Sampling Strategies Guide to Aid Your Research

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If you ask me, collecting data for the research paper and selecting a suitable method among dozens of sampling strategies are probably some of the most tedious tasks. It’s often compelling to even find relevant data, let alone collect it in a specific way!

If you’re currently assigned with a paper that requires using sampling strategy or technique, you need to know that it’s much easier than it seems! I know precisely how such tasks can make you feel totally lost, so in this article, I did my best to provide you a comprehensive guideline to sampling strategies, including:

   Sampling strategy 101: What Is It and How to Choose

Let’s get to know to sample a bit closer. Or, even better, let’s become friends with it and start by communicating in simple terms. Who really needs bulky definitions, right?

        ❓ What exactly is a sample?

A sample is a piece of something more substantial, which you use to figure that ‘something’ out. That way, a sample represents the population, which includes the people, animals, or objects that are researched. In a population, all subjects have at least one common characteristic.

If a sample is formed correctly, it will accurately reflect the larger entity (population) and be referred to as a representative sample. Making a sample representative is the main point of the research if you don’t have access to information about every subject in a population. This is why the selection of the sampling strategy is a big deal.

Example: If you get a piece of apple that is green, you’d assume that the whole apple is green.

If you ask ten people in a class of 25 people whether they like math and they say ‘yes,’ then probably you’ll assume that the whole class is likely to like math (which probably is impossible, but why not ¯\_(ツ)_/¯).

By all means, in a perfect world, we wouldn’t need to have samples and have access to the data about each item in the population, but the reality is that we need sampling.

        ❓ What is a sampling strategy? 

Sampling strategy is your method of choosing subjects from a population that will make a representative sample.

The stressed word here is ‘your’ because you need to choose the sampling strategy according to the design of your research, including:

  • Qualitative or quantitative research (see the following chapter to figure these out 👇)
  • Research design (exploratory, descriptive, or causal)
  • Research methods (experiment, survey, interview, etc.)

Let’s see how qualitative and quantitative research differ and how it will affect your choice of the sampling strategy.

   Qualitative vs. Quantitative Research

Checking features of qualitative and quantitative research for sampling strategy

Deciding whether your research is or will be qualitative or quantitative is as a piece of cake – it’s as simple as doing a checklist!

To choose a suitable sampling strategy, first, you need to figure out whether you’re working with qualitative or quantitative data. Take a look at the comparison table of these two types of research:

 Qualitative - details are in focusQuantitative - it’s all about numbers
PurposeInvestigate underlying trends and cause-and-effect relationshipsTest hypothesis and develop generalizations that can be applied to the statistical population
FocusWide - since the subject of research is often under-investigated, the researcher can come up with a wide range of conclusionsNarrow - the research uses statistical methods to test a specific hypothesis
NatureSubjective - researcher is involvedObjective - researcher’s bias doesn’t affect the results of the study
Type of dataNon-numericalNumerical
Common research designExploratory - investigating something that isn’t clear yet based on previous researchDescriptive and causal - investigation of something we already know about
Sample studiedTypically small and not randomly selectedTypically large and randomly selected
HypothesisRefers to the underlying causes of a particular phenomenon or eventNeeds a ‘yes’ or ‘no’ answer
Research methods Observations, focus groups, interviews - methods that result in non-numerical data collectionSurvey, questionnaire, experiments, etc - methods that provide numerical data
Data structure Not structuredAlways structured, e.g. the same questionnaire is filled in by different people
Generalization of conclusionsDifficult to generalize the results of the study to the general populationIs possible to generalize the conclusions of the research to the general population

Overall, it’s impossible to say that qualitative research is better than quantitative or vice versa. They both are good, but for different purposes. In fact, qualitative and quantitative research methods are often used together to obtain more in-depth results.

If you’re wondering how to tell if your research is qualitative or quantitative, answer these three simple questions:

1. Do you have numerical data or non-numerical data?

  • 🔤 Non-numerical data typically answers the questions like, “What? Which? How?” and isn’t expressed with numbers – qualitative
  • 📈 Numerical data always answers the question “how much?” or “how many?” – quantitative

2. Do you have a hypothesis that you’ll test or currently not sure about the outcomes of your research?

  • 🔤 In qualitative research, you’ll be exploring something unknown yet. Example of a hypothesis in the qualitative study would be, “women are more likely to take selfies at Starbucks than men because they have an appreciation for their coffee shop experience and try to capture a moment.”
  • 📈 Example of a hypothesis in the quantitative study would be, “women are more likely to take selfies at Starbucks than men.” You’ll be only able to answer if this hypothesis is true as a result of your research, but not answer why women are more likely to take selfies at Starbucks than men or not

3. Will you gather a lot of structured data or a small amount of unstructured data?

  • 🔤 In qualitative research, data is unstructured or semi-structured – the researcher can’t really predict how interviews will go, even though the questions might be ready
  • 📈 In quantitative research, all data is collected according to specific requirements – questions are rather closed than open-ended. For instance, Chi-square test is typically applied to test whether two variables are related or not in qualitative research and you are welcome to check a full, but straightforward chi-square guideline out.

