s anxious and avoidant subscale were significant predictor in the first block ( p  <.05) but did not reach significance in the second block ( p  =.328;0.105).

The participants that have been selected for this study are young female adults between the age of 18 to 30 (M ± SD = 22.08 ± 3.21 years) who are currently in a romantic relationship for more than three months. Regression analysis was done, and it was found that only agreeableness trait showed significant predictor on relational aggression in romantic relationship and the other four dimensions of the big five personality in the psychosocial factor variable, which are extraversion, openness, neuroticism, and conscientiousness are not predictors or contributors to relational aggression in romantic relationships. Therefore, the findings prove that the importance of the relative contribution of personality traits of agreeableness. Generally, in an interpersonal context, personality is known to play an important role in determining the likelihood of engaging in an aggressive act. Negative emotions are generally harmful to romantic relationships. The result from our study is contradictory with the research findings by Burton et al. [ 73 ] where they have found that higher relational aggression was associated with higher levels of neuroticism and lower level of conscientiousness.

In addition, in some studies it has been found that individuals who tend to engage in relational aggression are more likely to show lower traits of agreeableness, openness and conscientiousness [ 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 ]. In our study, none of the big five personality traits except for agreeableness show significant prediction towards relational aggression in romantic relationships. This may be due to in general agreeableness traits may have stronger predictive utility than other personality traits ( 78 – 79 ). It has also been shown that agreeableness trait is negatively associated with relational aggression [ 80 , 81 , 82 ]. Agreeableness characterized as cooperation and understanding is an aspect related to motivation to maintain positive interpersonal relationships [ 83 ]. Likewise, the relationship between agreeableness and mind suggests that the former is responsible for processing social information.

Furthermore, agreeableness supports altruism while relational aggression is a type of destructive and hostile behaviour that has anti-social tendencies [ 84 ]. Therefore, this can further explain the evidence we found that agreeableness trait is associated with a negative influence on relational aggression. The trait of agreeableness has also been referred to as adaptability or reliability. There are differences in the interpretation of the dimension of agreeableness. The trait of agreeableness is considered reliable whereas Asian people generally support a collectivist culture, emphasizing social harmony and avoiding conflict [ 84 ]. Agreeableness represents the obligation to act as a group member and to make sacrifices. This cultural difference can lead to the irrelevance of agreeableness traits against relational aggression among young female adults in Malaysia. Besides that, those with higher levels of neuroticism are thought to be more likely to be aggressive. This individual is considered to have fewer stable emotions. Therefore, people who exhibit many neurotic personality traits are more prone to emotional instability and more prone to conflict with others. Conversely, agreeableness and aggressiveness are consistently negatively correlated [ 84 ].

Loneliness shows positively significant prediction towards relational aggression in romantic relationships. This is consistent with the study done by Prinstein et al., [ 55 ] which revealed that both relationally aggressive children and youth are more likely to be depressed, lonely, anxious, and socially isolated. However, according to the study done by Povedano et al., [ 85 ] found that the relationship between loneliness and relational aggression is significant and positive for boys, but not for girls. The involvement in violent behaviour would not act as a buffer for victimized girls experiencing strong feelings of loneliness, whereas it would be for boys. Lonely people usually have a negative perception of others’ intentions and behaviours in their interpersonal relationships. Along with these findings, lonely people tend to assume that their interpersonal failures stem from unchangeable and undesirable traits in their own personality, and they have a negative interpretation of other people’s intentions and interactions. Individuals who have developed a negative perception of themselves because of loneliness, feeling undesirable and unaccepted by others may resort to relational aggression, a powerful tool in which one uses force in interpersonal relationships to control other people [ 27 ].

The results of this study found a positive and significant prediction between avoidant and anxious attachment styles with relational aggression in romantic relationships. It has been established that the quality of communication between parents and children plays a crucial role in the development of a secure attachment. Our findings are in line with previous research that suggests that adolescents who have a positive relationship with their parents and communicate well with them are less likely to engage in aggressive behaviours and engage in risky activities [ 86 ]. Moreover, early attachments shape not only an individual’s sense of self and view of the world, but also their social skills, overall well-being, and future relationships. This is supported by the findings of Dervishi et al., [ 87 ] who found that adolescents with anxious attachments had higher levels of physical and verbal aggression. Studies have also shown that communication between parents and teens is strongly linked to the emergence of aggressive behaviours, with better communication resulting in a higher sense of security and an active exchange with others throughout life [ 88 , 89 , 90 ]. Essentially, individuals who are highly insecure may have difficulties controlling their anger and are more likely to engage in aggressive behaviour.

