Unit 2: Designing Qualitative Research

NEURO_QUAL Podcast Designing Qualitative Research

🎧 Click here to listen to Charlie, Brandon and Santhani introduce Unit Two, on designing qualitative research.

2.1 Unit introduction

Welcome to Unit Two. In this unit, we will introduce you to the core characteristics of qualitative research and explain why it is relevant to the neurosurgical evidence base. We will outline some of the available research designs, common methods used to collect qualitative data and explain the value of non-probability sampling.

Unit Content: Designing Qualitative Research

Definition of qualitative research
Discussion of how qualitative research is important to the evidence-base
Exploration of the breadth and scope of qualitative methodology
Overview of data collection methods in qualitative research
Introduction to sample size and sample types

2.2 What is qualitative research?

Qualitative research is a heterogenous research field. There are multiple definitions and multiple ways in which it can be conducted.¹² But while this heterogeneity causes some challenges to the field, there are broad principles that characterise qualitative research (for a critical discussion of what is qualitative research See Aspers and Corte ³).

Qualitative researchers attempt to understand events and experiences through detailed descriptive and interpretive analysis of people’s experiences, views, perspectives and perceptions of their social reality. Most commonly (but not exclusively), qualitative studies use in-depth, non-numerical data collection methods to explore participants’ experiences.⁴ These methods can reveal important insights not possible from research using quantitative methods alone.⁵ There are many definitions of qualitative research, but a succinct definition is provided by Strauss and Corbin⁶:

“… any type of research that produces findings not arrived at by statistical procedures or other means of quantification” ⁶

In contrast, Denzin and Lincoln ¹ add the characteristics of interpretive inquiry and meaning making of the naturalistic world:

“… qualitative research involves an interpretive, naturalistic approach to the world. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or to interpret, phenomena in terms of the meanings people bring to them.” ¹ (p. 3)

A more recent definition is presented by Aspers and Corte ³ This emphasises the process underpinning qualitative research and its intended outcome, which is to advance understanding of a phenomenon by getting closer to it. In this context, ‘moving closer’ can only be achieved by analysing first-hand experience.

“… an iterative process in which improved understanding to the scientific community is achieved by making new significant distinctions resulting from getting closer to the phenomenon studied” ³

From these definitions we can begin to extract the important features of qualitative methodology, which in turn helps us to understand why, and in what circumstances, we would choose to use it.

Table 1: Characteristics of qualitative methodology

Term	Explanation
Non-numerical	Qualitative methodology is borne out of the understanding that not everything can or should be measured. In qualitative studies where there is some quantification of the data, these data must still be interpreted for what the quantification actually means.
Interpretive	Qualitative researchers do not have statistical formulae to decide what is significant within their data. Instead, they must rely on their own analytical skills to decide what is important.
Naturalistic	Qualitative studies are usually conducted about natural social reality.
Meaningful	Qualitative studies often want to understand how people make sense of events and experiences and what was important about these.
Iterative	An iterative approach in qualitative research is a process of systematic, recursive steps in which one moves forward and back using reflection to develop insight and deeper understanding of the data.

From these definitions, it is clear that qualitative research commonly uses approaches that are non-numerical, interpretive and iterative to make sense of naturally occurring social reality. However, qualitative methods can also favour description over interpretation, proceed in a linear rather than iterative fashion, include numerical data in its analysis, and be used to understand events that have been designed rather than occur naturally. Hence why a simple definition is incomplete and a comprehensive definition so elusive.

Key Points: What is qualitative research?

A heterogeneous research methodology
An attempt to understand the naturally occurring world
Research that involves non-numerical interpretation
Research that involves close interaction with the subject
An iterative process underpinned by reflexivity

2.3 Why conduct qualitative research?

Fred Kerlinger Quote

It is fair to say that qualitative methodology has been given less attention and less respect than quantitative approaches. Qualitative research has suffered from a label of ‘soft science’, which in turn has led to an assumption that it is not as valuable as ‘hard science’ associated with quantitative research. Therefore, qualitative research, and its contribution to evidence-based practice, has been viewed as less valuable than evidence generated through statistical measurement. However, research is about choosing the right methodology for the research question, and there are questions that simply cannot be answered, or answered well, if we do not commit to using in-depth methods that involve spending time with participants to understand their views and value their subjective experiences.

