Alcohol expectancy and drinking refusal self-efficacy

Addictive Behaviors, Vol. 25, No. 4, pp. 499–507, 2000 Copyright © 2000 Elsevier Science Ltd. Printed in the USA. All rights reserved 0306-4603/00/$–see front matter

Pergamon

PII S0306-4603(99)00044-1

ALCOHOL EXPECTANCY AND DRINKING REFUSAL SELF-EFFICACY: A TEST OF SPECIFICITY THEORY TIAN P. S. OEI and TAMARA BURROW University of Queensland

Abstract — Although alcohol expectancy (expectations about the effects of drinking alcohol on one’s behavior and mood) and drinking refusal self-efficacy (one’s perceived ability to resist drinking in high-risk situations) have consistently been demonstrated to be useful to our understanding of alcohol use and abuse, the specificity of these constructs to alcohol consumption has not been previously demonstrated. Using 161 first-year psychology students and multiple regression analyses this study indicated that alcohol expectancies and drinking refusal self-efficacy were specifically related to quantity of alcohol consumption, but not to caffeine or nicotine intake. These results provide empirical evidence to confirm the theoretical and practical utility of these two cognitive constructs to alcohol research and serve to strengthen the theoretical foundations of alcohol expectancy theory. © 2000 Elsevier Science Ltd.

Key Words. Alcohol expectancy, Drinking refusal self-efficacy, Alcohol consumption, Smoking, Caffeine.

Empirical support for a relationship between alcohol expectancy (AE) and alcohol consumption has been found in cross-sectional studies across a variety of populations (e.g., Baldwin, Oei, & Young, 1993; Brown, Christiansen, & Goldman, 1987; Oei & Jones, 1986). Although there has been less research into the relationship of drinking refusal self-efficacy (DRSE) to alcohol consumption, those studies that have been conducted indicate that this construct plays an even more important role than AE in alcohol-related behavior (Baldwin et al., 1993; Lee & Oei, 1993; Oei, Fergusson, & Lee, 1998). In combination, expectancies and refusal self-efficacy are related to the initiation and maintenance of drinking, the onset of problem drinking (Christiansen, Smith, Roehling, & Goldman, 1989), and the recovery from alcohol abuse (Solomon & Annis, 1990; Young & Oei, 1993). Overall, convergent evidence from experimental studies suggests that both the AE and DRSE constructs are important to our understanding of alcohol-related behavior. However, despite the theoretical positions (Oei & Baldwin, 1994) and experimental data supporting AE and DRSE, it is still unclear whether the link between AE and DRSE and alcohol consumption is unique and specific. That is, whether AE and DRSE are related only to alcohol consumption, but not to other drug behavior such as smoking. The aim of the present study is to test the hypothesis that both AE and DRSE are specific to alcohol behavior. Firstly, each of these constructs must be shown to be related to alcohol behavior. Secondly, each must be shown to be unrelated to other types of drug behaviors. Thus, two additional drug behaviors, cigarette smoking and

This study was partially supported by grants from Australia Research Council and National Health and Medicine Research Council to Professor Oei who is also the Team leader, Cognitive Behaviour Therapy Unit, Toowong Private Hospital. Requests for reprints should be sent to Dr. T.P.S. Oei, University of Queensland, School of Psychology, Brisbane QLD 4072, Australia; E-mail: [email protected] 499

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caffeine consumption, are examined in conjunction with alcohol behavior. Exercise behavior is included as a control measure because it is nondrug behavior and is conceptually unrelated to either AE or DRSE. It is included as a partial check on the discriminant validity of other measures used in the study. Exercise behavior is assessed in terms of the amount of time per day spent exercising. Two further cognitive variables are included in the design of the study. Smoking refusal self-efficacy is included as a nonfocal variable in order to examine its specificity to smoking behavior (i.e., related to smoking behavior, but not to alcohol behavior). Presence of automatic thoughts (Hollon & Kendall, 1980) is included to check for other cognitive constructs measured at the same time and in the same way that may contribute to the relationship (i.e., due to common method correlation). Because presence of automatic thoughts is a measure of the frequency of negative self-statements associated with depression, this measure is expected to be conceptually unrelated to the behavioral variables (i.e., alcohol consumption, smoking, caffeine intake, and exercise). M E T H O D