Previous research has demonstrated that individuals with insecure attachment patterns, particularly the anxious type, are at risk of experiencing negative consequences [ 91 , 92 , 93 ]. This can be attributed to a negative self-concept and high levels of rejection anxiety, leading to an over-reaction of excessive anger, and hurt in conflict situations. Research suggests that individuals with anxious attachment style have a history of persistent rejection from their partners and perceive themselves as unworthy of affection [ 94 ]. This can result in a perception of partners as untrustworthy and even threatening. It has been found that young adults with anxious attachment style are more prone to experiencing anger, compared to those with a secure or preoccupied attachment style who tend to have more positive expectations of their partners. In other words, those who have a strong sense of insecurity are likely to struggle with controlling their anger, while those with these insecurities are more likely to engage in aggressive behaviour.

Hierarchical regression analysis was carried out and it was found that social support as a moderator showed no significant effect between big five personality, avoidant and anxious attachment style with relational aggression in a romantic relationship except for loneliness subscale. The behaviour’s of loved ones that are in tune with the needs of the individual who is dealing with a stressful situation are referred to as social support [ 95 ]. The availability of support in the environment, the emotional response to stressful events, and the assessment of the consequences of these events can all be positively influenced by support from loved ones. Support from loved ones help to decrease the impact of stress by solving the victim’s problems, diminishing the perceived importance of the incident, facilitating the adoption of rational thoughts, and preventing or reducing inappropriate behaviour responses. According to previous research, social support may act as a moderator and buffer the effects of aggression and family functioning [ 96 ]. Due to the positive correlation between social support and a person’s family adjustment, social support helps to balance the negative effects of relational aggression on families [ 97 ].

This study’s finding is also consistent with the finding by Fortin et al. [ 98 ], where the moderating effect of social support is not present in female victims of physical violence. Thompson et al. [ 99 ] found that less women who have experienced relational aggression perceive the availability of social support, the more severe the violence they have experienced. The victim may also begin to blame herself more and ask for less support from her loved ones as the violence intensifies due to the bidirectional pattern of violence. Additionally, it seems that continuing in a relationship while having experienced physical abuse may have an impact on how satisfied they are with the assistance they have received [ 100 ]. These victims may also require additional forms of support, such as emotional, educational, and material support, even though they are generally happy with the assistance they have received.

Therefore, fewer confidants may have led to less robust social support. As a result, having fewer confidants may have led to social support that was insufficient and did not entirely satisfy the needs of the physical abuse victims. Besides that, social support is thought to be the most important factor that could significantly reduce loneliness [ 100 ], and it may be able to predict the trajectory of loneliness [ 101 ]. Indeed, numerous studies on the roles played by various forms of social support have found that perceived social support is more useful for predicting people’s mental health and may have a bigger impact on mental health than other forms of social support [ 102 , 103 , 104 ].

Both relational aggression and social support are empirically related to levels of loneliness, empirical literature is lacking on the interactive effects of relational aggression and social support on levels of loneliness [ 53 , 105 , 106 ]. Little is devoted in the existing literature to investigating the relationship triad. Ladd and Burgess [ 52 ] suggested that social support moderates the association between aggression and adjustment because it balances the dysfunction created by aggression. Family and peer support can act as a buffer in minimizing the negative effects of relational aggression in romantic relationships [ 107 ]. Adolescents who receive social support perform better in academic tasks and social interactions than individuals who do not have family and peer support [ 108 ]. Consistent with this research, social support, in general, and family support may act as moderating factors for the relationship between levels of loneliness and relational aggression.

Next in this study, it was found that there is no relationship between the role of social support as a moderator in the relationship between attachment style and relational aggression in romantic relationships among young female adults in Malaysia. This is contrary to the results of previous studies that suggest social support act as a moderator and minimizes or increases the effect of relational aggression on parental attachment style because social support is positively related to one’s family adjustment [ 99 ] and it has been hypothesized that social support moderates the relationship between relational aggression and parenting style. However, the findings of this current study highlight that social support as a moderator, relational aggression and parenting style are one of the factors that are very influential which affects the functioning of young people based on past studies [ 104 ]. The current findings show how social support moderates as an enhancer and buffer in attachment styles and relational aggression.

