The Journal of Primary Prevention, Vol. 14, No. 4, 1994

The Development of a Model to Predict Drinking Behavior from Attitudes in University Students Elizabeth W. Edmundson, 1,5 Patrick Clifford, 2 Debra S. Serrins, 3 and David Wiley 4

The major purposes of the study were: a) to examine the relationship between attitudes and self-reported levels of drinking; and b) to develop a stable prediction equation that included attitudes as a predictor of drinking behavior. A Likert-type survey was developed to measure college student's attitudes toward alcohol. The survey was administered to a sample of college students (n = 1049). The coefficient alpha reliability estimate was found to be .91. Stepwise multiple regression procedures were used to ascertain the relationships between attitudes and other psychosocial constructs of drinking. The dependent variable was an alcohol consumption index. The analysis revealed that the seven variable model was the most parsimonious (R 2 = .46), and attitudes toward drinking was the strongest predictor of self-reported drinking behavior. A double cross-validation of the regression model indicated that the model was very stable, and therefore couM be generalized to similar samples. The substantive findings related to students' self-reported drinking practices are also reported. KEY WORDS: attitudes; drinking; prevention.

1Elizabeth W. Edmundson, Ph.D., The University of Texas at Austin. 2patrick Clifford, Ph.D., Center for Alcohol & Addiction Studies, Brown University, Providence, Rhode Island. 3Debra S. Serrins, M.A., The University of Texas at Austin. 4David Wiley, Ph.D., Southwest Texas State University, San Marcos, Texas. 5Address correspondence to Elizabeth W. Edmundson, Ph.D., Department of Kinesiology & Health Education, Bel 222, University of Texas, Austin, Texas 78712. 243 O 1994HumanScicnc¢~Pre~ Inc.

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INTRODUCTION Adolescent and college student alcohol use has generated great concern regarding the high prevalence of usage, estimates of problematic use, and the number of alcohol-related traffic accidents and fatalities. Overall, the results of annual national surveys conducted by the National Institute on Drug Abuse (NIDA) indicate that self-reported use of alcohol among adolescents has gradually decreased since 1980, while use among college students has remained stable; however, the decreasing trend among adolescents appeared to level off in 1985 (United States Department of Health and Human Services, 1987). According to the results of a national survey conducted in 1986, approximately 92 percent of college students had consumed alcohol at least once, while the average prevalence of monthly use was 80 percent, and average prevalence of daily use was 5 percent (NIDA, 1987). These results remained stable through 1990; results of another NIDA survey revealed 94.3 percent lifetime consumption, 71.2 percent monthly use, and daily use of 4.7 percent for college students (Johnston, O'Malley, and Bachman, 1991). One of the most daunting results of the 1986 survey concerned episodes of heavy drinking among college students; approximately 45 percent reported at least one episode of heavy drinking (defined by NIDA as 5 or more drinks at one sitting) within the two weeks prior to the survey. Between 1981 and 1990, rates of heavy drinking in the prior two weeks decreased by 9.2 percent for high school seniors, and by 9.9 percent for non-college students age 19-22, but only by 2.6 percent for college students (Johnston et al., 1991). Among high school seniors, approximately 33 percent of this national sample did not view consuming 5 or more drinks once or twice every weekend as risky (NIDA, 1987). Moreover, motor vehicle accidents are the leading cause of death for 15-34 year olds. Approximately 40 percent of motor vehicle fatalities involved persons with blood alcohol concentrations of .10 percent or higher (Massachusetts Medical Society, 1990). The implications of these statistics in terms of negative interpersonal and social consequences is disconcerting and demonstrative of the need for alcohol education programs among this population. According to Moskowitz (1983), the three most common theoretical approaches currently utilized in alcohol/drug education programs are: the knowledge/attitudes approach, the values/decision-making approach, and the social competency approach. Of the three, educational efforts to affect alcohol/drug behavior via influencing knowledge and attitudes dominate the substance abuse prevention literature (Bangert-Drowns, 1988; Cahalan, 1991; Eiser, Eiser, Claxton-Oldfield & Pritchard, 1988; Goodstadt & Caleekal-John, 1984; Hennessy, 1991: Moskowitz, 1983, 1989; Rundall & Bruvold, 1988).

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The knowledge/attitudes approach is premised upon the idea that providing knowledge of the negative consequences of alcohol/drug use, primarily information regarding the pharmacological properties of the drugs, will instill negative attitudes toward drug use. These negative attitudes, in turn, will promote behavioral abstinence. Empirical evidence has not supported this model. The inconsistent results have been attributed to inadequate implementation and evaluation methodologies. Furthermore, knowledge has been found to be a necessary but insufficient condition for attitude and behavior change (Cahalan, 1991; Eiser, Eiser, Claxton-Oldfield & Pritchard, 1988; Goodstadt & Caleekal-John, 1984; Moskowitz, 1983, 1989; Swadi & Zeitlin, 1987). The values/decision-making approach, as implied by the name, focuses upon individual needs and values and how substance use fulfills those needs and influences values. Decision-making skills are taught to enhance responsibility and self-reliance, and the role of personal values in the decisionmaking process is illustrated to foster personal understanding (Cahalan, 1991; Moskowitz, 1983, 1989). Ideally, these skills and self-awareness should promote the notion of responsibility towards substance use. The social competency model has been heavily influenced by Bandura's social learning theory (Moskowitz, 1983, 1989). The upshot of this approach is that social situations, modeling, and social environments dictate the acquisition of individual psychosocial skills. A deficiency in these skills places the individual at higher risk for substance abuse. Subsequently, rectifying these deficiencies modifies attitudes and behaviors toward drug-taking. While this approach is theoretically promising, empirical research has only recently begun to test these assumptions regarding substance abuse prevention. Although many of the aforementioned programs have yet to be evaluated for long-term effects, preliminary findings have demonstrated some positive outcomes, particularly among young adolescents (Botvin, 1986; Goodstadt & Caleekal-John, 1984; Hansen, 1992). Unfortunately, alcohol/drug education programs remain plagued with methodological weaknesses that threaten the internal validity of the results and prohibi t accurate assessments of program effectiveness (Bangert-Drowns, 1988; Cahalan, 1991; Hansen, 1992; Moskowitz, 1983, 1989). Furthermore, research findings from studies attempting to ascertain the relationships between knowledge, attitudes and behavior toward alcohol and other psychoactive substances have been inconsistent. More specifically, the study of attitudes toward alcohol has been criticized as being unsystematic and as using inappropriate or inadequate measurement techniques and analyses (McCarty, Morrison & Mills, 1983; Torabi, & Veenker, 1986). Many studies that have purported to assess attitudes (through the use of self-report instruments) have neglected to report any theoretical bases for

