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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 plan for quantitative research

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

sampling plan for quantitative research

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

sampling plan for quantitative research

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

sampling plan for quantitative research

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.

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Sampling Methods & Strategies 101

Everything you need to know (including examples)

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

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

Overview: Sampling Methods & Strategies

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

Simple random sampling

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

  • How to choose the right sampling method

What (exactly) is sampling?

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

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

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

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

sampling plan for quantitative research

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.

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sampling plan for quantitative research

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.

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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.

Systematic sampling simply involves selecting participants at a set interval (e.g., every 10th person), starting from a random point.

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 .

Purposive sampling involves the researcher selecting participants using their own judgement, based on the purpose of the study.

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.

sampling plan for quantitative research

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.

sampling plan for quantitative research

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  • What Is Probability Sampling? | Types & Examples

What Is Probability Sampling? | Types & Examples

Published on July 5, 2022 by Kassiani Nikolopoulou . Revised on June 22, 2023.

Probability sampling is a sampling method that involves randomly selecting a sample, or a part of the population that you want to research. It is also sometimes called random sampling.

To qualify as being random, each research unit (e.g., person, business, or organization in your population) must have an equal chance of being selected. This is usually done through a random selection process, like a drawing.

Table of contents

Types of probability sampling, examples of probability sampling methods, probability vs. non-probability sampling, advantages and disadvantages of probability sampling, other interesting articles, frequently asked questions about probability sampling.

There are four commonly used types of probability sampling designs:

Simple random sampling

  • Stratified sampling

Systematic sampling

  • Cluster sampling

Simple random sampling gathers a random selection from the entire population, where each unit has an equal chance of selection. This is the most common way to select a random sample.

To compile a list of the units in your research population, consider using a random number generator. There are several free ones available online, such as random.org , calculator.net , and randomnumbergenerator.org .

Writing down the names of all 4,000 inhabitants by hand to randomly draw 100 of them would be impractical and time-consuming, as well as questionable for ethical reasons. Instead, you decide to use a random number generator to draw a simple random sample.

Stratified sampling collects a random selection of a sample from within certain strata, or subgroups within the population. Each subgroup is separated from the others on the basis of a common characteristic, such as gender, race, or religion. This way, you can ensure that all subgroups of a given population are adequately represented within your sample population.

For example, if you are dividing a student population by college majors, Engineering, Linguistics, and Physical Education students are three different strata within that population.

To split your population into different subgroups, first choose which characteristic you would like to divide them by. Then you can select your sample from each subgroup. You can do this in one of two ways:

  • By selecting an equal number of units from each subgroup
  • By selecting units from each subgroup equal to their proportion in the total population

If you take a simple random sample, children from urban areas will have a far greater chance of being selected, so the best way of getting a representative sample is to take a stratified sample.

First, you divide the population into your strata: one for children from urban areas and one for children from rural areas. Then, you take a simple random sample from each subgroup. You can use one of two options:

  • Select 100 urban and 100 rural, i.e., an equal number of units
  • Select 80 urban and 20 rural, which gives you a representative sample of 100 people

Systematic sampling draws a random sample from the target population by selecting units at regular intervals starting from a random point. This method is useful in situations where records of your target population already exist, such as records of an agency’s clients, enrollment lists of university students, or a company’s employment records. Any of these can be used as a sampling frame.

To start your systematic sample, you first need to divide your sampling frame into a number of segments, called intervals. You calculate these by dividing your population size by the desired sample size.

Then, from the first interval, you select one unit using simple random sampling. The selection of the next units from other intervals depends upon the position of the unit selected in the first interval.

Let’s refer back to our example about the political views of the municipality of 4,000 inhabitants. You can also draw a sample of 100 people using systematic sampling. To do so, follow these steps:

  • Determine your interval: 4,000 / 100 = 40. This means that you must select 1 inhabitant from every 40 in the record.
  • Using simple random sampling (e.g., a random number generator), you select 1 inhabitant.
  • Let’s say you select the 11th person on the list. In every subsequent interval, you need to select the 11th person in that interval, until you have a sample of 100 people.

Cluster sampling is the process of dividing the target population into groups, called clusters. A randomly selected subsection of these groups then forms your sample. Cluster sampling is an efficient approach when you want to study large, geographically dispersed populations. It usually involves existing groups that are similar to each other in some way (e.g., classes in a school).

There are two types of cluster sampling:

  • Single (or one-stage) cluster sampling, when you divide the entire population into clusters
  • Multistage cluster sampling, when you divide the cluster further into more clusters, in order to narrow down the sample size

Clusters are pre-existing groups, so each high school is a cluster, and you assign a number to each one of them. Then, you use simple random sampling to further select clusters. How many clusters you select will depend on the sample size that you need.

Multi-stage sampling is a more complex form of cluster sampling, in which smaller groups are successively selected from larger populations to form the sample population used in your study.

First, you take a simple random sample of departments. Then, again using simple random sampling, you select a number of units. Based on the size of the population (i.e., how many employees work at the company) and your desired sample size, you establish that you need to include 3 units in your sample.

In stratified sampling , you divide your population in groups (strata) that share a common characteristic and then select some members from every group for your sample. In cluster sampling , you use pre-existing groups to divide your population into clusters and then include all members from randomly selected clusters for your sample.

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See an example

sampling plan for quantitative research

There are a few methods you can use to draw a random sample. Here are a few examples:

  • The fishbowl draw
  • A random number generator
  • The random number function

Fishbowl draw

You are investigating the use of a popular portable e‐reader device among library and information science students and its effects on individual reading practices. You write the names of 25 students on pieces of paper, put them in a jar, and then, without looking, randomly select three students to be interviewed for your research.

All students have equal chances of being selected and no other consideration (such as personal preference) can influence this selection. This method is suitable when your total population is small, so writing the names or numbers of each unit on a piece of paper is feasible.

Random number generator

Suppose you are researching what people think about road safety in a specific residential area. You make a list of all the suburbs and assign a number to each one of them. Then, using an online random number generator, you select four numbers, corresponding to four suburbs, and focus on them.

This works best when you already have a list with the total population and you can easily assign every individual a number.

RAND function in Microsoft Excel

If your data are in a spreadsheet, you can also use the random number function (RAND) in Microsoft Excel to select a random sample.

Suppose you have a list of 4,000 people and you need a sample of 300. By typing in the formula =RAND() and then pressing enter, you can have Excel assign a random number to each name on the list. For this to work, make sure there are no blank rows.

This video explains how to use the RAND function.

Depending on the goals of your research study, there are two sampling methods you can use:

  • Probability sampling : Sampling method that ensures that each unit in the study population has an equal chance of being selected
  • Non-probability sampling : Sampling method that uses a non-random sample from the population you want to research, based on specific criteria, such as convenience

Probability sampling

In quantitative research , it is important that your sample is representative of your target population. This allows you to make strong statistical inferences based on the collected data. Having a sufficiently large random probability sample is the best guarantee that the sample will be representative and the results are generalizable and free from research biases like selection bias and sampling bias .

Non-probability sampling

Non-probability sampling designs are used in both quantitative and qualitative research when the number of units in the population is either unknown or impossible to individually identify. It is also used when you want to apply the results only to a certain subsection or organization rather than the general public. Non-probability sampling is at higher risk than probability sampling for research biases like sampling bias .

You are unlikely to be able to compile a list of every practicing organizational psychologist in the country, but you could compile a list with all the experts in your area and select a few to interview.

It’s important to be aware of the advantages and disadvantages of probability sampling, as it will help you decide if this is the right sampling method for your research design.

Advantages of probability sampling

There are two main advantages to probability sampling.

  • Samples selected with this method are representative of the population at large. Due to this, inferences drawn from such samples can be generalized to the total population you are studying.
  • As some statistical tests, such as multiple linear regression , t test , or ANOVA , can only be applied to a sample size large enough to approximate the true distribution of the population, using probability sampling allows you to establish correlation or cause-and-effect relationship between your variables.

Disadvantages of probability sampling

Choosing probability sampling as your sampling method comes with some challenges, too. These include the following:

  • It may be difficult to access a list of the entire population, due to ethical or privacy concerns, or a full list may not exist. It can be expensive and time-consuming to compile this yourself.
  • Although probability sampling reduces the risk of sampling bias , it can still occur. When your selected sample is not inclusive enough, representation of the full population is skewed .

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If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

  • Student’s  t -distribution
  • Normal distribution
  • Null and Alternative Hypotheses
  • Chi square tests
  • Confidence interval
  • Quartiles & Quantiles
  • Data cleansing
  • Reproducibility vs Replicability
  • Peer review
  • Prospective cohort study

Research bias

  • Implicit bias
  • Cognitive bias
  • Placebo effect
  • Hawthorne effect
  • Hindsight bias
  • Affect heuristic
  • Social desirability bias

When your population is large in size, geographically dispersed, or difficult to contact, it’s necessary to use a sampling method .

This allows you to gather information from a smaller part of the population (i.e., the sample) and make accurate statements by using statistical analysis. A few sampling methods include simple random sampling , convenience sampling , and snowball sampling .

Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous , so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous , as units share characteristics.

Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population .

A sampling frame is a list of every member in the entire population . It is important that the sampling frame is as complete as possible, so that your sample accurately reflects your population.

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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.

Graphical user interface, application Description automatically generated

  • 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?

Term

Definition

Importance

Application

Reliability

Measures the ability of a measure to
consistently
measure the same way

This is important for measures of a construct.

 

 

For example, when measuring a patient’s blood pressure, the blood pressure cuff should consistently measure in the same way.  So, when doing every 15-minute vital signs after surgery, the blood pressure cuff should measure consistently every 15 minutes.

 

 

Validity

Measures the concept it is
supposed
 to measure

This is important to be able to measure the intended construct.

For example, a measure of critical thinking is an accurate measure of critical thinking and not expert practice.  

 

Another example:  a measure of stress level should measure stress level, not pain level.

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.

sampling plan for quantitative research

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.

sampling plan for quantitative research

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.

sampling plan for quantitative research

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

sampling plan for quantitative research

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.

sampling plan for quantitative research

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

sampling plan for quantitative research

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

10.3 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 describe why a researcher may use one type over another

Quantitative researchers are often interested in making generalizations about groups larger than their study samples; they seek nomothetic causal explanations. While there are certainly instances when quantitative researchers rely on nonprobability samples (e.g., when doing exploratory 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 likelihood of being selected from the sampling frame is known. You might ask yourself why we should care about a potential participant’s likelihood of being selected for the 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.

animated people walking in unison in a crowd

Obtaining a representative sample is important in probability sampling because of 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 sampling frame have an equal chance of being selected for inclusion in the study. In research, this is the principle of random selection . Researchers use a computer’s random number generator to determine who from the sampling frame gets recruited into the sample.

Using random selection does not mean that your sample will be perfect. No sample is perfect. The only way to come with a perfect result would be to include everyone in the population in your sample, which defeats the whole point of sampling. Generalizing from a sample to a population always contains some degree of error. This is referred to as sampling error, a statistical calculation of the difference between results from a sample and the actual parameters of a population.

