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order norms), develop their own measures in lieu of those with established psychometric properties, or focus on pathogenic eating behaviors (eg, dietary restraint) rather than identifying specific eating disorders For example, studies that have used author-developed, as opposed to externally validated, measures generally report the highest prevalence rates among athletes (eg, Black & BurkesMiller, 1988; Rosen & Hough, 1988; Rosen, McKeag, Hough, & Curley, 1986) The use of different measurement instruments also may explain the large variation of prevalence rates among athletes For example, researchers may use questionnaires that directly assess Bulimia Nervosa (eg, Bulimia TestRevised; Thelen, Farmer, Wonderlich, & Smith, 1991; Thelen, Mintz, & Vander Wal, 1996), drive for thinness, or general eating pathology (eg, Eating Attitudes Test26; Garner, Olmstead, Bohr, & Garfinkel, 1982).

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Amos Amos (Arbuckle & Wothke, 1999) has become one of the more commonly used SEM programs available today Amos provides good, quick parameter estimation, along with reasonable standard errors, in the missing data case Two of the most desirable features of Amos are (a) that it has an excellent and extremely exible graphical interface and (b) that it is now part of the SPSS package This latter fact means that one can create one s SPSS data set, making any desired data modi cations, and then click on Amos as one of the available analyses within the SPSS package Despite the drawbacks described in the next paragraph, the array of nifty features makes Amos a highly desirable option for researchers in the social sciences.

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The measure used and how the disordered eating construct is defined in the study strongly influence the prevalence rates found in the sample Thus, researchers and practitioners need to take care in their instrument selection and description of the disordered eating construct being measured so more direct comparisons can be made among studies In addition, including only measures that have been standardized and have established psychometric properties, particularly those validated with current diagnostic criteria, should become a standard part of research and clinical methodologies A thorough review and critique of current measures of disordered eating and related constructs is beyond the scope of this chapter (see Kashubeck-West, Mintz, & Saunders, 2001, for a detailed review) However, because of this issue s importance, we provide an overview of two validated general eating disorder measures and highlight a few recently developed athlete-specific questionnaires.

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Although general eating disorder measures should continue to be used in the study of athletes, particularly those that have been tested in sport settings (eg, Eating Attitudes Test-26; Doninger, Enders, & Burnett, 2005), including validated populationspecific measures will aid in the assessment of the sport environment and improve our understanding of athletes experiences In addition, population-specific measures will provide differential validity information on general and athlete-specific measures Although the Eating Disorder Inventory and its revision (EDI-2; Garner, 1991) have been used extensively in athlete eating disorder research (Hausenblas & Carron, 1999), the Bulimia Test-Revised (BULIT-R; Thelen et al, 1991, 1996) and the Questionnaire for Eating Disorder Diagnoses.

Amos is not free, but it is available at a reasonable price, especially if one can obtain it at the same time one obtains the latest version of SPSS Unfortunately, Amos is not without limitations Perhaps the most important limitation is that one of its most desirable features, the graphical interface, becomes quickly loaded down with squares, circles, and arrows when more than a few latent variables are included in the model For example, a model with ve independent latent variables, three latent mediating variables, and three latent outcomes variables, would be a jumble of wires (regression and correlation paths) and extremely dif cult to read This problem is further exacerbated if one makes use of one of the models recommended for enhancing the missing data estimation (described later)..