Predictors of alcohol use and binge drinking among Asian Indian students.
|Abstract:||This study examined the extent to which selected social cognitive theory constructs predict alcohol use and binge drinking behaviors among Asian Indian students. A 46-item valid and reliable questionnaire was administered online to college students (n = 245) at two large Mid-western universities. Sixty two percent of Indian students consumed alcohol in the past 30 days. Alcohol-related self-efficacy (p < 0.001) and self control for quitting alcohol (p < 0.005) were significant predictors for average number of drinks consumed in a typical week. Alcohol use is a significant problem among Asian Indian college students and interventions for this community may be built based on SCT constructs.|
Universities and colleges
Students (Alcohol use)
Drinking of alcoholic beverages
|Publication:||Name: American Journal of Health Studies Publisher: American Journal of Health Studies Audience: Professional Format: Magazine/Journal Subject: Health Copyright: COPYRIGHT 2009 American Journal of Health Studies ISSN: 1090-0500|
|Issue:||Date: Spring, 2009 Source Volume: 24 Source Issue: 2|
|Product:||Product Code: 8220000 Colleges & Universities NAICS Code: 61131 Colleges, Universities, and Professional Schools SIC Code: 8221 Colleges and universities|
|Organization:||Government Agency: United States. National Institute on Drug Abuse|
|Geographic:||Geographic Scope: India Geographic Code: 9INDI India|
Alcohol misuse among college students has been documented extensively in literature. A frequent finding that has been reported is that alcohol misuse is more prevalent among Caucasians, African-Americans and Hispanics, than in Asian-Americans in the United States (So & Wong, 2006). This has led to the belief that Asian-Americans are less affected by alcohol use and can be classified as a low health risk group. Although this could be true for some of the ethnic groups within Asian-Americans in the United States, it is not accurately reflective of all Asian ethnic groups. Unlike other races, the Asian-American population is not a homogenous group. They consist of many different ethnic groups with a unique set of cultural and traditional beliefs (Makimoto, 1998; Yi & Daniel, 2001). The Asian American population consists of at least 30 different Asian ethnic groups. These ethnic groups include Indians, Chinese, Japanese, Vietnamese, Koreans, Filipinos and many other such nationalities. The culture of each country within Asia is different. Also, the immigration history and acculturation may vary within these ethnic groups (Makimoto, 1998). It would be inappropriate to study those health risk behaviors collectively. Instead, studying the health risk behaviors of individual ethnic groups would give more information on the patterns of alcohol use and to what extent that problem might be represented in that particular Asian ethnic group (Yi & Daniel, 2001).
Over the past two decades from the year 1980 to 2000, one of the fastest growing racial/ethnic minorities in the United States is Asian-American and Pacific Islanders (AAPIs) with an estimated 10.9 million AAPIs in 2000 (Makimoto, 1998; So & Wong, 2006; Varma & Siris, 1996). They are often called as the "model minority" with the second generation members being overrepresented in universities, colleges and other educational institutions (So & Wong, 2006; Varma & Siris, 1996). Among the AAPIs in the United States, the ethnic minority groups where alcohol use has been documented most frequently are Chinese and Japanese people, followed by Koreans and Filipinos (Makimoto, 1998). The studies found that alcohol use was more prevalent in Japanese than Koreans followed by Filipinos and Chinese populations respectively (Makimoto, 1998; Varma & Siris, 1996). These studies did not look at Indians. One of the probable reasons for lack of information on alcohol use and alcohol-related problems among Indians is due to short immigration history and smaller size of Indian immigrant population (Makimoto, 1998).
According to one study done in Asian-American college students (n = 248), a 94.5% lifetime prevalence and 78.6% current prevalence of alcohol use (past 30 days) was found. The sample in the study predominantly consisted of Chinese followed by Korean, Filipino and Vietnamese college students. The mean age of the students was 20 years. These findings have established that Asian American college students are also consuming alcohol (So & Wong, 2006). Another study done in India estimated the prevalence, patterns and predictors of alcohol consumption among college students (n = 610) from 9 different graduate level colleges. The mean age of the students was found to be 21 years. The study found that the overall prevalence of alcohol use (past 30 days) was 49.2% in male students and 5.2% in female students. The study found that the mean age of initiation of alcohol use was 18 years for males and 19 years for females. The common reasons found for initiation of drinking were encouragement from friends, curiosity, for fun, for celebrations and during depressed mood (Khosla, Thankappan, Mini, & Sarma, 2008).
One reason researchers have recognized in their studies regarding Asian-American students' drinking behavior is influence of the degree of acculturation. These studies have found that students who were more strongly acculturated reported higher levels of alcohol consumption compared to less strongly acculturated students (Gill, Wagner, & Vega, 2000; So & Wong, 2006; Yi & Daniel, 2001). Researchers have operationalized acculturation as language use, as the composite of language use and cultural preferences and as ethnic identity and pride (Gill, Wagner, & Vega, 2000). Acculturation may be looked at from a multidimensional point of view as it involves an array of factors that influences an immigrant behavior in adapting to the new culture. These factors are incorporating cultural values and behaviors, increased perceived discrimination, and trouble in adjusting and dealing with a multicultural environment (Vega & Gill, 1999). Studies have found that substance use among Asian-American students was more influenced by their cultural factors. Researchers have found that immigrant's traditional behavior will be modified over time when it is in continuous contact with the dominant culture. Some factors which might mediate this change are duration of stay in the country, accessibility and cost of alcoholic drinks, and the amount and quality of interaction with the dominant culture (O'Hare & Tran, 1998). Other factors which might contribute to higher levels of alcohol consumption among them are cultural conflict, minority group-status, social change, and lack of language or other marketable skills (Varma & Siris, 1996). Finally, a common factor which has been often linked with acculturation and alcohol use is stress associated with acculturation process. This association between alcohol use and acculturation reflects a stress management response. In an effort to reduce this perceived acculturation process, the immigrants resort to increased alcohol consumption. Along with the stress factors, acculturation also involves certain environmental factors that are particularly associated with heavy alcohol use (Vega, Zimmerman, Warheit, Apospori, & Gil, 1993).