Generalizability is a pretty easy concept to grasp. Imagine a professor were to take a sample of individuals in your class to see if the material is too hard or too easy. The professor, however, only sampled individuals whose grades were over 90% in the class. Would that be a representative sample of all students in the class? That would be a case of sampling error—a mismatch between the results of the sample and the true feelings of the overall class. In other words, the results of the professor’s study don’t generalize to the overall population of the class.

Taking this one step further, imagine your professor is conducting a study on binge drinking among college students. The professor uses undergraduates at your school as her sampling frame. Even if that professor were to use probability sampling, perhaps your school differs from other schools in important ways. There are schools that are “party schools” where binge drinking may be more socially accepted, “commuter schools” at which there is little nightlife, and so on. If your professor plans to generalize her results to all college students, she will have to make an argument that her sampling frame (undergraduates at your school) is representative of the population (all undergraduate college students).

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. Let’s build on the previous example. Imagine we were concerned with binge drinking and chose the target population of fraternity members. How might you go about getting a probability sample of fraternity members that is representative of the overall population?

three dice hitting the ground under a spotlight

Simple random samples are the most basic type of probability sample. A simple random sample requires a real sampling frame—an actual list of each person in the sampling frame. Your school likely has a list of all of the fraternity members on campus, as Greek life is subject to university oversight. You could use this as your sampling frame. Using the university’s list, you would number each fraternity member, or element , sequentially and then randomly select the elements from which you will collect data.

True randomness is difficult to achieve, and it takes complex computational calculations to do so. Although you think you can select things at random, human-generated randomness is actually quite predictable, as it falls into patterns called heuristics. To truly randomly select elements, researchers must rely on computer-generated help. Many free websites have good pseudo-random number generators. A good example is the website Random.org, which contains a random number generator that can also randomize lists of participants. Sometimes, 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.

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 possess a list of everyone in your sampling frame. Once you’ve done that, to draw a systematic sample you’d simply select every kth 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 10.2.

100 frat members divided by 25 fraternity members is 4 which is our selection interval or k

To determine where on your list of population elements to begin selecting the names of the 25 men you will interview, select a number between 1 and k, and begin there. If we 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 10.2 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.

Table 10.2 Systematic sample of 25 fraternity members
1 Jacob 51 Blake Yes
2 Ethan 52 Oliver
3 Michael Yes 53 Cole
4 Jayden 54 Carlos
5 William 55 Jaden Yes
6 Alexander 56 Jesus
7 Noah Yes 57 Alex
8 Daniel 58 Aiden
9 Aiden 59 Eric Yes
10 Anthony 60 Hayden
11 Joshua Yes 61 Brian
12 Mason 62 Max
13 Christopher 63 Jaxon Yes
14 Andrew 64 Brian
15 David Yes 65 Mathew
16 Logan 66 Elijah
17 James 67 Joseph Yes
18 Gabriel 68 Benjamin
19 Ryan Yes 69 Samuel
20 Jackson 70 John
21 Nathan 71 Jonathan Yes
22 Christian 72 Liam
23 Dylan Yes 73 Landon
24 Caleb 74 Tyler
25 Lucas 75 Evan Yes
26 Gavin 76 Nicholas
27 Isaac Yes 77 Braden
28 Luke 78 Angel
29 Brandon 79 Jack
30 Isaiah 80 Jordan
31 Owen Yes 81 Carter
32 Conner 82 Justin
33 Jose 83 Jeremiah Yes
34 Julian 84 Robert
35 Aaron Yes 85 Adrian
36 Wyatt 86 Kevin
37 Hunter 87 Cameron Yes
38 Zachary 88 Thomas
39 Charles Yes 89 Austin
40 Eli 90 Chase
41 Henry 91 Sebastian Yes
42 Jason 92 Levi
43 Xavier Yes 93 Ian
44 Colton 94 Dominic
45 Juan 95 Cooper Yes
46 Josiah 96 Luis
47 Ayden Yes 97 Carson
48 Adam 98 Nathaniel
49 Brody 99 Tristan Yes
50 Diego 100 Parker
In case you’re wondering how I came up with 100 unique names for this table, I’ll let you in on a little secret: lists of popular baby names can be great resources for researchers. I used the list of top 100 names for boys based on Social Security Administration statistics for this table. I often use baby name lists to come up with pseudonyms for field research subjects and interview participants. See Family Education. (n.d.). Name lab. Retrieved from names.familyeducation.com/popular-names/boys

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. (Bias will be discussed in more depth in the next section.) 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 binge drinking on campus each day of the week. Perhaps you need to have your observations completed within 28 days and you wish to conduct four observations on randomly chosen days. Table 10.3 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.

Table 10.3 Systematic sample of observation days
1 Monday Low 15 Monday Low
2 Tuesday Low Yes 16 Tuesday Low Yes
3 Wednesday Low 17 Wednesday Low
4 Thursday High 18 Thursday High
5 Friday High 19 Friday High
6 Saturday High 20 Saturday High
7 Sunday Low 21 Sunday Low
8 Monday Low 22 Monday Low
9 Tuesday Low Yes 23 Tuesday Low Yes
10 Wednesday Low 24 Wednesday Low
11 Thursday High 25 Thursday High
12 Friday High 26 Friday High
13 Saturday High 27 Saturday High
14 Sunday Low 28 Sunday Low

Do you notice any problems with our selection of observation days in Table 1? Apparently, we’ll only be observing on Tuesdays. Moreover, Tuesdays may not be an ideal day to observe binge drinking behavior. Unless alcohol consumption patterns have changed significantly since I was in my undergraduate program, I would assume binge drinking is more likely to happen over the weekend.

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 binge drinking, we want 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 sampling frame—for example, if we want to be sure to include in our study students who are in year five of their undergraduate program but this subgroup makes up only a small percentage of the population of undergraduates. There’s a chance simple random or systematic sampling strategy might not yield any fifth-year students, but by using stratified sampling, we could ensure that our sample contained the proportion of fifth-year students that is reflective of the larger population.

In this case, class year (e.g., freshman, sophomore, junior, senior, and fifth-year) is our strata , or the characteristic by which the sample is divided. In using stratified sampling, we are often concerned with how well our sample reflects the population. A sample with too many freshmen may skew our results in one direction because perhaps they binge drink more (or less) than students in other class years. Using stratified sampling allows us to make sure our sample has the same proportion of people from each class year as the overall population of the school.

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 binge drinking across fraternity members at each undergraduate program in your state. Just imagine trying to create a list of every single fraternity member in the state. 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 work through how we might use cluster sampling in our study of binge drinking. While creating a list of all fraternity members in your state would be next to impossible, you could easily create a list of all undergraduate colleges in your state. Thus, you could draw a random sample of undergraduate colleges (your cluster) and then draw another random sample of elements (in this case, fraternity members) from within the undergraduate college you initially selected. Cluster sampling works in stages. In this example, we sampled in two stages— (1) undergraduate colleges and (2) fraternity members at the undergraduate colleges we selected. However, we could add another stage if it made sense to do so. We could randomly select (1) undergraduate colleges (2) specific fraternities at each school and (3) individual fraternity members. 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)  [2] 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.

To summarize, probability samples allow a researcher to make conclusions about larger groups. Probability samples require a sampling frame from which elements, usually human beings, can be selected at random from a list. The use of random selection reduces the error and bias present in nonprobability samples reviewed in the previous section, though some error will always remain. In relying on a random number table or generator, researchers can more accurately state that their sample represents the population from which it was drawn. This strength is common to all probability sampling approaches summarized in Table 10.4.

Table 10.4 Types of probability samples
Simple random Researcher randomly selects elements from sampling frame.
Systematic Researcher selects every th element from sampling frame.
Stratified Researcher creates subgroups then randomly selects elements from each subgroup.
Cluster Researcher randomly selects clusters then randomly selects elements from selected clusters.

In determining which probability sampling approach makes the most sense for your project, it helps to know more about your population. A simple random sample and systematic sample are relatively similar to carry out. They both require a list all elements in your sampling frame. Systematic sampling is slightly easier in that it does not require you to use a random number generator, instead using a sampling interval that is easy to calculate by hand.

The relative simplicity of both approaches is counterweighted by their lack of sensitivity to characteristics in of your population. Stratified samples can better account for periodicity by creating strata that reduce or eliminate the effects of periodicity. Stratified samples also ensure that smaller subgroups are included in your sample, thus making your sample more representative of the overall population. While these benefits are important, creating strata for this purpose requires knowing information about your population before beginning the sampling process. In our binge drinking example, we would need to know how many students are in each class year to make sure our sample contained the same proportions. We would need to know that, for example, fifth-year students make up 5% of the student population to make sure 5% of our sample is comprised of fifth-year students. If the true population parameters are unknown, stratified sampling becomes significantly more challenging.

Common to each of the previous probability sampling approaches is the necessity of using a real list of all elements in your sampling frame. Cluster sampling is different. It allows a researcher to perform probability sampling in cases for which a list of elements is not available or pragmatic to create. Cluster sampling is also useful for making claims about a larger population, in our example, all fraternity members within a state. However, because sampling occurs at multiple stages in the process, in our example at the university and student level, sampling error increases. For many researchers, this weakness is outweighed by the benefits of cluster sampling.

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.
  • Probability samples usually require a real list of elements in your sampling frame, though cluster sampling can be conducted without one.
  • Cluster sampling- a sampling approach that begins by sampling groups (or clusters) of population elements and then selects elements from within those groups
  • 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
  • Periodicity- the tendency for a pattern to occur at regular intervals
  • Probability proportionate to size- in cluster sampling, giving clusters different chances of being selected based on their size so that each element within those clusters has an equal chance of being selected
  • Probability sampling- sampling approaches for which a person’s likelihood of being selected from the sampling frame is known
  • Random selection- using a randomly generated numbers to determine who from the sampling frame gets recruited into the sample
  • 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
  • Sampling error- a statistical calculation of the difference between results from a sample and the actual parameters of a population
  • Simple random sampling- selecting elements from a list using randomly generated numbers
  • Strata- the characteristic by which the sample is divided
  • Stratified sampling- dividing the study population into relevant subgroups and then draw a sample from each subgroup
  • Systematic sampling- selecting every kth element from a list

Image attributions

crowd men women by DasWortgewand CC-0

roll the dice by 955169 CC-0

  • Figure 10.2 copied from Blackstone, A. (2012) Principles of sociological inquiry: Qualitative and quantitative methods. Saylor Foundation. Retrieved from: https://saylordotorg.github.io/text_principles-of-sociological-inquiry-qualitative-and-quantitative-methods/ Shared under CC-BY-NC-SA 3.0 License (https://creativecommons.org/licenses/by-nc-sa/3.0/) ↵
  • Holt, J. L., & Gillespie, W. (2008). Intergenerational transmission of violence, threatened egoism, and reciprocity: A test of multiple psychosocial factors affecting intimate partner violence. American Journal of Criminal Justice, 33 , 252–266. ↵

Scientific Inquiry in Social Work Copyright © 2018 by Matthew DeCarlo is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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

Posted on 18th November 2020 by Mohamed Khalifa

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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

Simple sampling method example in stick men.

