The strategies of sampling and selection

In quantitative methods, the researcher uses probability sampling that is based on the assumption of representativeness of a sample for a given population. The statistical analyses enable inference about the probability of phenomena occurrence in this population with an assumed level of error (statistical generalization). In turn, in qualitative research involving interviews the

non-probability sampling is employed (Saunders, 2012) and to say it explicit: “probability sampling” (representative, random) is not an appropriate method of case and participants selection for qualitative research (Perry, 1998;

Yin, 2003).

The main recommended strategy for qualitative research is the purposive

sampling: cases or participants8 _{are selected to serve a very specifi c need or}

purpose and they do not constitute a subset of some larger population (sometimes it is even not possible to specify the population). The researcher chooses participants based on own judgment, knowledge about research phenomena and practical experiences, which cases and particular individuals may deliver

best insight for an issue of interest and provide as much information as possible to achieve the research objectives, both in terms of relevance and

depth (Patton (1990), in Perry (1998) calls it information rich cases). The research problem, key aims and objectives are a starting point (Saunders, 2012), although saying that sampling strategy and number of participant results directly from them would be too simplifi ed (Alvesson & Ashcraft, 2012). Illustrative example of the relationships between research aims and criteria of participant selection one may fi nd in Frame 4.1. (other examples one may also fi nd within this and other chapters).

8 _{Depending on a method, different terms are used to name individuals taking part in a research} project: participants in the literature on qualitative interviews (Saunders, 2012), participants or in- formants (to name experts who deliver information about the fi eld during repeated interviews) to name – in the literature on case studies (Alvesson & Ashcraft, 2012; Yin, 2003), respondents in the literature on surveys (Noga-Bogomilski, 2007) and subjects in the literature on experiments (www. apastyle.org). For all type of research, psychological academic publication standards (www.apastyle. org) recommend to use terms that are consistent with the traditions of the given fi eld with emphasis on terms that acknowledge participation (e.g., participants, individuals, respondents or more con- cise terms as managers, employees).

4.1. The strategies of sampling and selection

Frame 4.1. Example of purposive sampling for interviewing in fi nances fi eld

Calum Middleton, Suzanne Fifi eld and David Power (2007) investigated the perception of opportunities to undertake investments in Central and Eastern Europe (CEE) among institutional investors. Their objectives included investigation of the practitioners’ opinion on three main issues regarding investments in CEE: reasons, structure of the process and barriers, completed by the prediction of the CEE development. They conducted semi-structured individual interviews with two types of participants in the USA and UK. Seven interviews involved managers and analysts from two types of CEE funds (emerging and GEM), each having extensive knowledge and experience in investing in the CEE region and across the globe. Three interviews were attended by private equity fund managers. The interviews took place in the workplace of interviewees and lasted approx. 40 minutes. They were audio- recorded.

However, the purposive sampling based on assumed criteria is not always possible. As Saunders (2012) noticed, some compromises are required and the access is constraint by what is applicable (p. 35), particularly in organization setting. Thus, additional circumstances such as gaining access to organizations and participants, being granted permission to collect data, resources that may limit the amount of data (time, budget, number of researchers), willingness and capacity of individuals to take part in the research, may infl uence the decision.

Table 4.1 presents the list of sampling strategies for participant choice for qualitative interviewing. The list of strategies and their characteristics is based on refl ections of many authors. The distinction of purposive and non-purposive strategies of sampling is derived from the work by Mark Saunders (2012). As one may see, various strategies serve different aims.

In practice of management and economy sciences, most useful are strategies such as typical, critical, extreme, heterogeneous and homogenous. These strategies may co-occur, which means that – for instance – for criterion effectiveness of organization the extreme cases are chosen, but they represent different branches (heterogeneity) and participants represents similar job positions (homogeneity). Additionally, the selection based on most accessible cases (with an easy access to a large number of rich data sources) or opportunistic approach via acquaintances in organizations (Saunders, 2012; Yin, 2003; Zaborek, 2009b) and convenience sampling for focus group interviews are applied (Stewart et al., 2007). The convenience sampling may carry some risk if it was without any control (Saunders, 2012), but may be also benefi cial when one remembers that even the convenient sample needs to match criteria which result from research objectives (Stewart et al., 2007).

Table 4.1. Strategies of non-probability sampling for qualitative interviewing

Strategy Description Appropriateness of application

Purposive sampling strategies

Typical case _{• Participants considered} to be “average” representatives of a given group, the most characteristic of phenomena of interest

 For illustrative purpose, to deliver “representative” picture of the phenomena

 To capture the circumstances and conditions of ordinary phenomena

Critical case _{• Having strategic importance}

in relation to general problem (Flyvbjerg, 2006,

p. 425)

 To test (confi rm, challenge, or extend) an existing theory, to determine the relevance of theory’s propositions and of alternative set of explanations (Yin, 2003)  To verify or falsify the proposition

(to obtain information to validate the reasoning that if this occurs

(does not occur) in this particular case, then this is valid for all or many cases (any or only a few) cases) (Flyvbjerg, 2006, p. 425)

Extreme case • Participants having unusual characteristics 

To enlighten unusual  To achieve information on

particularly problematic or good examples of a given phenomena,

getting a point across in an especially dramatic way (Flyvbjerg,

2006, p. 425) Heterogeneous (maximizing differences) • Participants representing diversity of characteristics (different departments or levels of hierarchy)

 Providing maximum variation in the data → Revealing of key themes and

patterns of common understanding shared by the majority of the members of the wider population.

(Szabo, 2006 , p. 279) Homogeneous • Participant sharing similar

characteristics (e.g. same occupation, level of hierarchy)

 Going in-depth A rare/unique

case • The case is very rare but reveals important /new aspects of the issue

 To document or analyze rare phenomena

 Mostly in a single case design Revelatory case • The case that was not

examined previously although was visible for other researchers

 To investigate a phenomenon previously inaccessible to scientifi c examination even though the problems were widespread

Other non-probability sampling strategies

Self-selection sampling, vol- unteers, snow balling proce- dures

• Participants who are willing to take part in a research • Participants who give access

to next participants

 When access is diffi cult

 When criteria of choice are diffi cult to identify