Tag: purposeful sampling

Qualitative Research: Sampling

Purposive and Theoretical Sampling:

When identifying means for recording data, one must be wary in qualitative research to how they collect data as well, it can be via unstructured or semi-structured observations and interviews, documents, and visual materials (Creswell, 2014).  Purposeful sampling is to help select the (1) actors, (2) events, (3) setting, and (4) process that will best allow the researcher to get a firm grasp at understanding and addressing their central questions and sub-questions in their study.  Also, consider how many sites and participants there should be in the study (identifying your sample size).  The sample size can vary from 1-2 in narrative research, 3-10 in grounded theory, 20-30 for ethnographic studies, and 4-5 cases in case studies (Creswell, 2014).

However, you can reach data saturation (when the research stops the data collection because there exists no more new information that would reveal any other insights or properties addressing the question of the research) before any of these aforementioned numbers (Creswell, 2014). Theoretical sampling is theoretically bound around a concept, but this type of sampling touches more on this concept of data saturation.  Thus, when the researcher is trying to understand the data in order to help them define or understand their theory to the point of data saturation, rather than reaching a defined number.

Example:

An example of this could come from studying the effects of business decisions affecting the family through analyzing relocation decisions on non-military families. (PROCESS)  Purposefully I would like to sample in this example are three groups of families, ACTORS: those with no children, those with children that are no older than 12 years of age, and those with one or more children over the age of 13.  I want to see if there is a difference between the reactions based on having kids and having kids that are older versus younger, over the past decade (EVENT) at Boeing (SETTING).  I could aim for 20-30 families per group to a total of 60-90 sample size, or I could aim for data saturation between each of these groups (Theoretically sampling).  If I want to stick with 60-90 as a total sample size, I could aim for an open answer survey or conduct interviews (which is more costly on my end).  If I wish to aim for data saturation, it can be more easily done with interviews.

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Interviewing strategy and qualitative sampling

As an interviewing strategy, open-ended questions leave the responses open to participant experience and categories and don’t close down the discussion or allow the participant to answer the question in one word (Snow et al, 2005).  Though in the past it was rejected because it did not involve a precise measurement, sometimes data that may not be easily measurable or counted, have value because of its intrinsic complexity and showcase of the “conditional nature of reality” (Rubin, 2012).  A whole field of text-analytics is aiming to prove that this data, considered as unstructured data, is an important part of knowledge discovery and knowledge sharing. Thus, Rubin (2012) says that open-ended questions grant the participant the chance to respond to the question in any way they choose, as elaborated on a response, allow participants to raise issues that are important to them, or even raise new issues not thought of by the interviewer.  Creswell (2013), further states that the more open the questions the better because it will allow the interviewer to listen to what people say and how they say, which can allow the participants to share their own views.  Usually, there are a few open-ended questions.  Finally, open-ended questions are used primarily in qualitative studies, but a mixture of both close-ended and open-ended questions could be asked in mixed methods studies.

One thing is to have the right questions as part of your interviewing strategies, it is another thing to have the right qualitative sampling plan.

Sampling Plans {purposeful/judgmental sampling, maximum variation sampling, sampling extreme or deviant cases, theoretical sampling, snowball/chain-referral sampling, cluster sampling, single-stage sampling, random sampling} (Creswell, 2013, Rubin, 2012, & Lofland et al, 2005). Here are just three of the many sampling plans listed in the sampling plan space.

  • Purposeful/judgmental sampling: In order to learn about a selective character, group, or category or their variations, you group the population into different characters, groups, or categories to collect data from with the participants now representing those divisions. (Creswell, 2013 & Lofland et al, 2005)
  • Maximum variation sampling: Allows for an analysis of error and bias in a phenomenon, through sampling and discovering the widest range of diversity in the phenomena of interest. (Lofland et al, 2005)
  • Snowball/chain-referral sampling: Asking your initial set of contacts with characteristics X, if they can refer to you their network that has the same characteristics X that you are studying. This is a means to enlarge your sample size and break down barriers to the entrance of your future participant. (Lofland et al, 2005). Depending on the characteristic X, like domestic violence, sexual assault, etc., this technique may run into IRB issues (Rubin, 2012).  Rubin (2012), stated that the way to avoid IRB issues if you have the current participants contact the future participants on your behalf to participate in the interview process, but this can drastically reduce the number maximum number of participants you could have gotten.

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