Sampling for qualitative research

Family Practice
© Oxford University Press 1996
Vol. 13, No. 6
Printed in Great Britain
Sampling for qualitative research
Martin N Marshall
Marshall, MN. Sampling for qualitative research. Family Practice 1996; 13: 522-525.
The probability sampling techniques used for quantitative studies are rarely appropriate
when conducting qualitative research. This article considers and explains the differences
between the two approaches and describes three broad categories of naturalistic sampling:
convenience, judgement and theoretical models. The principles are illustrated with practical
examples from the author’s own research.
Keyword. Qualitative sampling.
Introduction
The benefits of a qualitative approach to health care
research are becoming increasingly recognized by both
academics and clinicians, but misunderstandings about
the philosophical basis and the methodological approach
remain. The impression is sometimes given that
qualitative research differs from the hypotheticodeductive model simply in terms of the way that data
is collected. The process of sampling is one of the principal areas of confusion, a problem not helped by the
inadequate way that it is covered in the literature, where
there is little agreement on definitions and authors frequently invent new and complex terms which cloud
simple fundamental issues. In this article I will describe
both quantitative and qualitative methods of sampling
and consider the basic differences between the two
approaches in order to explain why the sampling techniques used are not transferable. I will consider issues
relating to sample size and selection in qualitative
research and illustrate the principles with practical
examples.
Quantitative sampling
Choosing a study sample is an important step in any
research project since it is rarely practical, efficient or
ethical to study whole populations. The aim of all quantitative sampling approaches is to draw a representative
sample from the population, so that the results of
studying the sample can then be generalized back to
the population. The selection of an appropriate method
depends upon the aim of the study. Sometimes less
rigorous methods may be acceptable, such as incidental
or quota samples, but these methods do not guarantee
Received 30 May 1996; Accepted 15 July 1996.
Institute of General Practice, University of Exeter, Postgraduate
Medical School, Barrack Road, Exeter EX2 5DW, UK.
a representative sample. The most common approach
is to use random, or probability samples. In a random
sample the nature of the population is defined and all
members have an equal chance of selection. Stratified
random sampling and area sampling are variants of
random sampling, which allow subgroups to be studied
in greater detail.
The size of the sample is determined by the optimum
number necessary to enable valid inferences to be made
about the population. The larger the sample size, the
smaller the chance of a random sampling error, but since
the sampling error is inversely proportional to the square
root of the sample size, there is usually little to be gained
from studying very large samples. The optimum sample
size depends upon the parameters of the phenomenon
under study, for example the rarity of the event or the
expected size of differences in outcome between the intervention and control groups.
Comparing the quantitative and qualitative approaches
The choice between quantitative and qualitative research
methods should be determined by the research question, not by the preference of the researcher. It would
be just as inappropriate to use a clinical trial to examine
behavioural differences in the implementation of clinical
guidelines as it would be to use participant observation
to determine the efficacy of antibiotics for upper respiratory tract infections. The aim of the quantitative approach is to test ore-determined hypotheses and produce
generalizable results. Such studies are useful for answering more mechanistic ‘what?’ questions. Qualitative
studies aim to provide illumination and understanding
of complex psychosocial issues and are most useful
for answering humanistic ‘why?’ and ‘how?’ questions.
The principal fundamental differences in both the
philosophical foundation of and the methodological
approach to the two disciplines are summarized in
Table 1.
522
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Sampling for qualitative research 523
Why is random sampling inappropriate for
qualitative studies?
The process of selecting a random sample is well defined
and rigorous, so why can the same technique not be
used for naturalistic studies? The answer lies in the aim
of the study; studying a random sample provides the
best opportunity to generalize the results to the population but is not the most effective way of developing an
understanding of complex issues relating to human
behaviour. There are both theoretical and practical
reasons for this.
