how to write the sample in research

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GETTING STARTED
Introduction
FUNDAMENTALS
Acknowledgements
Research questions & hypotheses
Concepts, constructs & variables
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Sampling Strategy
Research Quality
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Data Analysis

How to structure the Sampling Strategy section of your dissertation

The Sampling Strategy section of your Research Strategy chapter (usually Chapter Three: Research Strategy ) needs to be well structured. A good structure involves four steps : describing , explaining , stating and justifying . You need to: (1) describe what you are studying, including the units involved in your sample and the target population ; (2) explain the types of sampling technique available to you; (3) state and describe the sampling strategy you used; and (4) justify your choice of sampling strategy. In this article, we explain each of these four steps:

STEP ONE: Describe what you are studying
STEP TWO: Explain the types of sampling technique available to you
STEP THREE: State and describe the sampling strategy you used
STEP FOUR: Justify your choice of sampling strategy

STEP ONE Describe what you are studying

First, the reader needs to know what you studied. This should include details about the following:

The units you measured (or examined).

Your target population .

If you used a probability sampling technique to select your sample , you will also need to describe:

Your sampling frame .

If you are unsure what of any of these terms mean (i.e., unit , sampling frame , population ), you might want to read the article, Sampling: The basics , before reading on. If you feel comfortable with these terms, let's imagine we completed a dissertation on the career choices of students at the University of Oxford, England. Below we describe our units , target population and sampling frame (imagining that we used a probability sampling technique ).

From this description , the reader learns the following:

Units: students Population: all undergraduate and postgraduate students, full-time and part-time, at the University of Oxford, England Sampling frame: all students enrolled at the University of Oxford, as of 05 January 2011 (i.e., according to Student Records, assuming this is the department that maintains a list of all students studying at the university)

Note the difference between the target population and the sampling frame, from which we select our sample (when using a probability sampling technique). They are the same in all respects apart from the fact that the sampling frame tells the reader that only those students enrolled in the university according to Student Records on a particular date (i.e., 05 January 2011) are being studied. If the list of students kept by Student Records is very different from the population of all students studying at the university, this should be made clear [see the article, Sampling: The basics, to understand more about sampling frames and potential sampling bias].

By the time you come to write up the Sampling Strategy section of your Research Strategy chapter, you should know whether the sampling frame is the same as the population. If it is not, you should highlight the difference between the two. This completes the first part of the Sampling Strategy section of your Research Strategy chapter.

STEP TWO Explain the types of sampling technique available to you

Once you know what units you are studying, as well as your population and sampling frame , the reader will often want to know what types of sampling technique you could use . We say could use rather than should use because whilst there are certain ideal choices of sampling technique, there is seldom a right or wrong answer. Instead, researchers choose sampling techniques that they feel are most appropriate to their study, based on theoretical and practical reasons.

Broadly speaking, you could choose to select your sample from (a) your sampling frame using either a probability sampling technique (e.g., simple random sampling, systematic random sampling, stratified random sampling) or (b) from your population using a non-probability sampling technique (e.g., quota sampling, purposive sampling, convenience sampling, snowball sampling). To understand the differences between these techniques, as well as their advantages and disadvantages, you may want to start by reading the articles: Probability sampling and Non-probability sampling .

When explaining the types of sampling technique that were available to you in this part of your Sampling Strategy section, you should take into account: (a) the research strategy guiding your dissertation; and (b) theoretical and practical sampling issues.

The research strategy guiding your dissertation

Theoretically , the ideal sampling technique for a piece of research (i.e., probability or non-probability sampling) differs depending on whether you are using a quantitative , qualitative or mixed methods research design .

Theoretical and practical sampling issues

Whilst there are theoretical ideals when it comes to choosing a sampling technique to use for your dissertation (i.e., probability or non-probability sampling), it is often practical issues that determine not only whether you choose one type of sampling technique over another (e.g., non-probability sampling over probability sampling ), but also the specific technique that you use (e.g., purposive sampling over quota sampling ; i.e., both are non-probability sampling techniques). Such practical issues range from whether your target population is known (i.e., whether you can get access to a list of the population) to whether you have the time and money to get access to such a list [click on the relevant article to understand the advantages and disadvantages (i.e., theoretical and practical considerations ) of the different probability sampling (e.g., simple random sampling , systematic random sampling , stratified random sampling ) and non-probability sampling techniques (e.g., quota sampling , purposive sampling , self-selection sampling , convenience sampling , snowball sampling )].

Assuming that you understand the differences between these sampling techniques, and their relative merits, let's consider what sampling choices are open to us using our example of career choices of students at the University of Oxford, England . The green text illustrates what we have already written above.

From this explanation , the reader learns the following:

Types of sampling strategy available: probability and non-probability sampling Ideal choice: probability sampling Preferred choice of probability sampling technique: stratified random sample Preferred choice of non-probability sampling technique: quota sample

When you are writing up this part of the Sampling Strategy section of your Research Strategy chapter, you may be expected to include a much more comprehensive list of reasons why you prefer one type of sampling strategy (i.e., probability or non-probability) and more specifically, a particular sampling technique (e.g., stratified random sampling over quota sampling). We provide information about the advantages and disadvantages of these different sampling strategies and sampling techniques in the following articles: for probability sampling , see simple random sampling , systematic random sampling , stratified random sampling ; for non-probability sampling techniques, see quota sampling , purposive sampling , self-selection sampling , convenience sampling , snowball sampling .

STEP THREE State and describe the sampling strategy you used

Third, you need to state what sampling strategy and sampling technique you used, describing what you did.

Again, let's consider this for our example of career choices of students at the University of Oxford, England . The green text illustrates what we have already written above.

Career choices of students at the University of Oxford, England We examined the career choices of all students at the University of Oxford, England. By all students we mean all undergraduate and postgraduate students, full-time and part-time, studying at the University of Oxford, England, enrolled as of 05 January 2011. Since our research drew on a quantitative research design , the ideal would have been to use a probability sampling technique because this allows us to make statistical inferences (i.e., generalisations ) from our sample of students to all students at the university. Such a probability sampling technique would provide greater external validity for our findings. Since we wanted to compare the career choices of different strata (i.e., groups of students), including males and females , the appropriate choice of probability sampling technique would have been a stratified random sample . However, if it were not possible to use a probability sampling technique , we could have used a non-probability sampling technique . Since we wanted to compare different strata (i.e., groups of students) and achieve a sample that is as representative as possible of our population , we could have used a quota sample . In the event, we used quota sampling to select the sample of students that would be invited to take part in our dissertation research. Student Records provided us with the appropriate quotas for male and female students, which showed a 53:47 male-female ration [ NOTE: this is a fictitious figure]. We selected a sample size of 200 students, which was based on subjective judgement and practicalities of cost and time. Therefore, we sampled 106 male students (i.e., 53% of our sample size of 200 students) and 94 female students (i.e., 47% of our sample size of 200 students). For convenience, we stood outside the main library where we felt the thoroughfare (i.e., number of students passing by) would be highest.

From this statement and description , the reader learns the following:

Sampling strategy chosen: non-probability sampling Specific sampling technique used: quota sampling

Details of quota sampling: strata (i.e., groups of students) of interest are males and females ratio of males-females at the university was 53:47 sample size selected was 200 students quota sample filled based on ease of access to students at the main university library.

Again, when you are writing up this part of the Sampling Strategy section of your Research Strategy chapter, it may be appropriate to include greater description of the sampling technique you used.

STEP FOUR Justify your choice of sampling strategy

Finally, you need to justify your choice of sampling strategy. When writing up the Sampling Strategy section of your Research Strategy chapter, you may find it easier to combine the third and fourth steps (i.e., stating and describing the sampling strategy you used, as well as justifying that choice). Taking our example of the career choices of students at the University of Oxford, England , we illustrate how the two steps can be integrated. As before, the green text illustrates what we have already written above.

Career choices of students at the University of Oxford, England We examined the career choices of all students at the University of Oxford, England. By all students we mean all undergraduate and postgraduate students, full-time and part-time, studying at the University of Oxford, England, enrolled as of 05 January 2011. Since our research drew on a quantitative research design , the ideal would have been to use a probability sampling technique because this allows us to make statistical inferences (i.e., generalisations ) from our sample of students to all students at the university. Such a probability sampling technique would provide greater external validity for our findings. Since we wanted to compare the career choices of different strata (i.e., groups of students), including males and females , the appropriate choice of probability sampling technique would have been a stratified random sample . However, if it were not possible to use a probability sampling technique , we could have used a non-probability sampling technique . Since we wanted to compare different strata (i.e., groups of students) and achieve a sample that is as representative as possible of our population , we could have used a quota sample . In the event, we used quota sampling to select the sample of students that would be invited to take part in our dissertation research. We were unable to use a stratified random sampling , our preferred choice, because we could not obtain permission from Student Records to access a complete list of all students at the university. Without any other way of attaining a list of all students, we had to use quota sampling . However, Student Records did provide us with the appropriate quotas for male and female students, which showed a 53:47 male-female ration [note: this is a fictitious figure]. We selected a sample size of 200 students, which was based on subjective judgement and practicalities of cost and time. Therefore, we sampled 106 male students (i.e., 53% of our sample size of 200 students) and 94 female students (i.e., 47% of our sample size of 200 students). For convenience, we stood outside the main library where we felt the thoroughfare (i.e., number of students passing by) would be highest.

From this justification , the reader learns the following:

Main reason for rejecting the ideal sampling strategy:

Access to a list of all students (i.e., the sampling frame needed for probability sampling ) was not granted by Student Records.

No other way of attaining a list of all students was available.

When you think about justifying your choice of sampling technique when writing up the Sampling Strategy section of your Research Strategy chapter, you should consider both practical reasons (e.g., what time you have available, what access you have, etc.) and theoretical reasons (i.e., those relating to the specific sampling technique , but also your choice of research paradigm , research design and research methods ).

Introduction to Research Methods

7 samples and populations.

So you’ve developed your research question, figured out how you’re going to measure whatever you want to study, and have your survey or interviews ready to go. Now all your need is other people to become your data.

You might say ‘easy!’, there’s people all around you. You have a big family tree and surely them and their friends would have happy to take your survey. And then there’s your friends and people you’re in class with. Finding people is way easier than writing the interview questions or developing the survey. That reaction might be a strawman, maybe you’ve come to the conclusion none of this is easy. For your data to be valuable, you not only have to ask the right questions, you have to ask the right people. The “right people” aren’t the best or the smartest people, the right people are driven by what your study is trying to answer and the method you’re using to answer it.

Remember way back in chapter 2 when we looked at this chart and discussed the differences between qualitative and quantitative data.

One of the biggest differences between quantitative and qualitative data was whether we wanted to be able to explain something for a lot of people (what percentage of residents in Oklahoma support legalizing marijuana?) versus explaining the reasons for those opinions (why do some people support legalizing marijuana and others not?). The underlying differences there is whether our goal is explain something about everyone, or whether we’re content to explain it about just our respondents.

‘Everyone’ is called the population . The population in research is whatever group the research is trying to answer questions about. The population could be everyone on planet Earth, everyone in the United States, everyone in rural counties of Iowa, everyone at your university, and on and on. It is simply everyone within the unit you are intending to study.

In order to study the population, we typically take a sample or a subset. A sample is simply a smaller number of people from the population that are studied, which we can use to then understand the characteristics of the population based on that subset. That’s why a poll of 1300 likely voters can be used to guess at who will win your states Governor race. It isn’t perfect, and we’ll talk about the math behind all of it in a later chapter, but for now we’ll just focus on the different types of samples you might use to study a population with a survey.

If correctly sampled, we can use the sample to generalize information we get to the population. Generalizability , which we defined earlier, means we can assume the responses of people to our study match the responses everyone would have given us. We can only do that if the sample is representative of the population, meaning that they are alike on important characteristics such as race, gender, age, education. If something makes a large difference in people’s views on a topic in your research and your sample is not balanced, you’ll get inaccurate results.

Generalizability is more of a concern with surveys than with interviews. The goal of a survey is to explain something about people beyond the sample you get responses from. You’ll never see a news headline saying that “53% of 1250 Americans that responded to a poll approve of the President”. It’s only worth asking those 1250 people if we can assume the rest of the United States feels the same way overall. With interviews though we’re looking for depth from their responses, and so we are less hopefully that the 15 people we talk to will exactly match the American population. That doesn’t mean the data we collect from interviews doesn’t have value, it just has different uses.

There are two broad types of samples, with several different techniques clustered below those. Probability sampling is associated with surveys, and non-probability sampling is often used when conducting interviews. We’ll first describe probability samples, before discussing the non-probability options.

