The cost of forgiveness: Observers prefer victims who leave unfaithful romantic partners

Ninety‐six male fraternity members (Study 1), 112 female voters (Study 2), and 219 undergraduates (Study 3) read scenarios in which a group representative forgave, retaliated, or left a romantic partner after the partner's sexual infidelity was publically revealed. Observers rated a victim who forgave his or her partner to be as mature as a victim who ended the relationship, but also as weaker and less competent. They rated a victim who forgave to be more mature but almost as weak and incompetent as a victim who retaliated. Symbolic concerns that the victim's behavior violated shared values (all three studies) or damaged the group's power/status (Study 3) mediated the relationship between victim behavior and victim ratings. These data demonstrate how the symbolic concerns that shape observers' judgments of an offender can extend to observers' judgments of the victim. Copyright © 2014 John Wiley & Sons, Ltd.     

Confirmatory Factor Analysis (CFA), Exploratory Structural Equation Modeling (ESEM), and Set-ESEM: Optimal Balance Between Goodness of Fit and Parsimony

Abstract

CFAs of multidimensional constructs often fail to meet standards of good measurement (e.g., goodness-of-fit, measurement invariance, and well-differentiated factors). Exploratory structural equation modeling (ESEM) represents a compromise between exploratory factor analysis’ (EFA) flexibility, and CFA/SEM’s rigor and parsimony, but lacks parsimony (particularly in large models) and might confound constructs that need to be kept separate. In Set-ESEM, two or more a priori sets of constructs are modeled within a single model such that cross-loadings are permissible within the same set of factors (as in Full-ESEM) but are constrained to be zero for factors in different sets (as in CFA). The different sets can reflect the same set of constructs on multiple occasions, and/or different constructs measured within the same wave. Hence, Set-ESEM that represents a middle-ground between the flexibility of traditional-ESEM (hereafter referred to as Full-ESEM) and the rigor and parsimony of CFA/SEM. Thus, the purposes of this article are to provide an overview tutorial on Set-ESEM, juxtapose it with Full-ESEM, and to illustrate its application with simulated data and diverse “real” data applications with accessible, heuristic explanations of best practice.