Participants One hundred and sixty eight first-year psychology students participated in the experiment for course credit. Mean age of the students was 20 years (SD: 4 years; Range: 17 to 41 years). Ninety eight percent of the students were of Caucasian origin. Only those who were both self-reported drinkers and smokers were recruited into the study. Their self-reported daily mean consumption of alcohol, nicotine, and caffeine were 47 ml of alcohol (SD: 33, Range: 3 to 178), nine cigarettes (SD: 7, Range: 0.5 to 31), and 239 mg of caffeine (SD: 187, Range: 0 to 831), respectively. Materials The questionnaires used in the experiment consisted of demographic information; the Khavari alcohol test (Khavari & Farber, 1978) the Drinking Expectancy Profile (Young & Oei, 1996; Young, Oei, & Crook, 1991), a measure of smoking refusal selfefficacy (Condiotte & Lichtenstein, 1981), and the Automatic Thoughts Questionnaire (Hollon & Kendall, 1980); and measures of alcohol, smoking, and caffeine consumption, and a measure of exercise behavior. All these measures have good psychometric properties and have been described previously (e.g., Oei et al., 1998). Procedure Participants completed the questionnaires in a single session (mean completion time was 40 minutes) in a classroom setting (in groups ranging from 5 to 20 students). Steps taken to maximize the validity of the self-reports included: (a) the confidentiality and anonymity of responses was emphasized to participants before the questionnaires were completed; and (b) students were told that their participation was voluntary. R E S U L T S

Prior to hypothesis testing, the data were screened for univariate and multivariate outliers and to ensure that the variables met assumptions of normality. Simple bivariate correlates between all variables used in the analyses (see Table 1) revealed there were no problems with multicollinearity or singularity. To test the hypotheses of the present study, the analyses were divided into three sections which are outlined here for purposes of clarity.

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Table 1. Correlation matrix

ALC SMOK CAFF EXER AE DRSE SRSE

ALC

SMOK

CAFF

EXER

AE

DRSE

SRSE

1.00 .13 ⫺.01 .11 .26* ⫺.40* ⫺.15

1.00 .49* ⫺.07 .04 ⫺.10 ⫺.70*

1.00 .05 ⫺.01 ⫺.10 ⫺.38*

1.00 ⫺.12 .05 .13

1.00 ⫺.49* ⫺.19

1.00 .36*

1.00

ALC ⫽ alcohol consumption; SMOK ⫽ smoking; CAFF ⫽ caffeine consumption; EXER ⫽ exercise; AE ⫽ alcohol expectancy; DRSE ⫽ drinking refusal self-efficacy; SRSE ⫽ smoking refusal self-efficacy. * p ⬍ .01.

Relationship of alcohol expectancy and drinking refusal self-efficacy to alcohol consumption The results of the first multiple regression analysis are shown in Table 2. Altogether, 17% (15% adjusted) of the variability in alcohol consumption was predicted by knowing the scores on alcohol expectancy, drinking refusal self-efficacy, smoking refusal self-efficacy, and automatic thoughts. In combination, these four variables predicted a significant proportion of the variance in alcohol consumption (F(4,155) ⫽ 7.83, p ⬍ .001). DRSE was the only variable to uniquely predict a significant proportion (8%) of the variance in alcohol consumption (sr2 ⫽ .08, p ⬍ .001). Two post hoc hierarchical regression analyses were conducted to determine the relative contribution of AE to the variance in alcohol consumption when the influence of DRSE is controlled (see Table 3). When entered at the first step, alcohol expectancy accounted for a significant proportion of the variance (6%) in alcohol consumption with R2 significantly different from zero (Fchange(1,158) ⫽ 10.83, p ⬍ .01). Entered at the second step, DRSE accounted for an additional 10% of the variance in prediction of alcohol consumption, and this increase was significant (Fchange(1,157) ⫽ 29.25, p ⬍ .001). When DRSE was entered at the first step it accounted for a significant proportion (16%) of the variability in alcohol consumption. When AE was entered second, there was no increase in the amount of variance accounted for in alcohol consumption. The results of these analyses suggest that both DRSE and AE are important predictors of alcohol consumption, but that AE contributes to the prediction only in an indirect manner through its relationship with DRSE. The analyses also suggest that neither smoking refusal self-efficacy nor automatic thoughts are important in the prediction of alcohol consumption.