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

Systematic sampling method example in stick men

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

Stratified sampling method example stick men

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

Cluster sampling method example with stick men

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)

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Mohamed Khalifa

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

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

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

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

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Very informative and useful for my study. Thank you

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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.

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

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Home » Sampling Methods – Types, Techniques and Examples

Sampling Methods – Types, Techniques and Examples

Table of Contents

Sampling Methods

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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sampling plan for quantitative research

What are sampling methods?

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  • (Choice A)   Simple random sampling A Simple random sampling
  • (Choice B)   Stratified random sampling B Stratified random sampling
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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.

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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:

Sampling Methods In Reseach: Types, Techniques, & Examples

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

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

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

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

On This Page:

Sampling methods in psychology refer to strategies used to select a subset of individuals (a sample) from a larger population, to study and draw inferences about the entire population. Common methods include random sampling, stratified sampling, cluster sampling, and convenience sampling. Proper sampling ensures representative, generalizable, and valid research results.
  • Sampling : the process of selecting a representative group from the population under study.
  • Target population : the total group of individuals from which the sample might be drawn.
  • Sample: a subset of individuals selected from a larger population for study or investigation. Those included in the sample are termed “participants.”
  • Generalizability : the ability to apply research findings from a sample to the broader target population, contingent on the sample being representative of that population.

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

The Purpose of Sampling

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

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

Sample Target Population

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

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

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

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

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

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

Probability and Non-Probability Samples

Random Sampling

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

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

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

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

Stratified Sampling

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

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

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

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

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

Opportunity Sampling

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

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

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

Systematic Sampling

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

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

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

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

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

Sample size

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

Reliability and Validity

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

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

Practical Considerations

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

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

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Maximise Research Impact with Diverse Sampling Strategies: How and Why to Increase Diversity in Samples

Dr Yuzana Khine Zaw

Dr Yuzana Khine Zaw

What is sampling in research.

Sampling is one of the first stages of a research study. It means deciding the group you will collect data from in your research. Sampling is used to study a smaller portion of the population to make generalisable claims about the larger population. 1 The extent to which the claims are generalisable or “valid” depends on having a good sampling strategy. The specific sampling strategies varies, depending on the aims and type of research (quantitative, qualitative).

Crafting a winning sampling strategy

When designing a sampling strategy, researchers already have a research question in mind and a plan of how the study should be executed. The goal may be to explore a phenomenon (inductive) or to test an existing theory (deductive). This plan may include quantitative or qualitative research methods.

The sampling strategy will be informed by these aims and objectives. Quantitative studies tend to prioritise having a large sample size or use a “random sampling” method to ensure validity and generalisability. 1 Qualitative research tends to have smaller samples and use a more “purposive,” non-probabilistic (not random) approach. This involves selecting specific participants who can provide rich information to address the aims and objectives of the research. 1

Importance and the benefits of encouraging diversity in sampling

Regardless of which sampling method is used, a good sampling strategy encourages diversity to ensure research findings are valid, reliable, and generalisable. For example, a clinical trial to test a drug intended to be used on the general population would need to include a very large and diverse sample to account for all demographics. As certain subgroups of patients may respond differently to interventions, a non-diversified sample could mean that the results of the trial are not valid (drug is not safe/not effective) for the entire population.

Diversity is also important for smaller qualitative studies. “Maximum variation” sampling in qualitative research aims to recruit participants who are widely different from each other to obtain as many diverse perspectives as possible. 1 While diversity in sampling within quantitative studies helps ensure validity and generalisability, diversity in qualitative studies supports researchers to capture a wide range of different perspectives, creating more inclusive and representative research findings. This is particularly important for minimising inequalities in healthcare research, as certain hard-to-reach or underprivileged populations often miss out on opportunities resulting from research outcomes.

Maximising Positive Impact

Beyond conducting “good” research (research that is valid, generalisable, and reliable), a key goal is to produce as much of a positive impact on the world as possible. Ensuring diversity in sampling helps researchers reach and take into consideration the voices of those who are normally marginalised and underprivileged. By actively encouraging a diverse sample that includes minority voices, researchers can produce a better impact on society through equitable, inclusive, and representative research.

Given, L. M. (Ed.). (2008). The Sage encyclopedia of qualitative research methods . Sage publications.

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I’m a qualitative researcher with expertise in patient-centred outcomes.

My Experience

With a background in qualitative research, I have been working in program evaluation and health outcomes research since I graduated with a Master of Education from Vanderbilt University in 2015. Before working in clinical outcome assessment (COA) research, my focus was on evaluating pediatric community health programs using mixed methods research. I have experience developing surveys, moderating focus groups, and interviewing pediatric and adult participants.

For the past three years, I have more directly worked in the development, validation, and modification of COAs through qualitative research. My experience includes working across a wide range of therapeutic areas including oncology, neurology, rheumatology, and rare disease.

My guiding principles

I am passionate about ensuring patient voice is included in a meaningful way to impact drug development and overall health outcomes research.

BA, ACMA / Finance Director (Headline FD)

Richard Simpson

I am an experienced Finance Director who has spent more than 30 years in senior financial positions and a specific interest in driving growth in small, owner-managed businesses.

Since qualifying as an accountant in 1990, I have worked senior finance and general management positions in the UK, mainland Europe, North America and South East Asia. My sector experience includes pharmaceuticals, advertising and marketing, professional services, broadcasting, food and technology.

For the past twenty years I have worked with a portfolio of smaller, usually owner-managed businesses helping them to achieve medium-term growth plans and enable their organisations to adapt to changing external environments.

I am passionate about providing owner-managers with the information and understanding they need to make their businesses succeed.

Project Manager

My background includes high-level administrative support and working with cross-functional teams in the healthcare and banking industries.

My experience

With nine years of executive administration and management support experience in the healthcare and banking industries, I am eager to bring my skills and passion to the Sprout team. I enjoy working in a collaborative environment where growth and exploration is actively encouraged.

I look forward to not only growing professionally, but also contributing to Sprout’s positive impact on patients’ lives.

Strive to be helpful and resourceful in any situation. Be open-minded and stay hungry.

MA | Senior Principal

Beverly Romero

I’m a qualitative resear cher with expertise in patient – centered outcomes and a focus in pediatrics and rare disease.

With an expertise in qualitative methods, I have been working in the field of patient – centered outcomes in a consulting environment since 2010. During this time, I have had the opportunity to hone my skills in the development and validation of clinical outcome assessments (COAs) and consulting on the selection of COA endpoints for clinical trials. I have conducted hundreds of qualitative interviews with clinicians, caregivers, and patients of all ages.

My experience includes a wide variety of therapeutic areas, including gastroenterology, CNS disorders, rare genetic disorders, immunology, and dermatology. I am particularly passionate about research in the area of rare disease and pediatrics.

My prior work experience includes over 5 years in the communication s field and 15+ years as a university – level writing instructor.

BSc (Hons) | Project Manager

Justina Tol

My background includes maintaining strong relationships with clients, project management and coordination, financial tracking and reporting, and risk management.

My experience is in various industries and working environments where I aimed for clients’ maximum satisfaction, thereby strengthening customer loyalty. While always aiming to create value, I have experience in taking accountability for a portfolio of clients that need regular management to support operationally and strategically. I enjoy having the responsibility of the full-service overseeing while also initiating and driving improvements to strategies and tactical plans resulting in enhanced efficiency and quality.

My focus is representing the stakeholders’ needs and goals to the various cross-functional teams to ensure quality service, resulting in client satisfaction and operational excellence, and encouraging new and repeat business opportunities.

Office Manager

Rachel Cotter

I am an Office Manager with a background in Bookkeeping and ISO Management.

Over the last thirteen years I have worked within the Information Technology industry which has given me great technical knowledge paired with finance experience. I started out as an Office Assistant but having gained exposure quite quickly to all aspects of the business I decided to focus on getting a bookkeeping certification. That soon developed into an Accounts Assistant role and then swiftly onto Office Management. During that time, I played a key part in obtaining ISO accreditation for the company and maintaining the QMS, ISMS and Environmental system, alongside the day to day tasks of Office Management and Accounts function.

I am very much looking forward to furthering my career and knowledge with Sprout.

I am passionate about finding better ways of working, utilising systems to their fullest and improving procedures for everyone; in the interest of saving time and cost. I get great satisfaction from helping others and being a supportive member of any team.

MPH | Scientist

Kara Giannone

Scientist with research expertise in qualitative research methodology, patient-reported outcomes, and digital health interventions.

I graduated with my MPH in Behavioral Science and Health Education from Emory University in 2019. After my graduate degree I worked for an academic institution both as a qualitative researcher and as a project manager in the digital health space. I have experience in translating public health and medical interventions to a digital platform while ensuring usability standards are maintained and user experience is maximized to meet study aims.

As a qualitative researcher I have experience working on projects in various areas including urology, oncology, immunology, psychiatry, hematology, gastroenterology, health policy and management, epidemiology, and health promotion.

I believe that any health-based organization or company that is aiming to positively impact quality of life needs to allow space for the patient voice. I am passionate about facilitating that exchange through qualitative research.

PhD, Public Health | Scientist

Yuzana Khine Zaw

I’m a medical anthropologist with qualitative research experience in clinical studies.

I have seven years of experience in academia, conducting qualitative social science research on antimicrobial resistance in Southeast Asia. I completed a PhD in Public Health and Policy at the London School of Hygiene and Tropical Medicine where I focused on exploring the social and political contexts of antimicrobial resistance and care in Myanmar through an ethnographic study.

Prior to this, I completed a master’s in International Health and Tropical Medicine at the University of Oxford where I examined the social role of diagnostic biomarker testing in antibiotic prescription. I also studied pre-medicine during my bachelor’s and spent four years doing lab-based research at Georgetown University’s Lombardi Comprehensive Cancer Center. There, I completed an undergraduate honors thesis on chemoprevention mechanisms. I am therefore, well-versed in health and medicine, across different disease and therapeutic areas including febrile illness, cancer, diabetes, malaria, and HIV.  

Real-world solutions should be holistic and go beyond the technical. To achieve this, I believe in the importance of listening to and incorporating everyone’s voices, particularly those whose lives we are most impacting – the patients.

MSEd  |  Scientist

Anna de la Motte

I’m a qualitative scientist with research and consulting expertise in patient-centered outcomes and qualitative research methods.

I have over five years’ experience in health outcomes research, four of which were in a pediatric setting. I completed my MSEd degree at the University of Pennsylvania where I focused on the role of health in education. My master’s thesis examined the effect of a free play intervention on feelings of social connectivity and academic performance.

For the last five years, I’ve worked on the development of novel pediatric patient-reported outcome (PRO) measures and modification of existing measures for specific pediatric clinical populations. Most recently, I worked in the patient-centered research division of a large CRO, providing project management, research, and consulting services to multiple international pharmaceutical companies. I have experience conducting qualitative research with both pediatric and adult populations and have experience across a broad range of therapeutic areas, including oncology, nephrology, gastroenterology, and sleep medicine.

My passion as a COA researcher is working creatively to ensure that the voices and priorities of different patient populations are central in clinical decision-making and the development of new treatments.

