Download Sampling in interview-based qualitative research and more Study Guides, Projects, Research Psychology in PDF only on Docsity!
Sampling in interview-based qualitative research: A theoretical and practical guide
Abstract
Sampling is central to the practice of qualitative methods, but compared with data collection and analysis, its processes are discussed relatively little. A four-point approach to sampling in qualitative interview-based research is presented and critically discussed in this article, which integrates theory and process for the following: (1) Defining a sample universe , by way of specifying inclusion and exclusion criteria for potential participation; (2) Deciding upon a sample size , through the conjoint consideration of epistemological and practical concerns; (3) Selecting a sampling strategy , such as random sampling, convenience sampling, stratified sampling, cell sampling, quota sampling or a single-case selection strategy; and (4) Sample sourcing , which includes matters of advertising, incentivising, avoidance of bias, and ethical concerns pertaining to informed consent. The extent to which these four concerns are met and made explicit in a qualitative study has implications for its coherence, transparency, impact and trustworthiness.
Keywords
Sampling, purposive sampling, random sampling, theoretical sampling, case study, stratified sampling, quota sampling, sample size, recruitment
Sampling is an important component of qualitative research design that has been given less attention in methodological textbooks and journals than its centrality to the process warrants (Mason, 2002). In order to help fill this void, the current article aims to provide academics, students and practitioners in Psychology with a theoretically-informed and practical guide to sampling for use in research that employs interviewing as data collection. Recognised methods in qualitative psychology that commonly use interviews as a data source include Interpretative Phenomenological Analysis (IPA), Grounded Theory, Thematic Analysis, Content Analysis, and some forms of Narrative Analysis. This article presents theoretical and practical concerns within the framework of four ‘pan- paradigmatic’ points: (1) setting a sample universe, (2) selecting a sample size, (3) devising a sample strategy and (4) sample sourcing. Table 1 summarises the principle features of these. All of the aforementioned methods can be used in conjunction with this four-point approach to sampling.
(Insert Table 1)
Point 1: Defining a sample universe
The first key concern in the four-point approach is defining the sample universe (also called ‘study population’ or ‘target population’). This is the totality of persons from which cases may legitimately be sampled in an interview study. To delineate a sample universe, a set of inclusion criteria or exclusion criteria, or a combination of both, must be specified for the study (Luborsky & Rubinstein, 1995; Patton, 1990). Inclusion criteria should specify an attribute that cases must possess to qualify for the study (e.g. a study on domestic violence that specifies that participants must be women who have suffered partner violence that was reported to the police or social services), while exclusion criteria must stipulate attributes that disqualify a case from the study (e.g. a study on exercise that stipulates that participants must not be smokers). Together, these criteria draw a boundary around the sample universe, as illustrated in Figure 1.
(Insert Figure 1)
Homogeneity and heterogeneity in the sample universe
The more inclusion and exclusion criteria that are used to define a sample universe, and the more specific these criteria are, the more homogenous the sample universe becomes. Sample universe homogeneity can be sought along a variety of parameters, such as demographic homogeneity, geographical homogeneity, physical homogeneity, psychological homogeneity or life history homogeneity (see Table 2 for descriptions of these). The addition of exclusion or inclusion criteria in these different domains increases sample homogeneity.
influential piece of research shows that cross-cultural qualitative research can be successfully conducted with a culturally heterogeneous sample universe, if resources are available.
There are however challenges inherent in using a heterogeneous sample. The first is that findings will be relatively removed from real-life settings, and the second is that the sheer diversity of data may lessen the likelihood meaningful core cross-case themes being found during analysis. Therefore, all researchers must consider the homogeneity/heterogeneity trade-off for themselves and delineate a sample universe that is coherent with their research aims and questions, and with the amount of research resources they have at their disposal.
The sample universe is not only a practical boundary that aids the process of sampling, it also provides an important theoretical role in the analysis and interpretation process, by specifying what a sample is a sample of , and thus defining who or what a study is about. The level of generality to which a study’s findings is relevant and logically inferable is the sample universe (Mason, 2002), thus the more clearly and explicitly a sample universe is described, the more valid and transparent any generalisation can be. If a study does not define a sample universe, or makes claims beyond its own sample universe, this undermines its credibility and coherence.
Point 2: Deciding on a sample size
The size of a sample used for a qualitative project is influenced by both theoretical and practical considerations. The practical reality of research is that most studies require a provisional decision on sample size at the initial design stage. Without a provisional number at the design stage, the duration and required resource-allocation of the project cannot be ascertained, that makes planning all but impossible. However a priori sample specification need not imply inflexibility – instead of a fixed number, an approximate sample size range can be given, with a minimum and a maximum.
