Latent factor structure of a behavioral economic marijuana demand curve

Abstract

Rationale Drug demand, or relative value, can be assessed via analysis of behavioral economic purchase task performance. Five demand indices are typically obtained from drug purchase tasks. Objectives The goal of this research was to determine whether metrics of marijuana reinforcement from a marijuana purchase task (MPT) exhibit a latent factor structure that efficiently characterizes marijuana demand. Methods Participants (n=99; 37.4% female, 71.5% marijuana use days [5 days/week], 15.2% cannabis dependent) were regular marijuana users who completed study assessments, including the MPT, during a baseline session. Principal components analysis was used to examine the latent structure underlying MPT indices. Concurrent validity was assessed via examination of relationships between latent factors and marijuana use, past quit attempts, and marijuana expectancies. Results A two-factor solution was confirmed as the best fitting structure, accounting for 88.5% of the overall variance. Factor 1 (65.8% variance) reflected “Persistence”, indicating sensitivity to escalating marijuana price, which comprised four MPT indices (elasticity, Omax, Pmax, and breakpoint). Factor 2 (22.7% variance) reflected “Amplitude”, indicating the amount consumed at unrestricted price (intensity). Persistence factor scores were associated with fewer past marijuana quit attempts and lower expectancies of negative use outcomes. Amplitude factor scores were associated with more frequent use, dependence symptoms, craving severity, and positive marijuana outcome expectancies. Conclusions Consistent with research on alcohol and cigarette purchase tasks, the MPT can be characterized with a latent two-factor structure. Thus, demand for marijuana appears to encompass distinct dimensions of price-sensitivity and volumetric consumption, with differential relations to other aspects of marijuana motivation.

The condom purchase task: A hypothetical demand method for evaluating sexual health decision‐making

“…Additional parameters were evaluated directly from the demand curve, including breakpoint (first price to suppress consumption to zero), O max (maximum expenditure), and P max (price at maximum expenditure). 2 Analyses focused on demand intensity and elasticity measures because these values generally reflect two distinct behavioral mechanisms and show separate loadings in factor analytic studies (Aston, Farris, MacKillop, & Metrik, 2017;Bidwell, MacKillop, Murphy, Tidey, & Colby, 2012;Epstein, Stein, Paluch, MacKillop, & Bickel, 2018;Murphy, MacKillop, Skidmore, & Pederson, 2009). Two approaches were used to evaluate condom purchase outcomes.…”

Utilizing the commodity purchase task to evaluate behavioral economic demand for illicit substances: a review and meta‐analysis

“…These differences could be due partially to the curve-observed nature of P max and breakpoint, which rely on estimation from a single location on the demand curve. 4 It is also possible that imprecise 4 O max values are also curve-observed; however, the estimation of O max relies on a combination of information from the relative amplitude of consumption at low cost and the relative persistence of composition across price changes (see cross loadings for O max in factor analytical studies [53][54][55]). Intensity can also be curve-observed or derived and reflects in part the trajectory of behavior aggregated along the inelastic portion of the demand curve.…”

Using behavioral economic variables to predict future alcohol use in a crowdsourced sample

“…The exponentiated demand equation also provided an excellent fit to group data (fit for mean demand data R 2 : alcohol=0.99; soda=0.99) and individual data (mean of individual demand curve fits R 2 : alcohol=0.87; soda=0.91). Intensity and elasticity were selected as the primary outcomes because prior factor analytic studies have demonstrated that these measures reflect the two factors underlying the purchase task factor structure for alcohol and other substances (Aston et al, 2017; Bidwell et al, 2012; Epstein et al, 2018; MacKillop et al, 2009). Recent evidence also suggests that these derived measures show greater stimulus-selectivity than other purchase task measures (e.g.…”