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the content of the items, and often have omitted information regarding the psychometric properties of the instrument(s). Hansen (1992) emphasizes the importance of either establishing a causal model in prevention programs even if the causal model used is only partly understood. Without a stable model, it is impossible to accurately predict drinking behavior. Only recently have attempts to investigate the underlying structure of attitudes toward alcohol, particularly among adolescents and college students, been reported in the scientific literature. Among those reported, Fishbein's work in attitude theory (Fishbein & Azjen, 1975) has been the predominant basis for item development. In this theory, one's value or attitude toward a behavior and one's estimation of others' attitude toward the behavior comprise intention to perform the behavior. The almost annual surveys instituted by NIDA, while quite comprehensive in scope, have examined global attitudes toward destructive drinking rather than detaiJed attitudes toward specific behavior patterns. For example, results are reported for items that measured perceived harmfulness, personal disapproval and perceived friends' disapproval of having 1 or 2 drinks almost daily (NIDA, 1991). NIDA (1991) also reported perceptions toward having 4 or 5 drinks almost daily and weekend binge drinking. However, they fail to address attitudes toward the more positive aspects of drinking, the different areas of life that may be affected by drinking, or more specific combinations of quantity and frequency that may be more representative of the college experience. This degree of detail could further elucidate the perspectives college students have on various drinking patterns and have substantial implications for the goals and objectives of university-based prevention programs. In consideration of the issues discussed above, the primary purposes of the present study were: a) to examine the relationship between detailed attitudes toward specific drinking patterns and self-reported levels of drinking; and b) to develop a stable prediction equation that included attitudes as a predictor of drinking behavior. These results could be used to design alcohol education programs and to help guide research on correlates of college students' drinking behavior.

METHODS Subjects The subjects for this study were a voluntary sample of college students (N = 1049) enrolled at several colleges or universities in the southwestern region of the United States. The subjects were drawn from

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a convenience sample; however, the sampling procedure did attempt to include students with various academic, demographic and socioeconomic backgrounds. Courses that are required for most undergraduates (e.g., introductory Government and English) or are popular among a large segment of students (e.g., Physical and Health Education) at the institutions served as the primary sources for participant selection for the sample. The subjects ranged in age from 18 to 50 years and included freshmen, sophomores, juniors, and seniors. Approximately 50% (n = 527) of the students were female and 46% (n = 484) were male; 4% (n = 38) of the sample did not respond to this item. The mean age for the sample was 21 years and the modal age was 19 years. The sample was predominantly White/Caucasian (approximately 73%), with Hispanics comprising about 15% (n = 156) of the sample while Blacks and Asians constituted 5% (n = 55) and 4% (n = 45), respectively. Very few students reported annual family income of less than $15,000 (approximately 4%); most students (approximately 75%) reported annual family income of $35,000 or higher. The majority of students (approximately 73%) reported either professional or white collar/management for the occupation of the parent who supported their family. Overall, the study sample could be described as white and middle class, with the second largest ethnic category being Hispanic. See Table I for a more detailed description of the biographical background characteristics of the study sample. Instrument

There was a paucity of theoretically based, psychometrically sound instruments designed to assess college students' attitudes toward alcohol use. Therefore, an instrument was developed using facet theory (Guttman, 1954) to address issues of content and construct validity of attitude measurement. The instrument development process and evidence supporting the content and construct validity of this instrument has been reported elsewhere (Edmundson, Koch, & Silverman, 1993). A brief summary of the item development procedure follows. For this investigation, facets representing conceptual aspects of variables previously reported in the substance abuse prevention literature (e.g., outcome of drinking, frequency and quantity of drinking) were identified for the purpose of forming a mapping sentence to measure attitudes toward alcohol. Mapping sentences, which are based upon Guttman's facet theory, provide a definitional framework for categorizing

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Table I. Demographic Characteristics of Study Sample (n -~ 1049) Response Variable

Frequency

Percent

484 527 38

46.1 50.2 3.6

68 384 258 188 55 32 23 41

6.5 36.6 24.6 17.9 5.2 3.1 2.2 4.0

Race Asian/Oriental Black White/Caucasian Hispanic Other Missing

45 55 763 156 13 17

4.3 5.2 72.7 14.9 1.2 1.6

Parents' Annual Income $75,000 Missing

46 181 291 205 289 37

17.3 27.8 19.5 27.6 3.5

Occupation of Parent Who Supported Family Blue Collar/Industrial Military Professional White Collar/Management Other Missing

117 45 346 424 97 20

11.2 4.3 33.0 40.4 9.3 1.9

Highest Level Education Achieved by Either Parent No School Grammar School High School College Graduate School Missing

4 37 189 432 353 34

.4 3.5 18.0 41.2 33.7 3.2

Gender Male Female Missing

Age 18 19 20 21-22 23-25 26-30 31-50 Missing

4.4

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variables of interest and are also useful as a template for item construction (Dancer, 1990; Guttman, 1954; Guttman, 1981; Levy, 1981). Within each facet category are subcategories, called elements. The survey items were developed from combinations of the elements, so that each element was represented proportionately among the items. The mapping sentence for this study was a modified version of a mapping sentence used by Levy (1982) to explore the structure of drug use behavior in Israel. The proposed facets for this study were: a) Outcome of drinking (positive or negative); b) Area of Life impacted by drinking (physical health, mental health, or social life); c) Frequency of drinking (daily, weekly, or monthly); and d) Quantity consumed (light, moderate, or heavy). The three elements of the quantity facet were defined by the number of drinks consumed in one sitting: light was equal to one or two drinks, moderate was equal to three or four drinks, and heavy was defined as 5 or more drinks. To illustrate the role of elements in the item development process, the two elements within the facet having to do with the outcome of drinking designated either a positive or negative impact from the drinking pattern. Therefore, one-half of the attitude items were written to represent outcomes from drinking having a positive component, and one-half were written to represent damages from drinking. It is important to note that the quantity categories described above were n o t analogous to the different types of drinkers. For example, an attitude toward daily, light use was not analogous to the attitude toward light drinkers (i.e., light drinkers may consume considerably less than one or two drinks per day). By combining a measure of frequency of use with a measure of quantity of use, this survey provided a specific, detailed account of attitudes toward various drinking patterns and the subsequent impact of those patterns. For example, Having one or two drinks once a week with friends is fun represents a positive outcome to social life from weekly drinking in a light fashion. Collectively, the facets of the mapping sentence provided a domain by which the content validity of the survey instrument could be addressed. Other features of facet analysis provided a means to estimate construct validity, and are discussed elsewhere (Edmundson, Koch, & Silverman, 1993). The instrument contained a total of 79 items, of which 54 represented attitudes toward various drinking patterns, 13 assessed self-reported drinking behaviors, and 12 measured demographic and psychosocial background characteristics. The survey instrument is included in the appendix to this paper.