Interview studies that have a nomothetic aim to develop or test general theory are to a degree reliant on sample size to generalise (Robinson, 2012). Sample size is by no means the only factor influencing generalisability, but it is part of the picture. O’Connor and Wolfe’s grounded theory of midlife transition, which was based on interviews with a sample of 64 adults between the ages of 35 and 50 (O’Connor and Wolfe, 1987) Illustrates this point. A way of working with larger sample sizes in qualitative research, which prevents analytical overload, is to combine separate studies together into larger syntheses. For example, I recently combined findings from a series of three studies on the topic of early adult crisis into a single analytical synthesis and single article. One contributing study had a sample of 16 cases, the second had a sample of 8 cases, and the third employed a sample of
26 cases. These were analysed and reported as separate studies originally, before being combined into the synthesis paper with a total sample of N=50 (Robinson, Wright & Smith, 2013).
Very large-scale qualitative interview projects include hundreds of individuals in their sample. For example the aforementioned EUROCARE project employed a sample size of approximately 280 ( persons of for each of 14 countries) (Murray, Schneider, Banerjee, & Mann, 1999), and the MIDUS study (The Midlife in the United States Study) is a study that has involved over 700 structured interviews (Wethington, 2000). While such projects do require time, money, many researchers and a robust purposive sampling strategy (see below), they are achieved by breaking up the research into smaller sub-studies that are initially analysed on their own terms before being aggregated together.
Interview research that has an idiographic aim typically seeks a sample size that is sufficiently small for individual cases to have a locatable voice within the study, and for an intensive analysis of each case to be conducted. For these reasons, researchers using Interpretative Phenomenological Analysis are given a guideline of 3 to 16 participants for a single study, with the lower end of that spectrum suggested for undergraduate projects and the upper end for larger-scale funded projects (Smith, Flowers & Larkin, 2009). This sample size range provides scope for developing cross-case generalities, while preventing the researcher being bogged down in data, and permitting individuals within the sample to be given a defined identity, rather than being subsumed into an anonymous part of a larger whole (Robinson & Smith, 2010).
Case study design is often referred to as a distinct kind of method that is separable from standard qualitative method (e.g. Yin, 2009). In relation to interview-based case-studies, a more integrative view is taken here, in which the decision to do a N=1 case study is a sample size decision to be taken as part of the four-point rubric set out in this guide. The resulting case study can then be analysed using an idiographic interview-focused method such as IPA. There are a number of different reasons for choosing a sample size of 1, and Table 3 lists six of these; psychobiography , theoretical or hermeneutic insight , theory-testing or construct-problematising , demonstration of possibility , illustration of best practice and theory-exemplification. All of these warrant a sample size of 1 and also require associated sample strategies, which are discussed later in this article.
These case study objectives are not mutually exclusive. An example of a paper that evidences multiple aims is Sparke’s narrative analysis of the autobiography of cyclist Lance Armstrong (Sparkes, 2004). It includes aspects of psychobiography, hermeneutic insight and construct problematizing.
(Insert Table 3)
Random and convenience sampling strategies
Random sampling is the process of selecting cases from a list of all (or most) cases within the sample universe population using some kind of random selection procedure. This process is used in opinion polls and social research surveys – typical methods include random selection of numbers from a phone book or of addresses from the electoral roll. Quantitative studies in psychology often claim to use a random sampling procedure, when they do not. Instead they typically locate a nearby source of potential participants who are convenient in their proximity and willingness to participate (i.e. Psychology students) and are in all likelihood not a random cross-section of the sample universe. This is called convenience sampling. It is used in quantitative research and sometimes in qualitative research too. It proceeds by way of locating any convenient cases who meet the required criteria and then selecting those who respond on a first-come-first-served basis until the sample size quotient is full. The problem of using this approach in quantitative research is that statistics function on the basis that samples are random, when they are typically not. For qualitative research, the danger of convenience sampling is that if the sample universe is broad, unwarranted generalisations may be attempted from a convenience sample. The best way of justifying the use of convenience samples in qualitative research is by defining the sample universe as demographically and geographically local and thus restricting generalisation to that local level, rather than attempting decontextualized abstract claims. For example, if the convenience sample is psychology students at a particular university in the UK, then by making the sample universe ‘young university-educated adults in the UK’ rather than ‘people in general’ the link between sample and target population is enhanced, while potential generalisation is narrowed and thus made more logically justifiable.
