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Identifying most typical and most ideal attribute levels in small populations of expert decision makers: Studying the Go/No Go decision of disaster relief organizations
Journal of Choice Modelling ( IF 2.8 ) Pub Date : 2020-01-30 , DOI: 10.1016/j.jocm.2020.100204
Paul Isihara , Chaojun Shi , Jonathan Ward , Leo O'Malley , Skyler Laney , Danilo Diedrichs , Gabriel Flores

This paper proposes the use of Most Typical (MT) and Most Ideal (MI) levels when an adaptive choice-based conjoint (ACBC) survey can only obtain a small sample size n from a small population size N. This situation arises when expert decision makers are surveyed from among important small populations such as executives of large companies or political leaders, for which the expert decision maker assumption is reasonable. The paper compares respondents' MT levels obtained using the Build Your Own (BYO) question with MI levels obtained using part-worth utilities. The MI levels are validated using the Potentially All Pairwise RanKings of all possible Alternatives (PAPRIKA) method. It then explores differences in MT/MI levels for two related populations using an application concerning disaster relief. For effective disaster relief coordination, humanitarian organizations must understand each other's response decisions. An ACBC survey on the “Go/No-Go” decision by 49 faith-based (FBOs) and 12 non faith-based (NFBOs) disaster relief organizations considered four attributes: Funding, Disaster Response Type, Need Assessment, and Community Access. There was disparity between MT/MI Funding levels: 18 of 19 respondents reported MT levels of 50% or less, but 12 of 19 estimated to have MI levels of at least 75%. Greatest similarity between FBOs and NFBOs was observed for MI Need Assessment. Greatest disagreement of MI levels determined by part-worths and PAPRIKA was for Need Assessment and Disaster Response Type. To handle zero counts in the sample frequency distributions, we include a mathematical appendix explaining our use of a Bayesian rather than maximum likelihood estimation of MT/MI population frequency distributions.



中文翻译:

在少数专家决策者中确定最典型和最理想的属性级别:研究救灾组织的通过/不通过决策

本文提出了当基于选择的自适应联合调查(ACBC)只能从较小的人口规模N中获得较小的样本量n时,使用“最典型”(MT)和“最理想”(MI)级别。当从重要的小群体(例如大公司的高管或政治领导人)中调查专家决策者时,就会出现这种情况,对此,专家决策者的假设是合理的。本文将使用“自行建造(BYO)”问题获得的受访者的MT级别与使用部分价值工具获得的MI的级别进行了比较。使用所有可能替代方法的潜在所有成对排名(PAPRIKA)方法验证MI级别。然后使用有关救灾的应用程序探索两个相关人群的MT / MI水平差异。为了有效地协调救灾工作,人道主义组织必须了解彼此的应对决策。ACBC对49个基于信仰的(FBO)和12个非基于信仰(NFBO)的救灾组织做出的“通过/不通过”决定的调查考虑了以下四个属性:资金,灾难响应类型,需求评估和社区访问。MT / MI资金水平之间存在差异:19位受访者中有18位报告的MT水平为50%或更低,但19位受访者中有12位的MI水平至少为75%。在MI需求评估中,观察到FBO与NFBO之间的最大相似性。由零件价值和PAPRIKA确定的MI水平的最大分歧在于需求评估和灾难响应类型。为了处理样本频率分布中的零计数,我们包括一个数学附录,解释了我们使用贝叶斯方法而不是MT / MI总体频率分布的最大似然估计。灾难响应类型,需求评估和社区访问。MT / MI资金水平之间存在差异:19位受访者中有18位报告的MT水平为50%或更低,但19位受访者中有12位的MI水平至少为75%。在MI需求评估中,观察到FBO与NFBO之间的最大相似性。由零件价值和PAPRIKA确定的MI水平的最大分歧在于需求评估和灾难响应类型。为了处理样本频率分布中的零计数,我们包括一个数学附录,解释了我们使用贝叶斯方法而不是MT / MI总体频率分布的最大似然估计。灾难响应类型,需求评估和社区访问。MT / MI资金水平之间存在差异:19位受访者中有18位报告的MT水平为50%或更低,但19位受访者中有12位的MI水平至少为75%。在MI需求评估中,观察到FBO与NFBO之间的最大相似性。由零件价值和PAPRIKA确定的MI水平的最大分歧在于需求评估和灾难响应类型。为了处理样本频率分布中的零计数,我们包括一个数学附录,解释了我们使用贝叶斯方法而不是MT / MI总体频率分布的最大似然估计。但19人中有12人的心梗水平至少为75%。在MI需求评估中,观察到FBO与NFBO之间的最大相似性。由零件价值和PAPRIKA确定的MI水平的最大分歧在于需求评估和灾难响应类型。为了处理样本频率分布中的零计数,我们包括一个数学附录,解释了我们使用贝叶斯方法而不是MT / MI总体频率分布的最大似然估计。但19人中有12人的心梗水平至少为75%。在MI需求评估中,观察到FBO与NFBO之间的最大相似性。由零件价值和PAPRIKA确定的MI水平的最大分歧在于需求评估和灾难响应类型。为了处理样本频率分布中的零计数,我们包括一个数学附录,解释了我们使用贝叶斯方法而不是MT / MI总体频率分布的最大似然估计。

更新日期:2020-01-30
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