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Laerd Dissertation Purposive Sampling Pdf

Sampling strategies and research ethics

Dissertations involve performing research on samples. The way that we choose a sample to investigate can raise a number of ethical issues that must be understood and overcome. When thinking about the impact of sampling strategies on research ethics, you need to take into account: (a) the sampling techniques that you use; (b) the sample size you select; and (c) the role of gatekeepers that influence access to your sample. Each of these aspects of sampling strategies and research ethics are discussed in turn:

Sampling techniques

When sampling, you need to decide what units (e.g., people, organisations, data) to include in your sample and which ones to exclude. Sampling techniques act as a guide to help you select these units. However, how units are selected varies considerably between probability sampling techniques and non-probability sampling techniques [see the articles, Probability sampling and Non-probability sampling to learn more about these types of sampling technique]. Moreover, there is also a lot of variation amongst non-probability sampling techniques, in particular.

Probability sampling techniques require a list of the population from which you select units for your sample. This raises potential data protection and confidentiality issues because units in the list (i.e., when people are your units) will not necessarily have given you permission to access the list with their details. Therefore, you need to check that you have the right to access the list in the first place.

When using non-probability sampling, you need to ask yourself whether you are including or excluding units for theoretical or practical reasons. In the case of purposive sampling, the choice of which units to include and exclude is theoretically-driven. In such cases, there are few ethical concerns. However, where units are included or excluded for practical reasons, such as ease of access or personal preferences (e.g., convenience sampling), there is a danger that units will be excluded unnecessarily. For example, it is not uncommon when select units using convenience sampling that researchers? natural preferences (and even prejudices) will influence the selection process. For example, maybe the researcher would avoid approaching certain groups (e.g., socially marginalised individuals, people who speak little English, disabled people). Where this happens, it raises ethical issues because the picture being built through the research can be excessively narrow, and arguably, unethically narrow. This highlights the importance of using theory to determine the creation of samples when using non-probability sampling techniques rather than practical reasons, whenever possible.

Sample size

Whether you are using a probability sampling or non-probability sampling technique to help you create your sample, you will need to decide how large your sample should be (i.e., your sample size). Your sample size becomes an ethical issue for two reasons: over-sized samples and under-sized samples.

Over-sized samples

A sample is over-sized when there are more units (e.g., people, organisations) in the sample than are needed to achieve you goals (i.e., to answer your research questions robustly). An over-sized sample is considered to be an ethical issue because it potentially exposes an excessive number of people (or other units) to your research. Let's look at where this may or may not be a problem:

  • Not an ethical issue

    Imagine that you were interested in the career choices of students at your university, and you were only asking students to complete a questionnaire taking no more than 10 minutes, all an over-sized sample would have done was waste a little of the students' time. Whilst you don't want to be wasting peoples' time, and should try and avoid doing so, this is not a major ethical issue.

  • A potential ethical issue

    Imagine that you were interested in the effect of a carbohydrate free diet on the concentration levels of female university students in the classroom. You know that carbohydrate free diets (i.e., no breads, pasta, rice, etc.) are a new fad amongst female university students because some female students feel that it helps them loose weight (or not put weight on). However, you have read some research showing that such diets can make people feel lethargic (i.e., low on energy). Therefore, you want to know whether this is affecting students? performance; or more specifically, the concentration levels of female students in the classroom. You decide to conduct an experiment where you measure concentration levels amongst 40 female students that are not on any specific diet. First, you measure their concentration levels. Then, you ask 20 of the students to go on a carbohydrate free diet and whilst the remaining 20 continue with the normal food consumption. After a period of time (e.g., 14 days), you measure the concentration levels of all 40 students to compare any differences between the two groups (i.e., the normal group and the group on the carbohydrate free diet). You find that the carbohydrate free diet did significantly impact on the concentration levels of the 20 students. So here comes the ethical issue: What if you could have come to the same conclusion with fewer students? What if you only needed to ask 10 students to go on the carbohydrate free diet rather than 20? Would this have meant that the performance of 10 students would not have been negatively for a 14 day period as a result? The important point is that you do not want to expose individuals to distress or harm unnecessarily.