Table 2. Standard multiple regression analysis predicting alcohol consumption from AE, DRSE, smoking refusal self-efficacy (SRSE), and automatic thoughts (ATQ) (N ⫽ 160) Variables AE DRSE SRSE ATQ

␤ weight

t value

sr2

.11 ⫺.34 ⫺.02 ⫺.08

1.30 ⫺3.78 .25 1.09

.01 .08** .01 .01

R ⫽ .41 R2 ⫽ .17 R adjusted ⫽ .15

AE ⫽ alcohol expectancy; DRSE ⫽ drinking refusal self-efficacy. * p ⬍ .01; ** p ⬍ .001.

F ⫽ 7.83**

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Table 3. Hierarchical regression analyses predicting alcohol consumption from alcohol expectancy (AE), drinking refusal self-efficacy (DRSE), smoking refusal self-efficacy (SRSE), and automatic thoughts (ATQ) Variables AE DRSE SRSE ATQ DRSE AE SRSE ATQ

Step

R2change

␤

(1) (2) (3) (4) (1) (2) (3) (4)

.06* .10** .00 .01 .16** .01 .00 .01

.25 ⫺.35 ⫺.01 ⫺.08 ⫺.40 .09 ⫺.01 ⫺.08

* p ⬍ .01; ** p ⬍ .001.

Relationship of alcohol expectancy and drinking refusal self-efficacy to other drug behavior Smoking behavior. A standard multiple regression analysis was performed to determine the relative importance of AE and DRSE and the other cognitive variables (i.e., smoking refusal self-efficacy and automatic thoughts) in their prediction of cigarette smoking. Results showed that R2 was significantly different from zero (F(4,155) ⫽ 36.83, p ⬍ .001) with 49% (47% adjusted) of the variability in smoking predicted by knowing the scores on these four variables (see Table 4). As expected, smoking refusal self-efficacy accounted for a significant proportion of the variance in smoking, uniquely contributing 48% to R2 (sr2 ⫽ .48, p ⬍ .001). Also, as expected, neither AE nor automatic thoughts significantly predicted smoking. Contrary to expectations, however, DRSE was found to be significant in its prediction of smoking (sr2 ⫽ .02, p ⬍ .05). DRSE was identified as a suppressor variable in this analysis because its simple bivariate correlation with the smoking dependent variable (r ⫽ ⫺.01) and its beta weight in the regression equation (.16) have opposite signs (Cohen & Cohen, 1975; Darlington, 1968). A suppressor variable served to enhance the importance of other independent variables by suppressing irrelevant variance in other independent variables or in the dependent variable (Tabachnick & Fidell, 1989). Darlington (1968) reports that suppressor variables usually have a low or zero true correlation with the independent variable. Thus, given that DRSE contributes only 2% to the prediction of smoking and that it is acting as a suppressor variable, it is very likely that its true relationship with smoking is unimportant. This conclusion is reinforced by the observation that the simple bivariate correlation between drinking refusal self-efficacy and smoking is not significant. The results of the second set of analyses suggest that AE and DRSE do not predict smoking behavior. Together, the findings of the first and second set of analyses offer converging evidence for the overall hypothesis that the AE and DRSE constructs are specifically related to alcohol behavior and not to other drug behavior. In support of the specificity of the self-efficacy constructs, smoking refusal self-efficacy was shown to predict smoking behavior, whereas an unrelated cognitive construct (i.e., automatic thoughts) was not predictive of smoking. Caffeine behavior. A standard regression analysis was performed to determine whether AE, DRSE, smoking refusal self-efficacy, and automatic thoughts were related to caffeine consumption. R2 was significantly different from zero (F(4,155) ⫽ 9.17, p ⬍ .001); the four variables in combination accounted for 19% (17% adjusted) of the

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Table 4. Standard multiple regression analysis predicting cigarette consumption from AE, DRSE, smoking refusal self-efficacy (SRSE), and automatic thoughts (ATQ) (N ⫽ 160) Variables AE DRSE SRSE ATQ

␤ weight

t value

sr2

⫺.01 .16 ⫺.74 ⫺.04

⫺.10 2.30* ⫺11.99** ⫺.70

.00 .02 .48 .00

R ⫽ .70 R2 ⫽ .49 R adjusted ⫽ .47

F ⫽ 36.83**

AE ⫽ alcohol expectancy; DRSE ⫽ drinking refusal self-efficacy. * p ⬍ .05; ** p ⬍ .001.