Current studies on college students and problematic drinking have come up with numerous predictors of drinking behavior. Researchers have defined problematic drinking taking into consideration two important factors (1) quantity and frequency of alcohol use and (2) alcohol-related negative consequences (Ham & Hope, 2003). There are a variety of factors predicting binge drinking and problematic drinking among American college students. The common predictors are likely to be male, Caucasian, single and members of fraternities and sororities (Strano, Cuomo, & Venable, 2004). Other significant predictors are engaging in risk and/or problem behaviors (Vollrath & Torgersen, 2002), drinking history (alcohol use before college), parental or family history (Hilderbrand, Johnson, & Bogle, 2001), peer influence or peer pressure, social and physical pressure (Borsari & Carey, 2001), alcohol expectancies (Martin & Hoffman, 1993), valuations of alcohol expectancies (Finn, Bobova, Wehner, Fargo, & Rickert, 2005), perceived norms (Baer, 2002), stress and coping (O'Hare & Sherrer, 2000), depressive symptomatology (Windle & Davies, 1999), anxiety (Berke & Stephens, 1999), tension reduction (Lewis & O'Neill, 2000), and parties or socializing (McCabe, 2002). Although there are many more factors which predict college student drinking, only those predictors which were relevant and significant to the study were discussed.
All the above mentioned predictors might not be true for Asian-Indian college students considering their differing cultural beliefs and traditional practices. However, there are some predictors such as drinking history, family history, peer influence or peer pressure, alcohol expectancies, tension reduction, and celebrations which were found to be significant predictors of alcohol use among Asian Indian college students according to a study done in India (Khosla et al., 2008).
Perceived norms, alcohol expectancy, and self-efficacy constructs were often a key part of any questionnaire measuring drinking frequency and drinking behaviors among college students. These constructs played an instrumental role in predicting college student drinking behavior (Kuther & Timoshin, 2003). The current study utilized social cognitive theory and its framework in predicting alcohol use and binge drinking among Asian Indian college students. Social cognitive theory has been a popular theory in health education for nearly three decades and is considered the most robust predictor of adolescent and young adult drinking (Bandura, 1986; Bandura, 2004; Kuther & Timoshin, 2003; McAlister, Perry, & Parcel, 2008). Social cognitive theory has shown great promise in predicting college student drinking and has been applied extensively in alcohol and drug education literature (Kuther & Timoshin, 2003; Sharma, 2005). Researchers have emphasized the importance of social cognitive theoretical constructs in developing and implementing alcohol cessation programs as it significantly predicts drinking behavior among youth and young adults (Callas, Flynn, & Warden, 2004; Metrik, McCarthy, Frissell, MacPherson, & Brown, 2004; Oei & Morawska, 2004; Winkleby et al., 2004). The key construct in social cognitive theory assumed to be an important predictor of college student drinking behavior is self-efficacy (Bandura, 1999; Oei, Ferugson, & Lee, 1998). Self-efficacy is commonly defined as confidence in one's ability to carry out a particular activity, but in the context of alcohol use, it is one's perceived skill to reject/resist alcohol in specific situations (Burke & Stephens, 1999). The other constructs are situational perception or the insights pertaining to alcohol use and its habits, expectations about expected costs and benefits for different health habits, and self control or goals that a person sets for himself or herself. Expectations are of three kinds and pertain to physical outcomes, social outcomes of approval and disapproval, and positive and negative self evaluative reactions. Expectations are a function of outcome expectations or anticipatory outcomes of a behavior and outcome expectancies or the value that person places on a given outcome. Self control involves setting goals that are proximal and distal and set the course for change (Sharma, 2005).
In the current study we examined the extent of alcohol use and binge drinking among Asian Indian students in Midwestern Universities. In this context we want to specify that our study has focused primarily on graduate students, as most Indian students studying in United States are graduate students as opposed to conventional undergraduates. In addition, we examined the role and relations among situational perception, alcohol-related outcome expectations, alcohol-related outcome expectancies, alcohol-related self-efficacy and self control for quitting alcohol as predictors of alcohol use and binge drinking among Asian Indian students based on social cognitive theory.
The design for the study was cross sectional in nature. The subjects selected were a convenience sample from a cross section of Asian Indian college students studying at two large Mid-western universities. Institutional Review Board (IRB) approval from both the universities was obtained prior to the initiation of the study. Approvals from the International Student Services Office (ISSO) and Indian Students Association (ISA) were also obtained from both the universities. The data were collected between March 2008 and July 2008.