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Procedures

Selected instructors (colleagues and other interested faculty) at the institutions were contacted and asked to participate in the study by allowing the survey to be administered during their classes. The cover sheet of the survey explained that participation was anonymous and voluntary. This information was read to the subjects at the time the survey was administered, and subjects were encouraged to ask any questions to clarify the purpose of the study and the conditions of anonymity and consent. Students who did not wish to participate were encouraged to work quietly at their desks during the administration of the survey. The overwhelming majority of students chose to participate. The subjects needed 20-30 minutes to complete the questionnaire. Ag~alysis Reliability was estimated using coefficient alpha in an effort to ascertain the internal consistency of the instrument. Descriptive statistics were computed for the demographic and personal consumption variables. Stepwise multiple regression procedures were used to ascertain the relationships between attitudes, other biographic and psychosocial constructs, and drinking. Additionally, the following analyses were performed to estimate the relationships between attitudes and drinking behavior through multiple regression and the stability of the regression model through cross-validation techniques. First, the dependent variable consumption index was created simply by summing across three i t e m s - those items that measured frequency of drinking within the previous year, frequency of drinking within the previous month, and the amount of alcohol usually consumed when drinking. Scores on the consumption index ranged from 3 to 15. Second, a total score was created for attitudes by summing across all of the 54 attitude items (following the reverse scoring of half the items so that a high score indicated a positive attitude and a low score indicated a negative attitude for all of the items). The values for the attitude total score ranged from 63 to 224. Third, 14 variables from the survey that had been previously identified in the scientific literature as being related to drinking, such as self-esteem and parental drinking, were included in the stepwise multiple regression analysis procedure utilized in the SAS package. Based upon the results of the stepwise procedure, the most parsimonious regression model was then selected for a double cross-validation of the model.

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Original data set

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A double cross-validation procedure was applied to test the stability of the derived regression model. First, the entire original sample was split into two groups, where sample A contained all of the odd numbered cases and sample B contained all of the even numbered cases. Then, the parsimonious regression model selected from the results of the Stepwise procedures described above was applied in a series of multiple regression equations for the double cross validation. Using the variables from that model, the first regression analysis was computed for the subjects in sample A, and the parameter estimates and multiple correlation coefficient for this equation was computed. Next, the regression parameter coefficients obtained from the sample A equation were subsequently applied to the sample B data in a separate multiple regression analysis to predict the consumption index for the subjects in sample B. The multiple correlation coefficient was then computed between the predicted values obtained for

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the dependent variable in sample B (obtained by using the sample A regression coefficients) and the actual values from sample B for the dependent consumption index variable. This procedure is depicted in Figure 1. The difference between the value of the multiple correlation coefficient from sample A and the value of the multiple correlation coefficient obtained between the predicted values of the dependent variable from sample B (using the sample A regression coefficients), sometimes called shrinkage, is an indicator of the stability of the model. When the difference or shrinkage between the two multiple correlations is small, the model demonstrates strong evidence of stability. The second cross-validation used the same general procedure described above. For this crossovalidation, a multiple regression analysis was computed on sample B using the predictor model previously discussed, and the multiple correlation coefficient was calculated. The regression parameter coefficients obtained from the equation derived from sample B were then applied to the sample A data to obtain predicted values of the dependent variable. Subsequently, the multiple correlation coefficient between the predicted values for the dependent variable (based upon the application of the coefficients from sample B) and the actual values of the dependent variable, alcohol consumption, from sample A was computed. The difference between these two multiple correlation coefficient values was also calculated for evidence of shrinkage.

RESULTS Personal Background Characteristics The substance abuse literature has suggested several psychosocial constructs that appear to be related to alcohol consumption and other drug-taking behavior. For example, some studies have reported that selfesteem and religiosity were substantially and negatively related to substance abuse. Therefore, personal characteristics such as attendance at religious services and perceived level of religiosity were measured. Indicators of self-esteem (perceived physical attractiveness, perceived successfulness in life and perceived academic ability) were surveyed along with the biographic variables. Most students (approximately 55 percent) responded that they attended religious services infrequently or occasionally. Less than 10% (n = 104) reported attending services very frequently. Interestingly, approximately 59% reported that they perceived themselves as religious or very religious.

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The majority of students (approximately 56%) rated their overall academic ability as good, with about 21% (n = 221) rating it as excellent. Most of the subjects (about 50%) described their lives up to this point as successful, with an additional 20% (n = 206) rating their lives as very successful. For the item that estimated perceived physical attractiveness, approximately one-half of the subjects (n = 508) perceived themselves as being physically attractive. Less than 4% (n = 37) perceived themselves as unattractive or very unattractive. Table I presents the demographic characteristics of the sample,

Self-Reported Drinking Behaviors Study subjects reported lifetime, previous year and previous month estimates of how often they had consumed alcohol. More than 94% (n --- 990) of the sample responded that they had drunk alcohol at least once during their lifetime, while 93% (n = 968) reported drinking it at least once in the previous year and 80% (n --- 842) reported drinking it at least once in the previous month. These results were consistent with reported national norms (NIDA, 1987; Johnston et al., 1991). One of the more interesting findings from a health education perspective was that apo proximately 25% (n = 259) of subjects responded that they had consumed alcohol more than once a week, but not every day, within the previous month. This indicated that a substantial proportion of the subjects were drinking fairly frequently and thus may be at higher risk for alcohol-related problems, such as driving while intoxicated. Subjects also reported the amount of alcohol they usually consumed in one sitting. Approximately 20% (n = 210) reported that when they drink, they usually consume 5 or more drinks in one sitting. Consuming this amount in one sitting has been traditionally categorized as an episode of heavy drinking (NIDA, 1986; Johnston et al., 1991). An additional 16% (n = 167) reported usually consuming 4 drinks when they drink alcoholic beverages; this amount would still increase an individual's risk for health-related problems when viewed in the context of usual amount consumed in one sitting. On a more positive note (in terms of influence upon health status), approximately 19% (n = 194) reported that they usually consumed one drink or less, while an additional 19% (n = 198) reported usually consuming 2 drinks when they drink. Table II provides prevalence estimates of the subjects' drinking behavior as well as their characterizations of their parents' drinking habits. Most subjects (about 81 percent) characterized the drinking habits of their mothers as either nondrinker or light drinker. Fathers were per-

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Table ii. Lifetime, Previous Year, and Previous Month Prevalence Estimates of Drinking (N = 1049) Response Variable

Frequency

Percent

Have you ever drunk alcohol? Yes No Missing

990 38 21

94.4 3.6 2.0

How often did you drink in the last year? Never At least once, but nc~t every m o ~ k At least once a month, ['ut not every, week At least once a week, but not every day At least once a day Missing

36 240 365 350 13 45

3.4 22.9 34.8 33.4 1.2 4.3

How often did you drink in the last month? Never At least once, but not every week About once a week More than once a week, but not every day At least once a day Missing

163 321 244 259 18 44

15.5 30.6 23.3 24.7 1.7 4.2

How much alcohol do you usually drink in one sitting? The same as one beer or less The same as two beers The same as three beers The same as four beers The same as five beers or more Missing

194 198 226 167 210 54

18.5 18.9 21.5 15.9 20.0 5.2

How would you characterize the drinking habits of your father? Nondrinker Light drinker Moderate drinker Heavy drinker Don't know Missing

225 377 281 107 21 38

21.5 35.9 26.8 10.2 2.0 3.6

How would you characterize the drinking habits of your mother? Nondrinker 417 Light drinker 437 Moderate drinker 135 Heavy drinker 19 Don't know 3 Missing 38

