Raw score

Abstract: Background: Although a number of measures of alcohol problems in college students have been studied, the psychometric development and validation of these scales have been limited, for the most part, to methods based on classical test theory. In this study, we conducted analyses based on item response theory to select a set of items for measuring the alcohol problem severity continuum in college students that balances comprehensiveness and efficiency and is free from significant gender bias. Method: We conducted Rasch model analyses of responses to the 48-item Young Adult Alcohol Consequences Questionnaire by 164 male and 176 female college students who drank on at least a weekly basis. An iterative process using item fit statistics, item severities, item discrimination parameters, model residuals, and analysis of differential item functioning by gender was used to pare the items down to those that best fit a Rasch model and that were most efficient in discriminating among levels of alcohol problems in the sample. Results: The process of iterative Rasch model analyses resulted in a final 24-item scale with the data fitting the unidimensional Rasch model very well. The scale showed excellent distributional properties, had items adequately matched to the severity of alcohol problems in the sample, covered a full range of problem severity, and appeared highly efficient in retaining all of the meaningful variance captured by the original set of 48 items. Conclusions: The use of Rasch model analyses to inform item selection produced a final scale that, in both its comprehensiveness and its efficiency, should be a useful tool for researchers studying alcohol problems in college students. To aid interpretation of raw scores, examples of the types of alcohol problems that are likely to be experienced across a range of selected scores are provided.

Abstract: The Mini-Mental State Examination (MMSE), a brief test to assess cognitive status, is heavily influenced by age and education. It was administered to 1019 elderly subjects (aged 65-89 years) living in three different Italian cities. A statistical non-linear regression model was built up in order to obtain adjustment coefficients to reduce the influence of demographic variables on the MMSE raw scores. Age and educational level were significantly and independently associated with the MMSE score. Results of a multiple linear regression with transformation of age and education provided adjustment coefficients of the MMSE raw scores. Data from this study will ameliorate the overall reliability of MMSE as a screening test for cognitive impairment in elderly people.