The type of sampling you’ll use will be based on the type of research you’re intending to do. There’s no sample that’s right or wrong, they can just be more or less appropriate for the question you’re trying to answer. And if you use a less appropriate sampling strategy, the answer you get through your research is less likely to be accurate.

7.1 Types of Probability Samples

So we just hinted at the idea that depending on the sample you use, you can generalize the data you collect from the sample to the population. That will depend though on whether your sample represents the population. To ensure that your sample is representative of the population, you will want to use a probability sample. A representative sample refers to whether the characteristics (race, age, income, education, etc) of the sample are the same as the population. Probability sampling is a sampling technique in which every individual in the population has an equal chance of being selected as a subject for the research.

There are several different types of probability samples you can use, depending on the resources you have available.

Let’s start with a simple random sample . In order to use a simple random sample all you have to do is take everyone in your population, throw them in a hat (not literally, you can just throw their names in a hat), and choose the number of names you want to use for your sample. By drawing blindly, you can eliminate human bias in constructing the sample and your sample should represent the population from which it is being taken.

However, a simple random sample isn’t quite that easy to build. The biggest issue is that you have to know who everyone is in order to randomly select them. What that requires is a sampling frame , a list of all residents in the population. But we don’t always have that. There is no list of residents of New York City (or any other city). Organizations that do have such a list wont just give it away. Try to ask your university for a list and contact information of everyone at your school so you can do a survey? They wont give it to you, for privacy reasons. It’s actually harder to think of popultions you could easily develop a sample frame for than those you can’t. If you can get or build a sampling frame, the work of a simple random sample is fairly simple, but that’s the biggest challenge.

Most of the time a true sampling frame is impossible to acquire, so researcher have to settle for something approximating a complete list. Earlier generations of researchers could use the random dial method to contact a random sample of Americans, because every household had a single phone. To use it you just pick up the phone and dial random numbers. Assuming the numbers are actually random, anyone might be called. That method actually worked somewhat well, until people stopped having home phone numbers and eventually stopped answering the phone. It’s a fun mental exercise to think about how you would go about creating a sampling frame for different groups though; think through where you would look to find a list of everyone in these groups:

Plumbers Recent first-time fathers Members of gyms

The best way to get an actual sampling frame is likely to purchase one from a private company that buys data on people from all the different websites we use.

Let’s say you do have a sampling frame though. For instance, you might be hired to do a survey of members of the Republican Party in the state of Utah to understand their political priorities this year, and the organization could give you a list of their members because they’ve hired you to do the reserach. One method of constructing a simple random sample would be to assign each name on the list a number, and then produce a list of random numbers. Once you’ve matched the random numbers to the list, you’ve got your sample. See the example using the list of 20 names below

and the list of 5 random numbers.

Systematic sampling is similar to simple random sampling in that it begins with a list of the population, but instead of choosing random numbers one would select every kth name on the list. What the heck is a kth? K just refers to how far apart the names are on the list you’re selecting. So if you want to sample one-tenth of the population, you’d select every tenth name. In order to know the k for your study you need to know your sample size (say 1000) and the size of the population (75000). You can divide the size of the population by the sample (75000/1000), which will produce your k (750). As long as the list does not contain any hidden order, this sampling method is as good as the random sampling method, but its only advantage over the random sampling technique is simplicity. If we used the same list as above and wanted to survey 1/5th of the population, we’d include 4 of the names on the list. It’s important with systematic samples to randomize the starting point in the list, otherwise people with A names will be oversampled. If we started with the 3rd name, we’d select Annabelle Frye, Cristobal Padilla, Jennie Vang, and Virginia Guzman, as shown below. So in order to use a systematic sample, we need three things, the population size (denoted as N ), the sample size we want ( n ) and k , which we calculate by dividing the population by the sample).

N= 20 (Population Size) n= 4 (Sample Size) k= 5 {20/4 (kth element) selection interval}

We can also use a stratified sample , but that requires knowing more about the population than just their names. A stratified sample divides the study population into relevant subgroups, and then draws a sample from each subgroup. Stratified sampling can be used if you’re very concerned about ensuring balance in the sample or there may be a problem of underrepresentation among certain groups when responses are received. Not everyone in your sample is equally likely to answer a survey. Say for instance we’re trying to predict who will win an election in a county with three cities. In city A there are 1 million college students, in city B there are 2 million families, and in City C there are 3 million retirees. You know that retirees are more likely than busy college students or parents to respond to a poll. So you break the sample into three parts, ensuring that you get 100 responses from City A, 200 from City B, and 300 from City C, so the three cities would match the population. A stratified sample provides the researcher control over the subgroups that are included in the sample, whereas simple random sampling does not guarantee that any one type of person will be included in the final sample. A disadvantage is that it is more complex to organize and analyze the results compared to simple random sampling.

Cluster sampling is an approach that begins by sampling groups (or clusters) of population elements and then selects elements from within those groups. A researcher would use cluster sampling if getting access to elements in an entrie population is too challenging. For instance, a study on students in schools would probably benefit from randomly selecting from all students at the 36 elementary schools in a fictional city. But getting contact information for all students would be very difficult. So the researcher might work with principals at several schools and survey those students. The researcher would need to ensure that the students surveyed at the schools are similar to students throughout the entire city, and greater access and participation within each cluster may make that possible.

The image below shows how this can work, although the example is oversimplified. Say we have 12 students that are in 6 classrooms. The school is in total 1/4th green (3/12), 1/4th yellow (3/12), and half blue (6/12). By selecting the right clusters from within the school our sample can be representative of the entire school, assuming these colors are the only significant difference between the students. In the real world, you’d want to match the clusters and population based on race, gender, age, income, etc. And I should point out that this is an overly simplified example. What if 5/12s of the school was yellow and 1/12th was green, how would I get the right proportions? I couldn’t, but you’d do the best you could. You still wouldn’t want 4 yellows in the sample, you’d just try to approximiate the population characteristics as best you can.

7.2 Actually Doing a Survey

All of that probably sounds pretty complicated. Identifying your population shouldn’t be too difficult, but how would you ever get a sampling frame? And then actually identifying who to include… It’s probably a bit overwhelming and makes doing a good survey sound impossible.

Researchers using surveys aren’t superhuman though. Often times, they use a little help. Because surveys are really valuable, and because researchers rely on them pretty often, there has been substantial growth in companies that can help to get one’s survey to its intended audience.

One popular resource is Amazon’s Mechanical Turk (more commonly known as MTurk). MTurk is at its most basic a website where workers look for jobs (called hits) to be listed by employers, and choose whether to do the task or not for a set reward. MTurk has grown over the last decade to be a common source of survey participants in the social sciences, in part because hiring workers costs very little (you can get some surveys completed for penny’s). That means you can get your survey completed with a small grant ($1-2k at the low end) and get the data back in a few hours. Really, it’s a quick and easy way to run a survey.

However, the workers aren’t perfectly representative of the average American. For instance, researchers have found that MTurk respondents are younger, better educated, and earn less than the average American.

One way to get around that issue, which can be used with MTurk or any survey, is to weight the responses. Because with MTurk you’ll get fewer responses from older, less educated, and richer Americans, those responses you do give you want to count for more to make your sample more representative of the population. Oversimplified example incoming!

Imagine you’re setting up a pizza party for your class. There are 9 people in your class, 4 men and 5 women. You only got 4 responses from the men, and 3 from the women. All 4 men wanted peperoni pizza, while the 3 women want a combination. Pepperoni wins right, 4 to 3? Not if you assume that the people that didn’t respond are the same as the ones that did. If you weight the responses to match the population (the full class of 9), a combination pizza is the winner.

Because you know the population of women is 5, you can weight the 3 responses from women by 5/3 = 1.6667. If we weight (or multiply) each vote we did receive from a woman by 1.6667, each vote for a combination now equals 1.6667, meaning that the 3 votes for combination total 5. Because we received a vote from every man in the class, we just weight their votes by 1. The big assumption we have to make is that the people we didn’t hear from (the 2 women that didn’t vote) are similar to the ones we did hear from. And if we don’t get any responses from a group we don’t have anything to infer their preferences or views from.

Let’s go through a slightly more complex example, still just considering one quality about people in the class. Let’s say your class actually has 100 students, but you only received votes from 50. And, what type of pizza people voted for is mixed, but men still prefer peperoni overall, and women still prefer combination. The class is 60% female and 40% male.

We received 21 votes from women out of the 60, so we can weight their responses by 60/21 to represent the population. We got 29 votes out of the 40 for men, so their responses can be weighted by 40/29. See the math below.

53.8 votes for combination? That might seem a little odd, but weighting isn’t a perfect science. We can’t identify what a non-respondent would have said exactly, all we can do is use the responses of other similar people to make a good guess. That issue often comes up in polling, where pollsters have to guess who is going to vote in a given election in order to project who will win. And we can weight on any characteristic of a person we think will be important, alone or in combination. Modern polls weight on age, gender, voting habits, education, and more to make the results as generalizable as possible.

There’s an appendix later in this book where I walk through the actual steps of creating weights for a sample in R, if anyone actually does a survey. I intended this section to show that doing a good survey might be simpler than it seemed, but now it might sound even more difficult. A good lesson to take though is that there’s always another door to go through, another hurdle to improve your methods. Being good at research just means being constantly prepared to be given a new challenge, and being able to find another solution.

7.3 Non-Probability Sampling

Qualitative researchers’ main objective is to gain an in-depth understanding on the subject matter they are studying, rather than attempting to generalize results to the population. As such, non-probability sampling is more common because of the researchers desire to gain information not from random elements of the population, but rather from specific individuals.

Random selection is not used in nonprobability sampling. Instead, the personal judgment of the researcher determines who will be included in the sample. Typically, researchers may base their selection on availability, quotas, or other criteria. However, not all members of the population are given an equal chance to be included in the sample. This nonrandom approach results in not knowing whether the sample represents the entire population. Consequently, researchers are not able to make valid generalizations about the population.

As with probability sampling, there are several types of non-probability samples. Convenience sampling , also known as accidental or opportunity sampling, is a process of choosing a sample that is easily accessible and readily available to the researcher. Researchers tend to collect samples from convenient locations such as their place of employment, a location, school, or other close affiliation. Although this technique allows for quick and easy access to available participants, a large part of the population is excluded from the sample.

For example, researchers (particularly in psychology) often rely on research subjects that are at their universities. That is highly convenient, students are cheap to hire and readily available on campuses. However, it means the results of the study may have limited ability to predict motivations or behaviors of people that aren’t included in the sample, i.e., people outside the age of 18-22 that are going to college.

If I ask you to get find out whether people approve of the mayor or not, and tell you I want 500 people’s opinions, should you go stand in front of the local grocery store? That would be convinient, and the people coming will be random, right? Not really. If you stand outside a rural Piggly Wiggly or an urban Whole Foods, do you think you’ll see the same people? Probably not, people’s chracteristics make the more or less likely to be in those locations. This technique runs the high risk of over- or under-representation, biased results, as well as an inability to make generalizations about the larger population. As the name implies though, it is convenient.

Purposive sampling , also known as judgmental or selective sampling, refers to a method in which the researcher decides who will be selected for the sample based on who or what is relevant to the study’s purpose. The researcher must first identify a specific characteristic of the population that can best help answer the research question. Then, they can deliberately select a sample that meets that particular criterion. Typically, the sample is small with very specific experiences and perspectives. For instance, if I wanted to understand the experiences of prominent foreign-born politicians in the United States, I would purposefully build a sample of… prominent foreign-born politicians in the United States. That would exclude anyone that was born in the United States or and that wasn’t a politician, and I’d have to define what I meant by prominent. Purposive sampling is susceptible to errors in judgment by the researcher and selection bias due to a lack of random sampling, but when attempting to research small communities it can be effective.

When dealing with small and difficult to reach communities researchers sometimes use snowball samples , also known as chain referral sampling. Snowball sampling is a process in which the researcher selects an initial participant for the sample, then asks that participant to recruit or refer additional participants who have similar traits as them. The cycle continues until the needed sample size is obtained.

This technique is used when the study calls for participants who are hard to find because of a unique or rare quality or when a participant does not want to be found because they are part of a stigmatized group or behavior. Examples may include people with rare diseases, sex workers, or a child sex offenders. It would be impossible to find an accurate list of sex workers anywhere, and surveying the general population about whether that is their job will produce false responses as people will be unwilling to identify themselves. As such, a common method is to gain the trust of one individual within the community, who can then introduce you to others. It is important that the researcher builds rapport and gains trust so that participants can be comfortable contributing to the study, but that must also be balanced by mainting objectivity in the research.

