Dishop (see record 2022-78260-001) identifies the consensus emergence model (CEM) as a useful tool for future research on emergence but argues that autoregressive models with positive autoregressive effects are an important alternative data-generating mechanism that researchers need to rule out. Here, we acknowledge that alternative data-generating mechanisms are possibility for most, if not all, nonexperimental designs and appreciate Dishop's attempts to identify cases where the CEM could provide misleading results. However, in a series of independent simulations, we were unable to replicate two of three key analyses, and the results for the third analysis did not support the earlier conclusions. The discrepancies appear to originate from Dishop's simulation code and what appear to be inconsistent model specifications that neither simulate the models described in the article nor include notable positive autoregressive effects. We contribute to the wider literature by suggesting four key criteria that researchers can apply to evaluate the possibility of alternative data-generating mechanisms: Theory, parameter recovery, fit to real data, and context. Applied to autoregressive effects and emergence data, these criteria reveal that (a) theory in psychology would generally suggest negative instead of positive autoregressive effects for behavior, (b) it is challenging to recover true autoregressive parameters from simulated data, and (c) that real data sets across a number of different contexts show little to no evidence for autoregressive effects. Instead, our analyses suggest that CEM results are congruent with the temporal changes occurring within groups and that autoregressive effects do not lead to spurious CEM results. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

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替代数据生成机制的合理性：对 Dishop (2022) 的评论和复制尝试。


Dishop（参见记录 2022-78260-001）将共识涌现模型 (CEM) 视为未来涌现研究的有用工具，但认为具有正自回归效应的自回归模型是一种重要的替代数据生成机制，研究人员需要排除这种机制。在这里，我们承认替代数据生成机制对于大多数（如果不是全部）非实验设计都是可能的，并且赞赏 Dishop 尝试识别 CEM 可能提供误导性结果的情况。然而，在一系列独立模拟中，我们无法复制三个关键分析中的两个，并且第三个分析的结果也不支持早期的结论。这些差异似乎源于 Dishop 的模拟代码和似乎不一致的模型规范，既不模拟文章中描述的模型，也不包含显着的正自回归效应。我们提出了四个关键标准，研究人员可以应用这些标准来评估替代数据生成机制的可能性，从而为更广泛的文献做出贡献：理论、参数恢复、适合真实数据和背景。应用于自回归效应和涌现数据时，这些标准表明（a）心理学理论通常会建议行为产生负面而不是正面的自回归效应，（b）从模拟数据中恢复真实的自回归参数具有挑战性，（c）许多不同背景下的真实数据集几乎没有显示自回归效应的证据。相反，我们的分析表明 CEM 结果与组内发生的时间变化一致，并且自回归效应不会导致虚假的 CEM 结果。 （PsycInfo 数据库记录 (c) 2024 APA，保留所有权利）。