Self-report methods offer a reliable and valid approach to measuring alcohol consumption. The accuracy of such methods, however, can be improved by research directed at understanding the processes involved in response behavior.

Substantial correlational evidence supports a causal (mediational) interpretation of alcohol expectancy operation, but definitive support requires a true experimental test. Thus, moderately to heavily drinking male college students were randomly assigned to 1 of 3 conditions in a pre-post design: Expectancy challenge (designed to manipulate expectancy levels), "traditional" information, and assessment-only control. Expectancy challenge produced significant drinking decreases, compared with the other 2 groups. Decreases in measured expectancies paralleled drinking decreases in the challenge condition. Significant increases in alcohol knowledge in the traditional program were not associated with decreased drinking. These experimental findings support a causal (mediational) interpretation of expectancy operation. The implications for a cognitive (memory) model of expectancies and for prevention and intervention programs for problem drinking and alcoholism are discussed.

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.

F. K. Del Boca, J. Darkes, P. E. Greenbaum, and M. S. Goldman (2004) examined temporal variations in drinking during the freshmen college year and the relationship of several risk factors to these variations. Here, using the same data, the authors investigate whether a single growth curve adequately characterizes the variability in individual drinking trajectories. Latent growth mixture modeling identified 5 drinking trajectory classes: light-stable, light-stable plus high holiday, medium-increasing, highdecreasing, and heavy-stable. In multivariate predictor analyses, gender (i.e., more women) and lower alcohol expectancies distinguished the light-stable class from other trajectories; only expectancies differentiated the high-decreasing from the heavy-stable and medium-increasing classes. These findings allow for improved identification of individuals at risk for developing problematic trajectories and for development of interventions tailored to specific drinker classes.

Expectancies' mediational (control) role in alcohol consumption has been supported by both correlational and experimental evidence (J. Darkes & M. S. Goldman, 1993; M. S. Goldman, P. E. Greenbaum, & J. Darkes, 1997; L. Roehrich & M. S. Goldman, 1995). This study assigned participants (n = 54) to 1 of 2 expectancy challenges targeting the expectancy dimensions of either arousal or sociability identified by B. C. Rather and M. S. Goldman (1994), or to a no-treatment control, to examine the relationship of the structure and process of change in alcohol expectancies. Both challenges resulted in reduced consumption and expectancies immediately posttreatment and 6 weeks later after a short "booster" session. These results may reflect the lack of "discrete" expectancy structure and provide further support for the exploration of these methods as a possible prevention strategy.