Snowball sampling is a useful method for locating hard to reach populations but cannot guarantee a representative sample because each contact will be based upon your last. For instance, let’s say you’re studying illegal fight clubs in your state. Some fight clubs allow weapons in the fights, while others completely ban them; those two types of clubs never interreact because of their disagreement about whether weapons should be allowed, and there’s no overlap between them (no members in both type of club). If your initial contact is with a club that uses weapons, all of your subsequent contacts will be within that community and so you’ll never understand the differences. If you didn’t know there were two types of clubs when you started, you’ll never even know you’re only researching half of the community. As such, snowball sampling can be a necessary technique when there are no other options, but it does have limitations.

Quota Sampling is a process in which the researcher must first divide a population into mutually exclusive subgroups, similar to stratified sampling. Depending on what is relevant to the study, subgroups can be based on a known characteristic such as age, race, gender, etc. Secondly, the researcher must select a sample from each subgroup to fit their predefined quotas. Quota sampling is used for the same reason as stratified sampling, to ensure that your sample has representation of certain groups. For instance, let’s say that you’re studying sexual harassment in the workplace, and men are much more willing to discuss their experiences than women. You might choose to decide that half of your final sample will be women, and stop requesting interviews with men once you fill your quota. The core difference is that while stratified sampling chooses randomly from within the different groups, quota sampling does not. A quota sample can either be proportional or non-proportional . Proportional quota sampling refers to ensuring that the quotas in the sample match the population (if 35% of the company is female, 35% of the sample should be female). Non-proportional sampling allows you to select your own quota sizes. If you think the experiences of females with sexual harassment are more important to your research, you can include whatever percentage of females you desire.

7.4 Dangers in sampling

Now that we’ve described all the different ways that one could create a sample, we can talk more about the pitfalls of sampling. Ensuring a quality sample means asking yourself some basic questions:

Who is in the sample?
How were they sampled?
Why were they sampled?

A meal is often only as good as the ingredients you use, and your data will only be as good as the sample. If you collect data from the wrong people, you’ll get the wrong answer. You’ll still get an answer, it’ll just be inaccurate. And I want to reemphasize here wrong people just refers to inappropriate for your study. If I want to study bullying in middle schools, but I only talk to people that live in a retirement home, how accurate or relevant will the information I gather be? Sure, they might have grandchildren in middle school, and they may remember their experiences. But wouldn’t my information be more relevant if I talked to students in middle school, or perhaps a mix of teachers, parents, and students? I’ll get an answer from retirees, but it wont be the one I need. The sample has to be appropriate to the research question.

Is a bigger sample always better? Not necessarily. A larger sample can be useful, but a more representative one of the population is better. That was made painfully clear when the magazine Literary Digest ran a poll to predict who would win the 1936 presidential election between Alf Landon and incumbent Franklin Roosevelt. Literary Digest had run the poll since 1916, and had been correct in predicting the outcome every time. It was the largest poll ever, and they received responses for 2.27 million people. They essentially received responses from 1 percent of the American population, while many modern polls use only 1000 responses for a much more populous country. What did they predict? They showed that Alf Landon would be the overwhelming winner, yet when the election was held Roosevelt won every state except Maine and Vermont. It was one of the most decisive victories in Presidential history.

So what went wrong for the Literary Digest? Their poll was large (gigantic!), but it wasn’t representative of likely voters. They polled their own readership, which tended to be more educated and wealthy on average, along with people on a list of those with registered automobiles and telephone users (both of which tended to be owned by the wealthy at that time). Thus, the poll largely ignored the majority of Americans, who ended up voting for Roosevelt. The Literary Digest poll is famous for being wrong, but led to significant improvements in the science of polling to avoid similar mistakes in the future. Researchers have learned a lot in the century since that mistake, even if polling and surveys still aren’t (and can’t be) perfect.

What kind of sampling strategy did Literary Digest use? Convenience, they relied on lists they had available, rather than try to ensure every American was included on their list. A representative poll of 2 million people will give you more accurate results than a representative poll of 2 thousand, but I’ll take the smaller more representative poll than a larger one that uses convenience sampling any day.

7.5 Summary

Picking the right type of sample is critical to getting an accurate answer to your reserach question. There are a lot of differnet options in how you can select the people to participate in your research, but typically only one that is both correct and possible depending on the research you’re doing. In the next chapter we’ll talk about a few other methods for conducting reseach, some that don’t include any sampling by you.

How it works

How to Write a Research Design – Guide with Examples

Published by Alaxendra Bets at August 14th, 2021 , Revised On October 3, 2023

A research design is a structure that combines different components of research. It involves the use of different data collection and data analysis techniques logically to answer the research questions .

It would be best to make some decisions about addressing the research questions adequately before starting the research process, which is achieved with the help of the research design.

Below are the key aspects of the decision-making process:

Data type required for research
Research resources
Participants required for research
Hypothesis based upon research question(s)
Data analysis methodologies
Variables (Independent, dependent, and confounding)
The location and timescale for conducting the data
The time period required for research

The research design provides the strategy of investigation for your project. Furthermore, it defines the parameters and criteria to compile the data to evaluate results and conclude.

Your project’s validity depends on the data collection and interpretation techniques. A strong research design reflects a strong dissertation , scientific paper, or research proposal .

Step 1: Establish Priorities for Research Design

Before conducting any research study, you must address an important question: “how to create a research design.”

The research design depends on the researcher’s priorities and choices because every research has different priorities. For a complex research study involving multiple methods, you may choose to have more than one research design.

Multimethodology or multimethod research includes using more than one data collection method or research in a research study or set of related studies.

If one research design is weak in one area, then another research design can cover that weakness. For instance, a dissertation analyzing different situations or cases will have more than one research design.

For example:

Experimental research involves experimental investigation and laboratory experience, but it does not accurately investigate the real world.
Quantitative research is good for the statistical part of the project, but it may not provide an in-depth understanding of the topic .
Also, correlational research will not provide experimental results because it is a technique that assesses the statistical relationship between two variables.

While scientific considerations are a fundamental aspect of the research design, It is equally important that the researcher think practically before deciding on its structure. Here are some questions that you should think of;

Do you have enough time to gather data and complete the write-up?
Will you be able to collect the necessary data by interviewing a specific person or visiting a specific location?
Do you have in-depth knowledge about the different statistical analysis and data collection techniques to address the research questions or test the hypothesis ?

If you think that the chosen research design cannot answer the research questions properly, you can refine your research questions to gain better insight.

Step 2: Data Type you Need for Research

Decide on the type of data you need for your research. The type of data you need to collect depends on your research questions or research hypothesis. Two types of research data can be used to answer the research questions:

Primary Data Vs. Secondary Data

Qualitative vs. quantitative data.

Also, see; Research methods, design, and analysis .

Need help with a thesis chapter?

Hire an expert from ResearchProspect today!
Statistical analysis, research methodology, discussion of the results or conclusion – our experts can help you no matter how complex the requirements are.

Step 3: Data Collection Techniques

Once you have selected the type of research to answer your research question, you need to decide where and how to collect the data.

It is time to determine your research method to address the research problem . Research methods involve procedures, techniques, materials, and tools used for the study.

For instance, a dissertation research design includes the different resources and data collection techniques and helps establish your dissertation’s structure .

The following table shows the characteristics of the most popularly employed research methods.

Research Methods

Step 4: Procedure of Data Analysis

Use of the correct data and statistical analysis technique is necessary for the validity of your research. Therefore, you need to be certain about the data type that would best address the research problem. Choosing an appropriate analysis method is the final step for the research design. It can be split into two main categories;

Quantitative Data Analysis

The quantitative data analysis technique involves analyzing the numerical data with the help of different applications such as; SPSS, STATA, Excel, origin lab, etc.

This data analysis strategy tests different variables such as spectrum, frequencies, averages, and more. The research question and the hypothesis must be established to identify the variables for testing.

Qualitative Data Analysis

Qualitative data analysis of figures, themes, and words allows for flexibility and the researcher’s subjective opinions. This means that the researcher’s primary focus will be interpreting patterns, tendencies, and accounts and understanding the implications and social framework.

You should be clear about your research objectives before starting to analyze the data. For example, you should ask yourself whether you need to explain respondents’ experiences and insights or do you also need to evaluate their responses with reference to a certain social framework.

Step 5: Write your Research Proposal

The research design is an important component of a research proposal because it plans the project’s execution. You can share it with the supervisor, who would evaluate the feasibility and capacity of the results and conclusion .

Read our guidelines to write a research proposal if you have already formulated your research design. The research proposal is written in the future tense because you are writing your proposal before conducting research.

The research methodology or research design, on the other hand, is generally written in the past tense.

How to Write a Research Design – Conclusion

A research design is the plan, structure, strategy of investigation conceived to answer the research question and test the hypothesis. The dissertation research design can be classified based on the type of data and the type of analysis.

Above mentioned five steps are the answer to how to write a research design. So, follow these steps to formulate the perfect research design for your dissertation .

ResearchProspect writers have years of experience creating research designs that align with the dissertation’s aim and objectives. If you are struggling with your dissertation methodology chapter, you might want to look at our dissertation part-writing service.

Our dissertation writers can also help you with the full dissertation paper . No matter how urgent or complex your need may be, ResearchProspect can help. We also offer PhD level research paper writing services.

Frequently Asked Questions

What is research design.

Research design is a systematic plan that guides the research process, outlining the methodology and procedures for collecting and analysing data. It determines the structure of the study, ensuring the research question is answered effectively, reliably, and validly. It serves as the blueprint for the entire research project.

How to write a research design?

To write a research design, define your research question, identify the research method (qualitative, quantitative, or mixed), choose data collection techniques (e.g., surveys, interviews), determine the sample size and sampling method, outline data analysis procedures, and highlight potential limitations and ethical considerations for the study.

How to write the design section of a research paper?

In the design section of a research paper, describe the research methodology chosen and justify its selection. Outline the data collection methods, participants or samples, instruments used, and procedures followed. Detail any experimental controls, if applicable. Ensure clarity and precision to enable replication of the study by other researchers.

How to write a research design in methodology?

To write a research design in methodology, clearly outline the research strategy (e.g., experimental, survey, case study). Describe the sampling technique, participants, and data collection methods. Detail the procedures for data collection and analysis. Justify choices by linking them to research objectives, addressing reliability and validity.

Research Methodology Example

Detailed Walkthrough + Free Methodology Chapter Template

If you’re working on a dissertation or thesis and are looking for an example of a research methodology chapter , you’ve come to the right place.

In this video, we walk you through a research methodology from a dissertation that earned full distinction , step by step. We start off by discussing the core components of a research methodology by unpacking our free methodology chapter template . We then progress to the sample research methodology to show how these concepts are applied in an actual dissertation, thesis or research project.

If you’re currently working on your research methodology chapter, you may also find the following resources useful:

Research methodology 101 : an introductory video discussing what a methodology is and the role it plays within a dissertation
Research design 101 : an overview of the most common research designs for both qualitative and quantitative studies
Variables 101 : an introductory video covering the different types of variables that exist within research.
Sampling 101 : an overview of the main sampling methods
Methodology tips : a video discussion covering various tips to help you write a high-quality methodology chapter
Private coaching : Get hands-on help with your research methodology

PS – If you’re working on a dissertation, be sure to also check out our collection of dissertation and thesis examples here .

FAQ: Research Methodology Example

Research methodology example: frequently asked questions, is the sample research methodology real.

Yes. The chapter example is an extract from a Master’s-level dissertation for an MBA program. A few minor edits have been made to protect the privacy of the sponsoring organisation, but these have no material impact on the research methodology.

Can I replicate this methodology for my dissertation?

As we discuss in the video, every research methodology will be different, depending on the research aims, objectives and research questions. Therefore, you’ll need to tailor your literature review to suit your specific context.

You can learn more about the basics of writing a research methodology chapter here .

Where can I find more examples of research methodologies?

The best place to find more examples of methodology chapters would be within dissertation/thesis databases. These databases include dissertations, theses and research projects that have successfully passed the assessment criteria for the respective university, meaning that you have at least some sort of quality assurance.

The Open Access Thesis Database (OATD) is a good starting point.

How do I get the research methodology chapter template?

You can access our free methodology chapter template here .

Is the methodology template really free?

Yes. There is no cost for the template and you are free to use it as you wish.

You Might Also Like:

Example of two research proposals (Masters and PhD-level)

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Knowledge Base
Methodology

Research Design | Step-by-Step Guide with Examples

Published on 5 May 2022 by Shona McCombes . Revised on 20 March 2023.