Purposive sampling strategies
Purposive sampling strategies are non-random ways of ensuring that particular categories of cases within a sampling universe are represented in the final sample of a project. The rationale for employing a purposive strategy is that the researcher assumes, based on their a-priori theoretical understanding of the topic being studied, that certain categories of individuals may have a unique, different or important perspective on the phenomenon in question and their presence in the sample should be ensured (Mason, 2002; Trost, 1986). Summarised below are stratified, cell, quota and theoretical sampling, which are all purposive strategies used in studies that employ multiple cases. Following this I describe significant case, intensity, deviant case, extreme case and typical case sampling, which are purposive strategies that are best employed when selecting a single case study.
All of these are processes for ensuring that certain types of individuals within a sample universe definitely end up in a final sample.
Stratified sampling
In a stratified sample, the researcher first selects the particular categories or groups of cases that he/she considers should be purposively included in the final sample. The sample is then divided up or ‘stratified’ according to these categories and a target number of participants is allocated to each one. Stratification categories can be geographical, demographic, socio-economic, physical or psychological – the only requirement is that there is a clear theoretical rationale for assuming that the resulting groups will differ in some meaningful way.
If there are just two stratification criteria in a study, the resulting framework can be illustrated as a simple cross-tabulated table, as shown in Figure 2.a. In this table, gender and age provide the basis for the sample stratification of a hypothetical study on the experience of life following divorce. If more than two variables are used in a sampling framework, an alternative way of illustrating the stratification is using a ‘nested table’, as shown in Figure 2.b (Trost, 1986). Here, the variable of ‘with children/without children’ is added to the divorce study sampling framework. It should be born in mind from a practical view that the more stratification criteria one includes in a sample frame, the more complicated recruitment becomes and the longer the process of finding participants. Therefore researchers should devise a sample strategy that takes into account how much time they have and the resources at their disposal.
(Insert Figure 2)
As previously mentioned, to include a purposive sampling stratification there must be clear theoretical grounds for the categories used. For example, in this hypothetical study on post-divorce experiences that Figure 2 refers to, the theoretical grounds for sampling for men and women could be that women are more likely to get custody of children than men in the UK and thus a systematic difference between sexes would be justifiably expected. Age could be justified as a sampling criterion on the basis that younger adults typically find it easy to re-partner than older adults, meaning the post-divorce experience may differ by age. The presence or absence of dependent children could be included because issues of child custody add a great deal of complexity and potential stress to post-divorce proceedings so those with and without children could be expected to differ. In a real study such theoretical rationales for purposive criteria would ideally have referenced sources.
Cell sampling
contrast with existing participants) or from new locations (Strauss, 1987), to build extra heterogeneity into the sample, or (b) re-structuring an already-gathered sample into a new set of categories that have emerged from analysis, and replacing any stratification/cells/quotas that were chosen a-priori (Draucker, Martsolf, Ross & Rusk, 2007). For example, Sandelowski et al. (1992) conducted a study of couples who were experiencing parental fertility problems. Initially a stratified frame of medical and social criteria was used. Then after analysis of the initial data, an alternative, more grounded way of grouping couples was found based on the explanations of fertility that participants gave. As a result, the researchers changed to an emergent sample categorisation, and purposively sought new participants to fill this new typology. Another example of theoretical sampling was the process of sampling used to develop a ‘theory of academic change’ in higher education institutions (Conrad, 1978). The researcher selected individuals to interview from positions in four higher education establishments according to emerging theory, without any a-priori purposive sample strategy at all. Each interviewee was selected based on the provisional analysis of previous interviews, followed by the researcher’s judgment as to what kind of person would help fill out the developing theory and explore its internal structure and external generalisability.
Purposive sampling strategies for selecting case study participants
If a decision has been taken by a researcher to do an N=1 case study, a strategy is then required to select the case in question, which fits with its overall objective (see Table 3). If the objective is psychobiography, an individual could be chosen due to his or her historical or theoretical significance
- this is called the significant sample approach (Simonton, 1999). If the objective is theoretical insight, a strategy of intensity sampling can be used – this aims to locate an information-rich case, chosen specifically to be insightful, comprehensive, articulate and/or honest (Miles & Hurberman, 1994). For example in my own research, an intensity sampling strategy was taken for the purposes of selecting a case study on early adult crisis; from a previous interview sample, individuals were selected who had provided a comprehensive account of both the inner and outer dimensions of their crisis experience, following which one person was selected randomly from this information-rich group (Robinson & Smith, 2010b).
