scaleslevels

Scaleslevels is a neologism used in data science and information design to describe the practice of pairing measurement scale type with a hierarchical level of aggregation or granularity for a variable. It is not part of formal measurement theory, but serves as a practical shorthand for documenting how data are measured and analyzed.

Core components are the scale type and the level. Scale type refers to the traditional levels of measurement—nominal, ordinal, interval, and ratio. Level describes the granularity or grouping, such as micro, meso, and macro, or local, regional, and national, as well as row-level versus group-level in data structures. Together, scaleslevels specifies both what is measured and at what resolution data are treated in analyses or interfaces.

In practice, using scaleslevels helps data stewards communicate measurement decisions. Example: a customer feedback variable uses an ordinal 1-to-5 satisfaction scale, with levels by product line to allow cross-sectional comparisons. Another example: climate data where temperature is on an interval scale, aggregated at city, state, and national levels for dashboards. In survey design or dashboards, scaleslevels can guide visualization choices and statistical methods appropriate to each combination.

Relation to related concepts and limitations. It overlaps with concepts such as level of measurement, data granularity, and hierarchical modeling. It can aid data governance but risks ambiguity if not standardized across teams, since different organizations may use the term differently.

See also: levels of measurement, data granularity, Likert scale, hierarchical modeling.