Results from previous studies differ from the current study due to several factors. Based on attachment style theory by Bowlby (1969), attachment style consists of secure attachment style, anxious attachment style, and avoidance attachment style but in this study only anxious attachment style, and avoidance attachment style alone were used to assess the attachment style of young adults. Avoidant attachment style involves fear of dependence and intimacy interpersonal, excessive need for independence and reluctance to self-disclosure. Anxious attachment styles involve fear of interpersonal rejection or neglect and distress when one’s partner is absent or unresponsive. People with an anxious attachment style always feel insecure about their romantic relationships and fear of abandonment by partner. Those with an avoidant attachment style have a common need to feel loved but not prepared emotionally to be in romantic relationships. Things like this can cause someone to use relational aggression in their romantic relationships such as manipulating partners, threatening partner to end the relationship. In addition, even if that individual has high social support but it does not affect if one is oriented in an avoidant attachment style and anxious attachment style.

Besides that, the findings of this study are consistent with a recent study by Egan and Bull [ 107 ] who found that there is no effect of social support as a moderator in the relationship between personality traits and relational aggression in romantic relationships. This is different from the perception based on personality theory developed by Goldberg [ 109 ] stating that social support is significantly associated with personality characteristics, especially extraversion, agreeableness, or emotional stability [ 107 ]. In general, from childhood to late adulthood, the relationships maintained by individuals with other people are related to individual differences in personality characteristics [ 110 ]. Personality traits that define interaction style can predict social interaction, available social support, and its perception. However, a supportive social context may also predict personality traits by providing individuals with opportunities to develop social skills, maintain social relationships, and foster prosocial behaviour. If personal experiences, roles, and social relationships can influence a person’s personality traits, social support is not only a proxy for the quality of social relationships but also a resource that can help to face the social challenges faced in middle adulthood and can predict personality traits by adapting to social roles expectations and developing social skills. Therefore, the relationship between the big five personalities and perceived social support is not only unidirectional but also reciprocal.

Limitations

As for limitations, all data used in this study were self-reported. The sensitive nature of some questions may have caused some participants to succumb to the social desirability bias and report. For instance, lower rates of relational aggression than their actual behaviour. Despite this, participants provided anonymous answers, making it less likely that they were prompted to provide biased answers. Furthermore, due to recall issues and inaccurate reporting it’s possible that both estimates of psychosocial factors and relational aggression contain measurement error. Another limitation for this study is the cross-sectional nature of these data, which precludes inferences about causal relationships is another drawback of this study.

Additionally, caution should be used when extrapolating the findings to all female samples since the participants in this study were a homogeneous sample of young female adults. Due to the study’s cross-sectional design, it is also impossible to draw conclusions about the cause-and-effect relationship between social support as a moderator in between psychosocial factors and relational aggression. To address the temporal ordering of people’s levels of social support from family and friends and their participation in relational aggression, longitudinal studies are required. Besides that, young female adults were not questioned regarding the opinions or involvement of friends in relational aggression. According to earlier studies, teenagers who have friends who engage in dating violence run a higher risk of doing so themselves [ 111 ]. Moreover, data was collected at one time point, so cause-and-effect conclusions could not be made. Besides that, the difference between the psychosocial factor’s groups couldn’t be identified clearly in relation to relational aggression in romantic relationship as only multiple regression has been conducted. A post hoc test can help in identifying the differences between specific groups and give a more meaningful finding.

Future studies

Future studies are needed on the impact of multiple placements, including their effects on unstable living situations, sibling attachment, adoption, frequent school changes, and difficulties. For instance, if an individual grew up in a family that shamed or condemned emotional expression or in a home with an abusive parent, this may associate anger with fear, danger, or damaged relationships, which will cause to develop more negative perception of their relationship with their parents and siblings. This study only focuses on female samples. Even though there are differences between the genders, both genders naturally experience anger. Men are thought to be more prone to rage despite evidence that women are more emotionally expressive. In addition, more research on gender disparities is necessary.

The current study suggests that preventive measures need to be taken to stop the symptoms of anger from getting worse. Uncontrollable anger can cause several problems, such as erratic behaviour, assault, abuse, addictions, and legal troubles. In these circumstances, anger impairs decision-making, harms relationships, and has other negative effects. Besides that, to manage anger and deal with triggers without repressing and storing it, as well as to deal without causing emotional harm, it’s crucial to recognize the warning signs of anger. Anger management techniques include breathing exercises under supervision, cognitive behavioural therapy, imagery, problem-solving, and the development of interpersonal and communication skills. Besides that, the findings of this study indicate that aimed at reducing and/or preventing relational aggression among young female adults should consider agreeableness traits ( 112 – 113 ). Young female adults who were less agreeable were likely to experience relational aggression. The findings highlight the need for additional research to pinpoint specific characteristics of the lower level of agreeableness female population that put them at risk for relational aggression in a romantic relationship.