BSc | Project Manager

Emily Whyte

I am a junior project manager providing support to senior project managers and specializing in pharma and wellness projects, behavior change initiatives and patient support programs.

I have worked in marketing and project management in a variety of roles including as a British Army Officer. With my immunology background, I have a deeper understanding of the relevancy of the project work we do at Sprout, and my army experience means I have the ability to work under pressure to meet important deadlines and budgets.

I am excited to play a role in delivering evidence-based programs which provide practical and effective solutions for patients and healthcare teams, ultimately improving the quality of healthcare given and received.

BCom | Senior Project Manager

Anna Werchowiecki

I specialize in setting up and delivering pharma-led projects, behavior change initiatives and patient support programs.

I’ve worked in account management, project management and resource management. With over five years’ experience in the pharma industry, I’ve collaborated with many of the top pharmaceutical companies on a variety of projects. These range from multi-channel international patient support programs to discrete local projects to disease awareness initiatives.

Always be prepared – as well as on time and to budget!

PhD |  Senior Scientist

Chloe Patel

I have recently completed my PhD at the University of Warwick that has assessed the role of maternal weight, experience and cognition, and media on the role of food-related parenting practices. Prior to undertaking a PhD, I worked for a global CRO on various patient centered outcomes projects.

I have over 10 years’ experience in health research. I have worked in academic and commercial settings with healthcare professionals, patients, and caregivers.

I am passionate about the impact that research has on the lives of patients, families, and wider healthcare setting; and I value turning research findings into meaningful actions.

PhD, CPsychol  | Lead Scientist

Laura Meade

My PhD in health psychology had a focus on public health, and I’m also a certified health and wellness coach. I now specialize in improving patient adherence to treatment recommendations, including adherence to non-pharmacological treatments.

I’ve worked with patients in a variety of contexts. As a coach, I support patients on a one-to-one basis to implement behavior changes recommended by HCPs. I’ve worked in primary care, as well as in the academic, clinical and commercial worlds. I’ve developed community health initiatives to enhance understanding of how emotions impact health behavior, and the personal, social and environmental impacts on patient adherence. Using psychological theory and techniques, I developed and delivered interventions for patients with long-term conditions, with a focus on enhancing exercise adherence in patients with chronic pain.

I’m passionate about translating research into practice. I enjoy working with both patients and healthcare providers to explore the best ways to support patients adhere to treatment recommendations.

PhD, CPsychol | Partner

Christina Jackson

I’m a behavioral psychologist specializing in improving adherence to medication for long-term conditions, and changing healthcare professional (HCP) behavior to improve services.

I’ve been working in the adherence field for more than a decade. I completed my PhD in adherence interventions at UCL and then worked for a behavior change agency before setting up Sprout with Lina in 2017. I focus on global and local initiatives to support people with long-term conditions with adherence and other self-management issues, and on projects focused on changing HCP behavior to improve services. I’ve worked for nine of the top 15 pharmaceutical companies, on more than 20 patient support programs.

I love translating the latest findings from academic research into workable, real-world solutions that can help people with long-term conditions and their healthcare team. I’m particularly interested in digital approaches to behavior change and maintaining participant engagement through a program journey.

PhD, CPsychol | Senior Scientist

Alicia Hughes

I am a Health Psychologist with expertise in symptom experience and adjustment to long-term conditions. I have a special focus on supporting patients with persistent physical symptoms, specifically fatigue.

I have over 10 years’ experience in health research and development of patient behaviour change initiatives. I have worked in academia and industry, with health-care professionals and directly with patients. I completed my PhD at King’s College London assessing psychological correlates of fatigue and have continued to build on this work exploring fatigue in a range of patient populations. The evidence-based models arising from my research have informed the development of novel and targeted interventions to help people manage fatigue and its impact on patient’s quality of life.

I am passionate about the role of psychology in improving patients’ health-related quality of life.

PhD, CPsychol  |  Partner

Lina Eliasson

I specialize in understanding, measuring and improving treatment adherence and other behaviors that impact health. I also have experience in identification and validation of clinical outcomes assessments (COAs) and in patient preference studies.

My background spans both academic research and the commercial world. Following completion of my PhD on adherence to oral oncology drugs from UCL School of Pharmacy, I worked as a post-doc researcher at Imperial College London. I then joined a behavior change agency, where I oversaw the development, implementation and evaluation of patient and caregiver support programs delivered by multi-disciplinary healthcare teams, which I trained and supervised. Before founding Sprout with Dr Christina Jackson in 2017, I led the European COA team for one of the world’s largest clinical research organisations.

Throughout my career, I’ve collaborated with numerous leading pharmaceutical and biotech companies globally across a wide range of disease categories.

I value transparency in processes and communication, and having the freedom to recommend the best possible strategies for my clients.

PhD, MSc | Partner

Sarah Clifford

I’m a health psychologist with research and consulting expertise in patient-centered outcomes and treatment adherence.

I have over 20 years’ experience in health research, including over 10 years of consulting with the life sciences industry. For my PhD, I designed and conducted treatment adherence-related studies at the London/UCL School of Pharmacy – my research helped inform the foundation of the New Medicines Service, a community pharmacy-delivered service in England to support patients with their prescribed medication.

For the last 10 years, I’ve worked in clinical research organizations (CROs) in the US, providing research, consulting and strategy to a wide range of national and international pharmaceutical and medical device companies. Most recently I was the US West Coast Divisional Principal for a large CRO, leading a multi-disciplinary patient-centered outcomes team. I have experience across a broad range of therapeutic areas, including gastroenterology, respiratory health, rheumatology and immunology.

I’m driven by a desire to have a meaningful impact on the lives of people with long-term conditions.

PhD, CPsychol | Principal

Vanessa Cooper

I am a psychologist who specializes in understanding patients’ perceptions and experiences of illness, treatment and healthcare services, and developing interventions to address unmet need.

I have over 20 years’ experience working as a health psychology researcher in universities, the NHS and behavior change agencies. After completing my PhD on adherence to antiretroviral therapy for HIV, I was a senior researcher at UCL School of Pharmacy as part of a multidisciplinary team developing and evaluating theory-based interventions to enhance adherence by addressing perceptual and practical barriers to treatment.

I then worked at Brighton and Sussex University Hospitals NHS Trust where I designed and managed a program of research to develop HIV services to better meet the needs of an ageing population. Since 2015 I’ve worked as a consultant behavioral scientist, conducting primary and secondary research to understand people’s perceptions and experiences of illness, treatment and healthcare services and developing and evaluating healthcare interventions and patient support programs. I’ve worked across a wide range of disease areas and multimorbidity.

I am passionate about co-creation – working with stakeholders including patients, patient organizations, healthcare professionals and pharmaceutical industry – to inspire and develop innovative support programs and services that meet the needs and preferences of end-users.

BSc | Head of Operations

Emma Lyon Carroll

I’m a senior project manager specializing in pharma, wellness, patient support and behavior change projects.

My background over the last nine years has been client service, account management and project management in medical communications, healthcare advertising and most recently patient behavior change and adherence support programs. I’ve worked on large and small, global and local projects for many of the top pharmaceutical companies in areas including cardiovascular, gastroenterology, dermatology, oncology and rare disease. I enjoy planning, organizing and bringing together a wide range of stakeholders to deliver evidence-based programs that provide valued support to patients. 

I am passionate about supporting patient populations whilst providing exceptional client service, delivering projects on time and budget.

PhD, Sociology | Lead Scientist

Roxana Bahar

I’m a medical sociologist with research and consulting expertise in patient-centered outcomes, shared decision-making and qualitative research methods.

I have over 15 years’ experience in health research, including 8 years consulting within the life sciences industry. For my PhD, I designed and implemented a multi-phase, qualitative research study to understand racial/ethnic disparities in Caesarean section rates, and to evaluate shared decision-making practices between clinicians and patients. This study involved multiple rounds of in-depth interviews with a diverse group of patients and clinicians, along with over 300 hours of participant observation on the labor and delivery floor of a major US hospital, where I observed how clinical decisions are made in real time.

For the last 8 years, I’ve worked in clinical research organizations (CROs) in the US, providing research, consulting and strategy to a wide range of national and international pharmaceutical and medical device companies. Most recently I was a patient-reported outcomes researcher at a large CRO, where I managed several multi-site research studies with the goal of evaluating, adapting or developing patient-centered clinical outcome measures for FDA approval.  I have experience across a broad range of therapeutic areas, including gastroenterology, oncology, nephrology and immunology, as well as experience conducting research with adult, adolescent and pediatric patients and their caregivers.

I am passionate about improving patients’ quality of life and health outcomes by bringing their voices, preferences and experiences to the forefront of healthcare research and decision making.

Quantitative vs. Qualitative Research Design: Understanding the Differences

sampling plan for quantitative research

As a future professional in the social and education landscape, research design is one of the most critical strategies that you will master to identify challenges, ask questions and form data-driven solutions to address problems specific to your industry. 

Many approaches to research design exist, and not all work in every circumstance. While all data-focused research methods are valid in their own right, certain research design methods are more appropriate for specific study objectives.

Unlock our resource to learn more about jump starting a career in research design — Research Design and Data Analysis for the Social Good .

We will discuss the differences between quantitative (numerical and statistics-focused) and qualitative (non-numerical and human-focused) research design methods so that you can determine which approach is most strategic given your specific area of graduate-level study. 

Understanding Social Phenomena: Qualitative Research Design

Qualitative research focuses on understanding a phenomenon based on human experience and individual perception. It is a non-numerical methodology relying on interpreting a process or result. Qualitative research also paves the way for uncovering other hypotheses related to social phenomena. 

In its most basic form, qualitative research is exploratory in nature and seeks to understand the subjective experience of individuals based on social reality.

Qualitative data is…

  • often used in fields related to education, sociology and anthropology; 
  • designed to arrive at conclusions regarding social phenomena; 
  • focused on data-gathering techniques like interviews, focus groups or case studies; 
  • dedicated to perpetuating a flexible, adaptive approach to data gathering;
  • known to lead professionals to deeper insights within the overall research study.

You want to use qualitative data research design if:

  • you work in a field concerned with enhancing humankind through the lens of social change;
  • your research focuses on understanding complex social trends and individual perceptions of those trends;
  • you have interests related to human development and interpersonal relationships.

Examples of Qualitative Research Design in Education

Here are just a few examples of how qualitative research design methods can impact education:

Example 1: Former educators participate in in-depth interviews to help determine why a specific school is experiencing a higher-than-average turnover rate compared to other schools in the region. These interviews help determine the types of resources that will make a difference in teacher retention. 

Example 2: Focus group discussions occur to understand the challenges that neurodivergent students experience in the classroom daily. These discussions prepare administrators, staff, teachers and parents to understand the kinds of support that will augment and improve student outcomes.

Example 3: Case studies examine the impacts of a new education policy that limits the number of teacher aids required in a special needs classroom. These findings help policymakers determine whether the new policy affects the learning outcomes of a particular class of students.

Interpreting the Numbers: Quantitative Research Design

Quantitative research tests hypotheses and measures connections between variables. It relies on insights derived from numbers — countable, measurable and statistically sound data. Quantitative research is a strategic research design used when basing critical decisions on statistical conclusions and quantifiable data.