First, samples for qualitative investigations tend to
be small, for reasons explained later in this article. Even
if a representative sample was desirable, the sampling
error of such a small sample is likely to be so large that
biases are inevitable. Secondly, for a true random sample to be selected, the characteristics under study of the
whole population should be known; this is rarely possible in a complex qualitative study. Thirdly, random
sampling of a population is likely to produce a representative sample only if the research characteristics are normally distributed within the population. There is no
evidence that the values, beliefs and attitudes that form
the core of qualitative investigation are normally
distributed, making the probability approach inappropriate. Fourthly, it is well recognized by sociologists1
that people are not equally good at observing, understanding and interpreting their own and other people’s
behaviour. Qualitative researchers recognize that some
informants are ‘richer’ than others and that these people
are more likely to provide insight and understanding
for the researcher. Choosing someone at random to
answer a qualitative question would be analogous to randomly asking a passer-by how to repair a broken down
car, rather than asking a garage mechanic—the former
might have a good stab, but asking the latter is likely
to be more productive.
Sample size
Quantitative researchers often fail to understand the
usefulness of studying small samples. This is related
to the misapprehension that generalizability is the
ultimate goal of all good research and is the principal
reason for some otherwise sound published qualitative
studies containing inappropriate sampling techniques.2
An appropriate sample size for a qualitative study is
one that adequately answers the research question. For
simple questions or very detailed studies, this might be
in single figures; for complex questions large samples
and a variety of sampling techniques might be
necessary. In practice, the number of required subjects
usually becomes obvious as the study progresses, as
new categories, themes or explanations stop emerging
from the data (data saturation). Clearly this requires
a flexible research design and an iterative, cyclical
approach to sampling, data collection, analysis and interpretation. This contrasts with the stepwise design of
quantitative studies and makes accurate prediction of
sample size difficult when submitting protocols to
funding bodies.
Sample strategies
There are three broad approaches to selecting a sample
for a qualitative study.
Convenience sample
This is the least rigorous technique, involving the selection of the most accessible subjects. It is the least costly
to the researcher, in terms of time, effort and money,
but may result in poor quality data and lacks intellectual credibility. There is an element of convenience
sampling in many qualitative studies, but a more
thoughtful approach to selection of a sample is usually
justified.
Judgement sample
Also known as purposeful sample, this is the most common sampling technique. The researcher actively selects
the most productive sample to answer the research question. This can involve developing a framework of the
variables that might influence an individual’s contribution and will be based on the researcher’s practical
knowledge of the research area, the available literature
and evidence from the study itself. This is a more intellectual strategy than the simple demographic
stratification of epidemiological studies, though age,
gender and social class might be important variables.
If the subjects are known to the researcher, they may
be stratified according to known public attitudes or
beliefs. It may be advantageous to study a broad range
of subjects (maximum variation sample), outliers
(deviant sample), subjects who have specific experiences
(critical case sample6
) or subjects with special expertise (key informant sample). Subjects may be able to
recommend useful potential candidates for study
(snowball sample). During interpretation of the data it
is important to consider subjects who support emerging explanations and, perhaps more importantly, subjects who disagree (confirming and disconfirming
samples).
Theoretical sample
The iterative process of qualitative study design means
that samples are usually theory driven to a greater or
lesser extent. Theoretical sampling necessitates building
interpretative theories from the emerging data and
selecting a new sample to examine and elaborate on this
theory. It is the principal strategy for the grounded
theoretical approach3
but will be used in some form
in most qualitative investigations necessitating
interpretation.
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524 Family Practice—an international journal
TABLE 1 Comparison of quantitative and qualitative methods
Philosophical
foundation
Aim
Study plan
Position of
researcher
Assessing quality
of outcomes
Measures of
utility of results
Quantitative
Deductive,
reducdonalist
To test pre-set
hypothesis
Step-wise,
predetermined
Aims to be detached
and objective
Direct tests of
validity and reliability using statistics
Generalizability
Qualitative
Inductive, holistic
To explore complex
human issues
Iterative, flexible
Integral part of
research process
Indirect quality
assurance methods of
trustworthiness
Transferability
It is apparent from the above description that there
is considerable overlap even between these three broad
categories. The relative balance will depend upon the
research question and the chosen style of data analysis
and interpretation. It is important to recognize that
the essence of the qualitative approach is that it is
naturalistic—studying real people in natural settings
rather than in artificial isolation. Sampling therefore
has to take account not only of the individual’s
characteristics but also temporal, spatial and situational
influences, that is, the context of the study. The researcher should consider the broader picture: would this
individual express a different opinion if they were interviewed next week or next month? Would they feel
differently if they were interviewed at home or at work?
