What (Exactly) Is Research Methodology?
A Plain-Language Explanation & Definition (With Examples)

By: Derek Jansen (MBA) | June 2020

If you’ve started working on your first piece of formal research – be it a dissertation, thesis or research project – you’re probably feeling a little overwhelmed by all the technical lingo that gets thrown around. “Research methodology”, “research methods”, “data collection and analysis”… it seems never-ending.

In this post, we’ll explain in plain, straightforward language:

• What exactly research methodology means
• What qualitative, quantitative and mixed methodologies are
• What sampling design is, and what the main sampling options are
• What the most common data collection methods are
• What the most common data analysis methods are
• How to choose your research methodology

Sounds scary? Don’t worry – we’ll explain it all below…

What is research methodology? 

Research methodology simply refers to the practical “how” of any given piece of research. More specifically, it’s about how a researcher systematically designs a study to ensure valid and reliable results that address the research aims and objectives. 

 For example, how did the researcher go about deciding: 

• What data to collect (and what data to ignore)
• Who to collect it from (in research, this is called “sampling design”)
• How to collect it (this is called “data collection methods”)
• How to analyse it (this is called “data analysis methods”) 

In a dissertation, thesis, academic journal article (or pretty much any formal piece of research), you’ll find a research methodology chapter (or section) which covers the aspects mentioned above. Importantly, a good methodology chapter in a dissertation or thesis explains not just what methodological choices were made, but also explains why they were made.

In other words, the methodology chapter should justify the design choices, by showing that the chosen methods and techniques are the best fit for the research aims and objectives, and will provide valid and reliable results. A good research methodology provides scientifically sound findings, whereas a poor methodology doesn’t. We’ll look at the main design choices below.

What are qualitative, quantitative and
mixed-method methodologies?

Qualitative, quantitative and mixed-methods are different types of methodologies, distinguished by whether they focus on words, numbers or both. This is a bit of an oversimplification, but its a good starting point for understandings. Let’s take a closer look.

Qualitative research refers to research which focuses on collecting and analysing words (written or spoken) and textual data, whereas quantitative research focuses on measurement and testing using numerical data. Qualitative analysis can also focus on other “softer” data points, such as body language or visual elements.

It’s quite common for a qualitative methodology to be used when the research aims and objectives are exploratory in nature. For example, a qualitative methodology might be used to understand peoples’ perceptions about an event that took place, or a candidate running for president. 

Contrasted to this, a quantitative methodology is typically used when the research aims and objectives are confirmatory in nature. For example, a quantitative methodology might be used to measure the relationship between two variables (e.g. personality type and likelihood to commit a crime) or to test a set of hypotheses.

As you’ve probably guessed, the mixed-method methodology attempts to combine the best of both qualitative and quantitative methodologies to integrate perspectives and create a rich picture.

What are the main sampling design approaches?

As we mentioned earlier, sampling design is about deciding who you’re going to collect your data from (i.e. your sample). There are many sample options, but the two main categories of sampling design are probability sampling and non-probability sampling.

Probability sampling means that you use a completely random sample from the group of people you’re interested in (this group is called the “population”). By using a completely random sample, the results of your study will be generalisable to the entire population. In other words, you can expect the same results across the entire group, without having to collect data from the entire group (which is often not possible for large groups).

Non-probability sampling, on the other hand, doesn’t use a random sample. For example, it might involve using a convenience sample, which means you’d interview or survey people that you have access to (perhaps your friends, family or work colleagues), rather than a truly random sample (which might be difficult to achieve due to resource constraints). With non-probability sampling, the results are typically not generalisable.

What are the main data collection methods?

There are many different options in terms of how you go about collecting data for your study. However, these options can be grouped into the following types:

• Interviews (which can be unstructured, semi-structured or structured)
• Focus groups and group interviews
• Surveys (online or physical surveys)
• Observations
• Documents and records
• Case studies

The choice of which data collection method to use depends on your overall research aims and objectives, as well as practicalities and resource constraints. For example, if your research is exploratory in nature, qualitative methods such as interviews and focus groups would likely be a good fit. Conversely, if your research aims to measure specific variables or test hypotheses, large-scale surveys that produce large volumes of numerical data would likely be a better fit.

What are the main data analysis methods?

Data analysis methods can be grouped according to whether the research is qualitative or quantitative.

Popular data analysis methods in qualitative research include:

• Qualitative content analysis
• Discourse analysis
• Narrative analysis
• Grounded theory
• IPA

Popular data analysis methods in quantitative research include:

• Descriptive statistics (e.g. means, medians, modes)
• Inferential statistics (e.g. correlation, regression, structural equation modelling)

Again, the choice of which data collection method to use depends on your overall research aims and objectives, as well as practicalities and resource constraints.

How do I choose a research methodology?

As you’ve probably picked up by now, your research aims and objectives have a major influence on the research methodology. So, the starting point for developing your research methodology is to take a step back and look at the big picture of your research, before you make methodology decisions. The first question you need to ask yourself is whether your research is exploratory or confirmatory in nature.

If your research aims and objectives are primarily exploratory in nature, your research will likely be qualitative and therefore you might consider qualitative data collection methods (e.g. interviews) and analysis methods (e.g. qualitative content analysis). 

Conversely, if your research aims and objective are looking to measure or test something (i.e. they’re confirmatory), then your research will quite likely be quantitative in nature, and you might consider quantitative data collection methods (e.g. surveys) and analyses (e.g. statistical analysis).

Designing your research and working out your methodology is a large topic, which we’ll cover in other posts. For now, however, the key takeaway is that you should always start with your research aims and objectives. Every methodology decision will flow from that.