If the objective of a case study is theory-testing or construct-problematising, a deviant case strategy may be used, whereby an individual is sought intentionally to explore the limits or problems inherent in a theory (Eysenck, 1976). Alternatively, if the aim is to demonstrate the possibility of a phenomenon, an extreme case strategy may be used, which locates who is not representative of others, but instead shows an extreme or unusual behaviour, ability or characteristic (Patton, 1990). Finally, if the research objective is illustration of best practice or theory-exemplification, a typical
case strategy (also called emblematic case or paradigmatic case) could be used (Miles & Huberman,
- in which the case is chosen precisely because it is a typical example of a theory or therapeutic application. For example, Hodgins and Peden (2000) describe a case study of using cognitive behavioural therapy for treating kleptomania, in which an individual was selected whose presentation was typical of the challenges involved.
Point 4: Sourcing sample
Once a researcher has decided on a sample universe, a provisional sample number and a sample strategy for their interview study, then comes the hands-on part – they need to go and source their participants from the real world. As well as practical and organisational skills, this stage of sampling requires ethical skills and sensitivity; all potential interviewees should be informed of the study’s aims, of what participation entails, of its voluntary nature, of how anonymity is protected and any other information that will help them reach an informed, consensual decision to participate, prior to any agreement to participate.
An aspect of voluntary participation is that individuals who consent to be involved in interviews may be different to those that do not, in ways that are not related to sampling criteria - this is called the self-selection bias (Costigan & Cox, 2001). In interviews, extensive intimate self-disclosure is sometimes required and this is likely to lead to a sample containing individuals who are more open, more patient and more interested in the topic than the general sample universe. My own research has found that women are more likely to put themselves forward to participate in qualitative research than men, which concurs with research showing a higher tendency towards self-disclosure in females than males (Dindia & Allen, 1992). This female bias can be easily counteracted in a mixed- gender purposive sampling frame that ensures male and female representation, but more subtle systematic biases in differences between participants and non-participants are harder to deal with. For example, Abrams (2010) refers to a study on ‘vulnerable youth’, which required individuals to respond to a flier and call the researcher to participate, but thereby was limited to those youth who were proactive and confident enough to actively call up a stranger to volunteer in a study and therefore in all probability not as psychologically vulnerable as those who did not call up. The self- selection bias is not possible to circumvent in interview-based research, as voluntary participation is central to ethical good practice, therefore all a researcher can do is be aware of the possibility for bias and consider it’s possible impact on findings and generalisability.
Referral processes: Snowball sampling
An alternative recruitment strategy to advertising is snowball sampling (also called chain sampling, chain-referral sampling or referral sampling). This involves asking participants for recommendations of acquaintances who might qualify for participation, leading to ‘referral chains’. This may be particularly useful when the population being studied is unlikely to respond to advertisements due to the stigmatising or illegal nature of the topic (Heckathorn, 2002). Such topic areas include drug use, crime, homelessness or stigmatizing illness such as HIV. Classic qualitative research that employed snowball sampling to investigate drug use includes the work by Lindesmith (1968) on opiate addiction, or Biernacki and Waldorf (1981) on recovery from heroin addiction.
The question of incentives and ‘respondent-driven sampling’
When recruiting people into an interview study, the question of whether to offer them a financial incentive for participation is a key decision that the researcher must make. The benefits of incentives are that they increase the likelihood of participation by adding additional motivation, and also increase retention in longitudinal studies (Yancey, Ortega & Kumanyika, 2006). The downside is that they provide a motivation for fabricating information in the interview in order to gain the money. The danger of gaining ‘dodgy data’ is such that if a researcher is confident that he/she can source the required sample without offering financial incentives, this is preferable. This problem is greater in low-income populations. A further ethical issue pertaining to incentives is that they may motivate participation in an interview on a topic that may elicit distress. Alternatives to financial incentives exist that can provide other perceived benefits to participation. Firstly participants can be offered a copy of their interview transcript and/or a report of the study’s findings, however researchers should be aware that this may not be an incentive to all participants and could in some cases lead to distress; therefore this decision should be made sensitively with a clear option to opt out. Secondly, making clear the potential applied benefits of the research can act as an incentive for many adults.
