The association between drinking motives and alcohol-related consequences - room for biases and measurement issues?

Aims  To investigate whether the predominant finding of generalized positive associations between self-rated motives for drinking alcohol and negative consequences of drinking alcohol are influenced by (i) using raw scores of motives that may weight inter-individual response behaviours too strongly, and (ii) predictor-criterion contamination by using consequence items where respondents attribute alcohol use as the cause. Design  Cross-sectional study within the European School Survey Project on Alcohol and other Drugs (ESPAD). Setting  School classes. Participants  Students, aged 13-16 (n = 5633). Measurements  Raw, rank and mean-variance standardized scores of the Drinking Motives Questionnaire-Revised (DMQ-R); four consequences: serious problems with friends, sexual intercourse regretted the next day, physical fights and troubles with the police, each itemized with attribution ('because of your alcohol use') and without. Findings  As found previously in the literature, raw scores for all drinking motives had positive associations with negative consequences of drinking, while transformed (rank or Z) scores showed a more specific pattern: external reinforcing motives (social, conformity) had negative and internal reinforcing motives (enhancement, coping) had non-significant or positive associations with negative consequences. Attributed consequences showed stronger associations with motives than non-attributed ones. Conclusion  Standard scoring of the Drinking Motives Questionnaire (Revised) fails to capture motives in a way that permits specific associations with different negative consequences to be identified, whereas use of rank or Z-scores does permit this. Use of attributed consequences overestimates the association with drinking motives.

Construcción y validación de un cuestionario para medir conductas, conocimientos y actitudes sobre la higiene de las manos en personal sanitario en formacion.

Background: Hand hygiene in the health context is a complex behaviour. There have been rarely given the role of the knowledge and attitudes as predictors of hand hygiene behaviour. The main objective of this work is the description of the development of a questionnaire on hand hygiene and the analysis of their measurement properties. Method: An instrument which was designed and validated a questionnaire. It was held in January 2009. It finally has had 50 items that assess risk behaviour intention before and after contact with the patient, declarative knowledge and attitudes about hand hygiene. It has been applied to 431 students of health sciences at the University of Granada. Results: There were three factor analysis, ultimately obtaining a general convergence value that explains 46.01% of the total variance and high reliability (a=0,843). There is correlation between knowledge and behavior intentions before and after patient contact (p <0.01).In turn, the attitude correlates only with behavioral intention before (p <0.05). The hand hygiene behavior refers to a higher mean after the completion of various health activities before the same (4.26 and 3.96 respectively). Both declarative knowledge and attitudes significantly predict behavioral intention, in particular the conduct before the contact with the patient (R2 = 0.100, standardized Beta 0.256 for knowledge and 0.145 for attitudes). Conclusions: The questionnaire shows high internal consistency. We have obtained a valid tool for assessing risk behavior, knowledge and attitudes about students’ hand hygiene in health sciences. The tool detects deficiencies in basic skills in students.

Correcting Measurement Error Bias in Interaction Models with Small Samples

Several methods have been suggested to estimate non-linear models with interaction terms in the presence of measurement error. Structural equation models eliminate measurement error bias, but require large samples. Ordinary least squares regression on summated scales, regression on factor scores and partial least squares are appropriate for small samples but do not correct measurement error bias. Two stage least squares regression does correct measurement error bias but the results strongly depend on the instrumental variable choice. This article discusses the old disattenuated regression method as an alternative for correcting measurement error in small samples. The method is extended to the case of interaction terms and is illustrated on a model that examines the interaction effect of innovation and style of use of budgets on business performance. Alternative reliability estimates that can be used to disattenuate the estimates are discussed. A comparison is made with the alternative methods. Methods that do not correct for measurement error bias perform very similarly and considerably worse than disattenuated regression