Assessing the correctness of a structural equation model is essential to avoid drawing incorrect conclusions from empirical research. In the past, the chi-square test was recommended for assessing the correctness of the model but this test has been criticized because of its sensitivity to sample size. As a reaction, an abundance of fit indexes have been developed. The result of these developments is that structural equation modeling packages are now producing a large list of fit measures. One would think that this progression has led to a clear understanding of evaluating models with respect to model misspecifications. In this article we question the validity of approaches for model evaluation based on overall goodness-of-fit indexes. The argument against such usage is that they do not provide an adequate indication of the "size" of the model's misspecification. That is, they vary dramatically with the values of incidental parameters that are unrelated with the misspecification in the model. This is illustrated using simple but fundamental models. As an alternative method of model evaluation, we suggest using the expected parameter change in combination with the modification index (MI) and the power of the MI test.In an influential paper, MacCallum, Browne, and Sugawara (1996) wrote, "If the model is truly a good model in terms of its fit in the population, we wish to avoid concluding that the model is a bad one. Alternatively, if the model is truly a bad one, we wish to avoid concluding that it is a good one" (p. 131). The mentioned two types of wrong conclusions correspond to what in statistics are known as Type I and Type II errors, the probabilities of occurrence of which are called ' and " respectively. Although everybody would agree that ' and " should be as small as

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PurposeIn order to understand the multidimensional mechanism of fear of cancer recurrence (FCR) and to identify potential targets for interventions, it is important to empirically test the theoretical model of FCR. This study aims at assessing the validity of Lee-Jones et al.’s FCR model.MethodsA total of 1205 breast cancer survivors were invited to participate in this study. Participants received a questionnaire booklet including questionnaires on demographics and psychosocial variables including FCR. Data analysis consisted of the estimation of direct and indirect effects in mediator models.ResultsA total of 460 women (38 %) participated in the study. Median age was 55.8 years (range 32–87). Indirect effects of external and internal cues via FCR were found for all mediation models with limited planning for the future (R 2 = .28) and body checking (R 2 = .11–.15) as behavioral response variables, with the largest effects for limited planning for the future. A direct relation was found between feeling sick and seeking professional advice, not mediated by FCR.ConclusionsIn the first tested models of FCR, all internal and external cues were associated with higher FCR. In the models with limited planning for the future and body checking as behavioral response, an indirect effect of cues via FCR was found supporting the theoretical model of Lee-Jones et al.Implications for Cancer SurvivorsAn evidence-based model of FCR may facilitate the development of appropriate interventions to manage FCR in breast cancer survivors.

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Incidence of mental disorders in young women was predicted by neuroticism and low global functioning. There seems to be a need for preventive interventions addressing high neuroticism and low global functioning. Remission in young women was predicted by positive mental health. It may be helpful to include resource-based interventions, which can strengthen or support general positive mental health. Relapse in young women was predicted by two negative psychological factors: high neuroticism and reporting many dysfunctional attitudes. Psychotherapy addressing the characteristics and behaviour of neurotic patients might be beneficial. Interventions should also focus on addressing and changing dysfunctional attitudes.