Assumptions in structural equation modeling.

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Student engagement scale: development, reliability and validity1 (2015, methodology): “As CFA is quite sensitive to missing data (Kline 2012), 50 participants with missing data were not included in the data-set, and the analyses were conducted for 332 individuals.”
Assessing statistical aspects of test fairness with structural equation modelling (2013, background): “Indeed, estimation of model parameters in SEM assumes that the model is correctly specified, a very strong assumption not always fully appreciated in practice (Kline, 2012).”
What is political incivility? (2016, unknown): “Such estimation is ideal for non-normally distributed ordinal data with larger sample sizes (Kline, 2012; Lei & Wu, 2012).”
The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers' adoption of digital technology in education (2019, methodology): “…of e ﬀ ects, the cross-sectional data used to specify the TAM with the help of structural equation models cannot deliver evidence for causality (Kline, 2012) — instead, longitudinal designs accounting for possible confounders and experimental studies are needed to substantiate causality…”