Quantitative research provides numerical-backed quantifiable data that may approve or discount a theory or hypothesis.

Quantitative data is…

  • often used in fields related to education, data analysis and healthcare; 
  • designed to arrive at numerical, statistical conclusions based on objective facts;
  • focused on data-gathering techniques like experiments, surveys or observations;
  • dedicated to using mathematical principles to arrive at conclusions;
  • known to lead professionals to indisputable observations within the overall research study.

You want to use quantitative data research design if:

  • you work in a field concerned with analyzing data to inform decisions;
  • your research focuses on studying relationships between variables to form data-driven conclusions;
  • you have interests related to mathematics, statistical analysis and data science.

Examples of Quantitative Research Design in Education

Here are just a few examples of how quantitative research design methods may impact education:

Example 1: Researchers compile data to understand the connection between class sizes and standardized test scores. Researchers can determine if and what the relationship is between smaller, intimate class sizes and higher test scores for grade-school children using statistical and data analysis.

Example 2: Professionals conduct an experiment in which a group of high school students must complete a certain number of community service hours before graduation. Researchers compare those students to another group of students who did not complete service hours — using statistical analysis to determine if the requirement increased college acceptance rates.

Example 3: Teachers take a survey to examine an education policy that restricts the number of extracurricular activities offered at a particular academic institution. The findings help better understand the far-reaching impacts of extracurricular opportunities on academic performance.

Making the Most of Research Design Methods for Good: Vanderbilt University’s Peabody College

Vanderbilt University's Peabody College of Education and Human Development offers a variety of respected, nationally-recognized graduate programs designed with future agents of social change in mind. We foster a culture of excellence and compassion and guide you to become the best you can be — both in the classroom and beyond.

At Peabody College, you will experience

  • an inclusive, welcoming community of like-minded professionals;
  • the guidance of expert faculty with real-world industry experience;
  • opportunities for valuable, hands-on learning experiences,
  • the option of specializing depending on your specific area of interest.

Explore our monthly publication — Ideas in Action — for an inside look at how Peabody College translates discoveries into action.

Please click below to explore a few of the graduate degrees offered at Peabody College:

  • Child Studies M.Ed. — a rigorous Master of Education degree that prepares students to examine the developmental, learning and social issues concerning children and that allows students to choose from one of two tracks (the Clinical and Developmental Research Track or the Applied Professional Track).
  • Cognitive Psychology in Context M.S. — an impactful Master of Science program that emphasizes research design and statistical analysis to understand cognitive processes and real-world applications best, making it perfect for those interested in pursuing doctoral studies in cognitive science.
  • Education Policy M.P.P — an analysis-focused Master of Public Policy program designed for future leaders in education policy and practice, allowing students to specialize in either K-12 Education Policy, Higher Education Policy or Quantitative Methods in Education Policy. 
  • Quantitative Methods M.Ed. — a data-driven Master of Education degree that teaches the theory and application of quantitative analysis in behavioral, social and educational sciences.

Connect with the Community of Professionals Seeking to Enhance Humankind at Peabody College

At Peabody College, we equip you with the marketable, transferable skills needed to secure a valuable career in education and beyond. You will emerge from the graduate program of your choice ready to enhance humankind in more meaningful ways than you could have imagined.

If you want to develop the sought-after skills needed to be a force for change in the social and educational spaces, you are in the right place .

We invite you to request more information ; we will connect you with an admissions professional who can answer all your questions about choosing one of these transformative graduate degrees at Peabody College. You may also take this opportunity to review our admissions requirements and start your online application today. 

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  • M.Ed. Degrees
  • Research Design
  • Open access
  • Published: 11 July 2024

The implementation and impacts of the Comprehensive Care Standard in Australian acute care hospitals: a survey study

  • Beibei Xiong 1 ,
  • Christine Stirling 2 ,
  • Daniel X. Bailey 1 , 3 &
  • Melinda Martin-Khan 1 , 4 , 5  

BMC Health Services Research volume  24 , Article number:  800 ( 2024 ) Cite this article

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Metrics details

Comprehensive care (CC) is becoming a widely acknowledged standard for modern healthcare as it has the potential to improve health service delivery impacting both patient-centred care and clinical outcomes. In 2019, the Australian Commission on Safety and Quality in Health Care mandated the implementation of the Comprehensive Care Standard (CCS). However, little is known about the implementation and impacts of the CCS in acute care hospitals. Our study aimed to explore care professionals’ self-reported knowledge, experiences, and perceptions about the implementation and impacts of the CCS in Australian acute care hospitals.

An online survey using a cross-sectional design that included Australian doctors, nurses, and allied health professionals in acute care hospitals was distributed through our research team and organisation, healthcare organisations, and clinical networks using various methods, including websites, newsletters, emails, and social media platforms. The survey items covered self-reported knowledge of the CCS and confidence in performing CC, experiences in consumer involvement and CC plans, and perceptions of organisational support and impacts of CCS on patient care and health outcomes. Quantitative data were analysed using Rstudio, and qualitative data were analysed thematically using Nvivo.

864 responses were received and 649 were deemed valid responses. On average, care professionals self-reported a moderate level of knowledge of the CCS (median = 3/5) and a high level of confidence in performing CC (median = 4/5), but they self-reported receiving only a moderate level of organisational support (median = 3/5). Only 4% (n = 17) of respondents believed that all patients in their unit had CCS-compliant care plans, which was attributed to lack of knowledge, motivation, teamwork, and resources, documentation issues, system and process limitations, and environment-specific challenges. Most participants believed the CCS introduction improved many aspects of patient care and health outcomes, but also raised healthcare costs.

Care professionals are confident in performing CC but need more organisational support. Further education and training, resources, multidisciplinary collaboration, and systems and processes that support CC are needed to improve the implementation of the CCS. Perceived increased costs may hinder the sustainability of the CCS. Future research is needed to examine the cost-effectiveness of the implementation of the CCS.

Peer Review reports

Introduction

Acute care hospital settings are characterised by a majority of patients with acute, serious or complex conditions [ 1 , 2 ], necessitating a range of care services from multiple care professionals across disciplines and settings [ 3 ]. However, a lack of care coordination may result in fragmented care, which can lead to unnecessary hospitalisation, an increased length of stay in hospital, and adverse events [ 1 , 4 ]. The traditional disease-specific approach to care delivery cannot meet the complex needs of patients resulting in a gradual shift by services to a more comprehensive care (CC) approach [ 5 ]. A rapid literature review identifying 16 articles on the effectiveness of CC indicated the potential of CC to improve health service delivery and positively impact both patient-centred care and clinical outcomes in acute care settings [ 6 ]. This literature review [ 6 ], conducted in 2015, was intended to inform Australian Commission on Safety and Quality in Health Care (ACSQHC) about the development of the Comprehensive Care Standard (CCS) in Australia.

Like most countries around the world, Australia’s population is aging, with people aged 65 and over increasing from 11% of the population in June 1992 to 17% in June 2022 [ 7 ]. This increase is associated with a significant increase in individuals suffering from chronic diseases, with approximately one-third of Australians self-reporting the presence of at least one long-term health condition in the 2021 Census [ 8 ]. The Australian health system is jointly run by three levels of government: local, state/territory, and federal (national) level [ 9 , 10 ]. Whilst the Federal Government is responsible for developing national health policies and allocating money to the healthcare system, local and State Governments are responsible for implementing and delivering health services. Medicare is Australia’s universal health care scheme, which covers the costs of all public hospital services and some or all of the costs of other health services, including medical services provided by general practitioners and medical specialists and medicine.

In 2017, the ACSQHC released the CCS, one of the National Safety and Quality Health Service (NSQHS) Standards [ 11 ]. The CCS is intended to minimise the risk of patient harm and reduce adverse events, improve the safety and quality of care delivered, and ensure patients receive the total health care required or requested by them. The NSQHS standards are mandatory in all Australian hospitals. From 2019 hospitals are accredited against these standards. The accreditation assessment of the CCS is based on four criteria and 36 actions that constitute the CCS. The four criteria are: “1. clinical governance and quality improvement to support CC, 2. developing the CC plan, 3. delivering CC, and 4. minimising patient harm” [ 11 ]. As per Xiong et al.‘s (2023) review of national standards for CC, the development of a CC plan is consistently recognised as a fundamental requirement for achieving CC. While the specifics of such a plan may vary based on patient needs and healthcare settings, the Australian CC plan is required to adhere to the seven specific actions outlined within the CCS criteria for developing a CC plan.

According to the ACSQHC, CC is defined as the “coordinated delivery of the total health care required or requested by a patient” [ 11 ]. The ACSQHC has described a set of six essential elements for CC delivery: (1) clinical assessment and diagnosis, (2) identify goals of care, (3) risk screening and assessment, (4) develop a single CC care plan, (5) deliver CC, and (6) review and improve CC delivery. These essential elements are closely interconnected and cover various stages or processes that a patient may experience in care delivery. They are also relevant to the well-established nursing process, encompassing assessment, diagnosis, planning, implementation, and evaluation, while also incorporating specific aspects such as identifying patients’ needs and preferences and minimising the risk of harm. As per Xiong et al.‘s review [ 12 ], risk screening and assessment and minimising patient harm is a unique component of the Australian national standard for CC. This review identified a significant knowledge gap regarding the impacts of addressing specific harms in a national standard for CC and emphasised the need for further studies to delve into this issue.

To support the implementation of the CCS, the ACSQHC also developed a conceptual model (referred to as “ACSQHC model”) (ACSQHC 2018). This model describes the key elements that health service organisations need to consider when implementing the CCS. The ACSQHC model also serves as a tool for identifying areas of improvement, enabling organisations to address gaps and enhance the delivery of CC. According to this model, the cultural conditions and systems and processes for CC fall into three groups: “1. a focus on patient experience, 2. systems, processes and protocols to deliver CC, and 3. organisational culture and governance that supports a CC approach” (p. 9) [ 13 ]. A focus on patient experiences is at the top of this pyramid-shaped model, emphasising the importance of person-centred principles in the policies, processes, and governance of the organisation for implementing the CCS.

Although the implementation of the CCS in hospitals is mandated, achieving 100% compliance has proven to be challenging, and the degree of compliance varies [ 14 ]. Two years after the CCS came into effect, 15% of the assessed health service organisations did not meet all the requirements of the CCS and 12% were provided with recommendations to meet the actions. This indicates underperformance of the implementation of the CCS [ 14 ]. In 2021, the ACSQHC surveyed health service organisations that had undergone accreditation assessments to identify the challenges associated with implementing the four criteria of the CCS and suggestions for resources [ 15 ]. This survey revealed that the most challenging criteria was developing a CC plan due to reasons such as no standard care plan used by all disciplines and difficulties in care planning with multidisciplinary teams (MDTs).

