The benefits and challenges of qualitative research

04 September 2017 - BY Kath McNiff

The benefits and challenges of qualitative research

 

“Not everything that can be counted counts and not everything that counts can be counted”. Albert Einstein.

Counting is important.

  • Political candidates need to predict how many constituents will vote for them.
  • Clinicians need to know what proportion of patients stop taking their medication.
  • Confectioners need to understand the correlation between temperature and ice cream sales.

These kinds of quantitative measures are crucial but they don’t tell the whole story.

Qualitative research can fill in the blanks and provide a more holistic picture. It’s the method of choice for researchers who want to explore the stories that people tell about themselves and the world they live in.

Why do people choose one political party over another? How does medication shape the lives of patients? What is it about ice cream on a sunny day?

Questions like these are not easily answered by numbers.

Its exploratory nature makes qualitative research ideal for studying complex social environments and unpacking sensitive issues. It focuses on behaviours, perceptions and attitudes within a specific setting or cultural context, and can be a powerful tool for social change.

Although it is flexible and exploratory, qualitative research is not a soft option – it is based on established protocols and rigorous methodological approaches.

 “Qualitative analysis is like the data with which one works: intense, engaging, challenging, non-linear, contextualised, and highly variable”.
(Bazeley, 2013, p.3)

Each research method comes with its own set of challenges, and this post explores six challenges that are common to most.

Dealing with time pressures

At its best, qualitative research generates large volumes of richly descriptive data. Analysis of this data takes time and significant intellectual effort.

Researchers (in academia, government, health and business) are being challenged to work quickly without forsaking rigour - and the current climate of accountability demands that they make their processes transparent, systematic and repeatable.

Digital tools like NVivo can relieve the organizational load and speed up thematic analysis by offering automatic coding techniques – but they can’t replace researcher know-how and the benefits of being immersed in the data.

To deal with time constraints, many researchers work in teams and take a strategic approach to coding and analysis. While offering gains in productivity, teamwork introduces other challenges around coding consistency, analytical style, philosophy, experience and skill.

Some methods lend themselves to more rapid analysis – for example, Framework Analysis skips the coding stage and focuses on summarizing and condensing case-based data.

If time is a make-or-break issue, then exploring these kinds of options makes good sense.

Developing a solid research design can also help to alleviate time-based pressures.

“Good design breaks down a complex issue and broad interest to something that can be managed in limited time, with limited resources, and still produce useful results." (Flick, 2007a).

Forming a question

A well-developed research question is at the heart of a successful qualitative study. It guides the choice of methodology and sets feasible boundaries for analysis.

But excellent research questions are hard to come by and rarely emerge fully formed at the start of a project.

First off, you need to:

  • Embark on a thorough literature review to find out what is already known about your topic
  • Identify the gap you want to fill or contribution you want to make
  • Establish your question in a broader theoretical framework
  • Set the context and scope of the study       
  • Refine the question in consultation with supervisors, mentors and peers.

Coming up with a great question takes time and can often be an iterative process.

In this video on Developing a Qualitative Research Question, Dr Leslie Curry from the Yale School of Public Health, uses a great before and after example to demonstrate the making of a good research question.

Choosing a methodology

Once you have a sound research question, you need to choose the best methodology to support it.

It can be tempting to choose a familiar methodology or one that is ‘easy to use’. But remember, you’ll be asked to justify your choice - so make sure it’s a considered one.

This table provides a snapshot of the most commonly used methods:

Methodology

Description

Case Study

Explores a programme, event, activity, a process or one or more individuals in depth. Some experts argue that all qualitative analysis is fundamentally case oriented (Bazeley 2013 p. 4).

Grounded Theory

Builds theory that is ‘grounded’ in the views of the participants. It’s more of an analysis technique than a research method. Especially useful when dealing with sensitive and complex issues.

Ethnography

Studies a cultural group in their natural setting over time.

Phenomenology

Identifies the essence of human experiences.

Narrative approaches

Focuses on the stories that individuals provide about their lives and experiences.

Mixed methods

Combines qualitative and quantitative approaches to provide a holistic picture of a complex problem.

Research methods are constantly evolving and it pays to read widely to find the one most suited to your research question.

Handling new sources of data

Along with the usual treasure trove of interviews, focus groups and observations - social media is fast cementing its place as a force to be reckoned with.

What are people saying? What motivates them? How are they connected to each other? 

Researchers from all disciplines are left wondering how best to harness this rich source of data - in a timely, ethical and nuanced way. Collaborative projects like New Social Media, New Social Science (NSMNSS) are tackling these challenges head-on.

“Should social science researchers embrace social media and, if we do, what are the implications for our methods and practice?”

Social media and the promises made by big data are softening the lines between qualitative and quantitative approaches and researchers are 'skilling-up' in response. They are realizing that the 'how many' can help to inform the 'why' - and they are looking for ways to handle this mixed-methods approach in their own research projects. 

Embracing the mess

Qualitative research involves gathering data and analysing the themes.

Sounds simple enough.

Well, it would be simple if people weren't so complex - if their perceptions, motivations and behaviours could be wrapped up in a single cohesive package.

But people are messy. Their experiences are rich, varied and nuanced.

Honouring this complexity while drawing useful conclusions is a major challenge faced by researchers across the board.

We need to address head-on the inconsistencies, irregularities, and downright messiness of the empirical world – not scrub it clean and dress it up for the special occasion of a presentation or a publication. (Clarke, 2005, p. 15)

To add to this, analysis is rarely linear – researchers move back and forth between gathering the data, coding it, writing memos, gathering more data, adjusting direction and so on.

There are no easy answers - stay organized but flexible, reflect in a journal, keep a comprehensive audit trail and enjoy the ride.

Dealing with researcher bias

In quantitative circles, surveys and questionnaires are known as ‘research instruments’ - they are carefully constructed ‘objective’ tools for capturing data and measuring the variables being studied.

Qualitative researchers don’t tend to rely on these types of structured instruments (except when taking a mixed methods approach).

Qualitative research is more subjective and relies on your skills as a researcher.

In some respects, you are “a living research instrument”. (Flipp, 2014)

Data is collected and filtered through your lens, so you’ll need to declare your personal bias and philosophical stance – and be aware of how your presence impacts what is being observed.

What else?

What qualitative challenges do you face? Are they being outweighed by the benefits? We’d love to hear about your experiences in the comments below.