The current study was novel in its examination of social support as a moderator of the association between psychosocial factor and relational aggression in romantic relationships. Future studies will need to test these associations further. Based on the findings from this study, there’s no evidence to support the prediction that social support would moderate this association, but future research with a better measure of social support or using different moderator variable may provide different results. Future research should investigate variables that are not included in this study that are possible predictors of relational aggression in romantic relationships. A post hoc test can be conducted further in identifying the differences between specific groups and give a more meaningful finding.

Relational forms of aggression tend to rise during adolescence (115), in part because more complex cognitive abilities are developed during this period that are necessary for successfully manipulating the relationships of others. We discovered a significant correlation between aggression and social support, which is crucial during adolescence. This research suggests that for some people, attachment style and relational aggression are highly overlapping, and possibly reciprocal. However, for some people, personality traits appear to be differentially linked to relational aggression. These results point to the need for additional research examining the moderating effects of significant correlates as well as a more nuanced strategy for relational forms of aggression during early adulthood’s prevention and intervention. Therefore, efforts to prevent young female adults from engaging in relational aggression should concentrate on all females and not just those who have been identified as perpetrators or victims. All females will be better equipped to spot relational aggression signs and help their friends if they are informed about the warning signs of relational aggression. Early adulthood could be taught about the warning signs of relational aggression through community-wide campaigns and in high school. This study will help to create awareness on the existence of relational aggression, public will be able to tackle this issue at an earlier stage rather than later and individuals will be able to identify the difference between a toxic and a non-toxic relationship.

In conclusion, many participants in this study reported having violent-free romantic relationships even though there are individuals who reported being the perpetrators of relational aggression. The current study was a first step in determining how psychosocial factors and relational aggression in romantic relationships are related to one another. Findings indicate that social support is also an important factor in understanding females’ relational aggression in romantic relationship. At the same time, results demonstrated that social support from friends and/or family has no significant effect with personality traits and attachment styles with relational aggression. This finding raises questions as to what may provide support to young female adults in relational aggression in romantic relationships. The current study’s greatest strength is the dialogue it has sparked about the importance of social support in romantic relationships between young female adults who is experiencing loneliness. This raised awareness could serve as a starting point for further study as well as the creation of programs and regulations that cater to the requirements of this population. It is necessary to create and carry out programs that encourage healthy dating interactions and inform young adults about dating violence which focuses on relational aggression. The findings also provide evidence for the significance of parental modelling in the development of romantic relationships in young adults. The findings are supported by social learning theory (Bandura, 1971), the concepts of which might be employed in investigating other areas of psychosocial factors on young adults’ relationships in the future.

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Big Five Inventory

Experiences in Close Relationships– II

Multidimensional Scale of Perceived Social Support

Measure of Relational Aggression and Victimization

UCLA Loneliness Scale

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Kamaluddin, M.R., Munusamy, S., Sheau Tsuey, C. et al. Relational aggression in romantic relationship: empirical evidence among young female adults in Malaysia. BMC Psychol 12 , 305 (2024). https://doi.org/10.1186/s40359-024-01670-4

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Determining the challenges and opportunities of virtual teaching during the COVID-19 pandemic: a mixed method study in the north of Iran

• Aram Ghanavatizadeh 1 ,
• Ghahraman Mahmoudi 1 ,
• Mohammad-Ali Jahani 2 ,
• Seyedeh Niko Hashemi 3 ,
• Hossein-Ali Nikbakht 2 ,
• Mahdi Abbasi 4 ,
• Alameh Darzi 5 &
• Seyed-Amir Soltani 6  

BMC Research Notes volume  17 , Article number:  148 ( 2024 ) Cite this article

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The aim of this study was to determine the challenges and opportunities of virtual education during the COVID-19 pandemic. This study was conducted in 2022–2023 with a mixed method. During the quantitative phase, we chose 507 students from Mazandaran Province medical universities (both governmental and non-governmental) by stratified random sampling and during the qualitative phase 16 experts were collected by purposive sampling until we reached data saturation. Data collecting tools consisted of questionnaires during the quantitative phase and semi-structured interview during the qualitative phase. Data was analyzed using SPSS21 and MAXQDA10. Mean scores of the total score was 122.28±23.96. We found a significant association between interaction dimension and background variables ( P  < 0.001). The most important privilege of virtual education is uploading the teaching material in the system so that students can access the material constantly and the most important challenge regarding virtual education is lack of proper network connection and limited bandwidth. Virtual education proved to be a suitable alternate to traditional methods of medical education during the COVID-19 pandemic in theoretical topics, we recommend that educational policymakers would take the necessary actions to provide the requirements and facilities needed to improve the quality of virtual education.