The ACSQHC survey primarily focused on investigating the implementation challenges of the CCS but did not explore potential facilitators. It targeted the contact person of health service organisations that are typically responsible for accreditation, which may not have captured the perspectives of individuals working directly with patients. Additionally, there is a notable lack of studies examining the impacts of the CCS. Therefore, there remains a research gap in understanding the implementation challenges, potential facilitators, and impacts of a national standard for CC [ 12 ]. A lack of knowledge of the implementation and impacts of a new care standard may hinder the sustainability of the care standard initiatives, waste financial resources, energy and time and increase the costs imposed on patients and governments [ 16 ]. The aim of our study is to develop a national picture of care professionals’ self-reported knowledge, experiences, and perceptions about the implementation and impacts of the CCS in Australian acute care hospitals. The findings of our study will contribute to a better understanding of the implementation and impacts of the Australian CCS in acute care hospitals and provide recommendations for further improvement. Moreover, these insights have the potential to advance the implementation of a national standard for CC in acute care hospitals not only within Australia but also internationally, benefiting healthcare systems worldwide.

Study design

The present study is part of a larger project exploring the implementation and impacts of the national standard for CC in Australian acute care hospitals from the perception of care professionals, patients, and informal carers [ 17 ]. This study uses survey methodology to understand the care professional perspective in a hospital setting, specifically acute care. A cross-sectional survey was created, distributed, and administered using the Checkbox survey platform (Checkbox Technology, Inc.), and was conducted from October 1, 2022, to April 30, 2023. This study is reported in compliance with the Consensus-Based Checklist for Reporting of Survey Studies [ 18 ].

Questionnaire development

As no existing survey instruments were identified through our previous literature review, a questionnaire was developed for this study, underpinned by the Commission’s evaluation of the CCS survey [ 15 ], the ACSQHC model [ 13 ], and previous literature [ 6 , 12 ]. BX, MMK, and CS participated in a collaborative and iterative process in the early stages of questionnaire development, refining and pre-testing the questions to ensure content validity and appropriate scope.

To pilot test the survey, we attached five survey evaluation questions (Supplementary Table 1 ) to the questionnaire and distributed it to 60 potential participants working at Australian acute care hospitals from July 1 to August 31, 2022. Participants were asked to fill in the questionnaire and then review the five aspects (i.e., readability, adequacy, relevance, practicality, and ethicality) of the survey. Twenty-nine care professionals responded to the online questionnaire, 21 met the inclusion criteria (care professionals who worked in acute care hospitals in Australia and had heard about the CCS) and 12 ( n  = 12/29, 41%) completed the validity questions.

Based on the results of the pilot survey (Supplementary Table 2 ), items, wording, and question order were revised before the subsequent, larger, formal investigation. Considering not all care professionals working in Australian hospitals knew about the CCS, we revised the screening questions and wording of the survey introduction to exclude these potential participants. Because of the importance of the CC plan in CC delivery, we added a question about the CC plan. Based on the results of the pilot survey, we also revised the response categories of two demographic questions. Due to incomplete responses, we moved the demographics section from the beginning to the end of the survey. Because the survey was anonymous, we could not send the revised questionnaire to the original respondents of the pilot test for feedback. Additionally, due to time and resource constraints, the revised version was reviewed by our research team for feedback before the formal launch.

Final questionnaire

The final online questionnaire consists of six sections, including: (1) screening section to confirm eligibility; (2) demographic section to obtain information regarding gender, employment location, rurality of location [ 19 ], type of hospital, work area, profession, leadership, and working experiences; (3) knowledge section to assess perceived knowledge about the CCS and confidence in performing CC; (4) practice section to examine received support for the CCS implementation and practices in CC delivery; (5) barriers and facilitators section to identify factors associated with the effective implementation of the CCS; and (6) perceived effects section to evaluate the perceived effects of the CCS implementation on patient care and health outcomes.

The questionnaire examines perceptions of 15 common effects of CC, as indicated by the review paper [ 6 ], and the definitions of these effects were included in the questionnaire (Supplementary Table 3 ).

The questionnaire includes 15 multiple-choice questions (including three matrix questions) and five free-text questions. The final version of the survey can be found in Supplementary Tables 3 and Supplementary Questionnaire.

Population and sampling

The population of interest is care professionals (doctors, nurses, and allied health professionals) who worked in acute care hospitals in Australia and had heard about the CCS at the time of the survey.

We employed convenience and snowballing sampling techniques to distribute the survey through our research team and organisation, healthcare organisations and facilities, and clinical networks using various methods, including websites, newsletters, magazines, emails, and social media platforms (Twitter, Linkedin, Facebook, and Instagram). In the survey invitation, recipients were informed that they were welcome to share the survey with their networks.

Sample size

According to Roscoe’s Simple Rule of Thumb, samples of 30 or more are recommended for one sub-sample [ 20 ]. The maximum number of sub-groups based on our demographic question is 8. According to Weisberg & Bowen [ 21 ], a sample size of 400 respondents is required if an error level of 5% is accepted in an e-survey. A sample size of at least 400 meets both criteria and was the minimum goal for our study.

Completion of the survey implied consent. Participation in the study was voluntary and confidentiality was assured as no identifiable information was collected. Participants were allowed to skip questions, except for the questions related to participant eligibility. Ethical approval was granted by the University of Queensland Human Research Ethics Committee (ID: 2022 /HE001036).

Data management

Survey data was collected without identifiable information and stored on the University of Queensland’s secure Research Data Management system, with access provided only to the researchers directly involved in its analysis.

Quality control

Five questions in the effect section examine negative outcomes, while the rest investigate positive outcomes. Respondents who chose “increase” or “decrease” for all the outcomes were deemed invalid and were excluded (reported as ”not responding logically”). Respondents who had the same IP address and had the same string of five or more words in the open-ended questions were considered repetitious and only their most recently completed responses were included in the analysis.

Statistical analysis

Survey data were included if at least one survey question related to the CCS implementation or CC delivery was answered. As the number of respondents completing each question varied, proportions reported were based on valid responses to each question.

Quantitative data were collated in Microsoft Excel for Mac (version 16.50) with statistical analyses carried out using R (version 4.1.0) and RStudio (version 1.4.1717) software. A two-sided p value of less than 0.05 was used to indicate statistical significance. Quantitative data were presented using descriptive statistics, including mean, standard deviation, median, range, cross-tabulations and proportions. Because all the variables were not normally distributed, we primarily used the median to provide a more accurate representation of the central tendency for non-normally distributed data. When the median values were the same, we reported the mean to highlight the differences.

Kruskal-Wallis tests were employed to assess potential differences in the variables within the ‘knowledge’ and ‘support’ sections among various demographic groups, as the assumption of normality was violated in their distributions. Post hoc analyses for Kruskal-Wallis were performed using Dunn’s test adjusted with the Bonferroni method. Chi-square tests were used to explore potential differences in the variables within the ‘effect’ section across demographic groups concerning organisation, profession, and leadership. Fisher’s exact tests were conducted due to the violation of the assumption of expected observations exceeding 5 within demographic groups for gender, location, work unit, and work experience. Post hoc analyses for both Chi-square and Fisher’s tests were adjusted using the Bonferroni method.

Free-text responses were analysed with NVivo software (version 12.3.0), coding thematically using a deductive approach [ 22 , 23 ]. During the analysis, BX began by thoroughly reading through the free-text responses to grasp the content and context. As BX read, BX started to notice recurring patterns, ideas, and concepts. These emergent patterns were then assigned codes, which served as labels for these common elements in the data. As the coding progressed, BX grouped similar codes together to form initial themes. Subsequently, all authors reviewed the themes.

Demographics

Our online survey received 864 responses, among which 215 responses were excluded due to: not working in an acute care hospital ( n  = 20) or having not heard about CCS ( n  = 99) or both ( n  = 9); starting the survey but not proceeding to the main body of the survey ( n  = 32); not responding logically ( n  = 8) or repeating responses ( n  = 65). After removing those invalid or ineligible responses, 649 responses were included in the analysis. The median time for completing the survey was 7 min (Q1 = 4 min, Q3 = 12 min).

Our sample consisted of registered nurses or midwives (44%, n  = 180), allied health professionals (31%, n  = 130), and doctors (25%, n  = 104) and 40% ( n  = 164) were a manager/director/leader in their profession. Our sample mirrors the distribution in the Australian health workforce ( n  = 642,000), which consists of 55% nurses ( n  = 350,000), 29% allied health professionals ( n  = 187,500), and 16% doctors ( n  = 105,300) [ 24 ]. Table  1 displays the characteristics of our sample. Females made up 52% ( n  = 216) of the sample, with 3–10 years of working experience comprising 58% ( n  = 240). All Australian states and territories were represented in the study, with one-third of respondents working in Queensland (35%, n  = 146). Additionally, 48% ( n  = 201) of the respondents were from regional areas, and three-quarters worked in public hospitals (76%, n  = 316). 47% ( n  = 188) of the respondents were from the emergency department (ED) or general medicine.

Doctors had a higher proportion of males (72%, n  = 75) than nurses (27%, n  = 48) in the questionnaire respondents. Nurses (71%, n  = 39) had a higher proportion of over 20 years of work experience than doctors (13%, n  = 7). Public acute care hospitals had a higher proportion of nurses (47%, n  = 147) and leaders (42%, n  = 134) among the questionnaire respondents compared to private acute care hospitals (33%, n  = 33; 30%, n  = 30).

The results from the knowledge, practice, and perceived effects sections are presented below. The results from the barriers and facilitators section are presented in another paper.

Perceived knowledge

The CCS is mandated in all acute care hospitals in Australia and its implementation relies on joint efforts from all care professionals. A 6-point Likert scale (0 = none to 5 = very high) measured the self-assessment of care professionals’ knowledge of the CCS. Analysis showed that on average respondents self-reported a ‘moderate’ level of knowledge of the CCS (median = 3/5) (Table  2 ).

The median level of self-reported knowledge for males (median = 4/5) was significantly higher than for females (median = 3/5) with z  = 3.4 and an adjusted p value of 0.002. Respondents from private acute care hospitals (median = 4/5) were identified as having a significantly higher average level of self-reported knowledge than those from public acute care hospitals (median = 3/5, χ 2 (1) = 6.3, p  = 0.012). On average, medical doctors (median = 4/5) sel-reported a higher level of knowledge than registered nurses (median = 3/5, z  = 3.0, p adj =0.008). On average, respondents from the Australian Capital Territory self-reported a significantly higher level of knowledge (median of 4/5 vs. 3/5) than those from New South Wales ( z  = 4.7, p adj <0.001), Queensland ( z  = 4.2, p adj <0.001), and Victoria ( z  = 5.6, p adj <0.001). No differences in the average level of self-reported knowledge were found in the rurality of locations, work units, working experiences, and having a leadership role or not ( p  > 0.05).

Perceived confidence

Performing CC involves six essential elements, and respondents were required to rate their confidence in performing them using a 5-point Likert scale (1 = very low to 5 = very high). On average, respondents self-reported a ‘high’ level of confidence in performing each of the six elements of CC (median = 4/5). However, the lowest average level of perceived confidence was observed in developing a single CC plan (mean = 3.53, SD = 1.07) (Supplementary Fig.  1 , Table  2 ).

