Identification and management of nonsystematic purchase task data: Toward best practice

Abstract

Experimental assessments of demand allow the examination of economic phenomena relevant to the etiology, maintenance, and treatment of addiction and other pathologies (e.g., obesity). Although such assessments have historically been resource intensive, development and use of purchase tasks-in which participants purchase 1 or more hypothetical or real commodities across a range of prices-have made data collection more practical and have increased the rate of scientific discovery. However, extraneous sources of variability occasionally produce nonsystematic demand data, in which price exerts either no or inconsistent effects on the purchases of individual participants. Such data increase measurement error, can often not be interpreted in light of research aims, and likely obscure effects of the variable(s) under investigation. Using data from 494 participants, we introduce and evaluate an algorithm (derived from prior methods) for identifying nonsystematic demand data, wherein individual participants' demand functions are judged against 2 general, empirically based assumptions: (a) global, price-dependent reduction in consumption and (b) consistency in purchasing across prices. We also introduce guidelines for handling nonsystematic data, noting some conditions in which excluding such data from primary analyses may be appropriate and others in which doing so may bias conclusions. Adoption of the methods presented here may serve to unify the research literature and facilitate discovery.

Figure 1

Example demand functions that that the three-criterion algorithm identifies as systematic (left panel) or nonsystematic for failing to meet the Trend, Bounce, or Reversal criteria (right panels).

Figure 2

Percent of demand functions that failed to meet the Trend (left panels) and Bounce (right panels) criteria when varying detection limits were used. See text for details.