User research data analysis
Two quick links that I still have to digest, think about, mix and adapt about user research and design by Steve Baty: First, Deconstructing Analysis Techniques: different sorts of analysis to apply to user research.
- "Deconstruction: breaking observations down into component pieces. This is the classical definition of analysis.
- Manipulation: re-sorting, rearranging and otherwise moving your research data, without fundamentally changing it. This is used both as a preparatory technique - i.e. as a precursor to some other activity - or as a means of exploring the data as an analytic tool in its own right.
- Transformation: Processing the data to arrive at some new representation of the observations. Unlike manipulation, transformation has the effect of changing the data.
- Summarization: collating similar observations together and treating them collectively. This is a standard technique in many quantitative analysis methods.
- Aggregation: closely related to summarization, this technique draws together data from multiple sources. Such collections typically represent a “higher-level” view made up from the underlying individual data sets. Aggregate data is used frequently in quantitative analysis.
- Generalization: taking specific data from our observations and creating general statements or rules.
- Abstraction: the process of stripping out the particulars - information that relates to a specific example - so that more general characteristics come to the fore.
- Synthesis: The process of drawing together concepts, ideas, objects and other qualitative data in new configurations, or to create something entirely new."
Second: Patterns in UX Research: different types of patterns one can find in user research that can be turned into actionable insights:
- "trends: a trend is the gradual, general progression of data up or down.
- repetitions: a repetition is a series of values that repeat themselves
- cycles: a cycle is a regularly recurring series of data.
- feedback systems: a feedback system is a cycle that gets progressively bigger or smaller because of some influence.
- clusters: a cluster is a concentration of data or objects in one small area.
- gaps: a gap is an area in which there is an absence of data.
- pathways: a pathway is a sequential pattern of data.
- exponential growth: in exponential growth, there is a rapidly increasing rate of growth.
- diminishing returns: when there are diminishing returns, there is a gradually decreasing rate of growth.
- long tails: the Long Tail is a pattern that rises steeply at the start, falls sharply, then levels off over a large range of low values."
Why do I blog this? material to rethink my methodologies. It's too rare to encounter hints, ideas, recommendations about the "analysis" part of user research.