Response biases related to wording effects, the inconsistent responding to regular and reversed self-report items, are pervasive in the behavioral and health sciences. Although several factor modeling strategies have been proposed to mitigate their adverse effects, there is limited simulation research assessing their performance with exploratory structural equation models (ESEM). The present study evaluated the impact of wording effects on ESEM models incorporating two popular method modeling

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... rategies, the correlated traits-correlated methods minus one (CTCM-1) model and random intercept item factor analysis (RIIFA), as well as the "do nothing" approach. Four variables were manipulated using Monte Carlo methods: the amount of wording effects, factor loadings, factor correlations, and sample size. Overall, the results showed that ignoring wording effects can lead to poor model fit and serious distortions of the ESEM estimates. The RIIFA approach, especially with one wording factor per substantive factor, performed best in countering these adverse impacts and recovering the unbiased factor structures. Relatedly, the current study exposed the inherent ambiguity of wording method factors and their nomological networks. A straightforward guide is offered to applied researchers who wish to use ESEM with mixed-worded scales.