A research design is a strategy for answering your research question using empirical data. Creating a research design means making decisions about:

Your overall aims and approach
The type of research design you’ll use
Your sampling methods or criteria for selecting subjects
Your data collection methods
The procedures you’ll follow to collect data
Your data analysis methods

A well-planned research design helps ensure that your methods match your research aims and that you use the right kind of analysis for your data.

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, frequently asked questions.

Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities – start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

How much time do you have to collect data and write up the research?
Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

Prevent plagiarism, run a free check.

Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types. Experimental and quasi-experimental designs allow you to test cause-and-effect relationships, while descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends, and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analysing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study – plants, animals, organisations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region, or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalise your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study, your aim is to deeply understand a specific context, not to generalise to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question.

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviours, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews.

Observation methods

Observations allow you to collect data unobtrusively, observing characteristics, behaviours, or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected – for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are reliable and valid.

Operationalisation

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalisation means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in – for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced , while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method, you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

How many participants do you need for an adequate sample size?
What inclusion and exclusion criteria will you use to identify eligible participants?
How will you contact your sample – by mail, online, by phone, or in person?

If you’re using a probability sampling method, it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method, how will you avoid bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organising and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymise and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well organised will save time when it comes to analysing them. It can also help other researchers validate and add to your findings.

On their own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyse the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarise your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarise your sample data in terms of:

The distribution of the data (e.g., the frequency of each score on a test)
The central tendency of the data (e.g., the mean to describe the average score)
The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

Make estimates about the population based on your sample data.
Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analysing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

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

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

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

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

The research methods you use depend on the type of data you need to answer your research question .

If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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Malays J Med Sci
v.28(2); 2021 Apr

A Step-by-Step Process on Sample Size Determination for Medical Research

Determination of a minimum sample size required for a study is a major consideration which all researchers are confronted with at the early stage of developing a research protocol. This is because the researcher will need to have a sound prerequisite knowledge of inferential statistics in order to enable him/her to acquire a thorough understanding of the overall concept of a minimum sample size requirement and its estimation. Besides type I error and power of the study, some estimates for effect sizes will also need to be determined in the process to calculate or estimate the sample size. The appropriateness in calculating or estimating the sample size will enable the researchers to better plan their study especially pertaining to recruitment of subjects. To facilitate a researcher in estimating the appropriate sample size for their study, this article provides some recommendations for researchers on how to determine the appropriate sample size for their studies. In addition, several issues related to sample size determination were also discussed.

Introduction

Sample size calculation or estimation is an important consideration which necessitate all researchers to pay close attention to when planning a study, which has also become a compulsory consideration for all experimental studies ( 1 ). Moreover, nowadays, the selection of an appropriate sample size is also drawing much attention from researchers who are involved in observational studies when they are developing research proposals as this is now one of the factors that provides a valid justification for the application of a research grant ( 2 ). Sample size must be estimated before a study is conducted because the number of subjects to be recruited for a study will definitely have a bearing on the availability of vital resources such as manpower, time and financial allocation for the study. Nevertheless, a thorough understanding of the need to estimate or calculate an appropriate sample size for a study is crucial for a researcher to appreciate the effort expended in it.

Ideally, one can determine the parameter of a variable from a population through a census study. A census study recruits each and every subject in a population and an analysis is conducted to determine the parameter or in other words, the true value of a specific variable will be calculated in a targeted population. This approach of analysis is known as descriptive analysis. On the other hand, the estimate that is derived from a sample study is termed as a ‘statistic’ because it analyses sample data and subsequently makes inferences and conclusions from the results. This approach of analysis is known as inferential analysis, which is also the most preferred approach in research because drawing a conclusion from the sample data is much easier than performing a census study, due to various constraints especially in terms of cost, time and manpower.

In a census study, the accuracy of the parameters cannot be disputed because the parameters are derived from all subjects in the population. However, when statistics are derived from a sample, it is possible for readers to query to what extent these statistics are representative of the true values in the population. Thus, researchers will need to provide an additional piece of evidence besides the statistics, which is the P -value. The statistical significance or usually termed as ‘ P -value less than 0.05’, and it shall stand as an evidence or justification that the statistics derived from the sample can be inferred to the larger population. Some scholars may argue over the utility and versatility of P -value but it is nevertheless still applicable and acceptable until now ( 3 – 5 ).

Why It is Necessary to Perform a Sample Size Calculation or Estimation?

In order for the analysis to be conducted for addressing a specific objective of a study to be able to generate a statistically-significant result, a particular study must be conducted using a sufficiently large sample size that can detect the target effect sizes with an acceptable margin of error. In brief, a sample size is determined by three elements: i) type I error (alpha); ii) power of the study (1-type II error) and iii) effect size. A proper understanding of the concept of type I error and type II error will require a lengthy discussion. The prerequisite knowledge of statistical inference, probability and distribution function is also required to understand the overall concept ( 6 – 7 ). However, in sample size calculation, the values of both type I and type II errors are usually fixed. Type I error is usually fixed at 0.05 and sometimes 0.01 or 0.10, depending on the researcher. Meanwhile, power is usually set at 80% or 90% indicating 20% or 10% type II error, respectively. Hence, the only one factor that remains unspecified in the calculation of a sample size is the effect size of a study.

Effect size measures the ‘magnitude of effect’ of a test and it is independent of influences by the sample size ( 8 ). In other words, effect size measures the real effect of a test irrespective of its sample size. With reference to statistical tests, it is an expected parameter of a particular association (or correlation or relationship) with other tests in a targeted population. In a real setting, the parameter of a variable in a targeted population is usually unknown and therefore a study will be conducted to test and confirm these effect sizes. However, for the purpose of sample size calculation, it is still necessary to estimate the target effect sizes. By the same token, Cohen ( 9 ) presented in his article that a larger sample size is necessary to estimate small effect sizes and vice versa.

The main advantage of estimating the minimum sample size required is for planning purposes. For example, if the minimum sample size required for a particular study is estimated to be 300 subjects and a researcher already knows that he/she can only recruit 15 subjects in a month from a single centre. Thus, the researchers will need at least 20 months for data collection if there is only one study site. If the plan for data collection period is shorter than 20 months, then the researchers may consider to recruit subjects in more than one centre. In case where the researchers will not be able to recruit 300 subjects within the planned data collection period, the researchers may need to revisit the study objective or plan for a totally different study instead. If the researcher still wishes to pursue the study but is unable to meet the minimum required sample size; then it is likely that the study may not be able to reach a valid conclusion at the end, which will result in a waste of resources because it does not add any scientific contributions.

How to Calculate or Estimate Sample Size?

Sample size calculation serves two important functions. First, it aims to estimate a minimum sample size that can be sufficient for achieving a target level of accuracy in an estimate for a specific population parameter. In this instance, the researcher aims to produce an estimate that is expected to be equally accurate as an actual parameter in the target population. Second, it also aims to determine the level of statistical significance (i.e. P -value < 0.05) attained by these desired effect sizes. In other words, a researcher aims to infer the statistics derived from the sample to that of the larger population. In this case, a specific statistical test will be applied and the P -value will be calculated by using the statistical test (which will determine the level of statistical significance).

For univariate statistical test such as independent sample t -test or Pearson’s chi-square test, these sample size calculations can be done manually using a rather simple formula. However, the manual calculation can still be difficult for researchers who are non-statisticians. Various sample size software have now been introduced which make these sample size calculation easier. Nevertheless, a researcher may still experience some difficulty in using the software if he/she is not familiar with the concept of sample size calculation and the statistical tests. Therefore, various scholars have expended some effort to assist the researchers in the determination of sample sizes for various purposes by publishing sample size tables for various statistical tests ( 10 – 12 ). These sample sizes tables can be used to estimate the minimum sample size that is required for a study. Although such tables may have only a limited capacity for the selection of various effect sizes, and their corresponding sample size requirements; it is nonetheless much more practical and easier to use.

For some study objectives, it is often much easier to estimate the sample size based on a rule-of-thumb instead of manual calculation or sample size software. Taking an example of an objective of a study that needs to be answered using multivariate analysis, the estimation of an association between a set of predictors and an outcome can be very complicated if it involves many independent variables. In addition, the actual ‘effect size’ can range from low to high, which renders it even more difficult to be estimated. Therefore, it is recommended to adopt the conventional rule-of-thumb for estimating these sample sizes in these circumstances. Although some scholars have initially thought that the concept of rule-of-thumb may not be as scientifically robust when compared to actual calculations, it is still considered to be an acceptable approach ( 13 – 15 ). Table 1 illustrates some published articles for various sample size determinations for descriptive studies and statistical tests.

Summary of published articles related to sample size determination for various statistical tests

In brief, the present paper will be proposing five main steps for sample size determination as shown in Figure 1 . The following provides an initial description and then a discussion of each of these five steps:

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Recommended steps in sample size determination

Step 1: To Understand the Objective of the Study

The objective of a study has to be measurable or in other words, can be determined by using statistical analysis. Sometimes, a single study may have several objectives. One of the common approaches to achieve this is to estimate the sample size required for every single objective and then the minimum required sample size for the study will be selected to be the highest number of all sample sizes calculated. However, this paper recommends that the minimum sample size be calculated only for the primary objective, which will remain valid as long as the primary objective is more important than all the other objectives. This also means that the calculation of minimum sample size for any other objectives (apart from the primary objective) will only be considered unless they are considered to be equally important as the primary objective. For the development of a research proposal, different institutions may apply different approaches for sample size determinations and hence, it is mandatory to adhere to their specific requirements for sample size determinations.

However, the estimation or calculation of sample size for every study objective can be further complicated by the fact that some of the secondary objectives may require a larger sample size than the primary objective. If the recruitment of a larger number of subjects is not an issue, then it will always be viable to obtain a larger sample size in order to accommodate the sample size requirements for each and every objective of the study. Otherwise, it may be advisable for a researcher to forgo some of the secondary objectives so that they will not be too burdensome for the him/her.

Step 2: To Select the Appropriate Statistical Analysis

Researchers have to decide the appropriate analysis or statistical test to be used to answer the study objective; regardless of whether the aim is to determine a single mean, or a prevalence, or correlation, or association, just to name a few. The formula that will be used to estimate or calculate the sample size will be the same as the formula for performing the statistical test that will be used to answer the objective of study. For example, if an independent sample t -test has to be used for analysis, then its sample size formula should be based on an independent sample t -test. Hence, there is no a single formula for sample size calculation or estimation which can apply universally to all situations and circumstances.

Step 3: To Calculate or Estimate the Sample Size

Estimating or calculating the sample size can be done either by using manual calculation, sample size software, sample size tables from scientific published articles, or by adopting various acceptable rule-of-thumbs. Since both the type I and type II errors are already pre-specified and fixed, hence only the effect size remains to be specified in order for the determination of an appropriate sample size. To illustrate this point, it will be easier to demonstrate by using a case scenario as an example. Say a researcher would like to study an effectiveness of a new diet programme to reduce weight. The researcher believes the new diet programme is better than the conventional diet programme. It was found that the conventional diet programme can reduce on average 1 kg in 1 month. How many subjects are required to prove that the new diet programme is better than the conventional diet programme?

Based on Step 1 and Step 2, a researcher has decided to apply the independent sample t -test to answer the objective of study. Next, the researcher will need to specify the effect size after having both type I error and power set at 0.05% and 80%, respectively (type II error = 20%). What margin of effect size will be appropriate? This shall depend on the condition itself or the underlying research rationale which can then be further classified into two categories. In the first category, the research rationale is to prove that the new diet programme (for reducing weight) is superior to the conventional diet programme. In this case, the researcher should aim for sizeably large effect size. In other words, the difference between means of the weight reduction (which constitutes part of the effect size for independent sample t -test) should be sufficiently large to demonstrate the superiority of the new diet programme over the conventional diet programme.

In the second category, the research rationale is to measure accurately the effectiveness of the new diet programme to reduce weight in comparison with conventional diet programme, irrespective of whether the difference between both programmes is large or small. In this situation, the difference does not matter since the researcher aims to measure an exact difference between them, which means that it can only tolerate a very low margin of difference. In this circumstance, the researcher will therefore only be able to accept the smaller effect sizes. The estimate of effect sizes in this instance can be reviewed either from literatures, pilot study, historical data and rarely by using an educated guess.

The acceptable or desirable effect size that can be found from the literature can vary over a wide range. Thus, one of the better options is to seek for the relevant information from published articles of recent studies (within 5 years) that applied almost similar research design such as used the same treatments and had reported about similar patient characteristics. If none of these published articles can provide a rough estimate of the desired effect size, then the researcher may have to consider conducting a pilot study to obtain a rough estimate of the closest approximation to the actual desired effect size. Besides, historical data or secondary data can also be used to estimate the desired effect size, provided that the researcher has access to the secondary data of the two diet programmes. However, it must be emphasised that deriving the effect size from secondary data may not always be feasible since the performance of the new intervention may still not yet have been assessed.