It is worth remembering that when we use descriptive and inferential statistics, we are often dealing with summary data and estimates of average values; moreover, ‘most is not all’, and statistical significance is not always meaningful to the lives of the population we are interested in helping. Measurement, and the resultant statistics, often give researchers a false sense of security and confidence in their conclusions. Yet if we know little about a concept at the start of a study, how can we be sure that we have the right tools to measure anything of value or importance to the population?

In contrast, qualitative researchers believe our lived experience cannot, or should not, be reduced to a simple count of observable features. Life, and our experience of living it, is about individual meaning and interpretation. Our lived experience is socially constructed, bound in our relationships with others, our cultural and societal norms, and shaped by both internal and external forces. These commitments support qualitative researchers to engage in inquiry that is in-depth and holistic.

Albert Einstein Quote

Key Points: Why conduct qualitative research?

Because a subject is poorly defined or understood
Because a subject cannot or should not be measured quantitatively
Because in-depth holistic inquiry will advance understanding
Because context is important to the research question

2.4 Why do we need qualitative research in neurosurgery?

NEURO_QUAL Podcast Why Qualitative Research is Important

🎧 Click here to listen a podcast from Dr Kathleen Khu about why she thinks qualitative research is important for neurosurgeons and neurosurgery.
🎧 Click here to listen a podcast from Charlie and Dr Ronnie Baticulon about the decision to conduct qualitative research in neurosurgery.

Qualitative studies in neurosurgery are important because they examine topics that are not amenable to quantitative analysis and empirical measurement or where little is known about a subject area. In an editorial to the Journal of Neurosurgery, Khu and Midha ⁷ strongly advocate for the contribution of qualitative research within medicine and healthcare, referring to the underpinning philosophy that medicine is an art as well as a science. Using the example of total avulsion brachial plexus injury, Khu and Midha ⁷ identify the benefit of research from the patient’s standpoint:

"The use of qualitative research in total avulsion brachial plexus injuries (BPIs) fills a void not addressed or explored by quantitative research. Clinicians are mostly concerned about treatment options and outcomes, but little has been written about the patient perspective of the disease […] it is imperative that we understand the condition from the patient’s standpoint to help them make informed decisions about their treatment." ⁷

The studies listed below showcase examples of qualitative studies relevant to neurosurgeons. These studies all aim to explore, examine or understand an issue that is poorly understood or where gaining more understanding of contextual factors can advance practice:

Reflection Point

Think for a moment why these studies were conducted using a qualitative approach and how knowledge generated in this way can advance practice.

Key Points: Why do we need qualitative research in neurosurgery?

To examine experiences, perspectives and preferences
To understand why a treatment is, or is not, effective
To explore decision making and capacity
To understand cultural, societal and contextual influences on care delivery

2.5 What are qualitative research questions

Qualitative research questions are usually broad and exploratory. These questions are particularly useful when a topic is under-researched or poorly understood. If a subject is not well understood, then it cannot be easily measured. This position of not knowing allows the researcher to feel more free to follow the data.

Qualitative research questions try to capture the essence of what it is the researchers are particularly interested in, such as: experience; context; culture or sense making. To answer such questions, researchers must be committed to analysing in depth and in detail.

Qualitative research questions are therefore commonly constructed around how, what and why. These styles of question reflect the exploratory nature of the investigation, which does not pre-suppose what the researchers will find out.

The following questions/aims are taken from recent qualitative studies listed above that are relevant to neurosurgery. The key descriptors are identified in bold.

To understand the contextual challenges associated with long-term follow-up of patients following TBI in LMICs. ⁸
To explore attitudes toward neurosurgery in a resource-poor setting. ⁹
To understand the perspectives and experiences in medical decision making for patients selecting management for UIAs. ¹⁰
To further explore neurosurgery patients' and caregivers' perceptions of the extent to which communication and patient education – preoperatively, during hospitalisation, and at discharge from hospital – met their needs and expectations. ¹¹
To explore how social media could be utilised to influence an individual’s motivation to pursue a neurosurgical career. ¹²
To understand neurosurgeons’ experiences of, aspirations for, and ability to conduct and disseminate clinical research in LMICs. ?13

Reflection Point

Take a moment to think of a research question relevant to neurosurgery that could not be answered using a quantitative approach. How would this advance neurosurgical care?

Key Points: What are qualitative research questions?