The study participants were students attending two large Mid-western universities. These schools have a large number of Asian Indian international students. The first university had 663 Asian Indian students and the second university had 673 Asian Indian students respectively in the academic year 2007-2008. To be included in the study, participants had to be of 'Asian Indian' origin and enrolled as full time students at either of the two universities. For the purpose of this study Asian Indian origin was defined by three requirements; a) respondents had to be natural Indian citizens, with both parents of Indian origin, b) they must have spent at least their first 15 years of life in India and c) the ownership of at least one university email account by each participant was a necessary condition imposed by the study structure. The participants included students of all class levels (undergraduates, graduates and doctoral), both genders and a variety of majors and concentrations. Participation in the survey was voluntary and identity of the respondents was kept anonymous. The students were recruited through the International Student Services Office (ISSO) and Indian Students Association (ISA) organizations in the campus by e-mails. The students were informed about a study being done among Asian Indian students alcohol use, its significance and future implications to the Asian Indian students community. The students were sent e-mails by their respective organizations and were asked to participate in the study. The approximate number of participants required to complete the study was 197. The sample size was calculated with an alpha of 0.05 and power of 0.80 with a population correlation coefficient of 0.20 (Polit & Hungler, 1999). The population correlation coefficient was assumed to be 0.20 based on previous studies done by Khosla and colleagues in 2008 (Khosla et al., 2008). The final sample for the study achieved was 245 college students from both the universities.
A 46-item self report questionnaire was developed and validated for face and content validity by a panel of six experts in a two round process. The survey questions were developed based on Core Alcohol and Drug Survey (CORE) and Alcohol Expectancy Questionnaire (AEQ)--Revised Adult (Brown, Christiansen, & Goldman, 1987; Presley, Meilman, & Lyerla, 1994). In the first round the experts were sent the operational definitions of each construct and the items corresponding to the definitions. They commented on readability, face validity, and content validity of the items. Based on their suggestions some of the items were modified. There were a total of 12 changes made to the items. Following the changes, the revised questionnaire was sent to the panel of experts for a second round. Consensus on the questionnaire was achieved after the second round. After face and content validation by the panel of experts, the questionnaire was pilot tested in a sample of thirty participants. The questionnaire was uploaded in the Survey Monkey TM software and was e-mailed to students in the form of a web link which included a cover letter and consent form by ISSO and ISA organizations. The participants were sent reminder e-mails three times over a period of five months. The participants were asked to report on their personal characteristics, frequency of alcohol use, amount of alcohol use, and behaviors pertaining to alcohol use. The questionnaire was distributed online and completed during the spring and summer quarters of 2008.
The first set of items in the questionnaire were about demographics and included information on age, gender, marital status, classification, GPA, number of years lived in United States, age of initiation of alcohol use, and number of years since drinking alcohol. The second set of items included information on frequency and quantity of alcohol use and binge drinking in the past 30 days. Students were asked to recall the average number of drinks consumed in a typical week, number of days alcohol consumed in the past 30 days, and number of days engaged in binge drinking in the past 30 days. Lastly, there were five set of items measuring the alcohol-related behaviors based on five constructs of Social Cognitive Theory (SCT). The first construct was situational perception. Situational perception was defined as how one perceives and interprets the environment around oneself (Sharma & Romas, 2008). The scale measuring this construct consisted of 5 items (e.g. everyone of my age drinks alcohol; people who drink alcohol have more friends; it is customary to drink alcohol at parties, etc.). The items were measured on a scale from strongly disagree (0) to strongly agree (4). A total score was computed by summing participant responses to the 5 items. The situational perception score could range from 0 to 20, where higher scores indicated the awareness about issues related to alcohol use more conducive to drinking. The Cronbach's alpha for this scale was 0.65 and test-retest reliability coefficient was 0.83.
The second construct was alcohol-related outcome expectations and was defined as anticipation of the probable outcomes that would ensue as a result of engaging in alcohol drinking behavior (Sharma & Romas, 2008). The scale measuring this construct consisted of 8 items (e.g. if I drink alcohol I will feel happy; if I drink alcohol I will be healthier; if I drink alcohol I will stand up to others, etc.). The items were measured on a scale from never (0) to always (4). The Cronbach's alpha for this scale was 0.86. The third construct was alcohol-related outcome expectancies and was defined as the values a person places on the probable outcomes that result from drinking alcohol (Sharma & Romas, 2008). The scale measuring this construct consisted of 8 items (e.g. how important is it to you that you feel happy when you drink alcohol; how important is it to you that you be healthier when you drink alcohol; how important is it to you that you can stand up to others when you drink alcohol, etc.). The items were measured on a scale from not at all important (0) to extremely important (4). The Cronbach's alpha for this scale was 0.75. The multiplicative score of alcohol-related outcome expectations and alcohol-related outcome expectancies and their cumulative score together gives the value of construct alcohol-related expectations. It is a standard practice when operationalizing alcohol-related expectations to multiply alcohol-related outcome expectations with corresponding alcohol-related outcome expectancies and then summing the total. This method of computation has been employed by several studies (Sharma, Wagner, & Wilkerson, 2006; Sharma, Petosa, & Heaney, 1999; Murnan, Sharma, & Lin, 2006-2007). Alcohol related expectations score could range from 0 to 128, where higher scores indicated the beliefs about the effects of alcohol use more conducive to drinking. The test retest reliability coefficient of alcohol-related expectations scale was 0.91.