Under-sized samples

A sample is under-sized when you are unable to achieve your goals (i.e., to answer your research questions robustly) because you insufficient units in your sample. These units could be people, organisation, data, and so forth. The important point is that you fail to answer your research questions not because a potential answer did not exist, but because your sample size was too small for such an answer to be discovered (or interpreted). Let's look where this may or may not be a problem:

  • Not an ethical issue

    Let's take the example of the career choices of students at your university. If you did not collect sufficient data; that is, you did not ask enough students to complete your questionnaire, the answers you get back from your sample may not be representative of the population of all students at your university. This is bad from two perspectives, but only one is arguably a potential ethical issue: First, it is bad because your dissertation findings will be of a lower quality; they will not reflect the population of all students at the university that you are interested in, which will most likely lead to a lower mark. This is bad for you, but not necessarily unethical. However, if the findings from your research are incorrectly taken to reflect the views of all students at your university, and somehow wrongly influence policy within the university (e.g., amongst the Career Advisory Service), your dissertation research could have negatively impacted other students. This is a potential ethical issue. Despite this, we would expect that the likelihood of this happening is fairly low.

  • A potential ethical issue

    Going back to the example of the effect of a carbohydrate free diet on the concentration levels of female university students in the classroom, an under-sized sample does pose potential ethical issues. After all, with the exception of students that just want to help you out, it is likely that most students are taking part voluntarily because they want to the effect of such a diet on their potential classroom performance. Perhaps they have used the diet before or are thinking about using the diet. Alternately, perhaps they are worried about the effects of such diets, and what to further research in this area. In either case, if no conclusions can be made or the findings are not statistically significant because the sample size was too small, the effort, and potential distress and harm that these volunteers put themselves through was all in vein (i.e., completely wasted). This is where an under-sized sample can become an ethical issue.

As a researcher, even when you're an undergraduate or master's level student, you have a duty not to expose an excessive number of people to unnecessary distress or harm. This is one of the basic principles of research ethics. At the same time, you have a duty not to achieve what you set out to achieve. This is not just a duty to yourself or the sponsors of your dissertation (if you have any), but more importantly, to the people that take part in your research (i.e., your sample). To try and minimise the potential ethical issues that come with over-sized and under-sized samples, there are instances where you can make sample size calculations to estimate the required sample size to achieve your goals.


Gatekeepers can often control access to the participants we are interested in (e.g., a manager's control over access to employees within an organisation). This has ethical implications because of the power that such gatekeepers can exercise over those individuals. For example, they may control what access is (and is not) granted to which individuals, coerce individuals into taking part in your research, and influence the nature of responses. This may affect the level of consent that a participant gives (or is believed to have given) you. Ask yourself: Do I think that participants are taking part voluntarily? How did the route I take to access participants affect not only the voluntary nature of individuals' participation, but how will it affect the data?

Problems with gatekeepers can also affect the representativeness of the sample. Whilst qualitative research designs are more likely to use non-probability sampling techniques such as purposive sampling, even quantitative research designs that use probability sampling can suffer from issues of reliability (dependability) associated with gatekeepers. In the case of quantitative research designs using probability sampling, are gatekeepers providing an accurate list of the population without missing out potential participants (e.g., employees that may give a negative view of an organisation)? In the case of qualitative research designs using non-probability sampling, are gatekeepers coercing participants to take part and/or influencing their responses?

Data analysis techniques and research ethics

It is often during the data analysis and reporting phases of dissertation research that issues of participant confidentiality and data privacy come to the fore. Since the use of quantitative data analysis techniques and qualitative data analysis techniques each present their own ethical challenges, these are addressed separately. These two types of data analysis technique are discussed in turn:

Quantitative data analysis techniques

For the most part, the aggregation of data (i.e., the summarising of data) when using quantitative data analysis techniques helps to protect the anonymity of respondents. However, there are occasions where quantitative data analysis techniques do not protect such anonymity.

For example, imagine that your dissertation used a quantitative research design and a survey as your main research method. In the process of analysing your data, it is possible that when examining relationships between variables (i.e., questions in your survey), a person's identity and responses could be inferred. For instance, imagine that you were comparing responses amongst employees within an organisation based on specific age groups. There may only be a small group (or just one employee) within a particular age group (e.g., over 70 years old), which could enable others to identify the responses of this individual (or small group of employees) when looking at a table summarising participants to the survey questions according to their age group.

Therefore, you need to consider ways of overcoming such problems, such as (a) further aggregating data in tables and (b) setting rules that ensure a minimum number of units are present before data/information can be presented. Indeed, whilst there is a danger that a lack of data aggregation can lead to the identification of research participants, this is (a) not that likely (unless you are looking at a small organisation where everyone generally knows each other) and (b) relatively easily rectified by aggregating the data at a higher level (e.g., resorting age categories).

Qualitative data analysis techniques

The greater richness of qualitative data and the way that qualitative data is often presented creates potential ethical challenges. On the one hand, there is the desire, especially amongst researchers following a qualitative research design to present qualitative data in all its richness. Failure to do so can not only limit the descriptive and explanatory power that is one of the advantages of using qualitative research designs, but also leads to criticisms of poor research quality because other researchers cannot easily validate the claims that are being made. On the other, there is the danger that such richness exposes research participants to greater risks since it is more likely that they can be identified through such qualitative data analysis techniques.

