BACKGROUND Analysis of interaction or moderation effects between latent variables is a common requirement in the social sciences. However, when predictors are correlated, interaction and quadratic effects become more alike, making them difficult to distinguish. As a result, when data are drawn from a quadratic population model and the analysis model specifies interactions only, misleading results may be obtained. METHOD This article addresses the consequences of different types of specification error in nonlinear structural equation models using a Monte Carlo study. RESULTS Results show that fitting a model with interactions when quadratic effects are present in the population will almost certainly lead to erroneous detection of moderation effects, and that the same is true in the opposite scenario. Simultaneous estimation of interactions and quadratic effects yields correct results. CONCLUSIONS Simultaneous estimation of interaction and quadratic effects prevents detection of spurious or misleading nonlinear effects. Results are discussed and recommendations are offered to applied researchers.

中文翻译：

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非线性SEM中的规格问题：不是适度的。

背景技术潜在变量之间的相互作用或调节作用的分析是社会科学中的普遍要求。但是，当预测变量相关时，交互作用和二次效应变得更加相似，从而使其难以区分。结果，当从二次总体模型中提取数据并且分析模型仅指定相互作用时，可能会产生误导性的结果。方法本文使用蒙特卡洛研究解决非线性结构方程模型中不同类型规范误差的后果。结果结果表明，当总体中存在二次效应时，使用相互作用对模型进行拟合几乎可以肯定会导致适度效应的错误检测，在相反的情况下也是如此。同时估计相互作用和二次效应会得出正确的结果。结论相互作用和二次效应的同时估计可防止检测到虚假或误导的非线性效应。讨论了结果，并向应用研究人员提供了建议。