Incentives can also be used within a snowball sampling process as a way of stimulating recruitment. Respondent-driven sampling is a form of snowball sampling that gives financial incentives for recruiting others into the study (Heckathorn, 1997; Johnston & Sabin, 2010; Wang et al., 2005). In the process, a researcher first recruits a small number of ‘seed’ subjects. These individuals are then given a number of ‘recruitment coupons’. They are then told that if they pass the coupons on to other persons who end up participating, they will be paid a fixed amount per referral. All new recruits are then offered the same recruitment coupons to continue the process. This incentivised
recruitment can be done in conjunction with a financial incentive for doing an interview – a combination referred to as ‘dual incentives’.
The importance of sampling to qualitative research validity
Addressing the four sampling issues that have been outlined in this article is central to enhancing the validity of any particular interview study. Yardley’s criteria for evaluating qualitative research are respected benchmarks for assessing the validity of a study (Robinson, 2008; Smith et al., 2009; Yardley, 2000). These are ‘sensitivity to context’, ‘rigour’, ‘transparency’, ‘coherence’, and ‘impact and importance’. Well-conducted sampling enhances all four of these.
With regards to ‘sensitivity to context’, a fully articulated, contextualised sample universe prevents unwarranted generalisation and helps to locate the study within a place, a time and a meaningful group. A sample universe can be given contextual richness by locating it clearly within a particular culture or subculture, and giving it a historical location if required. Historical sensitivity means that unwarranted generalisation of findings to the past or the future will be less likely. This is particularly relevant if a phenomenon is being studied that changes over historical time, for example marriage or retirement.
With regards to ‘rigour’, Yardley herself writes that sampling is integral to meeting this criterion: ‘Rigour…depends partly on the adequacy of the sample – not in terms of size but in terms of its ability to supply all the information needed for comprehensive analysis’ (2000, p.221). Key decisions to enhance rigour are the relationship of the sample to the sample universe, the appropriate choice of sampling strategy, the robustness of the sample sourcing approach, and the overall fit between research questions and total sample strategy.
The third criterion, ‘transparency’, is enhanced by being explicit in a final research report as to how all four points – sample universe, sample size, sample strategy and sample sourcing – were met. Articles that stipulate one or two of these, but omit to mention others, will lack transparency, repeatability and auditability. Furthermore, the process of recruitment is often influenced by the researcher’s own background, location and connections, and if that is the case, appropriate reflexive acknowledgement of any conflict of interests or possible bias also aids transparency.
‘Coherence’ is maximised by systematically fitting the sampling processes with research aims, research questions, data collection and analysis, so that all are mutually supported and theoretically consistent, and by maintaining consistency between the reach of the sample universe and attempted generalisations. The 4-point approach to sampling is ultimately aimed at linking the
References Abrams, L.S. (2010). Sampling 'Hard to Reach' Populations in Qualitative Research: The Case of Incarcerated Youth. Qualitative Social Work, 9 , 536-550. Biernacki, P. & Waldorf, D. (1981). Snowball sampling: Problems and techniques of chain referral sampling. Sociological Methods and Research, 10 , 141-163. Coleman, T., Williams, M. & Wilson, A. (1996). Sampling for qualitative research using quantitative methods. 1. Measuring GPs' attitudes towards discussing smoking with patients. Family Practice, 13 , 526-530. Conrad, C.F. (1978). A grounded theory of academic change. Sociology of Education, 51 , 101-112. Costigan, C.L. & Cox, M.J. (2001). Fathers' participation in family research: Is there a self-selection bias? Journal of Family Psychology, 15 f, 706-720. Coyne, I.T. (1997). Sampling in qualitative research. Purposeful and theoretical sampling; merging or clear boundaries? Journal of Advanced Nursing, 26 , 623–630. Devers, K.J. & Frankel, R.M. (2000).Study Design in Qualitative Research—2: Sampling and Data Collection Strategies. Education for Health, 13 , 263–271. Dindia, K. & Allen, M. (1992). Sex differences in self-disclosure: a meta-analysis. Psychological Bulletin, 112 , 106-24. Draucker, C.B., Martsolf, D.S., Ross, R., & Rusk, T.B. (2007). Theoretical sampling and category development in Grounded Theory. Qualitative Health Research, 17 , 1137-1148. Erikson, E. (1969). Ghandi's Truth. New York: W. W. Norton. Erikson, E. (1993). Young man Luther: A study in psychoanalysis and history. New York: W. W. Norton. Eysenck, H. (1976). Introduction. In H.Eysenck (Ed.), Case Studies in Behavioural Therapy (pp.1-15). London: Routledge and Kegan Paul. Festinger, L., Riecken, H.W., & Schacter, S. (1956). When prophecy fails: A social and psychological study of a modern group that predicted the end of the world. University of Minnesota Press. Glaser, B. (1978). Theoretical sensitivity: Advances in grounded theory. Mill Valley, CA: The Sociology Press. Guest, G., Bunce, A. & Johnson, L. (2006), How many interviews are enough? An experiment with data saturation and variability. Field Methods, 18, 59-82. Hamilton, R.J., & Bowers, B.J. (2006). Internet Recruitment and E-Mail Interviews in Qualitative Studies. Qualitative Health Research, 16 , 821-835. Heckathorn, D.D. (1997). Respondent-driven sampling: A new approach to the study of hidden populations. Social Problems, 44 , 174-199.
