Aims will be evaluated using multi-level regression analyses, often referred to as Hierarchical Linear Modeling (HLM) or mixed-effects modeling. With respect to evaluating main effects of experimental conditions on drinking, each participant will provide baseline, post-intervention, 1-month, 3-month, 6-month, and 12-month follow-up data. Hypotheses will be tested using specific contrast vectors, using a general linear hypothesis framework. The study consists of a (2×2+1+1) design, represented as a factorial design with the addition of a control group that will write about their first day of school and a computer-based PNF comparison group. Hypotheses will be tested with contrasts corresponding to the questions of interest. The first two hypotheses represent contrasts between the alcohol narrative conditions and the neutral control condition (H1a) and between the guilt narrative conditions and the neutral control condition (H1b). In examining these hypotheses, the investigators will construct two dummy coded variables reflecting alcohol versus non-alcohol narratives conditions and between guilt and non-guilt narrative conditions with the reference group being the neutral control condition. Thus, the PNF group will not be included in the tests of these two hypotheses. Dependent variables will include alcohol outcomes for H1a and psychological well-being for H1b. For these analyses each participant will provide up to 5 repeated measures (i.e., baseline, 1-month, 3-months, 6-months, and 12-months), yielding up to 3000 Level 1 cases (repeated-measures) across 600 Level 2 cases.