The last option is to estimate the desired effect size based on a scientifically or a clinically meaningful effect. This means the researcher, through his or her own knowledge and experience, is able to determine an expectation of the difference in effect, and then to set a target difference (namely, effect size) to be achieved. For example, a researcher makes an educated guess about the new diet programme, and requires it to achieve a minimum difference of 3 kg in weight reduction per month in order for it to demonstrate superiority over the conventional diet programme. Although it is always feasible to set a large effect size especially if the new diet programme has proven to be a more rigorous intervention and probably also costlier; however, there is also a risk for the study to might have possibly failed to report a statistically significant result if it has subsequently been found that the actual effect size is much smaller than that adopted by the study, after the analysis has been completed. Therefore, it is usually quite a challenging task to estimate an accurate effect size since the exact value of the effect size is not known until the study is completed. However, the researcher will still have to set the value of effect size for the purpose of sample size calculation or estimation.

Next is to calculate or estimate sample size either based on manual calculation, sample size software, sample size tables or by adopting a conventional rule of thumb. Referring to the example for illustration purposes, the sample size calculation was calculated by using the sample size software as follows; with a study setting of equal sample size for both groups, the mean reduction is set at only 1 kg with within group standard deviation estimated at 0.8 (derived from literature, pilot study or based on a reliable source), type I error at 0.05 and 80% power, a minimum sample size of 11 subjects are required for each group (both for new diet programme and conventional diet programme). The sample size was calculated using Power and Sample Size (PS) software (by William D Dupont and W Dale Plummer, Jr. is licensed under a Creative Commons Attribution-NonCommercial- NoDerivs 3.0 United States License).

Step 4: To Provide an Additional Allowance During Subject Recruitment to Cater for a Certain Proportion of Non-Response

After the minimum required sample size has been identified, it is necessary to provide additional allowances to cater for potential non-response subjects. A minimum required sample size simply means the minimum number of subjects a study must have after recruitment is completed. Thus, researchers must ideally be able to recruit subjects at least beyond the minimum required sample size. To avoid underestimation of sample size, researchers will need to anticipate the problem of non-response and then to make up for it by recruiting more subjects on top of the minimum sample size, usually by 20% to 30%. If, for example, the researcher is expecting a high non-response rate in a self-administered survey, then he/she should provide an allowance for it by adding more than 30% such as 40% to 50%. The occurrence of non-response could also happen in various other scenarios such as dropping out or loss to follow-up in a cohort study and experimental studies. Besides that, missing data or loss of records are also potential problems that can result in attrition in observational studies.

Referring to previous example as an illustration, by adding 20% of non-response rate in each group, 14 subjects are required in each group. The calculation should be done as follow:

Likewise, for a 30% non-response rate, the sample size required in each group will then be increased to 16 subjects (11/0.7 = 15.7 ≈ 16).

Step 5: To Write a Sample Size Statement

The sample size statement is important and it is usually included in the protocol or manuscript. In the existing research literatures, the sample size statement is written in various styles. This paper recommends for the sample size statement to start by reminding the readers or reviewers about the main objective of study. Hence, this paper recommends all the elements from Step 1 until Step 4 (study objective, appropriate statistical analysis, sample size estimation/calculation and non-response rate) should be fully stated in the sample size statement. Therefore, a proposed outline of this sample size statement of the previous example for two weight-losing diet programmes is as follows:

“This study hypothesised that the new diet programme is better than conventional diet programme in terms of weight reduction at a 1-month follow-up. Therefore, the sample size formula is derived from the independent sample t -test. Based on the results of a previous study (cite the appropriate reference), all the response within each subject group are assumed to be normally distributed with a within-group standard deviation (SD) of 0.80 kg. If the true mean difference of the new diet programme versus the conventional diet programme is estimated at 1.0 kg, the study will need to recruit 11 subjects in each group to be able to reject the null hypothesis that the population means of the new diet programme and conventional diet programme are found to be equal with a type I error of 0.05 and with at least 80% power of this study. By providing an additional allowance of 20% in sample recruitment due to possible non-response rate, the required sample size has been increased to 14 subjects in each group. The formula of sample size calculation is based on a study reported by Dupont and Plummer ( 31 ).”

Discussion on Effect Size Planning

Sample size is just an estimate indicating a minimum required number of sample data that is necessary to be collected to derive an accurate estimate for the target population or to obtain statistically significant results based on the desired effect sizes. In order to calculate or estimate sample size, researchers will need to provide some initial estimates for effect sizes. It is usually quite challenging to provide a reasonably accurate value of the effect size because the exact values of these effect sizes are usually not known and can only be derived from the study after the analysis is completed. Hence, the discrepancies of the effect sizes are commonly expected where the researchers will usually either overestimate or underestimate them.

A major problem often arises when the researchers overestimate the effect sizes during sample size estimation, which can lead to a failure of a study to detect a statistically significant result. To avoid such a problem, the researchers are encouraged to recruit more subjects than the minimum required sample size of the study. By referring to the same example previously (new diet programme versus conventional diet programme), if the required sample size is 11 subjects in each group, then researchers may consider recruiting more than 11 subjects such as 18 to 20 subjects in each group. This is possible if the researchers have the capability in terms of manpower and research grant to recruit more subjects and also if there are adequate number of subjects available to be recruited.

After the study is completed, if the difference in mean reduction was found not at least 1 kg after 1 month, then the result might not be statistically significant (depending on the actual value of the within-group SD) for a sample size of 11 subjects in each group. However, if the researchers had recruited 18 subjects in each group, the study will still obtain significant results even though the difference of mean reduction was 0.8 kg (if the within-group SD is estimated to be 0.8, and an equal sample size is planned for both groups, with type I error set at 0.05 and power of at least 80%). In this situation, researcher would still be able to draw a conclusion that the difference in mean reduction after one month was 0.8 kg, and this result was statistically significant. Such a conclusion is perhaps more meaningful than stating a non-significant result ( P > 0.05) for another study with only 11 subjects in each group.

However, it is necessary to always bear in mind that obtaining a larger sample size merely to show that P -value is less than 0.05 is not the right thing to do and it can also result in a waste of resources. Hence, the purpose of increasing the size of the sample from 11 to 18 per group is not merely for obtaining a P -value of less than 0.05; but more importantly, it is now able to draw a valid and clinically-significant conclusion from the smallest acceptable value of the effect size. In other words, the researcher is now able to tolerate a smaller effect size by stating that the difference in mean reduction of 0.8 kg is also considered to be a sizeable effect size because it is clinically significant. However, if the researcher insists that the difference in mean reduction should be at least 1.0 kg, then it will be necessary to maintain a minimum sample of only 11 subjects per group. It is now clear that such a subjective variation in the overall consideration of the magnitude of this effect size sometimes depends on the effectiveness and the cost of the new diet programme and hence, this will always require some degree of clinical judgement.

The concept of setting a desired value of the effect size is almost identical for all types of statistical test. The above example is only describing an analysis using the independent sample t -test. Since each statistical test may require a different effect size in its calculation or estimation of the sample size; thus, it is necessary for the researchers to be familiarised with each of these statistical tests in order to be able to set the desired values of the effect sizes for the study. In addition, further assistance may be sought from statisticians or biostatisticians for the determination of an adequate minimum sample size required for these studies.

Another Example of Sample Size Estimation Using General Rule of Thumb

Say a study aims to determine the association of factors with optimal HbA1c level as determined by its cut-off point of < 6.5% among patients with type 2 diabetes mellitus (T2DM). Previous study had already estimated that several significant factors were identified, and then included as three to four variables in the final model consisting of parameters that were selected from demographic profile of patients and clinical parameters (cite the appropriate reference). Now, the question is: How many T2DM patients should the study recruit in order to answer the study objective?

Step 1: To Understand the Objective of Study

The study aims to determine a set of independent variables that show a significant association with optimal HbA1c level (as determined by its cut-off point of < 6.5%) among T2DM patients.

Step 2: To Decide the Appropriate Statistical Analysis

In this example, the outcome variable is in the categorical and binary form, such as HbA1c level of < 6.5% versus ≥ 6.5%. On the other hand, there are about 3 to 4 independent variables, which can be expressed in both the categorical and numerical form. Therefore, an appropriate statistical analysis shall be logistic regression.

Step 3: To Estimate or Calculate the Sample Size Required

Since this study will require a multivariate regression analysis, thus it is recommended to estimate sample size based on the general rule of thumb. This is because the calculation of sample size for a multivariate regression analysis can be very complicated as the analysis will involve many variables and effect sizes. There are several general rules of thumb available for estimating the sample size for multivariate logistic regression. One of the latest rule of thumb is proposed by Bujang et al. ( 44 ). Two approaches are introduced here, namely: i) sample size estimation based on concept of event per variable (EPV) and ii) sample size estimation based on a simple formula.

i) Sample size estimation based on a concept EPV 50

For EPV 50, the researcher will need to know the prevalence of the ‘good’ outcome category and the number of subjects in the ‘good’ outcome category to fit the rule of EPV 50 ( 14 , 44 ). Say, the prevalence of ‘good’ outcome category is reported at 70% (cite the appropriate reference). Then, with a total of four independent variables, the minimum sample size required in the ‘poor’ outcome category will be at least 200 subjects in order to fulfil the condition for EPV 50 (i.e. 200/4 = 50). On the other hand, by estimating the prevalence of ‘good’ outcome at 70.0%, this study will therefore need to recruit at least 290 subjects in order to ensure that a minimum 200 subjects will be obtained in the ‘poor’ outcome category (70/100 x 290 = 203, and 203 > 200).

ii) Sample size estimation based on a formula of n = 100 + 50i (where i represents number of independent variable in the final model)

When using this formula, the researcher will first need to set the total number of independent variables in the final model ( 44 ). As stated in the example, the total number of independent variables were estimated to be about three to four (cite the appropriate reference). Then, with a total of four independent variables, the minimum required sample size will be 300 patients [(i.e. 100 + 50 ( 4 ) = 300].

Step 4: To Provide Additional Allowance for a Certain Proportion of Non-Response Rate

In order to make up for a rough estimate of 20.0% of non-response rate, the minimum sample size requirement is calculated to be 254 patients (i.e. 203/0.8) by estimating the sample size based on the EPV 50, and is calculated to be 375 patients (i.e. 300/0.8) by estimating the sample size based on the formula n = 100 + 50 i.

There were previously two approaches that were introduced to estimate sample size for logistic regression. Say, if the researcher chooses to apply the formula n = 100 + 50 i. Therefore, the sample size statement will be written as follows:

“The main objective of this study is to determine the association of factors with optimal HbA1c level as determined by its cut-off point of < 6.5% among patients with type 2 diabetes mellitus (T2DM). The sample size estimation is derived from the general rule of thumb for logistic regression proposed by Bujang et al. ( 44 ), which had established a simple guideline of sample size determination for logistic regression. In this study, Bujang et al. ( 44 ) suggested to calculate the sample size by basing on a formula n = 100 + 50 i. The estimated total number of independent variables was about three to four (cite the appropriate reference). Thus, with a total of four independent variables, the minimum required sample size will be 300 patients (i.e. 100 + 50 ( 4 ) = 300). By providing an additional allowance to cater for a possible dropout rate of 20%, this study will therefore need at least a sample size of 300/0.8 = 375 patients.”

Other Issues

Previously, there are four different approaches to estimate an effect size such as: i) by deriving it from the literature; ii) by using historical data or secondary data to estimate it; iii) by determining the clinical meaningful effect and last but not least and iv) by deriving it from the results of a pilot study. It is a controversial practice to estimate the effect size from a pilot study because it may not be accurate since the effect size has been derived from a small sample provided by a pilot study ( 52 – 55 ). In reality, many researchers often encounter great difficulties in the estimation of sample size either i) when the required effect size is not reported by the existing literature; or ii) if some new, innovative research proposals which may pose pioneering research questions that have never been addressed; or iii) if the research is examining a new intervention or exploring a new research area in where no similar studies have previously been conducted. Although there are many concerns about validity of using pilot studies for power calculation, further research is still being conducted in pilot studies in order to apply more scientifically robust approaches for using pilot studies in gathering preliminary support for subsequent research. For example, there are now many published studies regarding guidelines for estimating sample size requirements in pilot studies ( 54 – 61 ).

