# Visualizing Emergence, Abstraction & Theory - an Experiment with Moiré Patterns

On the right, you see your qualitative data. Your interviews with local Hmong entrepreneurs. The answers to your survey on workplace happiness. Your field notes from a classroom visit.

You stare at the pile. Tons of data points on top of each other. A multi-layered, thick blob of stuff. Somewhere in there are those themes, those models, those theories that people say 'emerge'... But how do they emerge? How do I abstract things from this mess? And what's the relationship between my analytic products and this wild pile of dots?

I will use a series of patterns that were custom-typed by the British artist Kasper Pincis to tackle these questions. In his artwork, Kasper creates patterns of dots and symbols on vintage typewriters. By intersecting these amazingly precise patterns, he creates moiré-patterns - complex, almost three-dimensional, fuzzy structures. Kasper explored these types of intersections in an art book, and he features several pieces on his website gallery.

In this post, I introduce the moiré effect as a visual device for explaining three tricky and hard-to-grasp aspects of qualitative research: Abstraction, emergence, and the relationship between data and theory. Kasper hand-typed the patterns for this post, and I printed them on transparency paper before intersecting them. **We're building & testing a classroom/workshop activity based on this visual metaphor - printable materials and a syllabus will be posted here soon! **

## Emerging Patterns

Your task as an analyst is to *discover* patterns in data (if you’re an objectivist), or to *construct* patterns from data (if you’re a constructionist). In either case, the first step towards getting there is to open your eyes and to pay attention. You start staring at your pile of data. You see a ⬤ somewhere, and you recognize that ⬤ is different from ★. You take note of this.

You keep looking, and you see another ⬤. You look at the relation between ⬤ and ⬤. They’re two finger lengths apart from each other. Hm. Mildly interesting. You take note of this.

You spot more pairs of ⬤⬤ , and these are also two finger lengths apart from each other. You take note of this, and you realize that you're seeing a pattern.

Our data points are the ⬤. There wouldn’t be a pattern without ⬤. But what’s pattern-y about the pattern - such as the one you see on the left - is not the ⬤. What’s pattern-y is how ⬤ and ⬤ and ⬤ relate to each other in a fairly stable way.

The person establishing - or making explicit - such a relationship is you. The essential strategy for this is comparison: How is this different from that? What makes this this, and that that? How is this related to that?

## Emerging Structures

Next, you intersect two patterns. You bring two sets of relationships into relationship with each other. That's how you escape the desert of non-abstraction. That's how structures emerge.

Above, look at the image on the left. Overlaying two simple patterns created a wondrous, deliciously complex, multi-dimensional *structure*. Compare the structure on the left with the structure on the right. Intersecting the same patterns in a different way - at a different angle - produces different patterns-of-patterns. Different perspectives yield different structures.

## Emerging Theories & Models

Patterns-of-patterns are the building blocks of your theory or model. You start building a theory or model of something by bringing patterns-of-patterns into relation with each other. Now you're on the third level of abstraction. You look at patterns-of-patterns-of-patterns.

Take a look at the two patterns-of patterns on the right and below. They emerged by bringing different patterns in relation with each other.

I highlighted the structures with markers (...wait, or did I construct them? Again, that depends on whether truth is 'out there' or constructed...). In either case, tracing the structures with a pen is the equivalent of writing. You emphasize certain relationships between relationships, and you search for regularities, for meaning, for sense.

The next step is to bring these structures in relation with each other.

The image on the right represents a first stab at a theory - or let's maybe say: a draft for an abstract model. Note how different this is from the the pile of data points you saw at the beginning of this post, and how different it is from the grid of dots we started out with.

Does this sketch of a model *consist* of the different data-dots? No, it does not. And yet, it is deeply grounded in them, because the model/theory was created by *establishing relationships between them on different levels of abstraction. *First we looked at relationships between dots, which yielded patterns. Then we looked at relationships between patterns, which yielded structures. Then we looked at relationships between structures, which yielded a draft for a model or theory. And that's what qualitative methods are all about. They're the tools and processes that guide you through the activity of establishing, discovering, debating, critiquing, improving, abandoning, doubting and communicating increasingly complex relationships.