To avoid breaching your duty of protecting participants' confidentiality, it is important to: (a) get permission to provide personally identifiable information and facts, especially quotations, before publishing the data (i.e., having your dissertation marked); (b) show participants what you are going to display and secure their permission to do so; (c) ask them to validate the conclusions you have made from their data and/or clearly distinguish your views from theirs when writing up; and (d) use different names for those individuals and/or organisations that took part in your research so that they cannot be identified.

Final thoughts

Research ethics should not be an afterthought. Instead, ethics should be built into the dissertation process. Therefore, when planning how you will tackle ethical issues and challenges in your dissertation, consider the research strategy that you have adopted and the impact this will have on these ethical issues and challenges.

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Sampling: The Basics

Sampling is an important component of any piece of research because of the significant impact that it can have on the quality of your results/findings. If you are new to sampling, there are a number of key terms and basic principles that act as a foundation to the subject. This article explains these key terms and basic principles. Rather than a comprehensive look at sampling, the article presents the sampling basics that you would need to know if you were an undergraduate or master's level student about to perform a dissertation (or similar piece of research). It also provides links to other articles within the Sampling Strategy section of this website that you may find useful. Some of the key sampling terms you will come across include population, units, sample, sample size, sampling frame, sampling techniques and sampling bias. Each is discussed in turn:


The word population is different when used in research compared with the way we think about a population under normal circumstances. Typically, we refer to the population of a country (or region), such as the United States or Great Britain. However, in research (and the theory of sampling), the word population has a different meaning. In sampling, a population signifies the units that we are interested in studying. These units could be people, cases and pieces of data. Some examples of each of these types of population are present below:

  • People

    Students enrolled at a university (e.g., Harvard University) or studying a particular course (e.g., Statistics 101)
    United States Senators or Congressman who are Democrats
    Users of Facebook or Twitter
    Presidents and CEOs of Fortune 500 or FTSE 100 companies
    Nurses working at hospitals in the State of Texas

  • Cases (i.e., organisations, institutions, countries, etc.)

    Recruitment agencies in Greater London, England
    Law firms in Manhattan, New York, United States
    The World Trade Organisation (WTO)
    The European Parliament
    Countries that are members of NATO
    Signatories of the Helsinki Accord

  • Pieces of data

    Customer transactions at Wal-Mart or Tesco between two time points (e.g., 1st April 2009 and 31st March 2010)
    The breaking distances (in kpm/m) of a particular model of car
    University applications in the United States in 2011
    Households with broadband subscriptions in the town of Carmarthen, Wales

When thinking about the population you are interested in studying, it is important to be precise. For example, if we say that our population is users of Facebook, this would imply that we were interested in all 500 million (or more) Facebook users, irrespective of what country they were in, whether they were male or female, what age they were, how often they used Facebook, and so forth. However, if the population you were interested in was more specific, you should make this clear. Perhaps our population is not Facebook users, but frequent, male Facebook users in the United States. When we come to describe our population further, we would also need to define what we meant by frequent users (e.g., people that log in to Facebook at least once a day).


As discussed above, the population that you are interested consists of units, which can be people, cases or pieces of data. These terms can sometimes be used interchangeably. In this website, we use the word units whenever we are referring to those things that make up a population. However, since you may find other textbooks referring to these units as people, cases, or pieces of data, we have provided some further clarification below:

  • The population you are interested in consists of one or more units. For example, if the population we were interested in was all 500 million (or more) Facebook users, each of these Facebook users would be a unit. So we would have 500 million (or more) units in our population. If we were interested in CEOs (or Presidents) of Fortune 500 companies, the CEOs (or Presidents) would be our units.

  • Sometimes the word units is replaced with the word cases. As highlighted in the population examples above, sometimes the populations we are interested in are organisations, institutions and countries. In such cases, it is often more appropriate to refer to each of these (e.g., recruitment agencies, law firms) as cases. You may be interested in a population that consists of only one case (e.g., the World Trade Organisation or European Parliament) or maybe you are interested in a population that has many cases (e.g., recruitment agencies in London, of which there must be hundreds).

  • Finally, researchers sometimes refer to populations consisting of data (or pieces of data) instead of units or cases. For example, researchers may be interested in customer transactions at a particular supermarket (e.g., Wal-Mart or Tesco) between two time points (e.g., 1st April 2009 and 31st March 2010); perhaps because they want to examine the effect of certain promotions on sales figures.