39.8 41.7 12.9 1.8 .3 3.6

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ceived as being heavier drinkers than mothers for the most part; still, 57% (n = 602) characterized their fathers' drinking habits as either nondrinker or light drinker. Table III presents a cross-tabulation of the responses to frequency of drinking during the previous month by the amount of alcohol usually consumed in one sitting. This presentation provides a better opportunity to estimate the percentage of subjects that regularly engaged in what has been commonly described as heavy drinking. Of those students who reported usually drinking 5 or more drinks in one sitting (n = 210), 108 (slightly over half) reported drinking more than once a week but not every day. An additional 61 individuals reported usually consuming four drinks in one sitting more than once a week, but not every day. Eight subjects reported usually consuming 5 or more drinks every day, and 2 subjects reported usually having 4 drinks every day. Thus, although the amount of alcohol consumed over a more specific measure of time, such as by the hour, would have provided an even better estimate of drinking patterns, approximately 17% (n = 179) of the study subjects could be classified as regularly engaging in heavy drinking, according to criteria utilized by most alcohol researchers. Other items attempted to measure place and time of consumption during the previous year as indicators of social and high risk drinking. The results indicated that while about 15% (n = 156) reported drinking while driving at least once a month, but not every week, approximately 27% (n = 286) responded that they had drunk alcohol at least once a month, but not every week as a passenger in a car. With the exception of the drinking estimates while driving or while a passenger, the results indicated that most students did not engage in what are commonly referred to as high risk drinking behaviors. For example, only about 9% (n = 93) reported drinking prior to attending class, and approximately 80% (n = 835) responded that they had never consumed alcohol in the morning. Additional place and time measures by frequency of drinking, primarily indicators of what is commonly called social drinking, were also reported. For example, about 33% (n = 347) reported drinking with close friends at least once a week, but not every day. Less than 1% (n = 6) reported drinking with close friends at least once a day. Approximately 24% (n -- 254) reported drinking at least once a week, but not every day at their college residences, while about 23% (n = 240) responded that they had not drunk alcohol at their college residence within the previous year. F u r t h e r m o r e , while approximately 33% (n = 350) reported that they drank alcohol at least once a month but not every week prior to attending social events, about 22% (n = 234)

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responded that they never consumed alcohol prior to attending social events. Reliability of the Attitude Scale Reliability was estimated using coefficient alpha in an effort to ascertain the internal consistency of the attitude component of the instrument. The attitude scale appeared very reliable, with coefficient t~ = .91. Item 21 was the only item with a corrected item-total correlation value of less than .10. Although the item-total correlation value for item 21 was fairly low (.056), coefficient alpha would only have increased by .0015 if the item was deleted from the scale. Overall, the scale was highly reliable. Relationship Between Levels of Attitude and Levels of Drinking Stepwise multiple regression procedures were used to ascertain the relationships between attitudes and drinking. The dependent variable was the alcohol consumption index. The predictor variables were 13 variables from the survey, along with the total attitude score, that had been previously identified in the scientific literature as being related to drinking, such as self-esteem and parental drinking. The variables included in the stepwise analysis were: gender, race, age, total attitude score, perceived academic ability, perceived life success, perceived physical attractiveness, parental income, parental education, parental occupation, perceived fathers' drinking patterns, perceived mothers' drinking patterns, frequency of attendance at religious services and frequency of use of drugs other than alcohol and tobacco. Although the race variable was initially scored with the integers one to five, it was a categorical rather than a quantitative variable. Therefore, the race variable was recoded to reflect the 5 distinct categories (also known dummy coding). The recoding of the race variable increased the number of predictor variables to 17. Nine variables were found to be statistically significant (p < .05). Although the original sample was n = 1049, some subjects automatically were excluded from the multiple regression analysis because their surveys did not have complete data. The nine variables that were not statistically significant and, therefore, did not enter into the stepwise model were: age, perceived life success, parental education level, parental occupation, mothers' drinking patterns, attendance at religious services, religiosity, race

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as White/Caucasian, and race as Hispanic. Although the two variables perceived physical attractiveness and fathers' drinking patterns were statistically significant, their overall contribution to the model was minimal. The R-Square value for the seven predictor-variable model that included attitudes, frequency of other drug use, race-Asian, race-Black, gender, parental income and perceived academic ability was .459, whereas the RSquare value for the nine variable model, which included the above seven variables along with perceived physical attractiveness and fathers' drinking patterns, was .466. (See Table IV.) Thus, the seven predictor variable model with the consumption index as the dependent variable was selected as the most parsimonious model for the study. The next step was to investigate whether or not a stable prediction equation could be developed that included attitudes to estimate drinking behavior. A double cross-validation procedure was used to test the stability of the equation. This procedure was described earlier and depicted in Figure 1. The seven-variable model described above was applied in multiple regression analyses in which the original entire sample was split into two groups, where sample A contained all of the odd numbered subjects and sample B contained all of the even numbered subjects. Then, the first regression analysis was computed for sample A, yielding a multiple correlation coefficient of R = .6851. Next, the regression analysis was completed from sample B. The regression coefficients obtained from the sample B equation were subsequently applied to the sample A data in a separate regression analysis to predict the consumption index. The multiple correlation coefficient was then computed between the predicted values obtained for the dependent variable (using the sample B coefficients) and the actual values from sample A for the dependent consumption index variable. The multiple correlation coefficient for this equation was R = .6641, The difference between the value of the multiple correlation coefficient from sample A and the value of the multiple correlation coefficient obtained between the predicted values of the dependent variable from sample A (using the sample B coefficients) and the actual sample A values is an indicator of the stability of the model. The decline in correlation was very minimal, only .0210, for the first cross-validation. The second cross-validation consisted of the same general procedure described above. For this cross-validation, a multiple regression analysis was computed on sample B; the multiple correlation coefficient was R = .6848. The regression coefficients obtained from the equation derived from sample A were then applied to the sample B data in a second regression analysis. Subsequently, the multiple correlation coefficient between the predicted values for the dependent variable (based

Attitudes and Drinking Behavior

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upon the application of the coefficients from sample A) and the actual values for the dependent alcohol consumption variable from sample B was computed; the multiple correlation coefficient for this model was R = .6604. The difference between these two Multiple R values was also very low: .0244. (See Table V.) These results demonstrated that the seven-predictor-variable model was very stable for predicting the alcohol consumption index. Also, the double cross-validation results indicated that the seven predictor variables had substantial utility and might generalize to other samples of college students for predicting alcohol consumption rates.