This article has sought to provide a brief but clear guidance on how to determine the minimum sample size requirements for all researchers. Sample size calculation can be a difficult task, especially for the junior researcher. However, the availability of sample size software, and sample size tables for sample size determination based on various statistical tests, and several recommended rules of thumb which can be helpful for guiding the researchers in the determination of an adequate sample size for their studies. For the sake of brevity and convenience, this paper hereby proposes a useful checklist that is presented in Table 2 , which aims to guide and assist all researchers to determine an adequate sample size for their studies.

A step-by-step guide for sample size determination

Acknowledgements

I would like to thank the Director General of Health, Ministry of Health Malaysia for his permission to publish this article. I would also thank Dr Ang Swee Hung and Mr Hoon Yon Khee for proofreading this article.

Conflict of Interest

Sample Size Considerations: Basics for Preparing Clinical or Basic Research

Affiliations.

1 Harvard Medical School, Boston, USA; Department of Orthopedic Surgery, Boston Children's Hospital, Boston, USA.
2 Institutional Centers for Clinical and Translational Research, Boston Children's Hospital, Boston, USA.
PMID: 37123495
PMCID: PMC10133938
DOI: 10.17996/anc.20-00122

Background : Sample size estimation is an important and integral part of a research protocol. While "how large a sample?" is a simple question, the answer is only meaningful within the context of the research question. Methods : Clear definitions of the variable of interest and target population parameters are key to estimating sample size. In turn the sample must be sized such that it can accurately detect the 'effect' of interest, adequately represent the target population and maintain maximum design efficiency. Four basic pieces of information are utilized in most sample estimation across all clinical research: significance level, power, magnitude of effect and variability of the variable of interest. Results/Discussion : Preliminary determination of these will greatly facilitate work with a biostatistician or a computer application to create a sample size estimation. While applications can support relatively simple sample size calculations consultation with a biostatistician is recommended.

Keywords: Magnitude of effect; Power; Sample size; Sampling.

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How to Write a Research Paper Introduction (with Examples)

The research paper introduction section, along with the Title and Abstract, can be considered the face of any research paper. The following article is intended to guide you in organizing and writing the research paper introduction for a quality academic article or dissertation.

The research paper introduction aims to present the topic to the reader. A study will only be accepted for publishing if you can ascertain that the available literature cannot answer your research question. So it is important to ensure that you have read important studies on that particular topic, especially those within the last five to ten years, and that they are properly referenced in this section. 1 What should be included in the research paper introduction is decided by what you want to tell readers about the reason behind the research and how you plan to fill the knowledge gap. The best research paper introduction provides a systemic review of existing work and demonstrates additional work that needs to be done. It needs to be brief, captivating, and well-referenced; a well-drafted research paper introduction will help the researcher win half the battle.

The introduction for a research paper is where you set up your topic and approach for the reader. It has several key goals:

Present your research topic
Capture reader interest
Summarize existing research
Position your own approach
Define your specific research problem and problem statement
Highlight the novelty and contributions of the study
Give an overview of the paper’s structure

The research paper introduction can vary in size and structure depending on whether your paper presents the results of original empirical research or is a review paper. Some research paper introduction examples are only half a page while others are a few pages long. In many cases, the introduction will be shorter than all of the other sections of your paper; its length depends on the size of your paper as a whole.

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The introduction in a research paper is placed at the beginning to guide the reader from a broad subject area to the specific topic that your research addresses. They present the following information to the reader

Scope: The topic covered in the research paper
Context: Background of your topic
Importance: Why your research matters in that particular area of research and the industry problem that can be targeted

The research paper introduction conveys a lot of information and can be considered an essential roadmap for the rest of your paper. A good introduction for a research paper is important for the following reasons:

It stimulates your reader’s interest: A good introduction section can make your readers want to read your paper by capturing their interest. It informs the reader what they are going to learn and helps determine if the topic is of interest to them.
It helps the reader understand the research background: Without a clear introduction, your readers may feel confused and even struggle when reading your paper. A good research paper introduction will prepare them for the in-depth research to come. It provides you the opportunity to engage with the readers and demonstrate your knowledge and authority on the specific topic.
It explains why your research paper is worth reading: Your introduction can convey a lot of information to your readers. It introduces the topic, why the topic is important, and how you plan to proceed with your research.
It helps guide the reader through the rest of the paper: The research paper introduction gives the reader a sense of the nature of the information that will support your arguments and the general organization of the paragraphs that will follow. It offers an overview of what to expect when reading the main body of your paper.

What are the parts of introduction in the research?

A good research paper introduction section should comprise three main elements: 2

What is known: This sets the stage for your research. It informs the readers of what is known on the subject.
What is lacking: This is aimed at justifying the reason for carrying out your research. This could involve investigating a new concept or method or building upon previous research.
What you aim to do: This part briefly states the objectives of your research and its major contributions. Your detailed hypothesis will also form a part of this section.

How to write a research paper introduction?

The first step in writing the research paper introduction is to inform the reader what your topic is and why it’s interesting or important. This is generally accomplished with a strong opening statement. The second step involves establishing the kinds of research that have been done and ending with limitations or gaps in the research that you intend to address. Finally, the research paper introduction clarifies how your own research fits in and what problem it addresses. If your research involved testing hypotheses, these should be stated along with your research question. The hypothesis should be presented in the past tense since it will have been tested by the time you are writing the research paper introduction.

The following key points, with examples, can guide you when writing the research paper introduction section:

Highlight the importance of the research field or topic
Describe the background of the topic
Present an overview of current research on the topic

Example: The inclusion of experiential and competency-based learning has benefitted electronics engineering education. Industry partnerships provide an excellent alternative for students wanting to engage in solving real-world challenges. Industry-academia participation has grown in recent years due to the need for skilled engineers with practical training and specialized expertise. However, from the educational perspective, many activities are needed to incorporate sustainable development goals into the university curricula and consolidate learning innovation in universities.

Reveal a gap in existing research or oppose an existing assumption
Formulate the research question

Example: There have been plausible efforts to integrate educational activities in higher education electronics engineering programs. However, very few studies have considered using educational research methods for performance evaluation of competency-based higher engineering education, with a focus on technical and or transversal skills. To remedy the current need for evaluating competencies in STEM fields and providing sustainable development goals in engineering education, in this study, a comparison was drawn between study groups without and with industry partners.

State the purpose of your study
Highlight the key characteristics of your study
Describe important results
Highlight the novelty of the study.
Offer a brief overview of the structure of the paper.

Example: The study evaluates the main competency needed in the applied electronics course, which is a fundamental core subject for many electronics engineering undergraduate programs. We compared two groups, without and with an industrial partner, that offered real-world projects to solve during the semester. This comparison can help determine significant differences in both groups in terms of developing subject competency and achieving sustainable development goals.

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The purpose of the research paper introduction is to introduce the reader to the problem definition, justify the need for the study, and describe the main theme of the study. The aim is to gain the reader’s attention by providing them with necessary background information and establishing the main purpose and direction of the research.

The length of the research paper introduction can vary across journals and disciplines. While there are no strict word limits for writing the research paper introduction, an ideal length would be one page, with a maximum of 400 words over 1-4 paragraphs. Generally, it is one of the shorter sections of the paper as the reader is assumed to have at least a reasonable knowledge about the topic. 2 For example, for a study evaluating the role of building design in ensuring fire safety, there is no need to discuss definitions and nature of fire in the introduction; you could start by commenting upon the existing practices for fire safety and how your study will add to the existing knowledge and practice.

When deciding what to include in the research paper introduction, the rest of the paper should also be considered. The aim is to introduce the reader smoothly to the topic and facilitate an easy read without much dependency on external sources. 3 Below is a list of elements you can include to prepare a research paper introduction outline and follow it when you are writing the research paper introduction. Topic introduction: This can include key definitions and a brief history of the topic. Research context and background: Offer the readers some general information and then narrow it down to specific aspects. Details of the research you conducted: A brief literature review can be included to support your arguments or line of thought. Rationale for the study: This establishes the relevance of your study and establishes its importance. Importance of your research: The main contributions are highlighted to help establish the novelty of your study Research hypothesis: Introduce your research question and propose an expected outcome. Organization of the paper: Include a short paragraph of 3-4 sentences that highlights your plan for the entire paper

Cite only works that are most relevant to your topic; as a general rule, you can include one to three. Note that readers want to see evidence of original thinking. So it is better to avoid using too many references as it does not leave much room for your personal standpoint to shine through. Citations in your research paper introduction support the key points, and the number of citations depend on the subject matter and the point discussed. If the research paper introduction is too long or overflowing with citations, it is better to cite a few review articles rather than the individual articles summarized in the review. A good point to remember when citing research papers in the introduction section is to include at least one-third of the references in the introduction.

The literature review plays a significant role in the research paper introduction section. A good literature review accomplishes the following: Introduces the topic – Establishes the study’s significance – Provides an overview of the relevant literature – Provides context for the study using literature – Identifies knowledge gaps However, remember to avoid making the following mistakes when writing a research paper introduction: Do not use studies from the literature review to aggressively support your research Avoid direct quoting Do not allow literature review to be the focus of this section. Instead, the literature review should only aid in setting a foundation for the manuscript.

Remember the following key points for writing a good research paper introduction: 4

Avoid stuffing too much general information: Avoid including what an average reader would know and include only that information related to the problem being addressed in the research paper introduction. For example, when describing a comparative study of non-traditional methods for mechanical design optimization, information related to the traditional methods and differences between traditional and non-traditional methods would not be relevant. In this case, the introduction for the research paper should begin with the state-of-the-art non-traditional methods and methods to evaluate the efficiency of newly developed algorithms.
Avoid packing too many references: Cite only the required works in your research paper introduction. The other works can be included in the discussion section to strengthen your findings.
Avoid extensive criticism of previous studies: Avoid being overly critical of earlier studies while setting the rationale for your study. A better place for this would be the Discussion section, where you can highlight the advantages of your method.
Avoid describing conclusions of the study: When writing a research paper introduction remember not to include the findings of your study. The aim is to let the readers know what question is being answered. The actual answer should only be given in the Results and Discussion section.

To summarize, the research paper introduction section should be brief yet informative. It should convince the reader the need to conduct the study and motivate him to read further. If you’re feeling stuck or unsure, choose trusted AI academic writing assistants like Paperpal to effortlessly craft your research paper introduction and other sections of your research article.

1. Jawaid, S. A., & Jawaid, M. (2019). How to write introduction and discussion. Saudi Journal of Anaesthesia, 13(Suppl 1), S18.

2. Dewan, P., & Gupta, P. (2016). Writing the title, abstract and introduction: Looks matter!. Indian pediatrics, 53, 235-241.

3. Cetin, S., & Hackam, D. J. (2005). An approach to the writing of a scientific Manuscript1. Journal of Surgical Research, 128(2), 165-167.

4. Bavdekar, S. B. (2015). Writing introduction: Laying the foundations of a research paper. Journal of the Association of Physicians of India, 63(7), 44-6.

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Writing Center: Research

Brainstorming and Planning
Thesis Statements
Rough Drafting
Editing and Revising
Citations and Style
Additional Resources
ENGL 110 Resources
Writing in the Major

Step 0.5: Pre-Search

Before researching, it is a good idea to engage in "Pre-Search." Click here for resources on pre-search.

Step 1: Formulate a Research Question

Make sure your question is ....

Worth exploring: Use who, what, how, and why to formulate interesting questions.
Focused: Make sure your question is specific enough that you can answer it given the length of your paper.
Debatable: Choose questions that reasonable people could answer in different ways. (Why and how questions are especially good for this).
Grounded in Evidence: Choose a question that will lead to concrete evidence, not simply your personal opinions of beliefs.

Good Sample Research Questions:

Who is responsible for the contaminated drinking water in Flint, Michigan?
What happens to the arts without public funding?
How will higher tariffs on imported cars create new auto industry jobs and help US carmakers become more profitable?
Why are boys diagnosed with attention deficit disorder more often than girls are?

Partially adapted from A Writer's Reference (pg. 109).

Step 2: Find Sources

Library research resources:.

Primo Search Engine

Research Subject Guides and Databases

Research Help Guide: How to find specific kinds of sources

Other Helpful Research Resources:

Google Scholar : Use this to find reliable articles and webpages on Google.

Encyclopedia Britannica: This online fact-checked encyclopedia has a wealth of information that can help students writing research papers. We recommend consulting this instead of Wikipedia!

Step 3: Evaluate Sources

Determining reliability and usefulness:.

Relevance: Is the source clearly related to your research topic/question and your argument? Ask, "will my readers understand why I included this source?" And "does this source add to my understanding of the research conversation ?"

Currency: When was this source written, and when was it published? Ask, "does my research topic require current information?" And "does my research benefit from information from older soources, such as primary texts?"