The fourth construct was alcohol-related self-efficacy and was defined as the confidence in one's ability that decides whether or not to drink alcohol (Sharma & Romas, 2008). The scale measuring this construct consisted of 6 items (e.g. how sure you that you can purchase alcohol alone; how sure are you that you can drink alcohol alone; how sure are you that you can drink alcohol with friends, etc.). The items were measured on a scale from not at all sure (0) to completely sure (4). A total score was computed by summing participant responses to the 6 items. The alcohol-related self-efficacy scores could range from 0 to 24, where higher scores indicated the confidence to drink alcohol. The Cronbach's alpha for this scale was 0.85 and test-retest reliability coefficient was 0.89.
The fifth and final construct was self control for quitting alcohol and was defined as setting goals and developing plans to accomplish quitting alcohol ((Sharma & Romas, 2008). The scale measuring this construct consisted of 7 items (e.g. how sure are you that you can reduce or quit drinking alcohol; how sure are you that you can make a plan to reduce or quit drinking alcohol; how sure are you that you can set goals for reducing or quitting alcohol, etc.). The items were measured on a scale from not at all sure (0) to completely sure (4). The self control for quitting alcohol scores could range from 0 to 28, where higher scores indicated the ability to reduce or quit drinking alcohol. The Cronbach's alpha for this scale was 0.90 and test-retest reliability coefficient was 0.80. The five construct and their definitions have been presented in figure 1.
[FIGURE 1 OMITTED]
All data were analyzed using the Statistical Package for Social Sciences (SPSS), version 16.0. Means and standard deviations were used to compute the scores on measures of alcohol use and binge drinking (dependent variables) and on five constructs of SCT (independent variables). Chi-Square analyses were done to measure the relationship between gender and dependent variables and between gender and independent variables. Chi-square analyses would reveal whether or not there is a significant influence of gender on frequency, quantity, and behaviors predicting alcohol use and binge drinking. For modeling the predictors for each of the three dependent variables, stepwise multiple regression analyses were used. The apriori criteria of probability of F to enter the predictor in the model was chosen as less than or equal to 0.05 and for removing the predictor as greater than or equal to 0.10. The predictors used were: gender, number of years lived in U.S., situational perception, alcohol-related expectations, alcohol-related self-efficacy, and self control for quitting alcohol. The three dependent variables used were average number of drinks consumed in a typical week, binge drinking in the past 30 days, and number of days alcohol consumed in the past 30 days.
DEMOGRAPHIC CHARACTERISTICS OF THE SAMPLE
From a total of 1,336 Asian Indian students from two universities, about 18.3% (245) students responded to the survey. In the entire sample of 245 college students, 68.6% (168) were males, and 31.4% (77) were females. The mean age of the participants was 25 years (S.D = 3.04). Most of the college students, 63.7% (156) were enrolled in the master's programs, 21.6% (53) were enrolled in the doctoral programs, and 11.1% (27) were undergraduates. Majority of the college students were single, 87.8% (215) followed by 9.4% (23) married couples. The mean grade point average (GPA) of the students was 3.61 (S.D = 0.28), and the average length of residence in the U.S. was two years and six months (S.D = 21.37 months). The mean age of initiation of alcohol use was 20.1 years (S.D = 2.38) for males and 21.9 years (S.D = 3.273) for females. The average number of years since the Asian Indian students started consuming alcohol was 4.66 years (S.D = 2.84).
The current prevalence of alcohol use (past 30 days) was found to be 62.0% (152) among the participants who answered the questions on alcohol use. From those who reported drinking alcohol (n = 152), 71.7% (109) were males and 28.3% (43) were females. The current prevalence of binge drinking (past 30 days) was 51.3% (78) in the study sample. From those who reported binge drinking (n = 78), 44.1% were males and 7.2% were females. The means and standard deviations of the three dependent variables: a) average number of alcoholic drinks consumed in a typical week b) binge drinking (defined as, over the past 30 days, number of days one had five or more drinks (male) and four or more drinks (female) and c) number of days alcohol consumed in the past 30 days are presented in Table 1.
Cross tabulations between gender and alcohol use were done among participants who responded to the items on alcohol use. The findings from the study show that gender does not play a significant role (p > 0.05) in influencing alcohol use among male or female students. Cross tabulations were also done between gender and the three dependent variables and between gender and the constructs of social cognitive theory. The study found significant relationship (p<0.01) between gender and binge drinking. It was found that 44.1% male students were binge drinkers as opposed to 7.2% female students. Significant relationship could not be found for other predictors with gender.
There were a total of 131 participants responding to the items under each scale measuring the five constructs of social cognitive theory. The observed range for each construct was measured from the responses and was compared with the possible range. The possible range for situational perception was 0-20 and the observed range was 0-20 with a mean of 8.05 units and standard deviation of 3.16 units. Similarly, the possible range for alcohol-related expectations was 0-128 and the observed range was 0-128 with a mean of 34.23 units and standard deviation of 29.00 units. The possible range for alcohol-related self-efficacy was 0-24 and the observed range was 0-24 with a mean of 16.19 units and standard deviation of 6.15 units. Finally, the possible range for self control for quitting alcohol was 0-28 and the observed range was 0-28 with a mean of 18.74 units and standard deviation of 7.54 units. Among all the constructs, alcohol-related self efficacy and self control for quitting alcohol were the only two constructs which had a mean range closer to the possible range.
