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:

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:

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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