3.1.1.2 Sampling issues in qualitative research: who and how many?

Selection of participants

In qualitative research we select people who are likely to provide the most relevant information (Huston 1998). In order to design the sample and cover all variability around the research issue, the researchers must have an idea about the different perspectives that should be represented in the sample. This is called “field mapping” of the key players who have a certain interest in the problem under study. The role of this explicit “field mapping” is often underestimated but essential in order to build a purposive sample. It is possible that this “field map” evolves during the data collection. The notion of “representativeness” here is not understood in the statistical way. The idea of representation is seen as a “representation of perspectives, meanings, opinions and ideas” of different stakeholders in relation to the problem researched and their interest. In order to select the participants for interviews or focus groups, one should ask “do we expect that this person can talk about (represent) the perspectives (meanings given to the situation) of this stakeholder group”. The aim is to maximize the opportunity of producing enough data to answer the research question ^{(Green 2004)}.

Ideally there should be a mixture of different “population characteristics” to ensure that arguments and ideas of the participants represent the opinions and attitudes of the relevant population. Also the unit of analysis should be taken into account. This could be for example “individuals for their personal opinions/experience/expertise” or “individuals because they represent organizational perspectives”.

Moreover in order to make comparisons within and between types of participants, the sample design should take this already into account. In Table 9, two criteria for comparison, for example age and socio-economic status, are already included to allow comparative analysis between age or status groups.

Sampling approaches

There is a wide range of sampling approaches (e.g. Miles and Huberman 1994, Patton 2002, Strauss and Corbin 2008). It is not uncommon in qualitative research that the research team continues to make sampling decisions during the process of collecting and analysing data. However, a clear documentation of the sampling criteria is needed when doing qualitative research. These criteria should cover all relevant aspects of the research topic. The researcher should identify the central criteria and translate them in observable sample criteria. In addition, the chosen criteria should leave enough variation to explore the research topic ^{(Mortelmans, 2009)}. For example, in a research about factors influencing the decision to have or refrain from having a refractive eye surgery in the two last years, sampling criteria were:

1. To have experienced or to have considered a refractive surgery. We want to explore both the pro and cons.
2. To be older than 20 and younger than 70. Refractive eye surgery is not an option for those younger than 20 or older than 70.

In what follows we describe a number of sampling strategies. All the sampling strategies are non-probabilistic. A randomized sample is not useful in qualitative research, since generalizability to the general population is not the aim. Moreover with a random sample the researcher would run the risk of selecting people who have no link with the research subject and thus nothing to tell about it ^{(Mortelmans, 2009)}. In purposive sampling the point of departure are the sampling criteria as described above. There are different forms of purposive sampling:

• Stratified purposive sampling (Patton, 2002):      
  Purposive samples can be stratified (or nested) by selecting particular persons that vary according to a key dimension/characteristic (e.g. a sample of people from large hospitals, and a different sample with people from small hospitals) and the selection ideally represents the different positions within the ‘system’ or phenomenon under investigation. The stratification criteria are the equivalent of independent variables in quantitative research. The researcher should think ahead about independent variables which could provide new information regarding the research topic. For example, in the research project on refractive eye surgery we expected that reasons to chose or refrain from chosing for refractive eye surgery vary with age, with financial resources and can be different in the Dutch- and French-speaking part of the country. Therefore we added age, socio-economic status and region as criteria introducing heterogeneity. This results in the following matrix:
• Homogeneous sampling:   
  In the case of homogeneous sampling variation between respondents is minimised. Participants are chosen because they are alike, in order to focus on one particular process or situation they have in common ⁽Mortelmans, 2009) . However the homogenous character does not exclude comparisons between types of participants, because for example unanticipated dimensions might emerge from the data. It is also useful to take into account hierarchy, hence not to put for example nurses and specialists working in the same hospital together in a focus group, as this might create bias in the responses.This sampling strategy is used when the goal of the research is to develop an in-depth understanding and description of a particular group with similar characteristics or people on equal foot. For example for the KCE research project on alternative medicines ^48-50 only regular users were sampled.

Table 9 – Example of stratified purposive sample