Should I study mem when they are under stress or relaxed? There is no correct answer to these questions,
just as there is no perfect way to sample, but the influence that these factors might have on the trustworthiness of the results should be acknowledged.
A practical example of sampling strategy
In practice, qualitative sampling usually requires a flexible, pragmatic approach. This may be illustrated by
my own study of the professional relationship between
GPs and specialists (in progress).
The way that the two branches of the medical
profession work together is a key component of the
primary-secondary care interface, which, in terms of
sociological interaction, is largely unresearched. The
study aims to describe the current relationship, compare this with the historical literature, and elucidate the
principal factors causing a change in the interaction between the two main branches of the medical profession.
Four methods of data collection have been used, each
of which view the interaction from differing perspectives
and each of which have required different sampling
strategies.
The first stage involved the use of key informant
interviews,4
an anthropological technique utilizing rich
information sources, which has defined sample selection criteria.5
A sample of 10 national figures in positions of leadership and responsibility within the
profession were chosen. Since the total population of
possible key informants is small, this was necessarily
a convenience sample, though there was an element of
a judgement approach, since efforts were made to ensure
that participants came from a range of clinical,
academic, managerial and political backgrounds. The
advantage of this approach lies in its simplicity but it
was difficult to determine at the sampling stage whether
the informants fulfilled the published selection criteria.
The second stage involved in-depth interviews with
practising clinicians throughout the South and West
Region. The aim was to develop an understanding and
an interpretative framework of the process of interaction
between specialists and GPs. I started with a judgement
sample framework including variables such as time since
qualification, gender, geographical location, rurality,
fundholding status and teaching hospital status. As the
data was collected and analysed, an interpretative
framework was constructed, so the sampling strategy
changed from largely judgement to largely theoretical,
in order to build on the developing theory. New themes
stopped emerging after about 15 interviews and an acceptable interpretative framework was constructed after
24 interviews—the stage of thematic and theoretical
saturation.
The third stage of the study brought GPs and
specialists together in focus groups to collect the different level of data produced by personal interaction.
For pragmatic reasons, this had to be conducted in my
own locality, and it was important for the study that
the participants were able to interact in a productive,
rather than dysfunctional way. I was able to use my
local knowledge to satisfy these sampling requirements
using a combination of convenience and purposive
techniques.
The three qualitative stages of the study will form
the basis of a Likert survey to test out emergent themes
and which will be distributed to a stratified random
sample of the whole population of clinicians working in the Region. This will represent a different, not
necessarily a stronger, perspective of the professional
relationship.
Conclusion
Sampling for qualitative research is an area of considerable confusion for researchers experienced in the
hypothetico-deductive model. This largely relates to
misunderstanding about the aims of the qualitative
approach, where improved understanding of complex
human issues is more important than generalizability
of results. This basic issue explains why probabilistic sampling is neither productive nor efficient for
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Sampling for qualitative research 525
qualitative studies and why alternative strategies are
used. Three broad categories of naturalistic sampling
techniques have been described—convenience, judgement and theoretical sampling—though in practice there
is often considerable overlap between these approaches.
Acknowledgements
I would like to thank Ms Nicky Britten for her comments on this paper. The study of the professional relationship between specialists and GPs is funded by a
Research and Training Fellowship from the Research
and Development Directorate of the South and West
Regional Health Authority.
References
1
Jackson JA. Professions and professionalisation. Cambridge:
Cambridge University Press, 1970.
2
Pound P, Bury M, Gompertz P, Ebrahim S. Stroke patients’
views on their admission to hospital. Br MedJ 1995; 311:
1&-22.
3
Glaser BG, Strauss AL. The discovery of grounded theory:
Strategies for qualitative research. London: Weidenfield and
Nicholson, 1968.
4
Marshall MN. The key informant technique. Fam Pract 1996;
13: 92-97.
5
Burgess RG (ed.). Field research: a sourcebook and manual.
London: Routledge, 1989.
6
Bradley C. Turning anecdotes into data—the critical incident
technique. Fam Pract 1992; 9: 98-103.
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