Broad and exploratory
Focused on experience, context, culture and/or sense making
Designed to elicit depth and detail
Framed using HOW, WHAT, WHY

2.6 Qualitative research in medical journals

Medical journals predominantly publish quantitative research. Although there is evidence that medical journals are increasingly publishing qualitative research, rates remain low.¹⁴¹⁵Historically, qualitative research has been categorised with low-impact evidence such as case series, case reports, opinions and anecdotal findings. This may explain why qualitative research has not had an easy route to publication in medical journals.¹⁵ However, qualitative methodology is becoming more widely understood and its contribution to evidence-based practice more valued. Medical journals are, therefore, increasingly publishing qualitative studies for their ability to contribute to ‘how’ and ‘why’ questions that advance practice – as demonstrated by Shuval, et al. ¹⁴ and their review of qualitative studies in medical journals, which illustrates a trend toward increasing publication (see Figure 1).

Figure 1: Qualitative studies in medical journals by Shuval et al (2011) Figure 1: Qualitative studies in medical journals

There has been some heated debate about the publishing guidelines for qualitative research in the British Medical Journal; the BMJ had suggested that qualitative research was a low priority because it was unlikely to be cited highly or have practical value.¹⁶¹⁷ However, more recently Retrouvey, et al. ¹⁵ conducted a bibliometric and altmetric comparison of the impact of qualitative and quantitative research in the BMJ. They concluded that the 42 qualitative studies identified (out of more than 7,000 screened) published between 2007 and 2017 had equivalent impact to the quantitative articles:

“Our analysis reinforces the findings that qualitative and quantitative articles have similar academic impact.” ¹⁵ (p.6)

As already noted, while medical journals are increasingly publishing qualitative research, rates remain low.¹⁴¹⁵ In neurosurgical journals specifically, there very few examples.

Key Points: Qualitative research in medical journals

Qualitative research is poorly represented in the medical literature.
The publication of qualitative research in medical journals is increasing.
Qualitative studies have been shown to have a similar academic impact as quantitative studies.

2.7 Methodological choice in qualitative approaches

As a heterogenous field of inquiry, qualitative research has many research designs, which it may be useful to think of as a continuum (see Figure 2). However, many authors would reject a typology for qualitative research because many of the methods simply do not fit such restrictive models. Nevertheless, it can be useful to think of these designs as being on a continuum.

Figure 2: Continuum of qualitative research Figure 2: Continuum of qualitative research

NEURO_QUAL Podcast Designing Qualitative Research

🎧 Click here to listen to Charlie, Brandon and Santhani introduce Unit Two, and for a critical discussion of the above figure.

On the left-hand side of Figure 2 are thematic approaches most closely aligned to quantitative principles. As such, these studies tend to describe the patterns in the data through counting the presence of certain features of interest. Therefore, these studies often retain their foothold in [(post)positivist] assumptions and rely heavily on rigid procedures, coding frames, and inter-rater reliability of coding decisions.¹⁸ Examples include content and framework analysis; however, it is important to note that these studies can also adopt a more interpretive stance. Qualitative research designs on the left-hand side of Figure 2 do not have to be informed by philosophy or theory (although it is important to note that both Schwandt ¹⁹ and Merriam ²⁰ maintain that research without theory is impossible, because no study can be designed without some influence or guiding interest, even if this is only implicit). There are examples in the medical literature simply referred to as ‘qualitative research’, which may explain why qualitative is seen by some as a methodology and a design; if or where theory did inform these studies is often well hidden.

A thematic approach described by Braun and Clarke ²¹ is particularly popular and has been cited more than 90,000 times. However, Braun and Clarke ²² have suggested that this approach has been misunderstood and poorly used by many researchers. More recently, their approach has been defined as ‘reflexive thematic analysis’ (RTA) to emphasise the subjectivity and reflexivity which is central to its interpretive stance. ²²

Reflexivity has been defined as: 'The process of critical self-reflection about oneself as researcher (own biases, preferences, preconceptions), and the research relationship (relationship to the respondent, and how the relationship affects participant’s answers to questions)'. ²³ (p.121)

Reflexive thematic analysis is situated within the centre of Figure 2 to illustrate its roots in the qualitative paradigm but also its freedom from specific pre-determined ontological and epistemological beliefs.¹⁸

On the right-hand side of the continuum are designs that are highly interpretive in nature and do have specific ontological and epistemological anchors. These are very specific about how the study is designed and can support a more interpretive and in-depth analysis of meaning making. Such designs are less common in the medical literature. These approaches are often (but not always) associated with more complex and sensitive analysis and so can take longer to complete. The ‘big five’ are: phenomenology, narrative, ethnography, grounded theory, and case study (However, there are many others, including discourse analysis, conversation analysis, and action research). While there are some commonalities, there are also important differences which set them apart (see Creswell and Poth ²⁴ for an extremely useful comparative text).