When we are interested in a population, it is often impractical and sometimes undesirable to try and study the entire population. For example, if the population we were interested in was frequent, male Facebook users in the United States, this could be millions of users (i.e., millions of units). If we chose to study these Facebook users using structured interviews (i.e., our chosen research method), it could take a lifetime. Therefore, we choose to study just a sample of these Facebook users.

Whilst we discuss more about sampling and why we sample later in this article, the important point to remember here is that a sample consists of only those units (in this case, Facebook users) from our population of interest (i.e., X million frequent, male, Facebook users in the United States) that we actually study (e.g., 500 or 1000 of these Facebook users).

Sample Size

The sample size is simply the number of units in your sample. In the example above, the sample size selected may be just 500 or 1000 of the Facebook users that are part of our population of frequent, male, Facebook users in the United States.

In practice, the sample size that is selected for a study can have a significant impact on the quality of your results/findings, with sample sizes that are either too small or excessively large both potentially leading to incorrect findings. As a result, sample size calculations are sometimes performed to determine how large your sample size needs to be to avoid such problems. However, these calculations can be complex, and are typically not performed at the undergraduate and master?s level when completing a dissertation.

Sampling frame

The sampling frame is very similar to the population you are studying, and may be exactly the same. When selecting units from the population to be included in your sample, it is sometimes desirable to get hold of a list of the population from which you select units. This is the case when using certain types of sampling technique (i.e., probability sampling techniques), which we discuss later in the article. This list can be referred to as the sampling frame. We explain more about sampling frames in the article: Probability sampling.

Sampling bias

Sampling bias occurs when the units that are selected from the population for inclusion in your sample are not characteristic of (i.e., do not reflect) the population. This can lead to your sample being unrepresentative of the population you are interested in.

For example, you want to measure how often residents in New York go to a Broadway show in a given year. Clearly, standing along Broadway and asking people as they pass by how often they went to Broadway shows in a given year would not make sense because a higher proportion of those passing by are likely to have just come out of a show. The sample would therefore be biased.

For this reason, we have to think carefully about the types of sampling techniques we use when selecting units to be included in our sample. Some sampling techniques, such as convenience sampling, a type of non-probability sampling (which reflected the Broadway example above), are prone to greater bias than probability sampling techniques. We discuss sampling techniques further next.

Sampling techniques

As we have mentioned above, when we are interested in a population, we typically study a sample of that population rather than attempt to study the whole population (e.g., just 500 of the X million frequent, male Facebook users in the United States). If we imagine that our desired sample size was just 500 of these Facebook users, the question arises: How do we know what Facebook users to invite to take part in our sample? In other words, what Facebook users will becomes part of our sample?

The purpose of sampling techniques is to help you select units (e.g., Facebook users) to be included in your sample (e.g., of 500 Facebook users). Broadly speaking, there are two groups of sampling technique: probability sampling techniques and non-probability sampling techniques.

  • Probability sampling techniques

    Probability sampling techniques use random selection (i.e., probabilistic methods) to help you select units from your sampling frame (i.e., similar or exactly that same as your population) to be included in your sample. These procedures (i.e., probabilistic methods) are very clearly defined, making it easy to follow them. Since the characteristics of the sample researchers are interested in vary, different types of probability sampling technique exist to help you select the appropriate units to be included in your sample. These types of probability sampling technique include simple random sampling, systematic random sampling, stratified random sampling and cluster sampling.

    We discuss probability sampling in more detail the article, Probability sampling. We also discuss each of these different types of probability sampling technique, how to carry them out, and their advantages and disadvantages [see the articles: Simple random sampling, Systematic random sampling and Stratified random sampling].

  • Non-probability sampling techniques

    Non-probability sampling techniques refer on the subjective judgement of the researcher when selecting units from the population to be included in the sample. For some of the different types of non-probability sampling technique, the procedures for selecting units to be included in the sample are very clearly defined, just like probability sampling techniques. However, in others (e.g., purposive sampling), the subjective judgement required to select units from the population, which involves a combination of theory, experience and insight from the research process, makes selecting units more complicated. Overall, the types of non-probability sampling technique you are likely to come across include quota sampling, purposive sampling, convenience sampling, snowball sampling and self-section sampling.

    We discuss non-probability sampling in more detail in the article, Non-probability sampling. We also discuss each of these different types of non-probability sampling technique, how to carry them out, and their advantages and disadvantages [see the articles: Quota sampling, Purposive sampling, Convenience sampling, Snowball sampling and Self-selection sampling].

If you want to know more about the sampling techniques you may use in your dissertation, read up on probability sampling and non-probability sampling.

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