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Introduction

Given the adoption of technological advances in healthcare in the context of the COVID-19 pandemic, the dissemination of telehealth practices has dramatically increased between 2020 and 2021 [ 1 ]. Virtual education serves as a dynamic substitute for learners to constantly continue their educational journey and preserve their goals [ 2 ].

Virtual education is defined as the use of computer-based technology to provide education which consists of online learning, offline learning, or a combination of them [ 3 ]. In the web-based virtual educational platforms, students can continue to participate in the live academic lectures which they used to attend in class [ 4 ]. Virtual education varies significantly from in-person education [ 5 ]. The unavailability of necessary infrastructure and effective organizational strategies have been a major challenge for the integration of virtual education and face-to-face education [ 6 ]; [ 7 ] and this pandemic not only created the need but also provided the opportunity for accelerating digital transformation in medical education [ 8 ]. Using virtual education, learners can save and review the lectures whenever and wherever they want to. however, most professors have less experience with virtual teaching [ 9 ]. In this educational model, social interactions between the teachers and the students are solely through internet. Yet, how can a professor provide all the necessary communication, guidance, and feedback through internet alone so that learning process would be effective? Moreover, how can professors make sure that all students have access to the same content and same feedback equally? There are so many online educational activities that not all students can access equally due to differences in network conditions and inequal internet access [ 10 ]. Internet inequality could be defined as inequal distribution or access to the internet [ 11 ]. However, internet-based learning should be adjusted to different educational modalities so it would be effective [ 12 ]. Online education has played an important role in the education of undergraduate and graduate level students, and even continuing medical education (CME) of graduate doctors [ 13 ]. Learners can participate in the educational classes anytime it suits them [ 14 ].

one study conducted in Saudi Arabia revealed that using web-based video conferences during COVID-19 pandemic resulted in medical students’ satisfaction [ 15 ]. In another study by Cataudella et al. in Italy titled “Teaching During the COVID-19 Pandemic” showed that teachers exhibited lower self-esteem and self-efficacy while teaching virtually [ 16 ]. Rossi et al.’s study in Brazil demonstrated that active learning tools are helpful for students during the pandemic and they have succeeded at improving their critical thinking, motivation, and their contribution to science [ 17 ]. Khalili’s study in the United States showed that e-learning is becoming the new norm in the universities and this development can bring challenges to some, because some teachers lack the proper knowledge and expertise to create an supportive, positive and interesting environment to engage their students [ 18 ].

Currently the question is whether the implementation of virtual education has been able to satisfy students in their academic progress? Because learners are important stakeholders during the entire teaching and learning process in all educational institutions [ 19 ] and learners’ engagement in this process has positive effects on their active learning [ 20 ]. Therefore, due to widespread use of virtual education and online teaching during the pandemic, it is necessary to conduct more studies to examine the challenges and opportunities of this type of education during the COVID-19 pandemic.

Methodology

Study design.

The current study was conducted using a mixed (qualitative-quantitative) method in 2022–2023 in the north of Iran.

Sample size

The study population in the quantitative phase consisted of 13,500 medical science students of all the medical universities in the province, both public and private, who benefited from virtual education during the COVID-19 pandemic. In the qualitative phase the population included experts and university professors of the same aforementioned universities.

In order to determine the sample size, it is appropriate to use between 5 and 15 observations for each variable measured [ 21 ], in this study we considered between 12 and 13 times the number of questions in the questionnaire, that is in the range of 480 and 520 participants.

The sampling method implemented in the quantitative phase was cluster sampling; first the university, then the academic major, and finally the class was considered as the cluster. 507 students were selected. 194 students from Babol university, 225 students from Sari university of Medical Sciences, and 88 students from Azad university of Sari participated in this research. Inclusion criteria in quantitative phase of this study was that participants had to be a student in one of the medical universities of Mazandaran and also to have consent to participate in the study. participants were excluded if they did not use online education methods.

In the qualitative phase we selected 16 academic staff members of both basic science and clinical stage, using purposive sampling, who were policymakers and planners in their universities and had online educational activities alongside their executive posts.