Table 2: Comparison of fice qualitative research designs

Qualitative research design	Main focus of inquiry	Some common sub-divisions
Phenomenology	The essence of experience	Descriptive; interpretive; interpretive phenomenological analysis
Narrative	Exploring the individual life story	The ‘what’ of a story / The ‘how’ of a story / Biographical; life story; oral history
Ethnography	Cultural interpretation	Realist; critical; rapid; case study
Grounded Theory	Developing theory	Classical; Straussian; constructivist
Case study	Examination of a bounded system	Intrinsic; instrumental; collective / Collective; descriptive; explanatory

The final column in Table 2 identifies some common sub-divisions of the five main approaches to further emphasise the heterogenous nature of qualitative methodology.

NEURO_QUAL Podcast What Qualitative Research Design?

🎧 Click here to listen to Charlie discuss the use of Framework Analysis with Santhani.
🎧 Click here to listen to Charlie discuss the use of Interpretive Phenomenological Analysis with Dr Harry Mee.

Key Points: Methodological choice in qualitative approaches

There are lots of different ways to conduct qualitative research.
Descriptive qualitative studies base analysis on counts and frequency of data items within the data set.
Interpretive qualitative approaches favour interpretation and meaning of data items over frequency of these items within the data set.
Some research designs provide a specific framework for conducting qualitative research. These may be theoretically or philosophically informed. Most notably these are: phenomenology, narrative, ethnography, grounded theory, and case study. However, there are many others.
It is important to choose a research design that will answer the research question in the most comprehensive way.

2.8 How should data be collected in qualitative research?

While quantitative studies often start from a position of knowledge and may be hypothesis-led, qualitative studies more commonly start from a more modest position of not knowing and thus being open to what they will find out. Qualitative researchers must spend time with the population of interest and ask them what they think, what their lives are like and what is meaningful to them. This is known as ‘inquiry from the inside’.

“I want to understand the world from your point of view. I want to know what you know in the way you know it. I want to understand the meaning of your experience, to walk in your shoes, to feel things as you feel them, to explain things as you explain them. Will you become my teacher and help me understand?” ²⁵

The aim of qualitative data collection is to find out more about the phenomenon of interest. The best way to do that is to talk to or observe people who have direct experience of the phenomenon of interest. There are a number of different ways to collect qualitative data, including interviews, focus groups, open ended survey questions, observations and secondary research. However, the most common is the semi-structured interview.

Semi-structured interviews: The staple of qualitative data collection is the semi-structured interview. A semi-structured approach allows the researcher to have some control over what is discussed within the interview. This is particularly useful if you have narrower research questions which want to find out specific information. Like questionnaire development, the interview schedule should reflect the research questions and be developed from a contemporary review of the literature relevant to the topic of interest. However, in contrast to a questionnaire, the questions should be broad, open, and invite in-depth responses. In addition, you should set some probes, prompts and follow-up questions to help you get the most out of the interview. To learn more about qualitative interviews go to Unit Three.

Interviews are: “… a craft and social activity where two or more persons actively engage in embodied talk, jointly constructing knowledge about themselves and the social world as they interact with each other over time, through a range of sense, and in a certain context” ²⁶ (p. 85)

Focus groups: A focus group is an interview with a group of people held to generate discussion of issues presented by the researcher. Focus groups are particularly helpful when multiple perspectives are required and insight would be enhanced if these perspectives were shared and discussed between participants. To learn more about focus groups go to Unit Four.

Focus groups are: “a number of people collaboratively sharing ideas, feelings, thoughts and perceptions about a certain topic or specific issues linked to the area of interest” ²⁶ (p. 83)

Open-ended survey questions: It is important to state that simply collecting qualitative data (i.e. non-numerical data) in a survey, for example, is not qualitative research if the qualitative data are to be analysed in a purely quantitative way – for example, frequency of recurring text with no interpretation of what this means. Some surveys will commit to a descriptive or interpretive analysis of open questions, but these are often limited as people do not commonly like to give in-depth responses to what is predominantly a closed-question questionnaire. In contrast are those surveys which are from the outset designed specifically to elicit more in-depth responses with broader, more open questions. These are more representative of a qualitative commitment for in-depth data and can be analysed in more detail.

NEURO_QUAL Podcast Collecting Qualitative Data

🎧 Click here to listen to Charlie and Dr. Tom Bashford discuss the concept of unstructured interviewing for collecting qualitative data.

