Qualitative research is a type of research approach that involves a more interpretive and natural approach to eliciting data. Instead of distributing a tool that can be quickly quantified (e.g., a survey), qualitative research relies on capturing data and gleaning insights from participants or artifacts in a more natural setting.
Qualitative research is an excellent tool to use when you need to delve deeper to understand customer motivations, insights, and preferences. As such, methods and settings for collecting data will vary depending on the research question, as will analysis techniques.
This post will offer an overview of what you need to know about qualitative research projects so you can conduct tighter studies.
Where does qualitative research originate?
Psychologists and behaviorists (eg., Carl Rogers, and Skinner) were interested in a more comprehensive way to study human behavior, according to Simple Psychology.
According to these scientists, the traditional quantitative approach of capturing data wasn’t the best as it didn’t look at the human experience as a whole and didn’t allow researchers enough insight into the experiences of participants.
As a result, behavioral scientists took a different approach to data collection and started studying human behavior by entering a natural setting or within a particular context.
The history of qualitative research provides significant insights into when researchers should use qualitative approaches. If you are not able to answer your research questions with a quantitative study, then a qualitative study may be in order.
What are the differences between qualitative and quantitative data?
Qualitative data includes large batches of texts, photos, videos, interviews, observation notes, or any other data that is difficult to count. Since qualitative data is so unstructured, to analyze it effectively, a researcher must first code the data. This means researchers must develop a method for grouping or ordering the data and then analyzing it. Qualitative research often involves fewer participants, but the data is rich.
Quantitative data is numerical and countable. It’s structured, easy to analyze through statistics, and easy to determine statistical significance. Quantitive data can give you insights about particular trends, patterns, preferences, etc. within a group of people that represent your target population.
Both research methods are credible, helpful, and will provide insights to help brands better understand their customers.
What are the scientific standards of qualitative data?
There are specific standards a qualitative study must meet, according to research published in Oxford Academic. Here are a few essential criteria to remember as you judge qualitative research.
Context
To understand qualitative data, you must first understand the context under which the data was captured. For example, the results of a study conducted in the field in natural surroundings will be drastically different than a study conducted in a lab with controlled surroundings.
Taking context into consideration is part of a successful study.
Credibility
Credibility is the metric that helps you measure the internal validity of the research. You can count it credible if its results include a description of the context, a reflection on the influence on the researcher and study, answers the questions via triangulation, a description of the interpretation process, and if it uses direct quotes to support data, and shows congruency.
In short, credibility is anything that supports how well and accurate the researcher carried out the study.
Trustworthy
To ensure a study is trustworthy, it should include a report with a detailed procedural description. This means detailing why you conducted the study, how you performed it, what procedural decisions you made, and how you generated and managed data.
The more transparency involved in this process, the more trustworthy the study is.
Applicability
Applicability is the metric that helps you evaluate external validity, or whether or not the research findings are transferable.
A study will meet this criterion if it fits into real-world contexts and if researchers can apply the insights into real-world situations.
Part of ensuring that the research meets this criterion is to make sure the researchers represent the target demographic well.
Consistency
Consistency is the way researchers determine reliability. Since the study is qualitative and involves different participants, results will vary. But, researchers should be able to find similar patterns if they were given the same data.
What are the methods for qualitative analysis?
Since qualitative research aims to get to the deeper root of a research question, the methods of analysis will vary.
Top research methods include:
- Participant observation in natural, controlled, or social settings
- Documents
- Open-ended questionnaires
- Analysis of artifacts
- Interviews
- Unstructured interviews
- Cultural records
- Diary accounts
- Ethnography
- Personal experience
- Visuals
- Photographs
- Videos
- Sound recordings
- Etc.
Anything that provides insight into why people behave the way they do could be used as a method for qualitative research.
Are there limitations to qualitative studies?
As with any research study, there are limitations. Here are a few of the main constraints that traditional qualitative researchers face:
- Small data sets. Qualitative studies are expensive and take time. This means it’s challenging to capture large-scale data sets, which can make it more difficult to meet the “applicability” criterion.
- Validity and reliability. It’s challenging to apply the traditional reliability and validity standards to these types of studies since research typically originates in single contexts, and because the researcher plays such a significant role. This means replication can be hard.
- Time constraints. Qualitative data collection is much more time consuming than quantitative studies.
- The analysis is more complicated. The analysis of qualitative data is more involved and more complicated than running a quick statistical test.
Digital qualitative solutions on the other hand, like ones offered by Fuel Cycle and its partners, exist to solve these pain points. To understand how modern technology is breaking down the barriers to running qualitative research, check out this infographic.
How does data analysis work in qualitative research?
Since qualitative research methods are so diverse, the analysis is not limited to a cut and dry approach like it can be with a quantitative study.
Qualitative data analysis may include a combination of creative approaches that involve coding data and following established techniques (e.g., content analysis, grounded theory, thematic analysis, discourse analysis, etc.) for making sense of data.
It’s also critical to remember that analyzing qualitative data can be as easy or as difficult as you make it. To make it easy, it’s crucial to invest in a tool that will help you code data, interpret data, and present data in a way that is clear and organized. For more information about qualitative data and for help with analysis by top software, schedule a demo of Fuel Cycle today.