Hodgins, D.C., & Peden, N. (2008). Cognitive-behavioral treatment for impulse control disorders. Revista Brasileira de Psiquiatria, 30 , S31-40. Johnstone, L.G., & Sabin, K. (2010). Sampling hard-to-reach populations with respondent driven sampling. Methodological Innovations Online, 5 , 38-48. Kraepelin, E. (1895). Psychologische Arbeiten. Leipzig: Wm Engelmann. Leshan, L. (1990). The dilemma of psychology. London: Penguin Books Ltd. Lindesmith, A.R. (1968). Addiction and Opiates. Chicago: Aldine. Luborsky, M.R. & Rubinstein, R.L. (1995). Sampling in qualitative research: Rationale, Issues and Methods. Research on Aging, 17 , 89-113. Lynd, R.S. & Lynd, H.M. (1929). Middletown. New York: Harcourt, Brace. Lynd, R.S. & Lynd, H.M. (1937). Middletown in transition. New York: Harcourt, Brace. Mason, J. (2002). Qualitative researching, 2nd^ Edition. London: Sage. McAdams, D., & Schultz, T. (2010). George W. Bush and the Redemptive Dream: A Psychological Profile. OUP USA. McAdams, D., & West, S. (1997). Introduction: Personality psychology and the case study. Journal of Personality , 65 , 757-783. Miles, M.B. and Huberman, A.M. (1994). Qualitative Data Analysis: An Expanded Sourcebook , 2nd edition. London: Sage. Murray, J., Schneider, J., Banerjee, S., & Mann, A. (1999). EUROCARE: A cross-national study of coresident spouse carers for people with Alzheimer’s disease: II--A qualitative analysis of the experience of caregiving. International Journal of Geriatric Psychiatry , 14 , 662-667. Neisser, U. (1981). John Dean’s memory: A case study. Cognition, 9 , 1-22. O’Connor, D.J. & Wolfe, D.M. (1987). On managing midlife transitions in career and family. Human Relations, 40 , 799-816. Patton, M. Q. (1990). Qualitative evaluation and research methods (2nd Edition). Newbury Park, CA: Sage. Querstret, D. & Robinson, O.C. (2013). Person, persona and personality modification: An in-depth qualitative exploration of quantitative findings. Qualitative Research in Psychology. 10 , 140-159. 10.1080/14780887.2011. Reicher, S. (1996). The ‘Battle of Westminster’: developing the social identity model of crowd behaviour in order to explain the initiative and development of collective conflict. European Journal of Social Psychology, 26 , 115-134. Robinson, O. (2012). A war of words: the history of the idiographic/nomothetic debate. The Psychologist, 25 , 164-167.
Emotion , 24 , 85–103. Yancey, A.K., Ortega, A.N. and Kumanyika, S.K. (2006). Effective recruitment and retention of minority research participants. Annual Review of Public Health, 27, 1-28. Yardley, L. (2000). Dilemmas in qualitative health psychology. Psychology and Health, 15 , 215-228. Yin, R.K. (2009). Case study research: Design and methods (4th^ Edition). London: Sage Publications.
Table 1. The four-point approach to qualitative sampling
Name Definition Key decisional issues Point 1 Define a sample universe
Establish a sample universe, by way of specifically a set of inclusion and/or exclusion criteria.
Homogeneity vs. heterogeneity Inclusion and exclusion criteria. Point 2 Decide on a sample size
Choose a sample size or sample size range, by taking into account what is ideal and what is practical.
Idiographic (small) vs. nomothetic (large)
Point 3 Devise a sample strategy
Select a purposive sampling strategy to specify categories of person to be included in the sample.
Stratified, cell, quota, theoretical strategies
Point 4 Source the sample Recruit participants from the target population.
Incentives vs. non- incentivised. Snowball sampling varieties. Advertising study.
