Nottingham Trent University, Nottingham, UK.
Br J Psychol. 2022 Nov;113(4):1143-1163. doi: 10.1111/bjop.12577. Epub 2022 Jun 23.
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.
小插曲方法在心理学和社会科学中被广泛应用于获取对多维场景或情况的反应。在收集定量数据的情况下，这对选择适当的统计模型提出了挑战。这取决于小插曲的设计和分配给参与者的微妙细节。一个关键的区别是，在析因调查实验中，每个参与者从可能的小插曲的完整宇宙中收到不同的小插曲分配，而在实验小插曲研究中，这种限制放宽了。前者导致具有单个随机因素的嵌套设计，后者导致具有两个交叉随机因素的设计。此外，小插曲的分配给参与者可能导致分数或不平衡的设计，以及因此效率的损失或感兴趣的效果的混淆。许多小插曲研究（包括一些析因调查实验）包括小插曲之间未建模的异质性，如果采用传统的回归方法，这可能会导致严重的问题。本文回顾了这些问题，并对小插曲研究的有效设计提出了建议，包括小插曲的分配给参与者。多层次模型被提出作为处理嵌套和交叉设计的一般方法，包括不平衡和分数设计。这通过一个小型小插曲数据集进行了说明，该数据集研究了对在线和离线欺凌和骚扰的判断。
