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How Dooyeweerd Can Help Snowball Sampling

(This page is a summary of ideas that could be written up into a research paper or a textbook. Anyone who is interested in research methods and has some understanding of Dooyeweerd should feel free to do either of these.)

Snowball sampling is a way to get participants for a research project, especially ones to interview. The researcher asks current participants to invite others. (The first few participants are ones the researcher has invited, and they invite others, who invite yet others, and so on.) Usually participants are interviewed in order to find out what issues are meaningful either about a particular community or more generally.

A major advantage of this method of finding participants for a research project is that it can bring in people whom the researcher knows little about and might not even be aware of. In that case, the range of issues that can come up in interviews might be widened beyond what the researcher originally expected.

However, a major disadvantage is twofold. 1. It is not clear that how well participants invited are representative of the community that is being researched as a whole. 2. Even where representativeness is good, community bias is likely to skew the results, in that participants often invite like-minded people, so that even though the range of issues might be widened, there are still likely to be issues that remain hidden and overlooked. For example, in a community where there is anger over some injustice, it is likely that participants, focusing on issues informed by their anger, might overlook whole kinds of issue that are not, such as enjoyments or acts of generosity. This might not matter if all the research wants to do is to describe a particular community. But it does matter when the people in a community are being interviewed in order to find meaningful issues more generally.

Using Dooyeweerd's aspects to guide or to analyse interviews can help reveal and uncover hidden or overlooked issues. Two methods have been developed for doing this.

Dooyeweerd's Aspects

Dooyeweerd's suite of aspects offers a comprehensive and philosophically sound set of fifteen aspects of reality. Aspects are irreducibly distinct ways of seeing things and understanding the different norms by which reality operates, both in the natural world and the human worlds like language, economics, art, morals and belief. Dooyeweerd called them "modalities of meaning" and "spheres of law" - each aspect is both. The fifteen aspects he identified include:

Quantitative: to do with quantity, amount
Spatial: to do with continuous extension, space
Kinematic: to do with movement; flowing movement
Physical: to do with energy + causality
Biotic: to do with life functions
Sensitive: to do with sense, feeling, emotion
Analytical: to do with distinguishing and logic
Formative: to do with deliberate forming, as in planning, design, structuring, technology, history and culture
Lingual: to do with symbolic signification that makes data possible and enables communication
Social: to do with social interaction and agreement that enables working together
Economic: to do with frugal use of resources
Aesthetic: to do with harmony, surprise, fun
Juridical: to do with appropriateness and 'due', rights and responsibilities
Ethical: to do with self-giving love, going beyond due
Pistic: to do with belief, aspiration, commitment, ultimate meaning, religion/ideology

For more on aspects, see below.

The main advantages Dooyeweerd's aspects offer include:

Separating out issues to they are not conflated (because aspects are irreducible to each other in their meanings and laws);
More deeply understanding and probing issues (because the kernel meaningfulness of each aspect has been studied and discussed, not least by Dooyeweerd);
Uncovering hidden issues (because in most circumstances every aspect is relevant yet those who research, analyse or indeed just think about something usually focus on certain aspects that are meaningful to them and ignore or overlook others);
Guiding practice towards the good (because each aspect includes a norm that contributes towards overall good).

A great advantage of Dooyeweerd's aspects during interviews and analysis is that their kernel (basic) meanings are grasped by intuition more than by theoretical thought.

Dooyeweerd's aspects may be used during research in the analysis phase, in analysing what participants say, to find out what is meaningful to them [Basden 2020, ===]. They may also be employed during interviews either by the interviewer steering the interview to ensure all aspects are covered [Basden 2020, ==], or to involve the participants in exploring what is meaningful to them [p.===]. Snowball sampling could use either.

Enhancing Snowball Sampling

I would like to suggest that these methods might be helpful to overcome the possible community bias in Snowball Sampling. This is because even though the community might find for example economic, social and juridical issues important, especially when there is a lot of anger, and ignore the aesthetic issues, much of self-giving love, and maybe also environmental issues (biotic). Yet, the wide range of people assembled by the Snowball technique will have among them much to say about such overlooked issues if suitably prompted.