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Now let’s proceed to the dessert – sampling strategies and their advantages and disadvantages.

   Sampling Strategies for Qualitative research: Advantages and disadvantages

This row of dice is a perfect example of a sample for qualitative research. They are selected carefully, intentionally aligned, and there aren’t many of them. When you take a look at them, you know immediately that they weren’t aligned like that by chance. In qualitative research, the task of the scientist is to find a way to create a sample where all participants will be “sixes” — and describe their color, shape, and whatnot.

Dice, all sixes, set like a sample for a qualitative research

Due to the nature of qualitative and quantitative research, certain sampling strategies suit best for each research:

  • Non-probability sampling strategies for qualitative research
  • Probability sampling strategies for quantitative research

For qualitative research, these sampling strategies include:

  • 👍 Convenience
  • ❄ Snowball
  • 👩‍⚖️ Judgemental
  • 🚫 Quota

        👍 Convenience Sampling

This sampling strategy is the simplest one and pretty self-explanatory: you collect a sample that you can. It’s referred to as convenience sampling strategy because the researcher chooses members of the sample based on convenience – their proximity or ability to contact them if we’re talking about conducting interviews. This sampling strategy isn’t random because it is chosen directly by the researcher, so the researcher’s bias influences the research results.

✅ Advantages of convenience sampling:

  • Convenient for the researcher
  • Inexpensive
  • Usually takes little time to collect the data because you won’t need to spend additional time searching suitable members of the sample
  • Ideal if you want to conduct several interviews or observations

❌ Disadvantages of convenience sampling:

  • You can’t be sure if the sample is representative
  • Researcher’s bias can influence the results of the study

        ❄ Snowball Sampling

Snowball sampling strategy for research is one step ahead of convenience sampling. In this sampling strategy, already existing members of the sample provide referrals for new subjects. To put it simply, if you choose this sampling strategy, you’ll need to find several people and then ask them to help you find someone else – that’s how the snowball effects occur. For instance, if you’re researching how members of a local book club compare ebooks and traditional ink-on-paper books, you could contact one of the members and ask them to introduce you to other members.

✅ Advantages of snowball sampling:

  • Quite quick to find members of the sample – people usually agree if they are asked by someone they know
  • Inexpensive compared to other methods
  • Chances are, you’ll be able to find as many members as you need for research

❌ Disadvantages of snowball sampling:

  • Researcher’s bias – the margin of error exists because the choice of members of the sample depends on the researcher
  • Some people are likely to be reluctant about participating in a research even if asked to by someone they know

        👩‍⚖️ Judgemental Sampling

Judgemental sampling, aka authoritative or purposive sampling, means that the researcher selects the members of the sample based on knowledge and experience. At the same time, the researcher strives to reduce the margin on error. This type of sampling strategy can be applied to rather small populations and require sufficient knowledge from the researcher.

✅ Advantages of judgment sampling:

  • Requires little time to execute
  • Allows the researcher to interact with the target audience directly

Disadvantages of judgment sampling:

  • Requires considerable preparation for the researcher
  • Researcher’s bias still persists

        🚫 Quota sampling

This type of sampling is used when you need to understand a specific feature of a specific group. Let’s assume that in the population, there are 45% Republicans and 55% Democrats. In the representative sample, there should also be 45% Republicans and 55% Democrats. This type of sampling strategy can only be applied if a certain characteristic allows categorizing the sample into subgroups.

✅ Advantages of quota sampling:

  • Easy to create subgroups in the sample
  • Cost-effective – inexpensive to conduct
  • Convenient, because enables the researcher to structure and categorize the data

Disadvantages of quota sampling:

  • There’s a potential selection bias
  • The process of sample member selection is not random – researcher’s bias persists

   Sampling Strategies for Quantitative Research: Advantages and Disadvantages

The sample in quantitative research is random and unintentional, like this pile of dice. When you take a look, you know immediately that they were distributed by chance. In quantitative research, the task of the researcher is to get the most representative selection of dices — regardless of their number, color or shape.

Sample in quantitative research - it is random like a pile of dice

Sampling strategies in quantitative research typically involve methods that help in dealing with large amounts of data. Quantitative sampling strategies and techniques are random, which helps minimize the researcher’s bias — the inherent disadvantage of qualitative sampling techniques.