DISCUSSION AND RECOMMENDATIONS The primary purpose of this research was to investigate the relationship between attitudes and self-reported behaviors related to alcohol and to discuss the implications of the survey results for alcohol misuse/abuse prevention programs. Overall, the set of background variables included on the attitude instrument did not appear to be strongly related to consumption. This was an interesting result because many theories attribute problem drinking to low self-esteem, one of the constructs on the instrument. Yet, this sample contained a substantial proportion of individuals who regularly engaged in heavy drinking (i.e,, 5 or more drinks in one sitting). These results suggest that perhaps heavy drinking among this population is not associated with low self-esteem. Or it could be that the current definition of "heavy drinking" is inappropriate for this population, or perhaps some combination of the two. The implication from this result is that substance abuse educators should design programs that would appeal to students with "normal" self-esteem. In drug education, we often portray students who become involved heavily with drugs as having low self-esteem; this does not necessarily appear to be the case with alcohol. Of all the self-esteem variables, only one, perceived academic ability, was statistically related to the consumption variable. Practically, its overall contribution to the R-Square value was minimal. This association shows no cause and effect relationship between drinking and perceived academic ability. It could be that students who drink heavily have low perceptions of their academic ability due to that drinking, or students who have low perceptions of their academic ability, drink to compensate for this weakness. Based on this finding, a recommendation for prevention specialists is to train gatekeepers to recognize the difference between perceived academic ability and actual G.P.A. The ability of gatekeepers to make this

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Table V. Results of Double Cross-Validation Procedure for Predicting the Alcohol Consumption Index Actual Sample A (Odd Numbered Cases n = 429) Regression Diagnostics R2 = .4694, R = .6851 Variable

Coefficient

Intercept Attitudes Other Drug Use Race-Asian Race-Black Gender Parental Income Academic Ability

-2.6111 0.0678 0.6312 -1.6069 -2.1462 -0.3064 0.2889 --0.3291

R = .6641 when sample B coefficients were applied to sample A data. Difference = .6851 N.6641 = .0210. Actual Sample B (Even Numbered Cases n = 428) Regression Diagnostics Rz = .4689, R = .6848 Variable

Coefficient

Intercept Attitudes Other Drug Use Race-Asian Race-Black Gender Parental Income Academic Ability

0.0415 0.0586 0.9826 -1.8487 --0.9960 -0.7835 0.1595 -0.3993

R =.6604 when sample A coefficients were applied to sample B data. Difference = .6848 -.6604 = .0244.

d i s t i n c t i o n c o u l d lead to g r e a t e r r e c o g n i t i o n o f p r o b l e m d r i n k i n g by college s t u d e n t s .

Patterns of Drinking Behavior O v e r a l l , this s a m p l e a p p e a r e d to m a t c h n a t i o n a l n o r m s for c o l l e g e s t u d e n t s c o n c e r n i n g f r e q u e n c y o f use d u r i n g t h e i r lifetimes, the p a s t 12 m o n t h s a n d t h e 30 days p r i o r to this s u r v e y (See N I D A , 1986; J o h n s t o n

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et al., 1991). About 50% could be described as regular drinkers, meaning that they had drunk alcohol at least once a week during the previous 30 days. An additional 31%, who reported that they had consumed at least one alcoholic beverage in the past month, could be described as occasional drinkers. The majority of students (greater than 50%) could be grouped into one of two categories of drinking behavior (light or heavy) based upon their responses to this item. Light was defined as usually consuming the equivalent of 2 drinks or less in one sitting, while heavy was defined as usually consuming the equivalent of 5 drinks or more in one sitting. With such a large proportion of students engaging in drinking behavior (many on a fairny frequent, regular ~a~is), primary prevention specialists at the college leve~ should ~eiect alcohol as the primary target for intervention. About 36% of the sample reported that they usually consumed 4 or more drinks in one sitting. In the context of long term health effects, the deleterious impact from chronic patterns of heavy drinking have been well documented in the alcohol abuse literature (e.g., cardiovascular disease, cirrhosis of the liver, certain types of cancer) (Duncan & Gold, i985; U.S. Department of Health, Education & Welfare, 1979; U.S. DHHS, 1987). Substance abuse prevention specialists should note that for females, usual consumption of this amount is associated with even greater risks. For example, it is well known that women in general do not metabolize alcohol at the same rate as their male counterparts, so the physiological and psychological effects may be even more pronounced (U.S. DHHS, 1987). Furthermore, for those female students who may be pregnant (knowingly or unknowingly), the risk of Fetal Alcohol Syndrome for their unborn children would be much greater at this level of consumption. Substance abuse educators should incorporate assessment of increased physiological danger to females and their fetuses to interventions. More immediate negative effects from this drinking pattern might include hangovers and increased probability of engaging in high risk behavior such as drinking and driving, or having unsafe sex. An important issue worthy of further investigation would be what percentage of those individuals usually drive an automobile after drinking. These results support the implementation of programs by program planners to reduce high risk behaviors in college students. Alternately, about 37% of the students reported that, when they drank, they usually consumed 1 or 2 drinks in one sitting. These results indicated that a substantial proportion of students could be characterized as engaging in "low risk" drinking patterns. A pattern of alcohol consumption at this level has been associated with protective health effects, particularly concerning cardiovascular disease. The immediate negative effects

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to physical and psychological health are virtually nonexistent, or very minimal at most. Practitioners can take heart that most college drinkers engage in responsible drinking behavior. A more comprehensive picture of drinking patterns was presented in Table III, which illustrated a cross-tabulation of frequency of drinking during the previous month and amount usually consumed. Again, the majority of students could be classified as engaging in either occasional, light drinking or regular, heavy drinking (recall that the definition proposed by NIDA of an episode of heavy drinking was the consumption of 5 or more drinks in one sitting). The upper left portion of the table included individuals (n = 293, 28%) with the following drinking patterns: they had not drunk alcohol at all in the past month or had consumed alcohol at least once but not every week, and when they drank, they usually consumed only 1 or 2 drinks. The lower right portion included students (n = 162, 16%) who drank in the following pattern: they drank alcohol at least once a week, and when they drank, they usually consumed 5 or more drinks. If the range were expanded to having 4 or more drinks in one sitting, the proportion who reported usually drinking in a moderate-heavy fashion increases substantially (n = 287, 29%). These percentages suggested several things for the prevention planner. First, there appeared to be a dichotomy in the drinking patterns of this sample. In other words, a majority of students either drink lightly or heavily, with the remaining students engaging in what might be described as moderate drinking patterns. The division of drinking patterns indicate that the educator should address the many students who could benefit from alcohol education programs that focus upon risk reduction through the modification of heavy drinking patterns. Other commonly utilized indicators of high-risk drinking also were measured. For the most part, drinking appeared to occur within social situations (e.g., with close friends, before going to social events). Very few students drank alone, in the morning, or prior to attending class. Prevention specialists should consider selecting more relevant criteria than these typical indices for college student problem drinking. Alcohol education programs might benefit from including more relevant criteria such as making regrettable decisions regarding sexual behavior while intoxicated or missing class due to hangovers as a measure of problem drinking. However, the items that measured drinking and driving behavior did appear relevant. More than 19% of respondents reported drinking while driving at least once a month. Interestingly, there was quite a disparity between the number of individuals who reported drinking while driving and those who reported drinking while a passenger (36.2%). The two most plausible explanations for this disparity are: a) the reported drinking while driving estimates

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are reasonably accurate and students are utilizing designated drivers, cabs, etc., or b) the drinking while a passenger estimates are equally reflective of actual drinking and driving b e h a v i o r - thus, students simply reported what they perceived to be the "socially acceptable" response to this item. This issue would be of interest for further study. The results indicated that planners of alcohol education programs should continue to promote responsible drinking and driving messages and interventions. Parental drinking patterns typically have been highly associated with the drinking patterns of their offspring (U.S. DHHS, 1987). Although the students did attempt to characterize their parents' drinking patterns, each student had his/her individual definition of a light, moderate or heavy drinker. The results indicated that only the father' drink/rig patterns were related to the students' personal consumption. Although this relationship was statistically significant, ether variables were revealed to be much better predictors of the student~' cor~,$umption. Given the results of this study and the lack of conclusive evidence on the effect of parental drinking on nonclinical populations, employing perceived parental drinking as a potential marker among college students is not recommended.

