Department of Psychology, Virginia Tech, Blacksburg, Virginia 24061, USA.
J Stud Alcohol Drugs. 2013 Sep;74(5):803-9. doi: 10.15288/jsad.2013.74.803.
Knowledge of the types of consequences assessed by a given measure of alcohol-related consequences is important, as it may affect how the scores from that measure relate to individual differences and how the measure is used in treatment. This study was designed to examine the factor structure of the modified Timeline Followback (TLFB).
Undergraduate students (n = 573; 68.9% female) who drank alcohol at least once in the past 30 days completed an online version of the modified TLFB, a measure of alcohol use and alcohol-related consequences.
A confirmatory factor analysis supported the previously proposed four-factor structure of the consequences assessed by the modified TLFB (i.e., personal, social, role functioning, and physical consequences). Internal consistency of the four categories, as assessed by deleted-item odds ratios, was good. Additionally, correlations between subscale scores and measures of alcohol use provided evidence of convergent validity, and intraclass correlations between two administration formats (online vs. in-person) indicated preliminary concurrent validity of the four factors.
Overall, this study found support for the factor structure that was previously proposed by Norberg et al. Both the results of the confirmatory factor analysis and the deleted-item odds ratios indicate that most items fit the model well. Four items, however, could not be included in the model as a result of either low endorsement or poor fit, suggesting that further research on these items is needed.
了解特定酒精相关后果评估量表所评估的后果类型很重要,因为这可能会影响该测量得分与个体差异的关系,以及该测量在治疗中的使用方式。本研究旨在检验改良时间线回溯(TLFB)的因子结构。
在过去 30 天内至少喝过一次酒的本科生(n = 573;女性占 68.9%)完成了改良 TLFB 的在线版本,这是一种评估饮酒和与酒精相关后果的测量工具。
验证性因子分析支持改良 TLFB 评估的后果的先前提出的四因素结构(即个人、社交、角色功能和身体后果)。通过删除项目的优势比评估的四个类别的内部一致性良好。此外,子量表得分与饮酒测量之间的相关性提供了收敛有效性的证据,两种管理格式(在线与面对面)之间的组内相关系数表明了四个因素的初步同时有效性。
总的来说,本研究支持了 Norberg 等人先前提出的因子结构。验证性因子分析的结果和删除项目的优势比都表明,大多数项目都很好地符合模型。然而,由于低认可度或较差的拟合度,有四个项目无法纳入模型,这表明需要对这些项目进行进一步研究。