        🎲 Simple Random Sampling

This sampling strategy in quantitative research implies that every member of a population has an equal chance to get picked into a sample. This means that all the members of the population are listed and then marked with a number. Afterward, the researcher uses a lottery method or random numbers method to pick the members into a sample. This sampling strategy is best when you’ve got a population where members are very similar to the critical variable or variables.

✅ Advantages of simple random sampling:

  • Ensures a high degree of representativeness, which is exactly what we want from a statistical sample
  • Researcher’s bias is removed – the sampling process is fair because every member of the population can be picked randomly
  • Simple to conduct even with minimal knowledge
  • The sample size isn’t limited – the researcher can choose the sample size freely from the population

Disadvantages of simple random sampling:

  • Can be expensive because it involves tedious data collection and creating lists of population members
  • Time-consuming and requires attention to detail
  • Might be difficult to collect all the necessary data

        🔢 Systematic Random Sampling

This sampling strategy is similar to the simple random sampling, but there’s some system to it — starting number and interval. In this strategy, each n’th subject is picked into the sample from the population. Interval is defined by dividing the population size on the desired sample size.

For instance, if you need to choose 10 people from a 100 people population, meaning that the interval would equal 100/10=10. Then you randomly start at 6 and pick 16,26,36,46,56,66,76,86, and 96th member of the population into your sample. This strategy is also the most suitable when the members of the population are similar to the important variable or variables.

✅ Advantages of systematic random sampling:

  • Ensures a high degree of representativeness
  • No need to use the table of random numbers, like in simple random sampling
  • Simple even for a beginner
  • Free from researcher’s bias
  • Provides even distribution of population members in a sample
  • Inexpensive

Disadvantages of systematic random sampling:

  • If there’s a hidden pattern in the population, there’s a risk that the systematic random sampling strategy will be affected by it
  • Can be time-consuming if the population is large

        📊 Stratified Sampling

As the name suggests, stratified sampling involves dividing the population into groups (strata) and then choosing the sample from each group. This sampling strategy is suitable when you’re dealing with a heterogeneous population that has several groups in it (they don’t overlap, but together represent the population in its entirety). These groups should be homogenous and can be divided by age, sex, political views, etc., while the groups should be somehow related to the topic of the research.

Advantages of stratified sampling:

  • Can help find a more accurate answer to the research question than simple random sampling, because groups are selected accurately
  • Highly representative of all groups of the population
  • Easy to conduct with little skills

Disadvantages of stratified sampling:

  • Time-consuming
  • Can be expensive, because requires processing a large amount of data
  • Can’t be applied to a population where strata overlap

        🧩 Cluster Sampling

This sampling strategy might confuse you because the name is similar to the previous one. The key difference is that in stratified sampling researcher selects sample members from every stratum, while in cluster sampling some groups are chosen fully, while other groups are not selected at all. That is, only a certain number of clusters (groups) is selected for research, and all the other clusters are unrepresented.

That being said, in cluster sampling, it is important to choose a sample that is convenient for research and representative. In this case, the research topic and the characteristic by which the researcher categorizes the population isn’t critical for the study. Cluster sampling is most often applied when the members of the community are spread out geographically – for instance, if you’re dealing with a whole state or country. Then they are easier to study if you pick several regions rather than process the data about the entire population.

Advantages of stratified sampling:

  • Easy and convenient, because many clusters are left out
  • Inexpensive and takes little time, as a rule
  • Easy to conduct

Disadvantages of stratified sampling:

  • By leaving a considerable part of the population unrepresented, the researcher faces the risk of sampling error, which means that the chosen sample won’t reflect the whole population

   Choosing sampling strategy: Pro tips

  • Remember that sampling involves only basic arithmetics – no calculus skill needed! If you can add up the number and divide them, you’re already prepared to nail it!
  • To make the process of calculating and registering data, create a spreadsheet in MS Excel or Google Docs before you start collecting the data – it’ll be convenient for both qualitative and quantitative research
  • If you’re not sure how to combine qualitative and quantitative research methods, skip it — while it can be great to challenge yourself, often it’s unnecessary, and one research method will be sufficient

This a full guide to choosing sampling strategies in qualitative and quantitative research. In this article, I did my best to put these concepts and terms simple, so that you can use them while working on your paper. I know that choosing a sampling strategy can turn into a pain the neck, so I made sure to create a guide that I’d like to have myself when I was a student.

If you’re struggling with sampling strategy for your term paper or research paper, you can contact our Geeks by sharing your task or leaving a comment below – I’ll do my best to help you figure any sampling strategy out.

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