On average, doctors (mean = 4.04, SD = 0.80) and nurses (mean = 3.82, SD = 0.84) self-reported a higher level of confidence in clinical assessment and diagnosis than allied health professionals (mean = 3.58, SD = 9.52), with doctors being significantly more confident ( z  = 3.8, p adj <0.001). Care professionals with 11–20 years of work experience (mean = 4.11, SD = 0.77) self-reported higher confidence in clinical assessment and diagnosis compared to those with more than 20 years of work experience (mean = 3.90, SD = 0.87, z  = 1.2, p adj =1.00), those with 3–10 years of work experience (mean = 3.72, SD = 0.88, z  = 3.2, p adj =0.009) and those with less than 3 years of work experience (mean = 3.68, SD = 0.89, z  = 2.6, p adj =0.052). No statistically significant differences in the average level of perceived confidence were found in other elements of CC among different demographic groups ( p  > 0.05).

Perceived support

According to the ACSQHC model, hospitals are required to provide organisational support to facilitate the implementation of the CCS. The perceived organisational support was measured using a 5-point Likert scale (1 = very low to 5 = very high). On average, respondents self-reported receiving a ‘moderate’ level (median = 3/5) of organisational support regarding education and training, systems and processes that support CC, and equipment and tools to implement the CCS (Supplementary Fig.  2 , Table  2 ). In contrast, respondents self-reported receiving an average of ‘high’ level (median = 4/5) of support in areas such as leadership across the organisation, ongoing quality improvement, and standardisation of hospital practices and policy. Perceptions towards support in education and training were at the lowest average level among the six aspects (mean = 3.37, SD = 1.03).

Among the care professionals surveyed, doctors self-reported receiving significantly more support than nurses in leadership across their organisations ( z  = 2.5, p adj =0.034) and ongoing quality improvement ( z  = 3.0, p adj =0.009).

When it came to support in education and training, doctors also self-reported receiving significantly more support than nurses ( z  = 3.7, p adj <0.001) and allied health professionals ( z  = 2.6, p adj =0.028). Males self-reported receiving significantly more support than females ( z  = 3.0, p adj  = 0.008), care professionals from private acute care hospitals self-reported receiving significantly more support than those in public acute care hospitals (χ 2 (1) = 4.2, p  = 0.040), and care professionals with 3–10 years of work experience self-reported receiving significantly more support than those with over 20 years ( z  = 3.0, p adj =0.018).

In terms of support for equipment and tools, the findings indicated that doctors also self-reported receiving significantly higher levels of support compared to nurses ( z  = 2.9, p adj =0.011). Males self-reported receiving significantly more support than females ( z  = 3.2, p adj =0.004), care professionals in private acute care settings self-reported receiving significantly more support than those in public acute care hospitals (χ 2 (1) = 7.1, p  = 0.008), and care professionals with 3–10 years of experience self-reported receiving significantly more support than those with over 20 years ( z  = 3.1, p adj =0.011).

Regarding support for systems and processes supporting CC, allied health professionals self-reported receiving more support in this aspect than nurses ( z  = 2.8, p adj =0.015). Care professionals in private acute care hospitals or working in intensive care units self-reported receiving more support than those in public acute care hospitals (χ 2 (1) = 11.5, p  < 0.001) or working in general medicine ( z  = 3.3, p adj =0.011). Additionally, care professionals with less than 20 years of work experience self-reported receiving more support than those with over 20 years (less than 3 years: z  = 2.9, p adj =0.020; 3–10 years: z  = 3.6, p adj =0.002, 11–20 years: z  = 3.4, p adj =0.004).

Support in standardisation of hospital practices and policy revealed that doctors self-reported receiving more support compared to nurses ( p adj =0.038). Males self-reported receiving more support than females ( z  = 2.8, p adj =0.017), and professionals in private acute care hospitals self-reported receiving more support than those in public acute care hospitals ( p adj =0.037). Furthermore, professionals with 3–20 years of experience self-reported receiving more support than those with over 20 years (3–10 years: z  = 3.1, p adj =0.012, z  = 3.0, 11–20 years: p adj =0.018).

Consumer involvement

According to the ACSQHC model, the implementation of the CCS should focus on patient experiences, and involving consumers is essential to reflect the person-centred principle in the policies, processes, and governance of the organisation. Participants were asked whether their organisations formally involved patients or care partners (also known as consumers) in the preparation, training, or implementation process of the CCS. Of the 449 respondents, 44% ( n  = 199) of respondents reported “Yes” to this question, 22% ( n  = 150) reported “No”, while 33% ( n  = 100) were unsure if their organisations involved consumers in these processes.

124 respondents reported the approaches to consumer involvement in their organisation. Seven common themes were identified and were presented here with examples (Table  3 ).

Establishing a committee or advisory group consisting of consumers was a common approach described that allowed consumers to provide input and perspectives on the implementation of the CCS. Another common approach was including consumer representatives in various working groups and meetings related to the CCS, ensuring that their voices were heard and considered in decision-making processes. Collaborating with consumers to jointly design and develop work processes and programs related to CC, and incorporating their perspectives and preferences were also mentioned by some respondents. Some respondents reported that their hospital offered educational resources and training programs specifically tailored for consumers to increase their understanding of CC principles and their role in their care. Actively involving consumers in making decisions about their own care, encouraging their participation, and supporting their autonomy were identified as routine practices. Additionally, some respondents reported that their hospital involved patients in sharing their stories and experiences related to CC, which increased publicity and awareness, promoted a patient-centred care culture, and fostered empathy among healthcare providers. Furthermore, respondents highlighted the importance of actively seeking feedback from consumers regarding their experiences of care, listening to their suggestions and concerns in various ways, and taking appropriate actions based on their feedback.

Developing a single CC plan is an essential element of CC. Participants were asked about the proportion of patients who had a care plan that met the requirement of the CCS (referred to as “CC plan”) in their unit, and the response was given on a 5-point Likert scale with options ranging from “none” (1) to “all” (5). 4% ( n  = 17) of respondents reported that “all” patients in their unit had a CC plan. Respectively, 29% ( n  = 120), 28% ( n  = 116), and 21% ( n  = 86) reported “most”, “half”, and “some” of the patients had a CC plan. On the other hand, 18% ( n  = 74) of respondents reported that “none” of the patients in their unit had a CC plan.

69 respondents reported the reasons for not all having a CC plan. Seven themes of not providing a CC plan were identified and were presented here with examples (Table  4 ).

Some respondents believed that non-compliant care plans with the CCS requirement were attributed to factors resulting from individuals, such as a lack of knowledge about creating a CC plan, insufficient motivation and commitment, and reliance on nursing staff and a lack of teamwork. On the other hand, others identified challenges related to the hospital itself such as scarcity of resources, including equipment, tools, staffing, and funding. This scarcity of resources led to competing priorities between clinical care and documentation. The challenges of documentation and limitations within the system and processes exacerbated the situation. Additionally, some respondents pointed out that the environment or setting, especially in the ED and day procedure environment, posed challenges for achieving compliant care plans.

Perceived effects

The questionnaire examined 15 effects that are commonly examined for evaluating CC. A 3-point rating scale (“worsened”, “no change”, “improved”) measured the perceived effects of the CCS. A small proportion of participants were not aware of the listed effects, ranging from 5% (shared decision-making and Interdisciplinary collaboration) to 20% (one-year survival) (Supplementary Table 4 ). Among those who were aware, about one-third (ranging from 26 to 35%) of the participants thought there were no changes in the effects of the introduction of CCS on patient care and health outcomes (Fig.  1 ).

Among those who were aware, more than half of the respondents thought there were improvements in interdisciplinary collaboration (62%, n  = 233), shared decision-making (61%, n  = 230), care continuity (59%, n  = 200), patient quality of life (57%, n  = 190), patient education (57%, n  = 209), patient satisfaction (55%, n  = 192), and emotional/social/spiritual support (52%, n  = 188), symptom control (51%, n  = 183), and patient compliance (51%, n  = 182) after the introduction of the CCS.

More than one-fourth of respondents believed there were worsened outcomes in length of stay (26%, n  = 88), 30-day readmission (28%, n  = 94), adverse events/clinical incidents (29%, n  = 104), and psychological distress (31%, n  = 112). An important caveat to these findings is that a greater proportion of participants thought that these metrics had improved since the introduction of the CCS, however the proportions thinking these had improved was towards the lower end of the Fig.  1 , below.

With all of these effects, a greater proportion of respondents did feel that patient care and health outcomes had improved, with the exception of healthcare costs. A greater proportion of respondents believed that health care costs increased (48%, n  = 159) than those who thought they decreased (18%, n  = 60) due to the introduction of the CCS.

Nurses, females, with more than 20 years of work experience, having leadership roles, and working in public acute care hospitals, tended to report ‘no change’ more frequently than ‘improved’ for certain outcomes compared to doctors, males, with less than 20 years of work experience, not having leadership roles, and working in private acute care hospitals. In metropolitan areas, there was a tendency for some outcomes to ‘improve’ or remain ‘unchanged’ compared to ‘worsening’, and ‘improvement’ was more common than ‘no change’ or ‘worsening’ for certain outcomes, in contrast to regional or remote/rural areas. Departments such as general medicine and others showed a tendency for ‘improved’ or ‘unchanged’ outcomes compared to ‘worsening’ for certain outcomes, as opposed to departments like ICU, ED, or surgery. Details of these specific outcomes by demographics are provided in the Supplementary Table 5 .

figure 1

Effects of the introduction of the Comprehensive Care Standard on patient care and health outcomes ranked in the descending order of improved effects

The aim of our study was to develop a national picture of care professionals’ self-reported knowledge, experiences, and perceptions about the implementation and impacts of the NSQHS CCS. In the five years since the release of the CCS, care professionals self-reported having a moderate level of knowledge of the CCS, a high level of confidence in performing CC, but experiencing only a moderate level of organisational support. This study also revealed seven common approaches for involving consumers in the CCS implementation process and highlighted seven recurring themes that contribute to non-compliant care plans aligned with CCS requirements. From the care professional perspective, many positive changes from the introduction of the CCS on patient care and health outcomes had been observed. More support in education and training as well as resources to support the development of the CC plan are needed to support the implementation of the CCS. Findings provide insights on what can be done to further improve the implementation of the CCS in Australia and might advance the implementation of national standards for CC internationally.

Despite the CCS being a mandatory national standard and its implementation relying on joint efforts from all care professionals, their perceived understanding of the CCS remains relatively limited. The lack of knowledge may result from ineffective communication strategies about the CCS and insufficient training and education. Previous research has highlighted the importance of communication in the delivery and dissemination of a new clinical policy within or beyond hospitals, thus facilitating more effective implementation [ 25 ]. However, it remains unclear how the CCS was communicated within and beyond the organisation [ 12 ]. Future research in this area would help the ACSQHC and hospitals identify strategies to improve the communication of the CCS, thus improving care professionals’ “awareness-knowledge” [ 25 ] of this Standard. Surprisingly, we found that males, doctors, and care professionals from private acute care hospitals believed they had higher levels of perceived knowledge of the CCS than females, nurses, and care professionals from public acute care hospitals. The differences in gender and hospital type may be attributed to variations in the professions. Based on our survey data, the workload of meeting the requirements of the CCS was primarily placed on nurses, despite their lower level of knowledge of the CCS compared to doctors. Nurses were frequently instructed on how to perform tasks without being provided with the underlying rationale. This highlights the importance of enhancing nurses’ knowledge of the CCS, as greater knowledge of guidelines and recommendations can increase nurses’ adherence to and compliance with the principles of CC [ 26 ].