variability in caffeine consumption (see Table 5). As expected, neither AE nor DRSE individually predicted a significant portion of the variance in caffeine consumption. However, smoking refusal self-efficacy and automatic thoughts both significantly contributed to the prediction of caffeine consumption. Smoking refusal self-efficacy uniquely contributed 12% to R2 (sr2 ⫽ .12, p ⬍ .001) and automatic thoughts uniquely contributed 4% to R2 (sr2 ⫽ .04, p ⬍ .01). To ascertain whether smoking refusal self-efficacy was only exerting an influence on caffeine consumption indirectly through its relationship with smoking behavior, a post hoc hierarchical multiple regression analysis was performed (see Table 6). When entered at the first step, smoking accounted for a significant proportion of the variance (21%) in caffeine consumption with R2 significantly different from zero (F(1,158) ⫽ 17.93, p ⬍ .001). When entered at the second step, smoking refusal self-efficacy did not add significantly to the prediction of caffeine consumption. Entered at the third step, automatic thoughts contributed an additional 4% of the variance in caffeine consumption. AE and DRSE did not add to the prediction of caffeine consumption. As expected, these results indicate that once smoking behavior is accounted for, smoking refusal self-efficacy does not significantly predict caffeine behavior. Relationship of alcohol expectancy and drinking refusal self-efficacy to exercise behavior. A standard multiple regression was performed to determine whether AE, DRSE, smoking refusal self-efficacy, or automatic thoughts were related to exercise behavior. As expected, none of these variables (either singly or in combination) added significantly to the prediction of the control variable, exercise. The standardized regression coefficients for AE, DRSE, smoking refusal self-efficacy, and automatic thoughts were .09, ⫺.04, .11, and ⫺.12, respectively. In addition, none of the bivariate correlations between each of the predictor variables and exercise was significant (see Table 1). These findings lend support to the hypothesis that AE and DRSE are unre-

Table 5. Standard regression analysis predicting caffeine consumption from AE, DRSE, smoking refusal self-efficacy (SRSE), and automatic thoughts (ATQ) (N ⫽ 160) Variable AE DRSE SRSE ATQ

␤ weight

t value

sr2

⫺.14 ⫺.03 ⫺.37 .22

⫺1.68 ⫺0.33 ⫺4.74** 2.88*

.01 .01 .12 .04

AE ⫽ alcohol expectancy; DRSE ⫽ drinking refusal self-efficacy. * p ⬍ .01; ** p ⬍ .001.

R ⫽ .43 R2 ⫽ .19 R adjusted ⫽ .17

F ⫽ 9.16**

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Table 6. Hierarchical regression analyses predicting caffeine consumption from smoking (SMOK), smoking refusal self-efficacy (SRSE), automatic thoughts (ATQ), alcohol expectancy (AE), and drinking refusal self-efficacy (DRSE) Variables SMOK SRSE ATQ AE DRSE

Step

R2change

␤

(1) (2) (3) (4) (5)

.21* .01 .04* .01 .01

.46* ⫺.12 .20* ⫺.10 ⫺.09

* p ⬍ .001.

lated to the control variable of exercise. They also provide support for the specificity of alcohol expectancy and drinking refusal self-efficacy to alcohol consumption. D I S C U S S I O N

