Eating disorder examination questionnaire (EDE-Q): validity and norms for Saudi nationals

The performance of a diagnostic test in the case of a binary predictor can be evaluated using the measures of sensitivity and specificity. However, in many instances, we encounter predictors that are measured on a continuous or ordinal scale. In such cases, it is desirable to assess performance of a diagnostic test over the range of possible cutpoints for the predictor variable. This is achieved by a receiver operating characteristic (ROC) curve that includes all the possible decision thresholds from a diagnostic test result. In this brief report, we discuss the salient features of the ROC curve, as well as discuss and interpret the area under the ROC curve, and its utility in comparing two different tests or predictor variables of interest.

A detailed comparison was made of two methods for assessing the features of eating disorders. An investigator-based interview was compared with a self-report questionnaire based directly on that interview. A number of important discrepancies emerged. Although the two measures performed similarly with respect to the assessment of unambiguous behavioral features such as self-induced vomiting and dieting, the self-report questionnaire generated higher scores than the interview when assessing more complex features such as binge eating and concerns about shape. Both methods underestimated body weight.

Clinicians and those in health sciences are frequently called upon to measure subjective states such as attitudes, feelings, quality of life, educational achievement and aptitude, and learning style in their patients. This fifth edition of Health Measurement Scales enables these groups to both develop scales to measure non-tangible health outcomes, and better evaluate and differentiate between existing tools.<br> <br> Health Measurement Scales is the ultimate guide to developing and validating measurement scales that are to be used in the health sciences. The book covers how the individual items are developed; various biases that can affect responses (e.g. social desirability, yea-saying, framing); various response options; how to select the best items in the set; how to combine them into a scale; and finally how to determine the reliability and validity of the scale. It concludes with a discussion of ethical issues that may be encountered, and guidelines for reporting the results of the scale development process. Appendices include a comprehensive guide to finding existing scales, and a brief introduction to exploratory and confirmatory factor analysis, making this book a must-read for any practitioner dealing with this kind of data.<br>