Using Dooyeweerd's aspects during interviews can help do this.

They could be used by the interviewer to guide the interviews to ensure that all aspects are covered. But, usually it is preferable if the participants themselves can use Dooyeweerd's aspects to uncover what is meaningful to them. Two research methods have been developed for the latter

MAKE: Multi-aspectual Knowledge Elicitation

MAKE, developed by Mike Winfield, is good for gleaning expertise, that is, knowledge from 'experts' in a field. It is useful especially for those who are accustomed to conceptual thinking, because the root question is, "In which aspects do you think that [an issue the interviewee has mentioned] is meaningful?"

After general introductions between himself and the interviewee, Mike would start an interview by taking 10 minutes to explain Dooyeweerd's aspects to the interviewee, usually having a list visual to them and himself. He would begin by asking, "Please can you suggest a couple of aspects that are meaningful in your expertise / line of work / etc." and would draw them onto a large piece of paper in full view of the interviewee. He would then ask them to suggest some issues or factors meaningful in those aspects, and plot them too near their aspects. Then ask for more. Eventually, some would be offered that are meaningful in other aspects, so he might ask, "Are these meaningful in other aspects too? Which ones? [referring to the list]" and those aspects would be plotted. He would ask about relationships among issues and among aspects and draw them as lines. Here is an example of a simple, tidied-up MAKE diagram:

A tidied-up MAKE diagram, with aspects, issues and the relationships among them. 923,462

Figure: A tidied-up MAKE diagram, with aspects, issues and the relationships among them.
Copyright (c) Mike Winfield, 2000, used by kind permission.
Click on diagram for full size.

Towards the end, when the rate of gleaning new issues is tailing off, he might ask the interviewee, "Are there any other aspects that have not yet been covered that you feel might be meaningful?" Asking that question helps to reveal hidden issues and some kinds of tacit knowledge.

An important rule is that what the interviewee says is taken, and not criticised (even if they seem to the interviewer to have mistaken an aspect's meaning) - because the aim of the interview is not 'correct' understanding of aspects but to help the interviewee separate out tangled issues and to think about issues that they often take for granted or are their tacit knowledge.

Mike found that almost every time, nearly every aspect was covered, and the interviewees thanked him for allowing them to think in this way because it helped them think more widely and see things they take for granted. For example, "helping each other" could be such an issue, even though extremely important in tense situations.

MAIT: Multi-aspectual Interview Technique

MAIT, developed by Suzanne Kane, is good for finding out what is meaningful to people more generally, such as their aspirations and expectations. It is useful for many kinds of people, for both those who are accustomed and those who are unaccustomed to conceptual thinking, because the root question is "Let us think about xxx aspect; in what ways might it be meaningful to you?"

Suzanne would start, as Mike did, by presenting and explaining Dooyeweerd's aspects, and making a list available. She found it useful to express them not conceptually by noun or adjectives, but by e.g. "When I ask you about your relationships with people, we are talking about the social aspect."

She would then invite the interviewee to select an aspect that might be meaningful to them, and begin to ask them in what ways it is meaningful. She would invite the interviewee to go through the entire set of aspects in a similar way, though in any order they chose (and to return to ones already discussed if they wish). Towards the end she would ask them, if some had not been covered, "Would you like to think about any other aspects?" and then "Are there any issues that are meaningful to you which you think do not come into any aspects?" Like Mike, she found that most aspects were covered every time, and very seldom did an interviewee bring up issues that they did not think were in any aspect.

Again, the interviewees valued such discussions and for similar reasons. In particular, they found it very freeing, with some explaining that they felt able to talk about things that were either embarrassing or that seemed trivial - two reasons why interviewees might fail to mention things.

Conclusion

In such ways, Dooyeweerd's aspects may help in research that employs Snowball Sampling, both systematize what interviewees say, and also draw out what is meaningful to them yet often taken for granted. Often issues are overlooked simply because of community bias, which is an acknowledged problem with Snowball Sampling.