NURS 350 DQ  Sampling Strategies for Qualitative research

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NURS 350 DQ  Sampling Strategies for Qualitative research

NURS 350 DQ  Sampling Strategies for Qualitative research

DQ1 Using examples, compare and contrast the sampling strategies of qualitative versus quantitative research designs. Describe some advantages and disadvantages of each.

DQ2 How is data used to evaluate health outcomes within your work environment and at the national level? Provide an example for both.


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?

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.

NURS 350 DQ  Sampling Strategies for Qualitative research

NURS 350 DQ  Sampling Strategies for Qualitative research

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   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 focus Quantitative – it’s all about numbers
Purpose Investigate underlying trends and cause-and-effect relationships Test hypothesis and develop generalizations that can be applied to the statistical population
Focus Wide – since the subject of research is often under-investigated, the researcher can come up with a wide range of conclusions Narrow – the research uses statistical methods to test a specific hypothesis
Nature Subjective – researcher is involved Objective – researcher’s bias doesn’t affect the results of the study
Type of data Non-numerical Numerical
Common research design Exploratory – investigating something that isn’t clear yet based on previous research Descriptive and causal – investigation of something we already know about
Sample studied Typically small and not randomly selected Typically large and randomly selected
Hypothesis Refers to the underlying causes of a particular phenomenon or event Needs a ‘yes’ or ‘no’ answer
Sample Small Large
Research methods Observations, focus groups, interviews – methods that result in non-numerical data collection Survey, questionnaire, experiments, etc – methods that provide numerical data
Data structure Not structured Always structured, e.g. the same questionnaire is filled in by different people
Generalization of conclusions Difficult to generalize the results of the study to the general population Is 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.

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.

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