Evidence of Reliability and Validity The results of the item analyses revealed a high internal consistency reliability estimate as measured by coefficient alpha for the attitude scale, meaning that on the average, the pairwise inter-item correlations were very high. Other evidence of reliability, such as equivalent forms or test-retest reliability, was not addressed in the present study. Evidence of concurrent validity was exhibited by the high correlation between attitudes and self-reported drinking behavior. The results of the regression analysis indicated that the students' scores on the attitude scale were statistically significantly related to their self-reported consumption. Furthermore, as shown in Table IV, the attitude total score was the single best predictor of the alcohol consumption index. Thus, these findings contradict the conclusions of previous studies in this area, which held that attitudes were not highly related to drinking behavior (Goodstadt et aL, 1978; Hanson, et al., 1988). Therefore, it is possible that the high reliability and the construct validity of this instrument permitted the empirical observation of the relationship between attitudes toward drinking and self-reported drinking behaviors.

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The other variables that were good predictors of consumption were: frequency of use of other drugs, gender, parental income, and race. The correlation between drinking and frequency of use of other drugs was positive. This indicated that as the scores on the consumption index increased so did the frequency with which students used other drugs. Also, there was a significant relationship between gender and levels of drinking. Males tended to have higher scores on the consumption index than did females. Parental income was positively and significantly related to the consumption index, meaning that as parental income increased, so did the students' scores on the consumption i n d e x - that is, the more affluent the students' backgrounds, the more frequent and/or higher quantities of alcohol consumed. Given these results, program planners should target male students, affluent students, and those students using other drugs for specific interventions. The results of the double cross-validation were encouraging, The results appeared quite stable and likely to generalize to other samples of college students with similar biographic characteristics. Because this model is so stable that it could be generalized to other samples, one implication for alcohol education programs is the possibility of developing a screening tool in the future that would be based upon students' attitudes. Significant variables, in particular attitude and use of other drugs, could be used to guide the development of a screening tool for college student populations. Because it is common for problem drinkers to experience denial, they may not be honest on a behaviorally based instrument. Once developed, this attitude based screening tool would help identify persons with high risk drinking behavior who might benefit from an educational intervention designed to promote responsible drinking. An additional implication is related to the different approaches to alcohol education previously discussed (e.g., values clarification/decisionmaking or social competency). Programs designed to influence attitudes directly in the hopes of influencing behavior, such as the values clarification/decision-making approach, could increase the likelihood of finding potential program effects by utilizing psychometrically sound instruments such as the one employed in the present study. Although the social competency model focuses on behavior directly and on attitudes indirectly, the strong relationship found between attitudes and self-reported behaviors indicates that perhaps attitudes should not be ignored. Moreover, a basic premise of the cognitive-behavior modification model has been that sustained behavior change is facilitated by attitude change (Meichenbaum, 1977). Whatever the approach, utilization of psychometrically sound instruments would

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enhance the overall integrity of the results from program evaluations or need assessment research. Limitations

One limitation of this study is that it was conducted on a sample of convenience; however, by administering this survey in required classes, the researchers attempted to obtain a representative sample of the student body. Additionally, although the students were promised anonymity, the results of this survey incorporate all the caveats of self-report techniques. An additional limitation of this research surrounds the item that measured quantity consumed (i.e., How much do you usually consume in one sitting?). Students were asked to report an average number which had to be self-estimated. Notably, some students voluntarily offered feedback after completion of the survey concerning the range of responses offered for this specific item. On several occasions, male students in particular commented that they usually consumed a twelve-pack of beer on Friday nights and on Saturday nights. Following their concerns and suggestions, planners developing an instrument as a screening tool should revise this item to ascertain a better estimate of quantity consumed. A final limitation of this survey was the unequal representation in this sample of all racial categories. Cell sizes for the categories of the race variable indicated that the group sizes for both the Asian category and the Black category were very small, n = 45 and n = 54, respectively. The mean for the dependent consumption variable was 6.26 for the Asian category and 6.55 for the Black category. The mean for the consumption variable f o r the White category (n = 763) was 9.06, and the mean for the Hispanic category (n = 156) was 8.30. The category Other contained only 11 individuals, with a mean of 8.82 on the consumption variable. Since the disparity between the number of individuals for each category was so great, particularly between those categories with the highest and lowest means for the consumption variable, the race variable was not very useful. Substance abuse prevention specialists should take the limitation concerning race in this sample into consideration in creating a screening tool or implementation appropriate for their college or university population.

REFERENCES Bangert-Drowns, R. L. (1988). The effects of school-based substance abuse education m a recta-analysis. Journal of Drug Education, 18(3), 243-264.

