CHAPTER 2: NON-VIOLENCE, THE HISTORY OF VIOLENCE AND THE ROLE OF THE CHURCH IN ZIMBABWE
6.2 Choosing a sample
6.2.2 Non-probability sampling
Non-probability sampling is when the choice of cases is not based on a randomised selection but on criteria that provides a sample that meets a particular need based on the aims of the research, particularly in qualitative researches. As mentioned earlier, various scholars group types of non-probability sampling differently. For example, on the one hand Babbie and Mouton (2001), Plowright (2011) and Remler and Van Ryzin (2011) identify at least four types of non-probability sampling as: purposive or judgmental sampling; convenience sampling;
quota sampling; and, snowball sampling. On the other hand, Tashakkori and Teddlie (2009) agree with the others on purposive sampling yet they do not go on to identify the other types as techniques. The impression that one gets is that to them non-probability sampling is purposive sampling. It is necessary to briefly look at the other three first, and then expand on purposive sampling, which this study employs, later. Remler and Van Ryzin (2011) also identify voluntary sampling and online sampling as emerging techniques especially with the growing popularity of Internet usage. At the core of non-probability sampling is the general acceptance that small non-probability samples can be used in experiments and qualitative research to produce generalisable knowledge, particularly about causation and when supported by strong theory.
Convenience sampling
According to Plowright (2011) and Ramler & Van Ryzin (2011), convenience sampling refers to a situation in which a researcher takes advantage of a natural gathering or easy access to people they can recruit in a study. Babbie and Mouton (2001, p166) refer to this type as “reliance on available subjects”. These scholars note that its main advantage is that it is quite easy and inexpensive, while little project management expertise is required. However, they recognise that its disadvantage is that it suffers from coverage bias because people who may happen to be available or convenient to the researcher may not represent the target population of interest.
150 Quota sampling
Quota sampling tries to address the issue of representativeness by identifying the specific characteristics such that the overall data will provide a reasonable representation of the total population. While this technique is ideal for use in a field research project, Babbie and Mouton note that it has inherent problems in terms of accuracy because personal and group information is not often updated. The idea of representativeness remains illusive particularly when studying people’s behaviours or attitudes as in this study.
Snowball sampling
The process of snowball sampling, as Babbie and Mouton (2011, p167) state, is implemented by collecting data on few members of the target population, especially the most influential. The snowball effect is realised when the number grows as information is passed on and more people are introduced to the process. The technique involves using participants to identify additional cases who may be included in the study. Plowright (2011) calls this viral ‘sampling’, anecdotally likening it to an electronic virus passed on from one computer to the next. In this research, snowball sampling was not a planned technique. However, as AVP training workshops were conducted with the Experimental group, a snowball effect was realised with time as more youths began to volunteer to be part of the process, as is revealed in detail in Part Four of this study.
Purposive sampling
According to Tashakkori and Teddlie (2003, p713), purposive sampling (also referred to as judgmental sampling by Babbie and Mouton), involves selecting certain units or cases “based on a specific purpose rather than randomly.” Tashakkori and Teddlie (2009, p173-4) further postulate that researchers using purposive sampling want to generate a wealth of detail from a few cases, and identify the following four critical characteristics:
Purposive sampling addresses specific purposes related to research questions, therefore the researcher selects cases that are information rich in regard to those questions.
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Purposive samples are often selected using expert judgment of researchers and informants [italics as in original].
Purposive sampling procedures focus on the ‘depth’ of information that can be generated by individual cases.
Purposive samples are typically small (usually 30 or fewer cases), but the specific sample size depends on the type of qualitative research questions.
Silverman (2010, p141) posits that purposive sampling allows researchers to choose a case because it illustrates some feature or process in which they are interested. It demands, however, that researchers think critically about the parameters of the population they are studying to choose their sample carefully. In that regard, the issue of setting also comes to the fore. Researchers are likely to choose a setting which, while demonstrating the phenomenon that they are interested in, it should also be accessible and have the capacity to provide appropriate data readily. Tashakkori and Teddlie (2009, p174) identify three ‘basic’ families of purposive sampling techniques, which this study adopts, as follows:
i. Sampling to achieve representativeness or comparability, which involves two general goals: firstly, selecting a purposive sample that represents, as closely as possible, a broader group, and secondly, selecting a purposive sample to achieve comparability across different types of cases on a dimension of interest.
In this study, participants were deliberately and strategically selected to represent youth militias, church youths and youths representing the main antagonist political formations in Zimbabwe presently, ZANU PF and MDC. Tashakkori and Teddlie (2009) note that while representativeness is most often associated with probability sampling, there are situations where the qualitative researcher is interested in the most typical or representative instances of a phenomenon of interest, such as violence as in this study.
ii. Sampling special or unique cases, where a qualitative researcher focuses on an individual case or group of cases as a major focus for investigation. Such cases would include sampling of politically significant or sensitive cases as in this study. However,
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research on sensitive topics may produce not only gains in knowledge but also effects that are directly beneficial to research participants.
In this study, it was assumed that the sample of youths would gain invaluable knowledge and skills in alternatives violence that would be beneficial not only to themselves but to the communities they live in.
iii. Sequential sampling, which involves a gradual or sequential selection of units or cases based on their relevance to the research questions. Tashakkori and Teddlie point out that this principle is used when the goal of the research project is the generation of theory, or when the sample evolves on its own accord as data are being collected. With sequential sampling the researcher examines particular instances of the phenomenon of interest so that he or she can define and elaborate on its various manifestations.
In this study, there were instances that I would ignite discussions about the impact of politically motivated violence at social gatherings in the community, or started a general discussion on violence in order to get people’s narratives of their experiences as individuals and as a community. This served the purpose of triangulation to corroborate information gathered either during interviews or focus group discussions. Findings of such narratives are discussed in Part Four of this thesis.
Mixed methods sampling
Tashakkori and Teddlie (2009, p178-9) provide a comparison between purposive and probability sampling before presenting mixed methods sampling techniques as combinations of quantitative and qualitative traits. They posit that a purposive sample is “typically designed to pick a small number of cases that will yield the most information about a particular phenomenon, whereas a probability sample is planned to select a large number of cases that are collectively representative of the population of interest.” This leads them to conclude that purposive sampling leads to greater depth of information from a smaller number of carefully selected cases, whereas probability sampling leads to greater breath of information from a larger number of units selected to be representative of the population of interest. This study
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fits well into this description as a small number of youths will be carefully selected for both experimental and control groups. The other difference is that purposive sampling can occur before or during data collection, while probability sampling is pre-planned and, unless a serious methodological problem occurs, does not change during data collection. “Whereas probability sampling is often based on pre-established mathematical formulas, purposive sampling relies heavily on expert opinion”, they argue. They further postulate that purposive sampling frames are informal and are based on the expert judgment of the researcher or some identified resource identified by the researcher.