Table 2 summarizes the parameter estimates from the stepwise regression of average number of alcoholic drinks consumed in a typical week. As the dependent variable was not normally distributed, log transformation was done to normalize the distribution. The same structure of predictors was found in both versions. The significant predictors found for average number of drinks consumed in a typical week were alcohol related self-efficacy (p < 0.001) and self control for quitting alcohol (p < 0.005). Together these two constructs accounted for approximately 16.7% of the variance in the dependent variable.
Table 3 summarizes the parameter estimates from the stepwise regression of the number of days students engaged in binge drinking over the past 30 days. The significant (p < 0.01) predictor found for binge drinking over the past 30 days was alcohol-related self-efficacy. This construct accounted for approximately 3.6% of the variance in the dependent variable.
Table 4 summarizes the parameter estimates from the stepwise regression of the number of days alcohol was consumed in the past 30 days. As the dependent variable was not normally distributed, log transformation was done to normalize the distribution. The same structure of predictors was found in both versions. The significant predictors found for the number of days alcohol was consumed in the past 30 days were alcohol-related self-efficacy (p < 0.001) and self control for quitting alcohol (p < 0.001). Together these two constructs accounted for approximately 17.7% of the variance in the dependent variable.
The purpose of the study was to determine the extent of alcohol use and binge drinking among Asian Indian students and to identify the role and relations of predictors based on the social cognitive theory. This study has contributed to the continually growing body of literature on alcohol use among Asian Americans focusing specifically on Asian Indian college students. The study found that Asian Indian college students are not far behind the drinking rates of college students from other ethnic races and that there is indeed a problem of alcohol use and binge drinking among them. Contrary to the popular belief of "model minority" attached to this community (So & Wong, 2006; Varma & Siris, 1996), the findings from this study establish and confirm that Asian-Americans are not resistant to alcohol use.
The current prevalence of alcohol use (past 30 days) among the sample was less than the rate reported in Asian American college students and American college students, currently estimated at 78.6% and 70% respectively in one month (Kuther & Timoshin, 2003; So & Wong, 2006). Although, the rate of alcohol use in Asian Indian students is less compared to other Asian American and American college students, they were significantly higher than the rate of alcohol use in college students studying in India (Khosla et al., 2008). This finding appears to support results of previous studies which have shown that immigrant students coming to the United States for higher studies may get acculturated with the mainstream American culture and adopt American drinking patterns (So & Wong, 2006; Varma & Siris, 1996; Yi & Daniel, 2001). Indian students in the process of adapting to new social environment, new culture and different lifestyles may experience personal stress and interpersonal conflicts. As a coping mechanism against these stressors they may resort alcohol. Another reason explained by researchers is experiencing a sense of loneliness by the immigrants when they migrate to foreign countries (Lala & Straussner, 2001). Often times raised in an extended family (with relatives and close friends) until their adulthood it is not uncommon for Asian Indian students to experience feelings of loneliness when they come to U.S. In order to overcome these feelings of loneliness and homesickness they may resort alcohol.
The mean age of initiation of alcohol use for the study sample was higher than the mean age of initiation of alcohol use for college students studying in Indian and also American colleges. The probable reason for this higher mean age of initiation may be due to the fact that Indian students are usually under parental control and are more focused on academics when they are in India. Once they come to the United States, Indian students become more independent and free from parental control and tend to choose pleasure seeking and unhealthy lifestyle behaviors. In addition in U.S, there is freedom from cultural restrictions and taboos which are common in certain castes and religions in India.
When compared to the binge drinking rates of American, African American, and Asian American college students, the binge drinking rates of the current sample seem to be higher. The reason for the paradox is due to the fact that the binge drinking rates in those national sample studies were measured for the past two weeks and not for the past 30 days (Wechsler et al., 2000). However, in one study by the National Institute on Drug Abuse (NIDA) in 1998, the researchers found that the binge drinking rate (past 30 days) was 30% in the age group of 18 to 20 years (NIDA, 1998). This shows that although Asian Indian students consume alcohol less frequently than their American counterparts they consume larger amounts of alcohol. This finding is supported by the fact that only 24.8% of college students in India engaged in binge drinking in the past 30 days (Khosla et al., 2008). A probable reason for higher binge drinking rates in Asian Indian students may be due to the fact that alcohol is readily available and easily accessible in U.S than in India (Lala & Straussner, 2001). In addition, with more number of Asian Indian students obtaining scholarships and stipend, they are in a better financial situation and can easily afford to purchase alcohol (Lala & Straussner, 2001). These differences in binge drinking rates among various ethnic groups however need to be further studied.
The results of this study indicate that the percentage of male drinkers were more than the percentage of female drinkers. This difference was also seen in the context of binge drinking. These findings are similar to those of previous studies that examined the role of gender in alcohol use (Ham & Hope, 2003; Khosla et al., 2008; So & Wong, 2006; Varma & Siris, 1996; Yi & Daniel, 2001). The probable reason for this gender difference is due to the stereotypical role attributed to women in the Indian society or due to cultural values. It is acceptable in many cultures and especially in Asian cultures for men to spend leisure time drinking with friends but similar behavior in women may be socially unacceptable (Yi & Daniel, 2001).