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Botvin, G. J. (1986). Substance abuse prevention research: Recent developments and future directions. Journal of School Health, 56(9), 369-374. Cahalan, D. (1991). An ounce of prevention. San Francisco: Josey-Bass Publishers. Duncan, D. & Gold, R. (1985). Drugs and the whole person. New York; Macmillan. Dancer, L. S. (1990). Introduction to facet theory and its applications. Applied psycholo~: An international review, 39(4): 365-377. Edmundson, E. W., Koch, W. R., & Siiverman, S. J. (1993). A facet analysis approach to content and construct validity. Educational and Psychological Measurement., 53, 351-368, Eiser, C., Eiser, J. R., Claxton-Oldfieid, S., & Pritchard, M. (1988). Attitudes, attributions, and persuasion: How young people's ideas about drugs relate to their preferences for different strategies of prevention. Journal of Substance Abuse, 1, 35-44. Fishbein, M. & Azjen, L (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Reading, Mass.: Addison-Wesley. Goodstadt, M. & CaleekaI-John, A. (1984). Alcohol education programs for university students: A review of their effectiveness. The International Journal of the Addictions, 19(7), 721-741. Goodstadt, M., Cook, G., Magid, S., & Gruson, V. (1978). The Drug Attitude Scale (DAS): Its development and evaluation. The International Journal of the Addictions, 13(8), 1307-1317. Guttman, L. (1954). An outline of some new methodology for social research. Public Opinion Quarterly, 18, 395-404. Guttman, L. (1981). What is not what in theory construction. In I. Borg (Ed.), Multidimensional data representations: When and why (pp. 47-64). Ann Arbor, Mh Mathesis Press. Hansen, W. B. (1992). School-based substance abuse prevention: A review of the state of art in curriculum, 1980-1990. Health Education Research, 7(3), 403-430. Hansen, W. B., Graham, J. W., Wolkenstein, B. H., Lundy, B. Z., Pearson, J., Flay, B. R., & Johnson, C. A. (1988). Differential impact of three alcohol prevention curricula on hypothesized mediating variables. Journal of Drug Education, 18(2), 143-153. Hennessy, M. (1991). Designing and evaluating alcohol problem community interventions: Quasi-lessons from the experience of medical trials. Journal of Primary Prevention, 11(3), 169-192. Johnston, L. D., O'Malley, P. M., & Bachman, J. G. (1991). Drug use among American high school seniors, college students and young adults, 1975-1990. (DHHS Publication No. ADM 91-1835). Rockville, MD: National Institute on Drug Abuse. Levy, S. (1981). Lawful role of facets in social theories. In I. Borg (Ed.), Multidimensional data representations: When and why (pp. 65-107). Ann Arbor, MI: Mathesis Press. Levy, S. (1982). Use of drug and medication in Israel (Institute Monograph No. 865). Jerusalem: The Israel Institute of Applied Social Research. McCarty, D., Morrison, S., & Mills, K. (1983). Attitudes, beliefs and alcohol use: An analysis of relationships. Journal of Studies on Alcohol, 44(2), 328-341. Massachusetts Medical Society. (1990). Alcohol-related mortality and years of potential life lost 1987-United States, 1990. Morbidity and Mortality Weekly Report, 39(11), 174-178. Meichenbaum, D. (1977)). Cognitive Behavior Modification. New York: Plenum. Moskowitz, J. M. (1983). Preventing adolescent substance abuse through drug education. National Institute on Drug Abuse Research Monograph Series, 47: 233-249. Moskowitz, J. M. (1989). The Primary prevention of alcohol problems: A Critical review of the research literature. Journal of Studies on Alcoho~ 50(I), 54-88. National Institute on Drug Abuse (1987). National trends in drug use and related factors among American high school students and young adults, 1975-1986. (DHHS Publication No. ADM 87-1535). Washington, DC: US Government Printing Office. National Institute on Drug Abuse. (1986). Drug Use Among American High School Students, College Students, and Other Young Adults. (DHHS Publication No. 81-1450). Washington D.C.: U.S. Government Printing Office. Rundall, T. G., & Bruvold, W. H. (1988). A Meta-analysis of school-based smoking and alcohol use prevention programs. Health Education Quarterly, 15(3), 317-334.

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Swadi, H. & Zeitlin, H. (1987). Drug education to school children: Does it really work7 British Journal of the Addictions, 82, 741-746. Torabi, M. R. & Veenker, C, H. (1986). An alcohol attitude scale for teenagers. Journal of School Health, 56(3), 96-100. U.S. Department of Health, Education and Welfare (1979). Healthy People: The Surgeon General's Report on Health, Promotion and Disease Prevention (DHEW Publication No. ADM 79-55071). Washington D.C.: U.S. Government Printing Office. U.S. Department of Health and Human Services (January, 1987). Shah Special Report to the U.S. Congress on Alcohol and Health. (DHHS Publication No. ADM 87-!519). Washington D.C.: U.S. Government Printing Office.

APPENDIX

A L C O H O L SURVEY I N S T R U M E N T P A R T A. T h e following statements concern your opinions toward alcohol use. T o i n d i c a t e h o w m u c h you a g r e e or d i s a g r e e with e a c h o f the statements listed below, please use the following scale:

A

B

C

D

E

I

t

I

I

I

strongly disagree undecided agree strongly disagree agree

Bubble in your response to each statement on the answer form using this scale for your response. Please be careful to put your response in the appropriate place on the answer sheet. Also, keep in mind that 1 beer contains about the same amount of alcohol as 1 shot of liquor, 1 glass of wine, or 1 wine cooler. The mixed drinks referred to in the items contain 1 shot of liquor (for example, a rum and coke). 1. People who have one or two beers every day tend to be depressed. 2. Drinking a six pack of beer at one time once a month might make a person feel depressed. 3. It's easier to cope with a relationship break-up by having 5 or 6 drinks every day. 4. Persons who have 5 or 6 glasses of wine about once a week tend to be lonely. 5. Having 3 or 4 drinks during the week should be good for your body.

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INSTRUCTIONS Please bubble in your response to each item of the survey in the appropriate area of the answer sheet. Please use the scale at the A B C D E right for your responses to each item. I I I I I strongly disagree undecided agree strongly disagree agree

6. Persons who drink 5 or 6 wine coolers one evening every week are probably hurting their bodies. 7. Having one or two drinks once a week with friends is fun. 8. People who drink one or two shots about once a month seem to be nervous. 9. People who have 3 or 4 drinks every day tend to have a positive outlook on life. 10. Friends who drink 5 or 6 wine coolers every day tend to have problems getting along with others. 11. Drinking 5 or more shots of tequila on Friday night once a month would be a great way to cope with the pressures of school. 12. Drinking a six-pack of beer every day can make a person violent. 13. People seem to feel good about life when they have one or two glasses of wine about once a month. 14. People who have one or two beers every evening seem to be relaxed. 15. People tend to vomit when they drink 5 or 6 wine coolers once a month. 16. The type of person who drinks five or more wine coolers once a month usually has a difficult time making friends. 17. It's okay to relax once a month by drinking 3 or 4 glasses of wine. 18. A person who drinks one or two glasses of champagne occasionally (at a wedding, for example) will probably get physically sick. 19. People who drink about three or four beers every day might expect to have more physical health problems than nondrinkers. 20. Having five or more beers every day should be good for your physical health. 21. People who drink 5 or 6 wine coolers every day tend to be popular on campus.

21o

INSTRUCTIONS Please bubble in your response to each item of the survey in the appropriate area of the answer sheet. Please use the scale at the A B C right for your responses to each item. I i .............. !. . . . . strongly disagree : t m ~ i ~ disagree

FAm~n

~ at.

D E I r I agree strongly agree

22. People who have 3 or 4 drinks every day tend to lose their friends. 23. It's hard for someone who drinks 3 or 4 beers during the week to get a date. 24. People who have 3 or 4 drinks once a month arc unlikely to be invited to parties. 25. Having 5 or more drinks every day would tend to make a person physically ill. 26. Friendships tend to end when a person has one or two drinks once a month. 27. People sometimes feel badly about themselves when they drink 3 or 4 wine coolers one evening each month. 28. Drinking one or two glasses of rum and coke about once a week can make a person depressed. 29. It's probably physically healthful to have 5 or 6 beers at one time once a month. 30. Going to movies with friends isn't much fun for a person who drinks one or two beers every day. 31. Physical health is probably harmed by having 3 or 4 drinks once a week. 32. Having a party once a month where persons can drink one or two beers would be a good way to have fun. 33. It's good for your physical health to have 3 or 4 wine coolers once a month. 34. A party is best when it happens about once a month and everyone has 5 or 6 drinks each. 35. Drinking a six-pack of beer or more every Saturday night is probably physically healthy. 36. Persons who drink three or four beers at a party every Saturday night often seem to get irritable.