Information gathering tools

After our proposal was accepted and we obtained the ethics committee approval, we commenced our executive phase of the research. For the literature review all the articles that were published between 2010 and 2021 in different national and international databases including ISI, Pubmed, Scopus, Google Scholar, Magiran, SID, and Irandoc were reviewed. We used Persian and English keywords including online teaching, virtual teaching, active learning, COVID-19, interaction in virtual education, feedback in online education, benefits of virtual learning, disadvantages of virtual learning, and types of virtual learning.

Data measurement tools

In the quantitative phase we used a questionnaire developed by Ünal Çakiroğlu and colleagues [ 22 ]. The questionnaire consists of 7 different principles. Each principle consists of approximately 5–6 questions and a total of 40 questions and answers vary between range of not satisfied to perfectly satisfied and each question has a score of 1 to 5.

In order to convert the English questionnaire to Persian we used the translation-retranslation method as described below. At first, two translator’s experts in this field translated the English version into Farsi. A conceptual translation instead of word-to-word translation was implemented; also, clarity, simplicity, brevity, type of audience, age and cultural factors were taken into consideration by the translators. In the second stage two translators fluent in English, who were not aware of the questionnaire’s content, translated the questionnaire back into English. In general, conceptual similarity was an important factor during the translation process. finally, in the third stage an expert panel consisting of people fluent in both languages reviewed the quality of the translations in the presence of researchers and in the case of inconsistency between translations, alternative words were suggested.

To perform face validity, the questionnaire was given to 20 students (10 males and 10 females) who met the inclusion criteria and a number of related experts. They were asked for feedback about the clarity of the questionnaire, its readability, writing style, easy understanding, confusing words, comprehensibility, disproportion and ambiguity. Any needed corrections were applied.

To check the reliability, Cronbach’s alpha was used as the index to evaluate internal consistency for the entire questionnaire and for each scale. Values above 0.7 were considered acceptable. To evaluate intraclass reliability, we used test-retest method. Data from 30 students, who met the inclusion criteria, were collected in two stages with a time interval of one month then the scores obtained in these two stages were evaluated using intraclass correlation coefficient (ICC).

To perform the qualitative phase, the data collection tool was semi-structured interview. We used the data from the quantitative phase of the study, the quantitative statistical outputs and items in the questionnaire that had the lowest points from the students’ point of view to formulate our interview questions. In the way that less favorable items from the students’ point of view were used as questions in the qualitative phase. At this point we used semi-structured interview and in-depth interview to gather our data. After conducting the interviews, handwriting, typing and listening to the files several times all the notes and writings were named and coded. in the initial coding process, the researcher reviewed the written and typed data line by line as analytical units; and then by identifying the related semantic units or determining the important parts of the text, the researchers would extract the explicit meanings and concepts from the interview texts and would write them next to the relevant sentence in the form of a code. At the same time in another text the researcher wrote down the codes with the relevant address.

Data analysis

After collecting the data and coding, the results of the quantitative phase were entered into the SPSS21 software. In order to perform the related statistical tests, first, the normality of the data was checked using the Kolmogorov-Smirnov test. In order to check the linear relationships between quantitative variables, Pearson correlation coefficient was used. Friedman’s test was used to rank principals and dimensions at a significance level of p  < 0.05. For analysis of the qualitative phase, content analysis method and MAXQDA10 software were carried out.

Results of the quantitative phase

From the 507 students participating in this study, regarding the demographic characteristics, the mean age of the participants was 21.47 ± 2.34 years with a range of 18 to 43 years. 319(62.92%) females and 174(34.32%) males participated in the study. The majority of the participants 145(28.6%) were medical students. (Table  1 )

Descriptive statistics of the scores of principles and dimensions of the questionnaire showed that the mean and standard deviation of the total score is 122.28±23.96 and for the three dimensions of interaction, learning and teaching. They were 34.54±8.23, 33.80±8.01 and 53.93±10.15. (Table  2 )

The results of the test about the correlation of the seven principles of the questionnaire showed that all seven principles had a positive correlation with each other and the correlation was of strong intensity. The strongest relationship was between high expectations and diverse talents ( r  = 0.73, p  < 0.001). The lowest correlations were found between cooperation among students and the time on task with other principles, the rest of the correlations are provided in the table. (Table  3 )

Also, in examining the relationship between the three dimensions of the questionnaire, the results showed that the highest correlation was between learning with teaching ( r  = 0.786, P  < 0.001) and the lowest correlation was between interaction with learning ( r  = 0.666, P  < 0.001), The correlation between teaching and interaction was ( r  = 0.738, P  < 0.001).