The study found alcohol-related self efficacy as the single most significant predictor for all the three outcome measures on alcohol use and binge drinking. This finding is consistent with other studies that have shown alcohol-related self-efficacy to be the most significant predictor of drinking behavior among college students (Bandura, 1999; Oei & Morawska, 2004). This shows that Asian Indian students may have a high perceived ability to drink alcohol in different social and psychological contexts. This high perceived ability in them may also be the reason for binge drinking. This finding is supported by the fact that mean scores for alcohol-related self-efficacy were recorded in the higher range. It should be noted that in this study we have measured the Indian students' perceived ability to decide whether or not to drink alcohol and did not measure the ability to reject/refuse drinking offers. The second significant predictor found for drinking behavior among Asian Indian students was self control for quitting alcohol. The study found an inverse relationship between students' self control for quitting alcohol and drinking behaviors. This shows that students who had low self control for quitting alcohol were more conducive to drinking and more conducive to heavy drinking.
The other two constructs of social cognitive theory, situational perception and alcohol related expectations were not found to be significant predictors. The mean scores for these two constructs were in the lower range. In the current study, the authors have focused on positive expectations and positive expectancies measuring the anticipated outcome benefits and the valuations placed on those benefits. The study found that the sample had low positive expectations and low positive expectancies. Although, these constructs did not contribute to the predictive potential of drinking behavior, it may be assumed that majority of the Asian Indian students did not consume alcohol assuming it will bring positive effect. To our understanding and knowledge, so far none of the research studies have shown any specific interventions being developed and implemented that have modified situational perception and alcohol-related expectations among individuals. Absence of such interventions and the relatively low scores seem to be the possible reasons that these constructs were not able to contribute to the predictive potential. The other probable reason can be that negative alcohol-related expectations may be a strong indicator of drinking behavior among Asian Indian students than positive alcohol-related expectations. To fully understand the magnitude and association of alcohol-related expectations with drinking behavior among this sample, importance may be given to both positive and negative alcohol-related expectations as well as expectancy valuations.
The current findings from our study predict that high alcohol-related self-efficacy and low self control for quitting alcohol were significant predictors of drinking behavior among Asian Indian students. The findings imply that heavy drinkers seem to have relatively high self-efficacy for drinking and relatively low self control for quitting. This would prevent them from quitting alcohol or making plans or setting goals for quitting alcohol. Further studies are needed to develop and measure drinking refusal skills and alcohol-related expectations in this target group, as together they contribute to significant understanding of drinking behavior.
The study was not without some limitations. There was no random selection of participants which could have possibly introduced some sampling bias. The study utilized self-reported questionnaires to collect alcohol use and so respondents may have under or over reported their drinking behaviors introducing measurement bias. Also, recall over one week and one month was employed to measure the primary dependent variables. Although, it offers an advantage of giving broader coverage, it may not accurately depict the true picture because of inaccurate memory. Approximately 11% of the sample comprised of undergraduate students and therefore generalization of alcohol problem cannot be made to this group. Finally, due to the cross-sectional nature of the study design, nothing can be said about the temporality of association.
IMPLICATIONS FOR HEALTH EDUCATION PRACTICE
It is evident from the study that health education interventions that reduce frequency and quantity of alcohol use, particularly binge drinking among Asian Indian college students, may be developed. Such interventions need to build self control to quit alcohol and decrease the alcohol-related self-efficacy. Self-efficacy toward alcohol use can be decreased by the support from credible Asian Indian role models who advocate non use of alcohol particularly focusing on binge drinking. Self-efficacy toward alcohol use can be further reduced by focusing on breaking down the task of refusing alcohol into smaller steps and developing mastery over each small step through health education programs. As stress related acculturation is associated with alcohol use, coping with stress, relaxation techniques, and extended family support can be incorporated in the interventions. In order to build self control, techniques such as making a plan to reduce or quit alcohol, setting goals to reduce or quit alcohol, and self rewarding oneself on reducing or quitting alcohol are helpful ways. The programs developed for Asian Indian students must take in to consideration their cultural relevance and traditional beliefs (Yi & Daniel, 2001). Similarly, health educators, college administrators, health providers, psychologists, and community leaders should not overlook the alcohol use problem among immigrant students (So & Wong, 2006). There is a growing trend of internet-based intervention programs in the university setting due to their customized, cost-effective, structured environment, and feasibility (Walter, Miller, & Chiauzzi, 2005). Modules related to alcohol attitudes and behavior should be developed and implemented in the target population. Interventions should be focused on strengthening the intercultural competencies of international students. In addition, there is a need for conducting more longitudinal studies that examine causal linkages between predictors of alcohol use and actual alcohol use..
Baer, J. S. (2002). Student factors: Understanding individual variation in college drinking. Journal of Studies on Alcohol, 14, 40-53.
Bandura, A. (1986). The social foundation of thought and action: A social cognitive theory. New Jersey: Prentice Hall.
Bandura, A. (1999). A sociocognitive analysis of substance abuse: An agentic perspective. Psychological Science, 10(3), 214-217.
Bandura, A. (2004). Health promotion by social cognitive means. Health Education and Behavior, 31(2), 143-164.
Berke, R. S., & Stephens R. S. (1999). Social anxiety and drinking in college students: A social cognitive theory analysis. Clinical Psychology Review, 19(5), 513-530.
Borsari, B., & Carey, K. B. (2001). Peer influences on college drinking: A review of the research. Journal of Substance Abuse, 13(4), 391-424.
