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A tailored-fit model evaluation strategy for better decisions about structural equation models

机译:用于结构方程模型的更好决策的量身定制的模型评估策略

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摘要

Proper measurement of technology knowledge and social change enables managers to advance strategies in technology management. Structural equation modeling is the ideal method in Technological Forecasting and Social Change (TFSC) and other leading journals to assess the measurement quality of the relevant decision variables and understand how they are related. However, a myriad of indicators are now available to judge how suitable these measurements are (i.e., how well they fit). Despite a consensus that fit indicators are highly context-dependent and no "one-fits-all approach" emerges, a more contingent perspective is surprisingly missing. To fill this gap, we advocate for a "tailored-fit model evaluation strategy" that is specific to the situation at hand to exploit the particular strengths of fit indicators. Motivated by a synthesis of structural equation modeling in TFSC, our simulation study finds that three critical distinctions regarding (a) model novelty, (b) focus on measurement or structural models, and (c) sample size are vital. The proposed strategy demonstrates that, in many contexts, only a few indicators are recommended to avoid artificially inflated Type I/II errors. We provide a decision tree to reach more accurate decisions in model evaluation in order to better theorize and forecast technological and social challenges.
机译:适当的技术知识和社会变革的测量使管理人员能够推进技术管理的策略。结构方程建模是技术预测和社会变革(TFSC)和其他主要期刊的理想方法,以评估相关决策变量的测量质量,并了解它们是相关的。然而,现在可以使用无数的指标来判断这些测量的合适程度(即它们的适应程度)。尽管达成共识,但拟合指标是高度上下文相关的,而且没有“一定的方法”出现,更有目的的观点令人惊讶地失踪。为了填补这一差距,我们倡导“定制适合的模型评估策略”,该策略是特定于手头的情况,以利用适合指标的特殊优势。通过TFSC的结构方程建模的合成,我们的仿真研究发现,关于(a)模型新颖性的三种致力于区别,(b)聚焦在测量或结构模型上,(c)样本量为至关重要。拟议的策略表明,在许多情况下,建议只有几个指标才能避免人为膨胀I / II型错误。我们提供了决策树,以便在模型评估中达到更准确的决策,以更好地理解和预测技术和社会挑战。

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