Credibility: Where does this source come from? Who is the author? The publisher? Ask, "how accurate and trustworthy is this information?" And "does this source cite other writers' research and ideas?"

Bias: Is the author engaging in a scholarly debate or providing their personal viewpoint? Ask, "does this author display biases that affect their ability to be objective on this topic?" And "is there evidence and counter evidence presented in a fair way in this source?"

Determining if a source is scholarly:

A scholarly source is written by "experts for a knowledgable audience" ( A Writer's Reference 126). Scholarly sources are also called refereed or peer-reviewed.
Determine whether a source is scholarly by looking for:
Formal language and presentation
Authors who are academics or scientists
Footnotes or a bibliography documenting the works cited in the source
Original research and interpretation (versus a summary of other people's work).

Detecting False and Misleading Sources:

Consider the author: Is this author anonymous or named? Can you find their credentials anywhere?
Look at the source's language: Is the language informal? Does the author use second person ("you") frequently?
Fact-check the information you find: Can the facts be objectively verifed? Look into the research and sources that your source cites to check for accuracy.

Adapted from A Writer's Reference (pg. 124, 126, 125)

Step 4: Taking Notes Responsibly

What to look for in the source:.

The Purpose of the Source: What is the argument? Look for the source's "thesis statement" or main claim and then put it into your own words.
Smaller Claims: In addition to the main claim of the source, look for smaller claims (often in the form of topic sentences) that the author uses to support his or her main claim or purpose.
Evidence: Determine and record the evidence the source uses to support its main claim (purpose) and smaller claims.

How to take notes:

Use an Organized System: Choose a way to record both factual notes on the source and your thoughts about the source, and make sure you keep these two kinds of notes distinct. Take time to think about how the information you write down from the source applies to your research question.
Strive for Accuracy: Keep the text close by to ensure that your notes are accurate, but avoid copying directly from the source unless you are quoting.

When to quote, summarize, and paraphrase:

Quote when ....

It is important to capture the exact words the author uses to accurately reflect their argument or a nuanced concept.
The author says something better than you could ever say it.

Summarize when ...

You are trying to record the author's main ideas or claims. This is often most helpful when you are trying to record large portions of the author's argument concisely.

Paraphrase when ...

You can put the athor's words into your own words. But make sure it is truly your own words; if you cannot, quote it instead.

** When in doubt, quote it! You can always change a quote into a summary or paraphrase later, but you cannot change a summary or a paraphrase into a quote!

Avoiding Plagarism:

Labelling: Make clear distinctions in your notes between quoting, summarizing, and paraphrasing.
Bibliography: Record complete bibliographic information for each source so that you can find it again easily and cite it accurately.

Partially adapted from A Writer's Reference pg. (123).

Step 5: Maintain a Working Bibliography

What is a working bibliography and why keep one:.

A working bibliography is a record of all the sources you have read or viewed. It is "working" because it is always changing as you add more sources. Eventually, you will narrow down sources to what you will actually keep in your bibliography. A working bibliography helps you keep track of the sources you have viewed for easy re-access. Also, it helps you get a head start on the final bibliography you will need at the end of your writing process.

How to Keep a Working Bibliography:

Create citations for all your sources as you read, listen to, or view them. This information includes, but is not limited to, the source title, the author, the publisher, and the publication date. Different types of sources (article, web source, book, interview, etc.) will require slightly different types of information to be recorded. and c itation formatting will be dependant on the documentation style you are using. Look here for more information on citations and styles.

Partially adapted from A Writer's Reference (pg. 118)

Research Assignment Sample Timeline

Research Essay

The Essay Wheel for Research

Research Strategies Essay Wheel Video!

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Annotated Bibliography

Background of The Study – Examples and Writing Guide

Table of Contents

Background of The Study

Definition:

Background of the study refers to the context, circumstances, and history that led to the research problem or topic being studied. It provides the reader with a comprehensive understanding of the subject matter and the significance of the study.

The background of the study usually includes a discussion of the relevant literature, the gap in knowledge or understanding, and the research questions or hypotheses to be addressed. It also highlights the importance of the research topic and its potential contributions to the field. A well-written background of the study sets the stage for the research and helps the reader to appreciate the need for the study and its potential significance.

How to Write Background of The Study

Here are some steps to help you write the background of the study:

Identify the Research Problem

Start by identifying the research problem you are trying to address. This problem should be significant and relevant to your field of study.

Provide Context

Once you have identified the research problem, provide some context. This could include the historical, social, or political context of the problem.

Review Literature

Conduct a thorough review of the existing literature on the topic. This will help you understand what has been studied and what gaps exist in the current research.

Identify Research Gap

Based on your literature review, identify the gap in knowledge or understanding that your research aims to address. This gap will be the focus of your research question or hypothesis.

State Objectives

Clearly state the objectives of your research . These should be specific, measurable, achievable, relevant, and time-bound (SMART).

Discuss Significance

Explain the significance of your research. This could include its potential impact on theory , practice, policy, or society.

Finally, summarize the key points of the background of the study. This will help the reader understand the research problem, its context, and its significance.

How to Write Background of The Study in Proposal

The background of the study is an essential part of any proposal as it sets the stage for the research project and provides the context and justification for why the research is needed. Here are the steps to write a compelling background of the study in your proposal:

Identify the problem: Clearly state the research problem or gap in the current knowledge that you intend to address through your research.
Provide context: Provide a brief overview of the research area and highlight its significance in the field.
Review literature: Summarize the relevant literature related to the research problem and provide a critical evaluation of the current state of knowledge.
Identify gaps : Identify the gaps or limitations in the existing literature and explain how your research will contribute to filling these gaps.
Justify the study : Explain why your research is important and what practical or theoretical contributions it can make to the field.
Highlight objectives: Clearly state the objectives of the study and how they relate to the research problem.
Discuss methodology: Provide an overview of the methodology you will use to collect and analyze data, and explain why it is appropriate for the research problem.
Conclude : Summarize the key points of the background of the study and explain how they support your research proposal.

How to Write Background of The Study In Thesis

The background of the study is a critical component of a thesis as it provides context for the research problem, rationale for conducting the study, and the significance of the research. Here are some steps to help you write a strong background of the study:

Identify the research problem : Start by identifying the research problem that your thesis is addressing. What is the issue that you are trying to solve or explore? Be specific and concise in your problem statement.
Review the literature: Conduct a thorough review of the relevant literature on the topic. This should include scholarly articles, books, and other sources that are directly related to your research question.
I dentify gaps in the literature: After reviewing the literature, identify any gaps in the existing research. What questions remain unanswered? What areas have not been explored? This will help you to establish the need for your research.
Establish the significance of the research: Clearly state the significance of your research. Why is it important to address this research problem? What are the potential implications of your research? How will it contribute to the field?
Provide an overview of the research design: Provide an overview of the research design and methodology that you will be using in your study. This should include a brief explanation of the research approach, data collection methods, and data analysis techniques.
State the research objectives and research questions: Clearly state the research objectives and research questions that your study aims to answer. These should be specific, measurable, achievable, relevant, and time-bound.
Summarize the chapter: Summarize the chapter by highlighting the key points and linking them back to the research problem, significance of the study, and research questions.

How to Write Background of The Study in Research Paper

Here are the steps to write the background of the study in a research paper:

Identify the research problem: Start by identifying the research problem that your study aims to address. This can be a particular issue, a gap in the literature, or a need for further investigation.
Conduct a literature review: Conduct a thorough literature review to gather information on the topic, identify existing studies, and understand the current state of research. This will help you identify the gap in the literature that your study aims to fill.
Explain the significance of the study: Explain why your study is important and why it is necessary. This can include the potential impact on the field, the importance to society, or the need to address a particular issue.
Provide context: Provide context for the research problem by discussing the broader social, economic, or political context that the study is situated in. This can help the reader understand the relevance of the study and its potential implications.
State the research questions and objectives: State the research questions and objectives that your study aims to address. This will help the reader understand the scope of the study and its purpose.
Summarize the methodology : Briefly summarize the methodology you used to conduct the study, including the data collection and analysis methods. This can help the reader understand how the study was conducted and its reliability.

Examples of Background of The Study

Here are some examples of the background of the study:

Problem : The prevalence of obesity among children in the United States has reached alarming levels, with nearly one in five children classified as obese.

Significance : Obesity in childhood is associated with numerous negative health outcomes, including increased risk of type 2 diabetes, cardiovascular disease, and certain cancers.

Gap in knowledge : Despite efforts to address the obesity epidemic, rates continue to rise. There is a need for effective interventions that target the unique needs of children and their families.

Problem : The use of antibiotics in agriculture has contributed to the development of antibiotic-resistant bacteria, which poses a significant threat to human health.

Significance : Antibiotic-resistant infections are responsible for thousands of deaths each year and are a major public health concern.

Gap in knowledge: While there is a growing body of research on the use of antibiotics in agriculture, there is still much to be learned about the mechanisms of resistance and the most effective strategies for reducing antibiotic use.

Edxample 3:

Problem : Many low-income communities lack access to healthy food options, leading to high rates of food insecurity and diet-related diseases.

Significance : Poor nutrition is a major contributor to chronic diseases such as obesity, type 2 diabetes, and cardiovascular disease.

Gap in knowledge : While there have been efforts to address food insecurity, there is a need for more research on the barriers to accessing healthy food in low-income communities and effective strategies for increasing access.

Examples of Background of The Study In Research

Here are some real-life examples of how the background of the study can be written in different fields of study:

Example 1 : “There has been a significant increase in the incidence of diabetes in recent years. This has led to an increased demand for effective diabetes management strategies. The purpose of this study is to evaluate the effectiveness of a new diabetes management program in improving patient outcomes.”

Example 2 : “The use of social media has become increasingly prevalent in modern society. Despite its popularity, little is known about the effects of social media use on mental health. This study aims to investigate the relationship between social media use and mental health in young adults.”

Example 3: “Despite significant advancements in cancer treatment, the survival rate for patients with pancreatic cancer remains low. The purpose of this study is to identify potential biomarkers that can be used to improve early detection and treatment of pancreatic cancer.”

Examples of Background of The Study in Proposal

Here are some real-time examples of the background of the study in a proposal:

Example 1 : The prevalence of mental health issues among university students has been increasing over the past decade. This study aims to investigate the causes and impacts of mental health issues on academic performance and wellbeing.

Example 2 : Climate change is a global issue that has significant implications for agriculture in developing countries. This study aims to examine the adaptive capacity of smallholder farmers to climate change and identify effective strategies to enhance their resilience.

Example 3 : The use of social media in political campaigns has become increasingly common in recent years. This study aims to analyze the effectiveness of social media campaigns in mobilizing young voters and influencing their voting behavior.

Example 4 : Employee turnover is a major challenge for organizations, especially in the service sector. This study aims to identify the key factors that influence employee turnover in the hospitality industry and explore effective strategies for reducing turnover rates.

Examples of Background of The Study in Thesis

Here are some real-time examples of the background of the study in the thesis:

Example 1 : “Women’s participation in the workforce has increased significantly over the past few decades. However, women continue to be underrepresented in leadership positions, particularly in male-dominated industries such as technology. This study aims to examine the factors that contribute to the underrepresentation of women in leadership roles in the technology industry, with a focus on organizational culture and gender bias.”

Example 2 : “Mental health is a critical component of overall health and well-being. Despite increased awareness of the importance of mental health, there are still significant gaps in access to mental health services, particularly in low-income and rural communities. This study aims to evaluate the effectiveness of a community-based mental health intervention in improving mental health outcomes in underserved populations.”

Example 3: “The use of technology in education has become increasingly widespread, with many schools adopting online learning platforms and digital resources. However, there is limited research on the impact of technology on student learning outcomes and engagement. This study aims to explore the relationship between technology use and academic achievement among middle school students, as well as the factors that mediate this relationship.”

Examples of Background of The Study in Research Paper

Here are some examples of how the background of the study can be written in various fields:

Example 1: The prevalence of obesity has been on the rise globally, with the World Health Organization reporting that approximately 650 million adults were obese in 2016. Obesity is a major risk factor for several chronic diseases such as diabetes, cardiovascular diseases, and cancer. In recent years, several interventions have been proposed to address this issue, including lifestyle changes, pharmacotherapy, and bariatric surgery. However, there is a lack of consensus on the most effective intervention for obesity management. This study aims to investigate the efficacy of different interventions for obesity management and identify the most effective one.