Attitudes and Drinking Behavior

INSTRUCTIONS Please bubble in your response to each item of the survey in the appropriate area of the answer sheet. Please use the scale at the A B C D right for your responses to each item. I 1 I I strongly disagree undecided agree disagree

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E 1 strongly agree

37. It would be nice to have 3 or 4 glasses of wine with friends about once a month. 38. Having 3 or 4 shots of whiskey at one time once a month could make a person physically sick. 39. Friends who have one or two drinks once a week seem to argue a lot. 40. Persons who have 3 or 4 drinks once a week tend to get along well with others at school. 41. It's fun to have one or two drinks with friends every evening. 42. People who have one or two glasses of wine one night a week might expect to have a headache the next morning. 43. Physical health should be improved by drinking three or four wine coolers every day. 44. People seem to feel good about themselves when they have 3 or 4 glasses of wine one evening during the week. 45. It's probably physically healthy for a person to have one or two glasses of wine every day. 46. It should be physically healthy for a person to have one or two beers about once a week. 47. It's hard for a person who drinks a six-pack of beer one night every week to have a good time at a party where no alcohol is served. 48. Persons who drink 3 or 4 glasses of wine every day tend to be unhappy. 49. Friends tend to get along better with each other when they share three or four beers every night at dinner. 50. Physically, drinking a couple of beers once a month should be beneficial.

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INSTRUCTIONS Please bubble in your response to each item of the survey in the appropriate area of the answer sheet. Please use the scale at the A B C D E right for your responses to each item. 1 I ...... I ........ I strongly disagree undecided agree strongly agree disagree

51. Persons who drink 5 or more beers every Saturday night seem to get a sense of confidence about themselves. 52. People who have 5 or 6 beers once a week seem to have an easier time getting a date. 53. Having one or two wine coolers every day would probably make a person physically sick. 54. One or two mixed drinks every Friday night helps a person to unwind after a hard week. P A R T B. T h e following questions c o n c e r n y o u r personal use o f alcohol. F o r e a c h question please bubble in the answer on the answer sheet that best describes y o u r drinking behavior.

55. Have you ever drunk alcohol (beer, wine, liquor, wine coolers)? A. Yes. B. No. If you answered "No", please skip to question 68. 56. In the last year, A. Never B. At least C. At least D. At least E. At least

how often did you drink alcohol? once, but not every month. once a month, but not every week. once a week, but not every day. once a day.

57. In the last month, how often did you drink alcohol? A. Never B. At least once, but not every week. C. About once a week D. More than once a week, but not every day.

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INSTRUCTIONS Please bubble in your response to each item of the survey in the appropriate area of the answer sheet. Please use the scale at the right for your responses to each item.

E. At least once a day. 58. How much alcohol do you usually drink in one sitting? To answer this question, please note that 1 beer contains about the same amount of alcohol as 1 shot of liquor, 1 glass of wine, or 1 wine cooler. A. The same as 1 beer or less. B. The same as 2 beers. C. The same as 3 beers. D. The same as 4 beers. E. The same as 5 or more beers. 59. How often do you drink alcohol by yourself?. A. Never. B. At least once a year, but not every month. C. At least once a month, but not every week. D. At least once a week, but not every day. E. At least once a day. 60. How often do you drink alcohol with close friends? A. Never. B. At least once a year, but not every month. C. At least once a month, but not every week. D. At least once a week, but not every day. E. At least once a day. 61. How often do you drink alcohol in the morning? A. Never. B. At least once a year but not every month. C. At least once a month, but not every week. D. At least once a week, but not every day. E. At least once a day. 62. How often do you drink alcohol while driving a car? A. Never.

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INSTRUCTIONS Please bubble in your response to each item of the survey in the appropriate area of the answer sheet. Please use the scale at the right for your responses to each item.

B. At C. At Do At E. At

least least least least

once once once once

a a a a

year, but not every month. month, but not every week. week, but not every day. day.

63. How often do you drink alcohol while a passenger in a car? A. Never. B. At least once a year, but not every month. C. At least once a month, but not every week. D. At least once a week, but not every day. E. At least once a day. 64. How often do you drink alcohol prior to going to class? A. Never. B. At least once a year, but not every month. C. At least once a month, but not every week. D. At least once a week, but not every day. E. At least once a day. 65. How often do you drink alcohol at your college residence? A. Never. B. At least once a year~ but not every month. C. At least once a month, but not every week. D. At least once a week, but not every day. E. At least once a day. 66. How often do you drink alcohol at another person's residence? A. Never. B. At least once a year, but not every month. C. At least once a month, but not every week. D. At least once a week, but not every day. E. At least once a day.

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INSTRUCTIONS Please bubble in your response to each item of the survey in the appropriate area of the answer sheet. Please use the scale at the right for your responses to each item.

67. How often do you drink alcohol before going to social events (movies, concerts, sports events)? A. Never. B. At least once a year, but not every month. C. At least once a month, but not every week. D. At least once a week, but not every day. E. At least once a day. P A R T C. T h e following questions concern general information a b o u t you. F o r each of the following items, please bubble in the answer on the form that best describes you.

68. Your race: A. Asian/Oriental. B. Black. C. White-Caucasian. D. Hispanic. E. Other. 69. Your parents' income per year: A. less than $15,000. B. $15,001 to $35,000. C. $35,001 to $55,000. D. $55,001 to $75,000. E. More than $75,000. '70. How often do you attend religious services? A. Never. B. Infrequently. C. Occasionally. D. Frequently. E. Very frequently.

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INSTRUCTIONS Please bubble in your response to each item of the survey in the appropriate area of the answer sheet. Please use the scale at the fight for your responses to each item.

71~ How would you describe the occupation of your parent who supported your family household? A. Blue collar/industrial. B. Military. C. Professional. D. White collar/management. E. Other. 72. What is the highest level of education achieved by either of your parents? A. No school. B. Grammar ~chool. C. High school. D. College. E. Graduate school. 73. How would you characterize the alcohol drinking habits of your father? A. Non-drinker. B. Light drinker. C. Moderate drinker. D. Heavy drinker. E. Don't know. 74. How would you characterize the alcohol drinking habits of your mother? A. Non-drinker. B. Light drinker. C. Moderate drinker. D. Heavy drinker. E. Don't know. 75. Compared to the other students who go to college, how would you rate your overall academic ability? A. Poor. B. Fair. C. Good.

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INSTRUCTIONS Please bubble in your response to each item of the survey in the appropriate area of the answer sheet. Please use the scale at the right for your responses to each item.

D. Excellent. 76. How religious are you? A. Very nonreligious. B. Nonreligious. C. Undecided. D. Religious. E. Very religious. 77. On the whole, how successful do you feel your life has been up to this point? A. Very unsuccessful. B. Unsuccessful. C. Average. D. Successful. E. Very successful. 78. How frequently do you use other drugs besides alcohol and tobacco? A. Never. B. Infrequently. C. Occasionally. D. Frequently. E. Very frequently. 79. On the whole, how physically attractive do you feel you are? A. Very unattractive. B. Unattractive. C. Average. D. Attractive. E. Very attractive.

Thank you very much for participating in this survey!