The present study was designed to examine the hypothesis that the AE and DRSE constructs are specific to alcohol behavior. The results are summarized in Figure 1. The study aimed firstly, to demonstrate that the constructs significantly predict alcohol consumption, and secondly, to demonstrate that the constructs do not predict either smoking or caffeine consumption. In support of the hypotheses, the results clearly show that AE and DRSE are predictive of alcohol consumption, and that they are not predictive of smoking or of caffeine consumption. These findings provide convergent evidence for the hypothesis that AE and DRSE are specifically associated with alcohol behavior. The present results also provide empirical evidence for the theoretical usefulness of these constructs in the explanation of alcohol behavior (see Oei & Baldwin, 1994). In demonstrating the specificity of these constructs to alcohol behavior, the results provide evidence for the theoretical distinctiveness of these constructs from other cognitive constructs. Furthermore, the present results do not support the idea that AE is not distinct from a general attitude construct (Leigh, 1989). In addition, the finding that these constructs are not related to smoking or caffeine behavior suggests that these constructs are not merely reflective of higher order drug expectancy and drug refusal self-efficacy cognitions. In terms of models of semantic memory (Rather, Goldman, Roehrich, & Brannick, 1992), the results suggest that expectancy and refusal self-efficacy cognitions regarding different drugs may be distally-related in memory networks rather than proximally-related. The finding that drinking refusal self-efficacy is specifically related to alcohol behavior also provides support for Bandura’s (1986) assertion that self-efficacy relates to specific behavior in specific situations. The present results do not support the idea that self-efficacy is generalizable across behavior (e.g., Hays & Ellickson, 1990). The findings of the present study, that these constructs are specifically related to alcohol behavior, have important implications for the content and efficiency of programs aimed at the prevention of alcohol and other drug abuse. There are some limitations in drawing conclusions about treatment given this study’s nonclinical sample. Nonetheless, the results suggest that drug prevention strategies target each specific drug category separately as they generally do with caffeine, nicotine, and alcohol. However, it remains to be determined if this should be the case for alcohol and illicit

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Fig. 1. Standardized regression coefficients (beta weights) for cognitive variables predicting (a) alcohol consumption (mls/day); (b) cigarette consumption (no./day); (c) caffeine consumption (mg/day); and (d) quantity of exercise behavior (mins/day). Solid lines indicate a significant predictor (p ⬍ .05); dashed lines indicate nonsignificance of a predictor.

drugs (e.g., cannabis, cocaine, heroin). It is likely that modifying high positive expectations for drugs in general and teaching overall resistance skills for drugs in general may be less effective than concentrating on modifications and training at the level of each specific drug category. Aside from the finding regarding the specificity of the AE and DRSE constructs, the present study also provides some insight into the nature of the relationships between these two constructs and alcohol consumption. Results from the first set of analyses, which examined the relationship of the AE and DRSE constructs to alcohol behavior, show that both constructs are related to alcohol consumption. However, AE exerted only an indirect influence on alcohol consumption via its relationship with DRSE. This result is consistent with recent findings that have shown DRSE plays a more important role than AE in terms of both the quantity and frequency of alcohol consumption (Baldwin et al., 1993; Lee & Oei, 1993). Moreover, these results provide further evidence for Oei and Baldwin’s (1994) suggestion that AE may be the more antecedent variable, involving a weighing up of the likely reinforcement value of drinking behavior, and that DRSE may be more of a mediating variable intervening between this weighing-up process and the behavioral response. The practical implication of these findings are that the modification of reinforcement expectancies may prove ineffective unless DRSE beliefs are also modified (Oei & Baldwin, 1994). Although smoking refusal self-efficacy was not a variable of primary importance in this study, the results do provide some insight into the specificity of this construct to smoking behavior. Specifically, it was expected that smoking refusal self-efficacy would predict cigarette consumption, but that this construct would not predict alcohol or caffeine consumption. The results provide evidence for this hypothesis. Smoking refusal self-efficacy was found to predict cigarette consumption, but not alcohol consumption. The analysis of the relationship of the smoking refusal self-efficacy con-

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struct to caffeine consumption suggests that once the relationship between smoking and caffeine consumption is accounted for, smoking refusal self-efficacy does not predict caffeine consumption. Thus, these results provide evidence that smoking refusal self-efficacy is specific to smoking behavior and provide support for the theoretical usefulness of this construct. Because this empirical study demonstrates the specificity of the AE and DRSE constructs, its findings are of considerable importance. However, one particular methodological issue should be kept in mind when generalizing from the results of this study. Specifically, the data were obtained from first-year psychology students and therefore may not generalize to clinical populations or to persons in the general community. Future research will need to be conducted to determine the nature of these constructs in these other populations. To summarize, the present study has demonstrated that the AE and DRSE constructs are specifically related to alcohol behavior and that they are unrelated to other drug behaviors. In doing so, the results provide evidence that these constructs are distinct from other cognitive constructs, and that the two constructs are not reflective of higher-order cognitions. The findings from the present study reinforce the tenets of, and empirical findings from, alcohol expectancy theory (Oei & Baldwin, 1994) and highlight the usefulness of this theory to our understanding of alcohol use and abuse.

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