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Model selection uncertainty and multimodel inference in partial least squares structural equation modeling (PLS-SEM)

机译：局部最小二乘结构方程建模中的模型选择不确定性和多模推断（PLS-SEM）

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摘要

Comparing alternative explanations for behavioral phenomena is central to the process of scientific inquiry. Recent research has emphasized the efficacy of Information Theoretic model selection criteria in partial least squares structural equation modeling (PLS-SEM), which has gained massive dissemination in a variety of fields. However, selecting one model over others based on model selection criteria may lead to a false sense of confidence as differences in the criteria values are often small. To overcome this limitation researchers have proposed Akaike weights, whose efficacy however, has not been assessed in the PLS-SEM context yet. Addressing this gap in research, we analyze the efficacy of Akaike weights in PLS-SEM-based model comparison tasks. We find that Akaike weights derived from BIC and GM are well suited for separating incorrectly specified from correctly specified models, and that Akaike weights based on AIC are useful for creating model-averaged predictions under conditions of model selection uncertainty.
机译：比较行为现象的替代解释是科学探究过程的核心。最近的研究强调了信息理论模型选择标准在局部最小二乘结构方程模型（PLS-SEM）中的功效，这在各种领域中获得了大规模的传播。然而，根据模型选择标准选择一个模型可以导致错误的置信感，因为标准值的差异通常很小。为了克服这种限制研究人员提出了Akaike权重，其有效性尚未在PLS-SEM背景下进行评估。解决这一研究的这种差距，我们分析了Akaike权重在PLS-SEM的模型比较任务中的功效。我们发现，来自BIC和GM的Akaike权重非常适合从正确指定的模型分离错误，并且基于AIC的Akaike权重可用于在模型选择不确定性的条件下创建模型平均预测。

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