The result of the Friedman test showed that from the participants’ point of view, there was a difference between dimensions of online teaching including interaction, teaching and learning. In other words, according to participants, online teaching during the COVID-19 pandemic had the best performance in teaching and the weakest performance in learning. (Table  4 )

The results of the qualitative phase

In the qualitative phase of the research, interview method was used to collect the data. The characteristics of the interview participants are provided in Table  5 .

In Table  6 , there are Strengths, opportunities and challenges of virtual education according to experts. Some solutions were suggested to improve this teaching method.

Qualitative phase data analysis

One of the most important opportunities of virtual teaching according to experts is the possibility of uploading the educational material in the electronic domains. The most important challenge of virtual teaching was the lack of proper and desirable communication infrastructure (internet) and poor-quality internet. The first solution proposed by the professors to increase the quality and efficacy of online teaching is recognizing the problems related to this educational method. the most important influential weakness of this method was the lack of proper communication and internet infrastructure.

The results of the study showed that all the principles of the study were positively correlated to each other and this relation was strong. The strongest relation was between high expectations and diverse talents and also between active learning and diverse talents. The least correlation was between cooperation among students and time on activity with other principles. In a study conducted by Alahmadi et al. it was shown that students believe online classes help them overcome some learning obstacles for example the fear of communication in English. One can argue that virtual classes are helping learners, especially timid learners, to interact more and overcome their fears of interaction in face-to-face classes [ 23 ]. Tanis article revealed that quick interaction between peers is helpful for their learning, whereas isolation and lack of communication was harmful, However, group project was not the best way of learning. Students found delayed feedback and limited work by their peers harmful for learning and preferred to work at their own pace [ 24 ].

In investigating the relationship between different principles, the strongest relationship was between teaching and learning and the least relation was between interaction and learning. Alenezi study mentions that it is necessary to design an effective electronic learning environment in which the content is presented based on the characteristics of the teachers and learners, the structure of the educational material and interaction creation [ 25 ]. In another study by Adnan et al. inaccessibility to internet, lack of proper interaction among teachers and learners and inefficient technology were the major challenges of students [ 26 ].

The results of the Friedman test in the present study showed that there are differences between various dimensions of virtual teaching including interaction, teaching and learning. In other words, online teaching has had the best performance in the teaching dimension and the weakest in the learning dimension during the COVID-19 pandemic. Çakiroğlu observed in his study that although the electronic learning system has advantages, there are still challenges related to the cooperation between students, and active learning was very low in virtual education [ 22 ]. Other studies showed that active learning using simulations improves conceptual learning and memory, increases motivation and study intensity, and also reduces the achievement gap in basic students [ 27 , 28 ].

According to the results of the study, one of the most important opportunities of virtual teaching is the ability to upload the teaching material in the electronic system, because it enables the students to receive and use the material as many times as they need to; this will eventually result in improvement of learning quality provided that we address the issues and challenges related to this process [ 29 ]. In electronic teaching, educational content can be quickly delivered to learners and standardized and updated if necessary. The material can be delivered using different approaches including self-directed and coaching [ 30 ].

Another strong point in online and virtual education was the increased ability of professors in working with different educational software, uploading assignments, holding online exams, and learning different ways of feedback. In Bjekic’s study, a teacher’s capability in electronic teaching is considered a combination of educational, communicational and content creation capability [ 31 ] and teacher’s ability to conduct better tests results in students’ satisfaction [ 32 , 33 ]. In this regard the faculty can use both online and offline tools for the education of students [ 34 ]. However, we should keep in mind that medical education is not solely the theoretical matters, there are also other aspects of teaching including laboratory techniques, clinical skills and patient exposure; so electronic methods alone will not be sufficient for medical education [ 35 ].

One of the other opportunities provided by virtual teaching is decreased expenses of both teachers and students due to less commute and the easy access of students to their professors through social network and also lack of limitations because of geographical distance between one individual’s residence and the place of their institute, Fedynich states that one of the advantages of remote education is that it is not limited by the learners’ location [ 36 ], also, it can be provided by the professors regardless of their location [ 37 ], it decreases students’ expenses [ 33 , 38 ] and students can ask questions whenever they have trouble with their studies and receive answers in a short amount of time and they can also see questions asked by others [ 39 ].