Example 2: Antibiotic resistance has become a major public health threat worldwide. Infections caused by antibiotic-resistant bacteria are associated with longer hospital stays, higher healthcare costs, and increased mortality. The inappropriate use of antibiotics is one of the main factors contributing to the development of antibiotic resistance. Despite numerous efforts to promote the rational use of antibiotics, studies have shown that many healthcare providers continue to prescribe antibiotics inappropriately. This study aims to explore the factors influencing healthcare providers’ prescribing behavior and identify strategies to improve antibiotic prescribing practices.

Example 3: Social media has become an integral part of modern communication, with millions of people worldwide using platforms such as Facebook, Twitter, and Instagram. Social media has several advantages, including facilitating communication, connecting people, and disseminating information. However, social media use has also been associated with several negative outcomes, including cyberbullying, addiction, and mental health problems. This study aims to investigate the impact of social media use on mental health and identify the factors that mediate this relationship.

Purpose of Background of The Study

The primary purpose of the background of the study is to help the reader understand the rationale for the research by presenting the historical, theoretical, and empirical background of the problem.

More specifically, the background of the study aims to:

Provide a clear understanding of the research problem and its context.
Identify the gap in knowledge that the study intends to fill.
Establish the significance of the research problem and its potential contribution to the field.
Highlight the key concepts, theories, and research findings related to the problem.
Provide a rationale for the research questions or hypotheses and the research design.
Identify the limitations and scope of the study.

When to Write Background of The Study

The background of the study should be written early on in the research process, ideally before the research design is finalized and data collection begins. This allows the researcher to clearly articulate the rationale for the study and establish a strong foundation for the research.

The background of the study typically comes after the introduction but before the literature review section. It should provide an overview of the research problem and its context, and also introduce the key concepts, theories, and research findings related to the problem.

Writing the background of the study early on in the research process also helps to identify potential gaps in knowledge and areas for further investigation, which can guide the development of the research questions or hypotheses and the research design. By establishing the significance of the research problem and its potential contribution to the field, the background of the study can also help to justify the research and secure funding or support from stakeholders.

Advantage of Background of The Study

The background of the study has several advantages, including:

Provides context: The background of the study provides context for the research problem by highlighting the historical, theoretical, and empirical background of the problem. This allows the reader to understand the research problem in its broader context and appreciate its significance.
Identifies gaps in knowledge: By reviewing the existing literature related to the research problem, the background of the study can identify gaps in knowledge that the study intends to fill. This helps to establish the novelty and originality of the research and its potential contribution to the field.
Justifies the research : The background of the study helps to justify the research by demonstrating its significance and potential impact. This can be useful in securing funding or support for the research.
Guides the research design: The background of the study can guide the development of the research questions or hypotheses and the research design by identifying key concepts, theories, and research findings related to the problem. This ensures that the research is grounded in existing knowledge and is designed to address the research problem effectively.
Establishes credibility: By demonstrating the researcher’s knowledge of the field and the research problem, the background of the study can establish the researcher’s credibility and expertise, which can enhance the trustworthiness and validity of the research.

Disadvantages of Background of The Study

Some Disadvantages of Background of The Study are as follows:

Time-consuming : Writing a comprehensive background of the study can be time-consuming, especially if the research problem is complex and multifaceted. This can delay the research process and impact the timeline for completing the study.
Repetitive: The background of the study can sometimes be repetitive, as it often involves summarizing existing research and theories related to the research problem. This can be tedious for the reader and may make the section less engaging.
Limitations of existing research: The background of the study can reveal the limitations of existing research related to the problem. This can create challenges for the researcher in developing research questions or hypotheses that address the gaps in knowledge identified in the background of the study.
Bias : The researcher’s biases and perspectives can influence the content and tone of the background of the study. This can impact the reader’s perception of the research problem and may influence the validity of the research.
Accessibility: Accessing and reviewing the literature related to the research problem can be challenging, especially if the researcher does not have access to a comprehensive database or if the literature is not available in the researcher’s language. This can limit the depth and scope of the background of the study.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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IMAGES

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COMMENTS

Sampling Methods
The sample is the group of individuals who will actually participate in the research. To draw valid conclusions from your results, you have to carefully decide how you will select a sample that is representative of the group as a whole. This is called a sampling method. There are two primary types of sampling methods that you can use in your ...
How to write a great Sampling Strategy section
Sampling strategy. The sampling strategy that you select in your dissertation should naturally flow from your chosen research design and research methods, as well as taking into account issues of research ethics.To set the sampling strategy that you will use in your dissertation, you need to follow three steps: (a) understand the key terms and basic principles; (b) determine which sampling ...
Research Methodology
How to Write Research Methodology. Writing a research methodology involves explaining the methods and techniques you used to conduct research, collect data, and analyze results. ... Discuss your sample: Describe the participants or subjects you included in your study. Include details such as their demographics, sampling method, sample size, and ...
Sampling Methods
1. Simple random sampling. In a simple random sample, every member of the population has an equal chance of being selected. Your sampling frame should include the whole population. To conduct this type of sampling, you can use tools like random number generators or other techniques that are based entirely on chance.
How to structure the Sampling Strategy section
A good structure involves four steps: describing, explaining, stating and justifying. You need to: (1) describe what you are studying, including the units involved in your sample and the target population; (2) explain the types of sampling technique available to you; (3) state and describe the sampling strategy you used; and (4) justify your ...
Sampling: how to select participants in my research study?
TO SAMPLE OR NOT TO SAMPLE. In a previous paper, we discussed the necessary parameters on which to estimate the sample size. 1 We define sample as a finite part or subset of participants drawn from the target population. In turn, the target population corresponds to the entire set of subjects whose characteristics are of interest to the research team.
7 Samples and Populations
So if you want to sample one-tenth of the population, you'd select every tenth name. In order to know the k for your study you need to know your sample size (say 1000) and the size of the population (75000). You can divide the size of the population by the sample (75000/1000), which will produce your k (750).
How to Write a Research Design
Step 2: Data Type you Need for Research. Decide on the type of data you need for your research. The type of data you need to collect depends on your research questions or research hypothesis. Two types of research data can be used to answer the research questions: Primary Data Vs. Secondary Data.
A Practical Guide to Writing Quantitative and Qualitative Research
INTRODUCTION. Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses.1,2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results.3,4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the ...
PDF Methodology Section for Research Papers
The methodology section of your paper describes how your research was conducted. This information allows readers to check whether your approach is accurate and dependable. A good methodology can help increase the reader's trust in your findings. First, we will define and differentiate quantitative and qualitative research.
Research Methodology Example (PDF + Template)
In this video, we walk you through a research methodology from a dissertation that earned full distinction, step by step. We start off by discussing the core components of a research methodology by unpacking our free methodology chapter template. We then progress to the sample research methodology to show how these concepts are applied in an ...
Research Design
Table of contents. Step 1: Consider your aims and approach. Step 2: Choose a type of research design. Step 3: Identify your population and sampling method. Step 4: Choose your data collection methods. Step 5: Plan your data collection procedures. Step 6: Decide on your data analysis strategies.
A Step-by-Step Process on Sample Size Determination for Medical Research
Step 5: To Write a Sample Size Statement . ... In the existing research literatures, the sample size statement is written in various styles. This paper recommends for the sample size statement to start by reminding the readers or reviewers about the main objective of study. Hence, this paper recommends all the elements from Step 1 until Step 4 ...
What Is a Research Design
Step 1: Consider your aims and approach. Step 2: Choose a type of research design. Step 3: Identify your population and sampling method. Step 4: Choose your data collection methods. Step 5: Plan your data collection procedures. Step 6: Decide on your data analysis strategies. Other interesting articles.
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Research proposal examples. Writing a research proposal can be quite challenging, but a good starting point could be to look at some examples. We've included a few for you below. Example research proposal #1: "A Conceptual Framework for Scheduling Constraint Management".
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Methods section is a crucial part of a manuscript and emphasizes the reliability and validity of a research study. And knowing how to write the methods section of a research paper is the first step in mastering scientific writing. Read this article to understand the importance, purpose, and the best way to write the methods section of a research paper.
Research Paper
Definition: Research Paper is a written document that presents the author's original research, analysis, and interpretation of a specific topic or issue. It is typically based on Empirical Evidence, and may involve qualitative or quantitative research methods, or a combination of both. The purpose of a research paper is to contribute new ...
Sample Size Considerations: Basics for Preparing Clinical or Basic Research
Background: Sample size estimation is an important and integral part of a research protocol.While "how large a sample?" is a simple question, the answer is only meaningful within the context of the research question. Methods: Clear definitions of the variable of interest and target population parameters are key to estimating sample size.In turn the sample must be sized such that it can ...
Writing Strong Research Questions
A good research question is essential to guide your research paper, dissertation, or thesis. All research questions should be: Focused on a single problem or issue. Researchable using primary and/or secondary sources. Feasible to answer within the timeframe and practical constraints. Specific enough to answer thoroughly.
Writing a Research Paper Introduction
Table of contents. Step 1: Introduce your topic. Step 2: Describe the background. Step 3: Establish your research problem. Step 4: Specify your objective (s) Step 5: Map out your paper. Research paper introduction examples. Frequently asked questions about the research paper introduction.
How to Write a Conclusion for Research Papers (with Examples)
Generate the conclusion outline: After entering all necessary details, click on 'generate'. Paperpal will then create a structured outline for your conclusion, to help you start writing and build upon the outline. Write your conclusion: Use the generated outline to build your conclusion.
How to Write a Research Paper Introduction (with Examples)
Define your specific research problem and problem statement. Highlight the novelty and contributions of the study. Give an overview of the paper's structure. The research paper introduction can vary in size and structure depending on whether your paper presents the results of original empirical research or is a review paper.
Research
How to take notes: Use an Organized System: Choose a way to record both factual notes on the source and your thoughts about the source, and make sure you keep these two kinds of notes distinct. Take time to think about how the information you write down from the source applies to your research question. Strive for Accuracy: Keep the text close ...
Background of The Study
Here are the steps to write the background of the study in a research paper: Identify the research problem: Start by identifying the research problem that your study aims to address. This can be a particular issue, a gap in the literature, or a need for further investigation. Conduct a literature review: Conduct a thorough literature review to ...
How to Write a Research Paper
Create a research paper outline. Write a first draft of the research paper. Write the introduction. Write a compelling body of text. Write the conclusion. The second draft. The revision process. Research paper checklist. Free lecture slides.
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How to structure the Sampling Strategy section of your dissertation

STEP ONE Describe what you are studying

STEP TWO Explain the types of sampling technique available to you

STEP THREE State and describe the sampling strategy you used

STEP FOUR Justify your choice of sampling strategy

Introduction to Research Methods

7.1 Types of Probability Samples

7.2 Actually Doing a Survey

7.3 Non-Probability Sampling

7.4 Dangers in sampling

7.5 Summary

How to Write a Research Design – Guide with Examples

Step 1: Establish Priorities for Research Design

Step 2: Data Type you Need for Research

Primary Data Vs. Secondary Data

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Step 3: Data Collection Techniques

Step 4: Procedure of Data Analysis

Quantitative Data Analysis

Qualitative Data Analysis

Step 5: Write your Research Proposal

How to Write a Research Design – Conclusion

Frequently Asked Questions

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Research Methodology Example

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Research Design | Step-by-Step Guide with Examples

Table of contents

Practical and ethical considerations when designing research

Prevent plagiarism, run a free check.

Types of quantitative research designs

Types of qualitative research designs

Defining the population

Sampling methods

Case selection in qualitative research

Survey methods

Observation methods

Other methods of data collection

Secondary data

Operationalisation

Reliability and validity

Sampling procedures

Data management

Quantitative data analysis

Qualitative data analysis

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Shona McCombes

A Step-by-Step Process on Sample Size Determination for Medical Research

Introduction

Why It is Necessary to Perform a Sample Size Calculation or Estimation?

How to Calculate or Estimate Sample Size?

Step 1: To Understand the Objective of the Study

Step 2: To Select the Appropriate Statistical Analysis

Step 3: To Calculate or Estimate the Sample Size

Step 4: To Provide an Additional Allowance During Subject Recruitment to Cater for a Certain Proportion of Non-Response

Step 5: To Write a Sample Size Statement

Discussion on Effect Size Planning

Another Example of Sample Size Estimation Using General Rule of Thumb

Step 1: To Understand the Objective of Study

Step 2: To Decide the Appropriate Statistical Analysis

Step 3: To Estimate or Calculate the Sample Size Required

Step 4: To Provide Additional Allowance for a Certain Proportion of Non-Response Rate

Other Issues

Acknowledgements

Sample Size Considerations: Basics for Preparing Clinical or Basic Research

Publication types

How to Write a Research Paper Introduction (with Examples)

Table of Contents

What are the parts of introduction in the research?