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n real data analysis with structural equation modeling, data are unlikely to be exactly normally distributed. If we ignore the non-normality reality, the parameter estimates, standard error estimates, and model fit statistics from normal theory based methods such as maximum likelihood (ML) and normal theory based generalized least squares estimation (GLS) are unreliable. On the other hand, the asymptotically distribution free (ADF) estimator does not rely on any distribution assumption but cannot demonstrate its efficiency advantage

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Affect can influence judgments and decision-making in multiple ways. One way is through (a) integral affect, or affect related to the choice at hand, and another way is through (b) incidental affect, or affect unrelated to the choice at hand. Research suggests integral affect influences risk-related decision-making, especially in the context of risky choice framing. However, the role of affect in other forms of framing (e.g., attribute framing) has received little attention. We examined how integral affect (Study 1) along with incidental