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Investigating gender differences in alcohol problems: A latent trait modeling approach

Research output: Contribution to journal › Article › peer-review

Abstract

Background: Inconsistent results have been found in research investigating gender differences in alcohol problems. Previous studies of gender differences used a wide range of methodological techniques, as well as limited assortments of alcohol problems. Methods: Parents (1,348 men and 1,402 women) of twins enrolled in the Minnesota Twin Family Study answered questions about a wide range of alcohol problems. A latent trait modeling technique was used to evaluate gender differences in the probability of endorsement at the problem level and for the overall 105-problem scale. Results: Of the 34 problems that showed significant gender differences, 29 were more likely to be endorsed by men than women with equivalent overall alcohol problem levels. These male-oriented symptoms included measures of heavy drinking, duration of drinking, tolerance, and acting out behaviors. Nineteen symptoms were denoted for removal to create a scale that favored neither gender in assessment. Conclusions: Significant gender differences were found in approximately one-third of the symptoms assessed and in the overall scale. Further examination of the nature of gender differences in alcohol problem symptoms should be undertaken to investigate whether a gender-neutral scale should be created or if men and women should be assessed with separate criteria for alcohol dependence and abuse. ©

Original languageEnglish (US)
Pages (from-to)783-794
Number of pages12
JournalAlcoholism: Clinical and Experimental Research
Volume31
Issue number5
DOIs
StatePublished - May 2007

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being
  2. SDG 6 - Clean Water and Sanitation
    SDG 6 Clean Water and Sanitation

Keywords

  • Alcohol Problems
  • Gender Differences
  • Latent Trait Modeling

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