Brown, S. A., Christiansen, B. A., & Goldman M. S. (1987). The alcohol expectancy questionnaire: An instrument for the assessment of adolescent and adult alcohol expectancies. Journal of Studies on Alcohol, 48(5), 483-491.
Burke, R. S., & Stephens, R. S. (1999). Social anxiety and drinking in college students: A social cognitive theory analysis. Clinical Psychology Review, 19(5), 513-530.
Callas, P. O., Flynn, B. S., & Warden, J. K. (2004). Potentially modifiable psychosocial factors associated with alcohol use during early adolescence. Addictive Behaviors, 29(8), 1503-1515.
Finn, P. R., Bobova, L., Wehner, E., Fargo, S., & Rickert, M. E. (2005). Alcohol expectancies, conduct disorder and early-onset alcoholism: Negative alcohol expectancies are associated with less drinking in non-impulsive versus impulsive subjects. Addiction, 100(7), 953-962.
Gill, A. G., Wagner, E. F., & Vega, W. A. (2000). Acculturation, familism, and alcohol use among Latino adolescent males: Longitudinal relations. Journal of Community Psychology, 28(4), 443-458.
Ham, L. S., & Hope, D. A. (2003). College students and problematic drinking: A review of the literature. Clinical Psychology Review, 23(5), 719-759.
Hildebrand, K. M., Johnson, D. J., & Bogle, K. (2001). Comparison of patterns of alcohol use between high school and college athletes and non-athletes. College Student Journal, 35(3), 358-365.
Khosla, V., Thankappan, K. R., Mini, G. K., & Sarma, P. S. (2008). Prevalence & predictors of alcohol use among college students in Ludhiana, Punjab, India. The Indian Journal of Medical Research, 128(1), 79-81.
Kuther, T. L., & Timoshin, A. (2003). A comparison of social cognitive and psychological predictors of alcohol use by college students. Journal of College Student Development, 44(2), 143-154.
Lala, S., & Straussner, A. (2001). Ethnocultural background and substance treatment of Asian Indian Americans. In Ethnocultural factors in substance abuse treatment. pp. 368-392. New York: Guilford Press.
Lewis, B. A., & O'Neill, H. K. (2000). Alcohol expectancies and social deficits relating to problem drinking among college students. Addictive Behaviors, 25(3), 295-299.
Makimoto, K. (1998). Drinking patterns and drinking problems among Asian-Americans and Pacific Islanders. Alcohol Health and Research World, 22(4), 270-278.
Martin, C. M., & Hoffman, M. A. (1993). Alcohol expectancies, living environment, peer influence, and gender: A model of college-student drinking. Journal of College Student Development, 34(3), 206-211.
McAlister, A. L., Perry, C. L., & Parcel, G. S. (2008). How individuals, environments, and health behaviors interact: social cognitive theory. In Glanz, K., Rimer, B. K., & Viswanath, K., eds. Health Behavior and Health Education. Theory, Research, and Practice, 4th ed. pp. 167-188. San Francisco: Jossey-Bass.
McCabe, S. E. (2002). Gender differences in collegiate risk factors for heavy episodic drinking. Journal of Studies on Alcohol, 63(1), 49-56.
Metrik, J., McCarthy, D. M., Frissell, K. C., MacPherson, L., & Brown, S. A. (2004). Adolescent alcohol reduction and cessation expectancies. Journal of Studies Alcohol, 65(2), 217-226.
Murnan, J., Sharma, M., & Lin, D. (2006-2007). Predicting childhood obesity prevention behaviors using social cognitive theory: Children in China. International Quarterly of Community Health Education, 26(1), 73-84.
National Institute on Drug Abuse (1998). National survey results on drug use from The Monitoring the Future Study, 1975-1997, Volume I: Secondary school students, Rockville, MD: Department of Health and Human Services.
Oei, T. P. S., Ferugson, S., & Lee, N. K. (1998). The differential role of alcohol expectancies and drinking refusal self-efficacy in problem and nonproblem drinkers. Journal of Studies on Alcohol, 59(6), 704-711.
Oei, T. P. S., & Morawska, A. (2004). A cognitive model of binge drinking: The influence of alcohol expectancies and drinking refusal self-efficacy. Addictive Behaviors, 29(1), 159-179.
O'Hare, T., Sherrer, M. V. (2000). Co-occurring stress and substance abuse in college first offenders. Journal of Human Behavior in the Social Environment, 3(1), 29-44.
O'Hare, T., & Tran, T. V. (1998). Substance use among Southeast Asians in the U.S: Implications for practice and research. Social Work in Health Care, 26(3), 69-80.
Polit, D. F., & Hungler, B. P. (1999). Nursing research. Principles and methods. (6th Ed.) Philadelphia: Lippincott.
Presley, C. A., Meilman, P.W., & Lyerla, R. (1994). Development of the core alcohol and drug survey: Initial findings and future directions. Journal of American College Health, 42(6), 248-255.
Sharma, M. (2005). Enhancing the effectiveness of alcohol and drug education programs through social cognitive theory. Journal of Alcohol and Drug Education, 43(7), 3-7.
Sharma, M., Petosa, R., & Heaney, C. A. (1999). Evaluation of a brief intervention based on social cognitive theory to develop problem-solving skills among sixth-grade children. Health Education and Behavior, 26(4), 465-477.
Sharma, M., & Romas, J. A. (2008). Theoretical foundations of health education and health promotion. Sudbury, Massachusetts: Jones and Bartlett publishers.
