A maximum likelihood approach for asymmetric non-normal data using a transformational measurement model

A transformational measurement model for structural equation modeling (SEM) of asymmetric non-normal data is proposed. This aligns with the expectation-maximization (EM) algorithm maximum likelihood estimation (MLE) method, creating adaptability to that deviate from normality. Distinctive properties connection and EM are maintenance normality assumption, which at core algorithm, applicability non-normality observed mediated by distortion coefficients. An evaluation using a mixture normal severely analyzed MLE (MLE ASN) demonstrated efficiency model. Comparisons robust DWLS WLS yielded better fit results under ASN estimation.