Observations: Observational research is particularly helpful if you are trying to examine behaviours that people may not be aware of themselves or something that participants may find difficulty in disclosing. Benefits include examining life in ‘real time’ and aiding the development of contextual understanding of people’s lives.²⁶ Studies of culture usually include some form of observational data collection so the researcher can immerse themselves in the setting and watch how people behave. These observations are recorded in detailed field notes.

Secondary research: Collecting existing textual data and subjecting it to a qualitative analysis is an interesting and compelling form of qualitative inquiry. Increasing use of online blogs, chat rooms and social media mean there is an ever-growing corpus of data which could, under the correct ethical approval, be used to understand people’s experience in a way that lies outside the traditional research interview.

Visual methods: Going beyond traditional forms of knowing the world, visual methods represent the world in a way that isn’t written or spoken. Photographs, drawings and paintings etc. show (rather than tell) others what life is like on the inside. A particular method gaining popularity for its participatory method is PhotoVoice, which asks participants to share their lived experience through photography.²⁷

Key Points: How should the data be collected?

The aim of data collection is to find out more about the phenomenon of interest.
The researcher needs to choose a method that will provide data that is in-depth enough to answer the research question.
Qualitative data collection methods include: interviews, focus groups, open-ended survey questions, observations, secondary research and visual methods.
The most common method for data collection is the semi-structured interview.
Data collection methods must be balanced with the time and resources available to complete the study.

2.9 How do I choose a sample?

Just like quantitative research, choosing a qualitative sample must be both systematic and rational.²⁸ Sampling is complex and requires pre-planning so that the sample is appropriate (i.e. fits the aim of the research) and adequate (i.e. generates adequate, rich and sufficient data).²⁹ As with all studies, the researcher will have a population that is relevant to the research aim (sampling frame); from this, a subset is identified who have specific characteristics of interest, including their knowledge, experience and accessibility, which will make them rich informants. A study sample can then be recruited from the target population.

Figure 3: Choosing a qualitative sample Figure 3: Choosing a qualitative sample

While random and stratified samples can be used in qualitative studies, it is more likely that a ‘purposive sampling’ technique is used where there is a careful selection of information-rich cases to address the research aim. (‘Purposive’, ‘purposeful’ and ‘criterion-based’ tend to be used interchangeably in the literature.)

The logic and power of purposeful sampling lies in selecting information-rich cases for study in depth. Information-rich cases are those from which one can learn a great deal about issues of central importance to the purpose of the inquiry, thus the term purposeful sampling. ³⁰ (p. 230)

In qualitative studies, participants should be ‘information rich’ and have an in-depth understanding of the experience.³¹ Sampling is therefore deliberate and non-random. The most common forms of purposive sampling are summarised in Table 3. Researchers should be transparent about their inclusion and exclusion criteria, and they must understand the impact of these decisions on their findings. In addition, Flick ³² warns that a sample that is too homogenous may stifle meaningful comparison, whereas it may be difficult to find areas of commonality if a sample is too heterogenous.

Table 3: Types of non-random sampling

Sampling approach	Definition
Purposive/purposeful/criterion based	Selection of participants based on a priori criterion
Typical	To illustrate what is ‘normal’ or average
Unique	Atypical presentation, rare or unusual presentation
Maximum variation	To document unique variations and shared patterns despite heterogeneity
Convenience/opportunistic	Participants are easily accessible to the researcher
Snowball/chain	Asking interviewees to identify others relevant to the aims of the study
Theoretical	Characteristics of the sample evolve during data collection and analysis

In contrast to studies which identify the characteristics of the sample before they recruit participants, theoretical sampling is used when all the preferred sample characteristics are not known a priori. Researchers may start with a criterion-based sample and then make progressive sampling decisions during data collection and analysis, as characteristics of interest evolve. These evolving decisions, regarding who should be interviewed next, support the researcher to develop and test their emergent theory – this is commonly associated with grounded theory methodology.²⁰

Key Points: How do I choose a sample?

Samples in qualitative research are usually non-random i.e., purposive
Must address the research aim and generate enough data to be deemed sufficient to answer the research question.
There are a range of purposive sampling techniques including, typical, convenience, snowball and maximum variation.
In samples which are too heterogenous it may be difficult to find areas of commonality.
Samples which are too homogenous may not facilitate meaningful comparison.

