DRINK-DRIVING & RISKY DRIVING IN ADOLESCENTS & YOUTH

这项研究旨在促进对风险的理解。 驾驶，包括酒后驾驶及药后驾驶， 青少年和年轻人。 的主要目的 研究的目的是确定这些行为在多大程度上 反映行为和社会心理的更大生活方式的一部分 常规--非常规。 该研究将建立 酒后驾驶和药驾的地点， 危险驾驶行为的结构，如以下也 “快，快。 这种危险驾驶行为与其他各种危险驾驶行为之间的关系 问题行为，包括问题饮酒，使用大麻， 其他非法药物，和犯罪类型的行为;它将 研究一般危险驾驶的关系，喝酒- 特别是毒驾，到传统 问题的变量所代表的非常规性- 行为理论，并通过反映风险承担倾向的措施 以及对敌意/侵略的一般倾向。 研究 我也会研究年龄和性别的差异， 酒驾和毒驾的转变 非常规性 危险驾驶行为的措施，其他问题行为， 社会心理常规-非常规将通过以下方式获得 调查问卷邮寄给1,350名年龄在16- 18岁之间的有执照的科罗拉多司机， 24. 参与者将通过分层随机抽样进行选择 来自科罗拉多州机动车部门的技术 全州的司机数据库 两个独立但平行的抽样 帧将用于建立两个完全独立的样本 可用于提供复制和确认 分析。 此外，还将使用官方驾驶员记录数据 以验证自我报告的驾驶数据，并作为依赖 使用自我报告重复分析的变量。 两 将在数据中使用单变量和多变量方法 分析，包括相关分析，多元回归 分析，结构方程建模技术，和验证 因子分析

The proposed study is designed to advance understanding of risky driving, including drink-driving and drug-driving, among adolescents and young adults. The primary objective of the research is to determine the extent to which these behaviors are part of a larger lifestyle reflecting behavioral and psychosocial conventionality-unconventionality. The research will establish the location of drink-driving and drug-driving in the larger structure of risky driving practices, such as following too closely, speeding, and unsafe passing; it will determine the relationship of such risky driving practices to a variety of other problem behaviors, including problem drinking, use of marijuana and other illicit drugs, and delinquent-type behavior; and it will examine the relationship of risky driving in general, and drink- driving and drug-driving in particular, to conventionality-- unconventionality as represented by the variables of Problem- Behavior Theory, and by measures reflecting risk-taking propensity and a general orientation to hostility/aggression. The research will also examine age and gender differences in the relationship of drink-driving and drug-driving to conventionality- unconventionality. Measures of risky driving behavior, other problem behaviors, and psychosocial conventionality-unconventionality will be obtained by questionnaires mailed to 1,350 licensed Colorado drivers aged 16- 24. Participants will be selected by stratified random sampling techniques from the Colorado State Division of Motor Vehicles statewide driver database. Two separate but parallel sampling frames will be used to establish two completely independent samples that can be used to provide replications and confirmations of the analyses. In addition, official driver record data will be used to validate the self-report driving data and to serve as dependent variables to replicate analyses using self-reports. Both univariate and multivariate methods will be used in the data analyses, including correlational analysis, multiple regression analysis, structural equation modeling techniques, and confirmatory factor analysis.