Del Boca Frances K, Darkes Jack, Greenbaum Paul E, Goldman Mark S
Department of Psychology, University of South Florida, Tampa, FL 33620-8200, USA.
J Consult Clin Psychol. 2004 Apr;72(2):155-64. doi: 10.1037/0022-006X.72.2.155.
Surveys have documented excessive drinking among college students and tracked annual changes in consumption over time. This study extended previous work by examining drinking changes during the freshman year, using latent growth curve (LGC) analysis to model individual change, and relating risk factors for heavy drinking to growth factors in the model. Retrospective monthly assessments of daily drinking were used to generate weekly estimates. Drinking varied considerably by week, apparently as a function of academic requirements and holidays. A 4-factor LGC model adequately fit the data. In univariate analyses, gender, race/ethnicity, alcohol expectancies, sensation seeking, residence, and data completeness predicted growth factors (ps <.05); gender, expectancies, residence, and data completeness remained significant when covariates were tested simultaneously. Substantive, methodological, and policy implications are discussed.
调查记录了大学生中的过度饮酒情况，并追踪了随时间推移饮酒量的年度变化。本研究通过以下方式扩展了先前的工作：考察大一期间的饮酒变化，使用潜在增长曲线（LGC）分析对个体变化进行建模，并将重度饮酒的风险因素与模型中的增长因素相关联。通过对每日饮酒情况的回顾性月度评估来生成每周估计值。饮酒量每周变化很大，显然这是学术要求和节假日的函数。一个四因素LGC模型充分拟合了数据。在单变量分析中，性别、种族/族裔、酒精预期、寻求刺激、居住情况和数据完整性预测了增长因素（p值<.05）；当同时检验协变量时，性别、预期、居住情况和数据完整性仍然显著。文中讨论了实质性、方法学和政策方面的影响。
