Statistical modelling of vignette data in psychology.
Vignette methods are widely used in psychology and the social sciences to obtain responses to multi‐dimensional scenarios or situations. Where quantitative data are collected this presents challenges to the selection of an appropriate statistical model. This depends on subtle details of the design a...
| Published in: | British Journal of Psychology Vol. 113; no. 4; pp. 1143 - 1164 |
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| Main Authors: | , , |
| Format: | Article |
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Wiley-Blackwell
Nov2022
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=159610010&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 159610010 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00071269 BJP jtl: British Journal of Psychology issn: 00071269 maglogo: Y pubinfo: dt: Nov2022 vid: 113 iid: 4 pid: 480 pub: Wiley-Blackwell artinfo: ui: 159610010 10.1111/bjop.12577 ppf: 1143 ppct: 21 formats: fmt: – @attributes: type: T – @attributes: type: C – @attributes: type: P size: 905KB tig: atl: Statistical modelling of vignette data in psychology. aug: au: Baguley, Thom Dunham, Grace Steer, Oonagh affil: Nottingham Trent University, Nottingham, UK su: Judgment (Psychology) Psychology Bullying Case studies Statistical models sug: subj: Judgment (Psychology) Psychology Bullying Case studies Statistical models keyword: factorial survey experiments multilevel modeling vignette data factorial survey experiments multilevel modeling vignette data ab: Vignette methods are widely used in psychology and the social sciences to obtain responses to multi‐dimensional scenarios or situations. Where quantitative data are collected this presents challenges to the selection of an appropriate statistical model. This depends on subtle details of the design and allocation of vignettes to participants. A key distinction is between factorial survey experiments where each participant receives a different allocation of vignettes from the full universe of possible vignettes and experimental vignette studies where this restriction is relaxed. The former leads to nested designs with a single random factor and the latter to designs with two crossed random factors. In addition, the allocation of vignettes to participants may lead to fractional or unbalanced designs and a consequent loss of efficiency or aliasing of the effects of interest. Many vignette studies (including some factorial survey experiments) include unmodeled heterogeneity between vignettes leading to potentially serious problems if traditional regression approaches are adopted. These issues are reviewed and recommendations are made for the efficient design of vignette studies including the allocation of vignettes to participants. Multilevel models are proposed as a general approach to handling nested and crossed designs including unbalanced and fractional designs. This is illustrated with a small vignette data set looking at judgements of online and offline bullying and harassment. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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