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Evaluating a theory-based hypothesis against its complement using an AIC-type information criterion with an application to facial burn injury.
Psychological Methods ( IF 10.929 ) Pub Date : 2020-04-01 , DOI: 10.1037/met0000238
Leonard Vanbrabant 1 , Nancy Van Loey 2 , Rebecca M Kuiper 3
Affiliation  

An information criterion (IC) like the Akaike IC (AIC), can be used to select the best hypothesis from a set of competing theory-based hypotheses. An IC developed to evaluate theory-based order-restricted hypotheses is the Generalized Order-Restricted Information Criterion (GORIC). Like for any IC, the values themselves are not interpretable but only comparable. To improve the interpretation regarding the strength, GORIC weights and related evidence ratios can be computed. However, if the unconstrained hypothesis (the default) is used as competing hypothesis, the evidence ratio is not affected by sample-size nor effect-size in case the hypothesis of interest is (also) in agreement with the data. In practice, this means that in such a case strong support for the order-restricted hypothesis is not reflected by a high evidence ratio. Therefore, we introduce the evaluation of an order-restricted hypothesis against its complement using the GORIC (weights). We show how to compute the GORIC value for the complement, which cannot be achieved by current methods. In a small simulation study, we show that the evidence ratio for the order-restricted hypothesis versus the complement increases for larger samples and/or effect-sizes, while the evidence ratio for the order-restricted hypothesis versus the unconstrained hypothesis remains bounded. An empirical example about facial burn injury illustrates our method and shows that using the complement as competing hypothesis results in much more support for the hypothesis of interest than using the unconstrained hypothesis as competing hypothesis. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

中文翻译:

使用AIC型信息标准评估基于理论的假设及其补充,并应用于面部烧伤。

诸如赤池集成电路(AIC)之类的信息标准(IC)可用于从一组基于理论的竞争性假设中选择最佳假设。开发用于评估基于理论的限序假设的IC是广义限序信息标准(GORIC)。像任何IC一样,这些值本身无法解释,只能比较。为了提高对强度的解释,可以计算GORIC权重和相关证据比率。但是,如果将无约束假设(默认值)用作竞争假设,则在相关假设(也)与数据一致的情况下,证据比率不受样本量或效应量的影响。实际上,这意味着在这种情况下,高证据率无法反映出对顺序受限假说的有力支持。因此,我们使用GORIC(权重)介绍了针对其补语的阶数受限的假设的评估。我们展示了如何计算补码的GORIC值,这是当前方法无法实现的。在一个小型模拟研究中,我们表明,对于较大样本和/或效应量,顺序受限假说与补语的证据比率增加,而顺序受限假说与无约束假设的证据比率仍然有限。一个关于面部烧伤的经验例子说明了我们的方法,并表明,使用补语作为竞争假设比使用无约束假设作为竞争假设能为目标假设提供更多的支持。(PsycINFO数据库记录(c)2019 APA,保留所有权利)。
更新日期:2020-04-01
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