Identification and management of nonsystematic purchase task data: Toward best practice.
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...
| Publicado en: | Experimental & Clinical Psychopharmacology Vol. 23; no. 5; pp. 377 - 387 |
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| Autores principales: | , , , , |
| Formato: | research Journal Article |
| Publicado: |
American Psychological Association
Oct 2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=110181413&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 110181413 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10641297 82W jtl: Experimental & Clinical Psychopharmacology issn: 10641297 maglogo: N pubinfo: dt: Oct 2015 vid: 23 iid: 5 pid: 34 pub: American Psychological Association place: Washington, District of Columbia artinfo: ui: 110181413 110181413 NLM26147181 110181413 10.1037/pha0000020 NLM26147181 PMC4579007 [Available on 10/01/16] 110181413 ppf: 377 ppct: 10 formats: tig: atl: Identification and management of nonsystematic purchase task data: Toward best practice. aug: au: Stein, Jeffrey S Koffarnus, Mikhail N Snider, Sarah E Quisenberry, Amanda J Bickel, Warren K affil: Addictions Recovery Research Center, Virginia Tech Carilion Research Institute sug: subj: Data Collection Algorithms Practice Guidelines Substance Use Disorders Economics Middle Age Female Adult Male Young Adult Human Funding Source Middle Aged: 45-64 years Adult: 19-44 years Female Male ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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