Although respondents’ knowledge level of CCS was moderate, their confidence level in their ability to perform CC was high. This may be because many clinicians had received training in various aspects of CC before the introduction of the CCS (such as risk screening and multidisciplinary teamwork). However, the lowest level of confidence in performing CC was in developing a CC plan, which parallels previous findings that developing a CC plan was the most challenging criterion to implement and an area for improvement [ 14 , 15 ]. The lack of confidence in developing a CC plan corresponds to the low compliance of care plans with the CCS requirements. This low compliance is consistent with previous research findings that a patient-centred care plan is often not visible in the patient record [ 27 ]. Addressing the challenges of CC plan implementation requires a multifaceted approach that targets both individual and systemic factors. At the individual level, targeted training to enhance staff knowledge, incentives to boost motivation and commitment, and inter-professional collaboration initiatives to enhance teamwork and reduce reliance on nursing staff are essential. On a systemic level, hospitals must address resource scarcity, streamline documentation processes, and re-evaluate and restructure systems and processes to overcome these limitations.

The development of a CC plan should be a collaborative effort involving the MDTs rather than the sole responsibility of nursing staff. Research indicates multidisciplinary collaboration in care planning is essential for achieving better patient outcomes [ 28 ]. However, the current practice of paper-based documentation poses significant challenges for MDTs, as it complicates the process of generating and updating across disciplines and settings. This approach often leads to redundant and time-consuming tasks of recording repetitive documents, which does not add value to actual patient care [ 29 ]. Fortunately, the shift towards electronic medical records (EMR) offers a promising solution, enabling intelligent and efficient documentation and facilitating better collaboration. However, most existing EMR programs have focused on the development of care plan applications for use by a single discipline (e.g., nursing) or department [ 30 ]. This silo approach is unlikely to meet the intended effect of the care plan that improves MDT communication and does not reflect an MDT-based approach to care planning and delivery. This highlights a need to build Integrated Electronic Medical Records (ieMR) that supports the integration of care planning documentation into workflows that exist across the continuum and facilitate input by all team members (including patients) to truly reflect an MDT approach [ 31 , 32 ]. However, implementing ieMRs presents its own challenges, especially if these systems fail to meet the needs of care professionals, particularly in terms of functionality [ 33 ]. It is critical that ieMRs are designed to support effective communication and coordination, rather than simply serving as a tick-box task. Actual patient care should always be emphasised despite changes in policies and clinical administrative processes [ 27 , 29 ]. By addressing the systemic barriers to effective care planning, hospitals can better support their staff and improve patient care.

Supported by both structured questions and free-text responses, respondents indicated a clear desire for increased knowledge and training, as well as for more resources and improved systems and processes, especially from nurses. Lack of education and training may hinder knowledge, awareness, and belief in implementing the CCS. Care professionals require education, training, and developmental support to grasp CC principles and how various components contribute to its delivery, particularly within their specific settings [ 13 ]. Training should cover the adoption of new policies, processes, approaches, and tools throughout different organisational layers, promoting alignment with the goal of delivering CC for a more consistent approach [ 13 ]. Lack of resources (such as funding, staffing, and equipment) may have greatly impacted the implementation of the CCS. In line with previous research, high workload, poor staffing, and time pressure are very common barriers to adherence to compliance with guidelines and recommendations, especially in low-resource settings and at the time of COVID-19 [ 34 , 35 ]. Previous research reveals the availability of resources, equipment and tools, and digitalisation increased the likelihood of adherence to patient safety principles [ 26 ]. Furthermore, the establishment of systems and processes to support the delivery of CC [ 13 ], ensuring standardised content, information, messaging, and terminology models for care plans across various disciplines and settings, is crucial for effective communication and seamless continuity of care. Achieving this high level of standardisation necessitates coordination at both local and national levels.

Providing care that responds to consumers’ needs is a requirement of the NSQHS Standards [ 36 ], as part of the CCS and the Partnering with Consumers Standard [ 11 ]. This responds to the growing consensus that involving consumer in the development, implementation and evaluation of healthcare contributes to more targeted initiatives, better resource utilisation, and improvement in the safety, quality, and overall performance of health services organisations [ 37 , 38 ]. Evidence suggests that patient involvement in goal setting and ongoing status updates is also crucial because patients who actively engage in their disease management are less likely to be re-hospitalised after an acute exacerbation [ 39 ]. This underscores the importance of integrating consumer engagement strategies into clinical workflows, not merely as a compliance measure but as a core component of effective healthcare delivery. Our study identified seven common approaches to consumer involvement. A one-size-fits-all approach to consumer partnership is neither feasible nor desirable. Instead, strategies should be tailed to fit the specific nature and context of each organisation, ensuring that consumer engagement is meaningful and sustainable.

Our research findings hold significant implications for healthcare practice and policy. Following the introduction of the CCS, our study indicates that positive perceived changes were observed in patient care and health outcomes. This aligns with the existing body of literature, exemplified by a review of 16 papers on the effectiveness of CC conducted by Grimmer et al. [ 6 ]. However, it’s noteworthy that care professionals in our study did not uniformly perceive significant reductions in adverse events at this time. These varied perspectives shed light on the complexity of the Australian national standard for CC, which includes a unique emphasis on minimising patient harm, and underscores the necessity for further examination and refinement in this area to enhance patient safety and healthcare quality. Contrary to the findings of Grimmer et al. [ 6 ], care professionals in our study showed a perception of increases in healthcare costs. Grimmer et al. [ 6 ] found that costs of care were shown to decrease in 83% of the articles included. When specifically focusing on articles that examined CC in older adults, all of the articles reported a significant decrease in the cost of care. The benefits of the CC model may be limited to specific patient groups. Additionally, care plans that comply with CCS requirements in certain hospital settings have been perceived as challenging or unfeasible by some participants. Future research is needed to investigate both the impacts of CC in the acute care setting and the relationship between healthcare costs and the implementation of the CCS.

This study is the first survey of care professionals’ perceived knowledge, experiences, and perceptions of a national standard for CC. It contributes significantly to the understanding of the practical implementation and potential impacts of such a standard. The diverse composition of the sample, including care professionals from various disciplines and work settings across all Australian states and territories, ensures a comprehensive national perspective. The survey was developed through a robust process of preliminary pre-testing and pilot-testing, which effectively enhanced its face validity.

This study also has several limitations. The inclusion of only care professionals who had heard about the CCS may have also introduced a selection bias. Respondents may have self-selected, potentially leading to a bias where those with greater interest and understanding of the CCS were more inclined to participate. If so the knowledge and awareness of the CCS among care professionals would have been lower than indicated in this study. Results may be subject to recall bias regarding the implementation and impacts of the CCS as there is no baseline survey before the implementation of the CCS and our survey administration occurred five years after the release of the CCS. Further, the readiness and attitudes of care professionals may vary substantially owing to differences in baseline hospital procedures prior to the introduction of the CCS. Finally, an increased occurrence of missing data in the latter parts of the survey might suggest respondent fatigue or drop-out, possibly attributed to the survey’s length or limited understanding of CCS implementation, and therefore further exploration of care professionals’ perceptions about the CCS may be warranted to confirm these findings. However, despite these limitations, our study brings to light significant issues that should be taken into account to improve CCS implementation. It also serves as an important initial step in addressing knowledge gaps related to the implementation of a national standard for CC.

Although Australian acute care hospitals have been mandated to implement the CCS, it is not easy to implement it successfully. Developing a CC plan is a key aspect of the CCS, yet developing these plans is challenging. Further education and training, resources, and collaboration may be required to increase care professionals’ capability and commitment to develop CC plans for patients. Nurses may benefit more from greater CCS education, as their knowledge of the CCS is lower than that of doctors, despite doing the bulk of CC delivery. More education and training as well as resources to support the development of MDT CC plans are needed to support the implementation of the CCS. Overall, more than half of care professionals felt that most care metrics had improved since the introduction of CCS but, almost half felt costs of care had also increased. Future research that involves investigating the implementation, costs and impacts of the CCS is warranted.

Data availability

The datasets generated and/or analysed during the current study are not publicly available due to privacy or ethical restrictions but are available from the corresponding author on reasonable request.

Abbreviations

Australian Commission on Safety and Quality in Health Care

  • Comprehensive care

Comprehensive care standard

Electronic Medical Records

Integrated Electronic Medical Records

Multidisciplinary team

National Safety and Quality Health Service

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Acknowledgements

The authors wish to acknowledge the support of our friends, colleagues, and work organisations (University of Queensland, University of Tasmania), healthcare organisations (Agency for Clinical Innovation, Australian Medical Association, Queensland Health, Health Translation Queensland, Alfred Health), as well as clinical networks (Queensland Statewide Clinical Networks, New South Wales Statewide Clinical Networks) for their invaluable assistance in disseminating this survey. We also extend our appreciation to those individuals and organisations that have assisted us in distributing the survey, despite remaining unidentified. We wish to acknowledge the University of Queensland eQC Patient and Carer Advisory Board for their support for this project from inception to dissemination.

Beibei Xiong is supported by Graduate School Scholarships from the University of Queensland. This work is part of the project “Improving quality of care for people with dementia in the acute care setting (eQC)” which is funded by the National Health and Medical Research Council of the Australian Government (ID: APP1140459). The research was designed, implemented, and analysed independently, with no involvement from the funding organisation.

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Beibei Xiong, Daniel X. Bailey & Melinda Martin-Khan

School of Nursing, University of Tasmania, Hobart, TAS, 7000, Australia

Christine Stirling

Centre for Clinical Research, The University of Queensland, Brisbane, QLD, 4102, Australia

Daniel X. Bailey

Department of Health and Life Sciences, University of Exeter, EX1 2HZ, Exeter, England, United Kingdom

Melinda Martin-Khan

School of Nursing, University of Northern British Columbia, British Columbia, V2N 4Z9, Prince George, Canada

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Contributions

BX, MMK, & CS conceptualised and designed the study. BX, MMK, & CS contributed to the design and pilot of the survey. BX distributed and administered the survey and analysed the survey results. All authors contributed to interpreting the results. BX drafted the manuscript with guidance from CS, DB, & MMK. CS, DB, & MMK critically reviewed the manuscript, provided input and made suggestions. All authors read and approved the final manuscript.

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Correspondence to Beibei Xiong .

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Ethical approval was granted by the University of Queensland Human Research Ethics Committee (ID: 2022 /HE001036). Informed consent was obtained from all study participants. The study was performed in accordance with the Declaration of Helsinki guidelines and regulations.

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Xiong, B., Stirling, C., Bailey, D.X. et al. The implementation and impacts of the Comprehensive Care Standard in Australian acute care hospitals: a survey study. BMC Health Serv Res 24 , 800 (2024). https://doi.org/10.1186/s12913-024-11252-0

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DOI : https://doi.org/10.1186/s12913-024-11252-0

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