Of the important challenges regarding virtual education, we can mention the lack of proper communication infrastructure (internet) and low-quality internet which can affect the quality of online classes as well as examinations. An important weakness of virtual teaching is inaccessibility to digital products needed for the education by the students [ 40 ]. Not having reliable internet connection [ 41 , 42 ], hardware and software issues of virtual educational platforms [ 43 , 44 ], problems related to speed and quality of internet [ 45 , 46 ] and audio and video streaming issues are other disadvantages of this teaching method [ 47 ].

Another engagement of professors in online education is the topic of conducting online examinations, because they believe online platforms do not hold the capacity to perform reliable and valid examinations, the results of several studies showed that due to the lack of supervision during the test, students’ grades are significantly higher compared to their previous educational records [ 37 , 48 , 49 ]. On the contrary, in Lara et al.’s study scores recorded from 49 medical students in OSCE did not have a significant difference from scores obtained from the same exam conducted in-person [ 50 ], it seems like there is a difference between the nature of theoretical and applied examinations.

The other challenge was organizing practical courses and clinical rotations. Due to several infrastructural and human limitations, holding online practical classes was not possible, and students passing their clinical rotations or those who had applied courses faced many problems. In this regard, the main issue is due to the very nature of medical education and the main problem is the inability to practice and obtain clinical skills online [ 51 ]. Clinical courses have suffered from the suspension/reduction of undergraduate student internships with a knock on impact on education. The fulfilment of professional skills in clinical training present both educational and professional challenges. Medical teachers will need to innovate and think outside the box to maintain the value of medical education in extreme circumstances. A solution may be represented and the introduction of telemedicine technologies that may contribute to the improvement of core competencies, medical knowledge, overall learning, and higher quality patient care [ 52 ]. We should keep in mind that online education cannot replace the face-to-face education of laboratory skills and techniques [ 53 ]. And most students do not feel good about learning practical skills alone or online [ 39 ].

Study limitations

This study was limited to Mazandaran Province. Also, the study population was limited to the students of the Medical Sciences universities. On the other hand, we evaluated all academic levels together, and considering the fact that challenges are different in various stages of training and in different majors this may affect the overall results. For example, students in their clinical rotations have very different problems than those passing theoretical courses and topics.

Since virtual education proved to be a suitable replacement for traditional educational methods in theoretical subjects during the COVID-19 pandemic and considering the recognition of factors affecting the quality of virtual teaching; it is crucial for policymakers in the field of education to take these factors into consideration, and implement goal-oriented plans and do their best to provide the necessary requirements to improve the quality of virtual teaching, so that it ultimately leads to an increase in the quality of learning of medical students.

Data availability

No datasets were generated or analysed during the current study.

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Acknowledgements

We would like to thank the officials of the universities of medical sciences as well as all the experts and experts who have helped us with their valuable views.

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Hospital Administration Research Center, Sari Branch, Islamic Azad University, Sari, Iran

Aram Ghanavatizadeh & Ghahraman Mahmoudi

Social Determinants of Health Research Center, Health Research Institute, Babol University of Medical Sciences, Babol, Iran

Mohammad-Ali Jahani & Hossein-Ali Nikbakht

Doctorate of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran

Seyedeh Niko Hashemi

Department of Health Economics and Management, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran

Mahdi Abbasi

Babol Education Department, Babol, Iran

Alameh Darzi

Golestan University of Medical Sciences, Gorgan, Iran

Seyed-Amir Soltani

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Contributions

Aram Ghanavatizadeh writing—original draft; writing—review and editing. Ghahraman Mahmoudi: Conceptualization; Formal analysis; Methodology. Mohammad-Ali Jahani: Conceptualization; data curation; formal analysis; investigation; methodology; project administration; software; super-vision; validation; visualization; writing—original draft; writing—review and editing. Hossein-Ali Nikbakht: Conceptualization; writing—original draft. Seyedeh Niko Hashemi: Formal analysis; investigation. Mahdi Abbas: data curation; Writing—original draft. Alameh Darzi writing—review and editing. Seyed Amir Soltani: writing—review and editing.

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Correspondence to Mohammad-Ali Jahani .

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Ghanavatizadeh, A., Mahmoudi, G., Jahani, MA. et al. Determining the challenges and opportunities of virtual teaching during the COVID-19 pandemic: a mixed method study in the north of Iran. BMC Res Notes 17 , 148 (2024). https://doi.org/10.1186/s13104-024-06806-8

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Received : 11 April 2024

Accepted : 20 May 2024

Published : 27 May 2024

DOI : https://doi.org/10.1186/s13104-024-06806-8

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