Sharma, M., Wagner, D. I., & Wilkerson, J. (2005-2006). Predicting childhood obesity prevention behaviors using social cognitive theory. International Quarterly of Community Health Education, 24(3), 191-203.
So, D. W., & Wong, F. Y. (2006). Alcohol, drugs, and substance use among Asian-American college students. Journal of Psychoactive Drugs, 38(1), 35-42.
Strano, D. A., Cuomo, M. J., & Venable R. H. (2004). Predictors of undergraduate student binge drinking. Journal of College Counseling, 7(1), 50-63.
Varma, S. C., & Siris, S. G. (1996). Alcohol abuse in Asian Americans: Epidemiological and treatment issues. American Journal on Addictions, 5(2), 136-143.
Vega, W. A., & Gil, A. G. (1999). A model for explaining drug use behavior among Hispanic adolescents. Drugs & Society, 14, 57-74.
Vega, W. A., Zimmerman, R. S., Warheit, G. J., Apospori, E., & Gil, A. G. (1993). Risk factors for early adolescent drug use in four ethnic and racial groups. American Journal of Public Health, 83(2), 185-189.
Vollrath, M., & Torgersen, S. (2002). Who takes health risks? A probe into eight personality types. Personality and Individual Differences, 32(7), 1185-1197.
Walters, S. T., Miller, E., & Chiauzzi, E. (2005). Wired for wellness: E-interventions for addressing college drinking. Journal of Substance Abuse Treatment, 29(2), 139-145.
Wechsler, H., Lee, J. E., Kuo, M., & Lee, H. (2000). College binge drinking in the 1990s: A continuing problem. Results of the Harvard School of Public Health 1999 College Alcohol Study. Journal of American College Health, 48(5), 199-210.
Windle, M., & Davies, P. T. (1999). Depression and heavy alcohol use among adolescents: Concurrent and prospective relations. Development and Psychopathology, 11(4), 823-844.
Winkleby, M. A., Feighery, E., Dunn, M., Kole, S., Ahn, D., & Killen, J. D. (2004). Effects of an advocacy intervention to reduce smoking among teenagers. Archives of Pediatrics and Adolescent Medicine, 158(3), 269-275.
Yi, J. K., & Daniel, A. M. (2001). Substance use among Vietnamese American college students. College Student Journal, 35(1), 13-23.
Samrat Yeramaneni, MBBS, MS, is affiliated with Health Promotion and Education program, University of Cincinnati. Manoj Sharma PhD, CHES, is affiliated with Health Promotion and Education program, University of Cincinnati. Please address all correspondence to Samrat Yeramaneni, MBBS, MS, Health Promotion & Education, University of Cincinnati, Teachers College 526 J, PO Box 210068, Cincinnati, OH 45221-0068, (513) 556-3878 (Phone), (513) 556-3898 (Fax), yeramas@email. uc.edu (e-mail).
Table 1. Means and standard deviations for dependent variables Dependent Variable Mean Std. Deviation Average number of drinks n = 152 4.02 4.48 consumed in a typical week 1-2 drinks 52.6% (80) 3-5 drinks 28.9% (44) 6-9 drinks 7.8% (12) 10 or more drinks 10.5% (16) Binge drinking (> 5 drinks n = 78 1.69 2.52 for males and > 4 drinks for females) over the last 30 day 1-2 days 26.9% (41) 3-5 days 17.1% (26) 6-9 days 3.2% (5) 10 or more days 3.9% (6) Number of days alcohol n = 152 6.05 4.68 consumed in the past 30 days 1-2 days 19.7% (30) 3-5 days 42.7% (65) 6-9 days 17.1% (26) 10 or more days 20.3% (31) Table 2. Parameter estimates from the final regression model for average number of drinks consumed in a typical week as predicted by alcohol-related self-efficacy and self control for quitting alcohol (adjusted [R.sup.2] = 0.167) (n = 131) * Model Unstandardized Standardized t P-value Coefficients Coefficients B Std. Beta Error (Constant) 0.321 0.115 2.795 0.006 Alcohol-related 0.020 0.005 0.340 4.234 0.000 self-efficacy Self-control -0.011 0.004 -0.229 -2.855 0.005 for quitting alcohol * Dependent variable was log transformed to normalize the distribution Table 3. Parameter estimates from the final regression model for binge drinking as predicted by self-efficacy for drinking (adjusted [R.sup.2] = 0.036) (n = 78) Model Unstandardized Standardized t P-value Coefficients Coefficients B Std. Beta Error (Constant) 0.335 0.628 0.534 .594 Alcohol-related 0.088 0.036 .0209 2.431 0.016 self-efficacy Table 4. Parameter estimates from the final regression model for number of days alcohol consumed in the past 30 days as predicted by self-efficacy for drinking and self control for quitting (adjusted [R.sup.2] = 0.177) (n = 131) * Model Unstandardized Standardized t P-value Coefficients Coefficients B Std. Beta Error (Constant) 0.577 0.103 5.606 0.000 Alcohol-related 0.018 0.004 0.342 4.285 0.000 self-efficacy Self-control -0.011 0.004 -0.246 -3.079 0.000 for quitting alcohol * Dependent variable was log transformed to normalize the distribution
|Gale Copyright:||Copyright 2009 Gale, Cengage Learning. All rights reserved.|