This study reports the results of an empirical investigation of the distributional properties of the item fit statistics that are commonly used in Rasch model calibration programs as indices of the fit of the responses to individual items to the measurement model. There are two aspects of this study: an investigation of the distributional properties of the item fit statistics when the data fit the model, and the power of the item fit statistics to detect measurement disturbances. This study is based on simulated data to control for the presence of confounding factors, such as multidimensionality, differences in the slopes of item characteristic curves, and guessing. The results indicate that, when that data fit the model, the distributional properties of the item fit statistics, corrected for use of estimated person parameters, are close to hypothesized mean and standard deviation and that it is possible to construct reasonable Type I error rates that can be used as a frame of reference when investigating the fit of actual data to the Rasch model. The item fit statistics also have the power to detect measurement disturbances of reasonable magnitude.

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Whilst further work is required, the findings provide a deeper understanding of individual midwives' transition period. The importance of forming longitudinal relationships not only with women but with midwifery colleagues is highlighted. Developing continuity models that adequately support graduates and student's needs are likely to assist in addressing practices issues in both the academic and clinical setting.

The idea of whether a person's response pattern is "believable" given his estimated ability is one of the central issues in measurement. This article traces the development of this idea from Thurstone to Rasch. It also presents the rationale behind the development of person analysis in the Rasch model. Finally, it presents data based on simulated response patterns which provide insight into the power and Type I error rates of the Rasch unweighted total and between person fit statistics under a variety of simulated measurement disturbances.

The aim of this investigation was to examine a theoretically based mechanism by which men’s adherence to antifeminine norms is associated with their perpetration of sexual aggression toward intimate partners. Participants were 208 heterosexual men between the ages of 21–35 who had consumed alcohol in the past year. They were recruited from a large southeastern United States city. Participants completed self-report measures of hegemonic masculinity (i.e., antifemininity, sexual dominance), masculine gender role stress, and sexual aggression toward an intimate partner during the past 12 months. Results indicated that adherence to the antifemininity norm and the tendency to experience stress when in subordinate positions to women were indirectly related to sexual aggression perpetration via adherence to the sexual dominance norm. Thus, the men who adhere strongly to these particular hegemonic masculine norms may feel compelled to be sexually aggressive and/or coercive toward an intimate partner in order to maintain their need for dominance within their intimate relationship. Implications for future research and sexual aggression prevention programming are discussed.

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This study employs simulated data to assess the appropriateness of using the separate calibration and between fit approaches to detecting item bias in the Rasch rating scale model. The development of these methods in the dichotomous Rasch model is discussed to delineate these methods from those often used in the true-score model or in multi-item parameter latent trait models. The results indicate that the Type I error rates for the null distribution hold even under circumstances in which there are different levels of ability of the reference and focal groups. The power and Type II error rates are estimated using differing levels of bias introduced by varying the numbers of persons in the focal group, varying the proportion of biased items, and varying the magnitude of the bias introduced. The results indicate that using the traditional 2 critical values for these methods will produce unacceptable Type II error rates and that some inspection of the item plots and the use of other information is needed to make a final determination regarding bias.

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