What did the system do?
- Which predictions were supported?
- Which candidate mechanisms remain plausible?
- How did behavior change when constraints changed?
- Which observations require a revised explanation?
Research approach
Questions guide measurement. Learning renews the question.
A recursive research approach in which consequential questions guide measurement development, observations support bounded learning, and what is learned refines both the question and the measurement.
Measurement enables learning, and learning enables better measurement.
The process is recursive rather than a one-way pipeline. A failed measurement may revise the operational definition. A surprising observation may expose a missing variable. Better theory may reveal that the original instrument measured the wrong thing.
A recurring research logic
Move from a consequential question to a more defensible next question.
- 01
Question
What do we need to know, and why would knowing it matter?
- 02
Criterion
What would count as knowing it, and which competing explanations must be distinguished?
- 03
Observation
What must become observable at the relevant scale and timescale?
- 04
Measurement
How can that observation be produced, calibrated, validated, and bounded?
- 05
Learning
What do the observations support, and what do they leave unresolved?
- 06
Revision
How should the question, construct, experiment, or measurement change next?
Two outcomes
Learn about the phenomenon and the measurement.
What could the method reveal?
- Which construct did the method actually operationalize?
- Which processing choices changed the result?
- Under which conditions was the estimate reliable?
- What additional observation is now required?
Published clinical example
Performance first, endurance next, then a more demanding task.
Figure 2 of the 2020 heart-failure clinical practice guideline first asks whether a limited activity can be performed. If it can, the algorithm asks whether endurance limits it. If it does not, the clinician identifies a new task or higher workload.
The algorithm is a clinical example of defining the objective, observing performance, locating the next limitation, and allowing the result to determine the next question.