2.10 How big should the sample be?

Deciding how many participants to include in a qualitative study is an important step. While a large sample size may be appealing, it is more typical for qualitative researchers to limit themselves to a small sample ranging from the single case study to samples of between 4 and 40.²⁸ While this may not seem a large enough sample for those more familiar with quantitative studies, these smaller samples facilitate the in-depth and in-detail analysis that is required for qualitative studies.

When thinking about sample size, it is worth bearing in mind that the aim of qualitative research is not statistical generalisation. Rather, the aim of qualitative research is to find out what can be learnt about the field of interest from a specific case or cases, which can then be transferred from those cases to others. In this sense, the aim of qualitative research is [transferability]. Therefore the context and selection of participants plays an important role in the final ‘fittingness’ between the data source and the target population.³³

While it may be misleading to cite typical sample sizes, it is useful to illustrate that different research designs come with different expectations from the sample. A case study, for example, may be as small as a sample of one, but it might include multiple datasets for an extremely in-depth analysis. In contrast, a content analysis conducted by a team of researchers may support a much larger sample of 20-50 as the focus is largely on frequency of data items rather than in-depth interpretation. Holloway and Galvin ²⁸ suggest that even a sample of one can yield useful results. However, medical journals, funders and possibly even examiners do seem to have a preference for larger sample sizes, even in qualitative research. Despite this preference, it is important to state that sample size is not a determinant of quality or importance.²⁸ Studies which do commit to larger sample sizes must be adequately resourced to ensure analysis can be completed in the required depth and complexity of the analytical technique proposed.

Table 4: Typical sample sizes in qualitative research

Research design	Typical samples*
Phenomenology	5 - 15
Grounded theory	20 - 30
Case study	May be as little as one
Narrative	5 - 10
Content / Thematic analysis	20 - 50
	* HIGHLY variable and context-specific.

Despite the broad guidelines offered in Table 4, there really are no set sample sizes in qualitative research. Instead, researchers frequently cite having a sample size large enough to reach ‘data saturation’ (discussed below).

Key Points: How big should the sample size be?

There are no sample size calculators for qualitative studies.
Qualitative studies do not usually aim for generalisation; instead they aim for transferability, therefore sample sizes can be small.
Typical samples range from 4 to 40, but this is highly variable and study specific.
Sample size must be balanced with data points, data types, methodological/analytical approach and resources available.

2.11 What is data saturation?

Data saturation is an important concept in qualitative studies. Data saturation is defined as:

“Data saturation is reached when no new analytical information arises anymore, and the study provides maximum information on the phenomenon.” ³⁴

Data saturation therefore refers to the completeness of the final findings, and as such it can be used to justify sample size. However, as an analytical technique, data saturation occurs during the study; but as a justification for sample size, data saturation must be determined a priori. So researchers must estimate – based on their understanding of the phenomenon under investigation, the needs of the research question, and their specific research design – at what point they think data saturation will be reached. This calculation should be balanced with the availability of time, resources and the proposed analytical strategy. During the analytical phase of the study, the researchers will then develop more confidence about whether their study reached data saturation and the completeness of their findings. Unfortunately, data saturation is often glossed over in published manuscripts, and researchers often give little justification or grounds for their claim that data saturation has been reached.³⁵

In the definition of data saturation by Moser and Korstjens ³⁴ there are two distinct points: the first is the point beyond which no new analytical information arises; the second is the point at which the maximum amount of information on the topic of interest is provided.³⁵ The latter has been identified as occurring after as little as nine interviews.³⁵ However, Hennink, et al. ³⁵ argue that this is not enough to develop a meaningful and rich interpretation that is sensitive to contextual and conceptual issues and that more data is needed. It is far harder to be confident about when this second point in data saturation has been reached; this will depend on the sample demographics as well as the sample size.

Key Points: What is data saturation?

Data saturation is both an overused term and a poorly described process in medical literature.
It includes both saturation of information from the sample and saturation of meaning relevant to the target population.
During protocol development, researchers should estimate a sample size based on where they think saturation may occur; and during data analysis, they should evaluate if data saturation was indeed reached.

2.12 Unit summary

In this unit we have introduced you to the principles of qualitative methodology and how it can help advance the neurosurgical evidence base. We have explained that qualitative research is a diverse and expansive field which can facilitate an in-depth and rich understanding of people’s experiences. We have introduced the main approaches to data collection and signposted you to Units Three and Four where interviews and focus groups will be examined in more depth. Finally, we have described sampling in qualitative research and explored principles of data saturation and non-probability sampling.

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