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What is the relationship between a construct and a measure?

Because an experiment involves examining the relationship between independent variables and changes in one or more dependent variables, defining what is measured—dependent variables—is crucial. The dependent variables are what can be measured and relate to the outcomes described in the research questions.
The research questions are often stated in terms of theoretical constructs, where constructs describe abstract entities that cannot be measured directly. Common constructs in human factors studies include: workload, situation awareness, fatigue, safety, acceptance, trust, and comfort. These constructs cannot be measured directly and the human factors researcher must select variables that can be measured, such as subjective ratings and performance data that are strongly related to these constructs. To assess how smartphones affect driving, the underlying construct might be safety and the measure that relates to safety might be error in lane keeping where the car’s tire crosses a lane boundary. Safety might also be measured by ratings from the drivers indicating how safe they felt. Subjective ratings are often contrasted with objective performance data, such as error rates or response times. The difference between these two classes of measures is important, given that subjective measures are often easier and less expensive to obtain, with a larger sample size. Both objective and subjective measures are useful. For example, in a study of factors that lead to stress disorders in soldiers, objective and subjective indicators of event
stressfulness and social support were predictive of combat stress reaction and later posttraumatic stress disorder. The subjective measure was a stronger predictor than the objective measure [68].
In considering subjective measures, however, what people rate as “preferred” is not always the system feature that supports best performance [69]. For example, people almost always prefer a color display to a monochrome one, even when color undermines performance.
Furthermore, people cannot always predict how they would respond to surprising events in different conditions, like during system failures. Human factors is much more than intuitive judgment (of either the designer OR the participant). It is for this reason that objective data from controlled experiments are needed to go beyond the expert judgments in heuristic evaluations and subjective data.
Subjective and objective dependent variables provide important and complementary information. We often want to measure how causal variables affect several dependent variables at once.
For example, we might want to measure how use of a smartphone affects a number of driving performance variables, including deviations from the lane, reaction time to cars or other objects in front of the vehicle, time to recognize objects in the driver’s peripheral vision, speed, acceleration, and so forth. Using several dependent variables helps triangulate on the truth—if all the variables indicate the same outcome then one can have much greater confidence in that outcome.

P3.28 For an evaluation of a vehicle entertainment system, identify possible dependent variables.

P3.29 What are the benefits of subjective measures?

P3.30 What are the limitations of subjective measures?

P3.31 What is the relationship between a construct and a measure?