Printer Friendly

The relationship between emotional intelligence and abuse of alcohol, marijuana, and tobacco among college students.


This study examined the relationship between emotional intelligence (EI), alcohol, marijuana, and tobacco use. A correlation analysis was used to explore the relationship between EI and the use of alcohol, marijuana, and tobacco among college students (n = 199). EI abilities (perception, utilization, understanding, and regulation of emotions) were measured in college students who completed the valid and reliable Schutte Self Report Inventory (SSRI), the Alcohol Use Disorders Identification Test (AUDIT), the Fagerstrom Test for Nicotine Dependence (FTND), and the Marijuana Screening Inventory (MSI). The results demonstrated that El constructs (Perception, Utilization, Regulation, and Management of Emotion) scores were significant predictors of alcohol and marijuana use. An association between the El and cigarette smoking was not supported by this study.


The use of alcohol and illicit substances among youth have been studied by many (Diala, Muntaner, & Walrath, 2004; Dube, Feliti, Dong, Chapman, Giles et al., 2003; Gordon, Kinlock, & Battjes, 2004). Substance abuse in youth contributes to internalizing and other psychosocial impairment that sets the foundation for potential and subsequent drug and alcohol use in adulthood (National Survey on Drug Use and Health [NSDUH], (2006); King, Meehan, Trim, & Chassin, 2007). The National Adolescent Health Information Center (NAHIC, 2007) reported that the use of substances, such as alcohol, marijuana, and cigarettes, has tripled for adolescents and young adults in all racial and ethnic groups. Marijuana, for example, is the most frequently abused illicit drug in the United States, mostly among adolescents 12-years and older. Its use accounts for one third of the most mentioned abused drug in drug-related emergencies in emergency departments nationwide (National Institute on Drug Abuse [NIDA], 2007). Similarly, cigarette smoking among ages 18 to 25 is 39.5% (Substance Abuse and Mental Health Services Administration [SAMHSA], 2005).

The early use of illicit drugs increases risky behaviors that can lead to: contracting or transmitting sexually transmitted diseases, crime and violence, and chronic substance abuse and dependence (Burrow-Sanchez, 2006; Guiao, Blakemore, & Boswell-Wise, 2004; Miller, Naimi, Brewer, & Everett-Jones, 2007; Nanda, & Konnur, 2006). In the United States between 1998 and 2001, among 18 to 24 year old college students that drank or were binge drinkers, there was a significant increase in deaths, which out proportioned the increase in that population (Hingson, Heeren, Winter, & Weschler, 2005; Weschler, et al, 2002). Similarly, the Centers for Disease Control and Prevention (CDC, 2005) reported that the leading cause of motor vehicle-crash-related deaths among adolescents is associated with alcohol.

The yearly cost of substance abuse often includes multiple treatments through medical and detoxification efforts, criminal prosecutions, school dropouts, and lack of productivity (Adrian, 2001; Compton, & Volkow, 2006). Little is known regarding the total cost of substance abuse to the health care system, but the National Drug Intelligence Center (2011) reported the cost of substance use to be at $193 billion in 2007.

Researchers continue to explore ways in which to understand the underpinnings of substance abuse (Burrow-Sanchez, 2006; Dooley & Prause, 2006; Hampson, Andrews, & Barckley, 2008; Hollist & McBroom, 2006) and other risk behaviors among populations. They have focused attention to the application of social theories (Cooper, May, Soderstrom, & Jarjoura, 2009) and the linkages of theoretical perspectives in human behavior.

There is limited research on the theory of emotional intelligence (EI) (Bar-On, 2006; Goleman, 1995; Salovey & Mayer, 1990) as it relates to substance use and other risky behaviors among freshmen college students. Mayer and Salovey (1997) defined EI as a subset of social intelligence that includes the ability of the individuals to recognize, manage, understand the emotions in others, and to utilize this information to guide their thoughts and actions (Mayer & Salovey, 1997). EI principles such as social competence, self-awareness, impulse control, and empathy were identified as necessary characteristics of leaders in the work place, essential to success, and they may provide a framework to safeguard youth against risky behaviors (Bar-On, 2006; Goleman, 1995; Mayer & Salovey, 1997). EI has been linked to decreased risk behaviors, improved relationships, and feelings of well-being (Afifi, Cox, & Katz, 2007; Petrides et al., 2006; Schutte et al., 2002). However, research examining the ability to identify, understand, use, and manage emotions, and apply findings to substance abuse among young adults, is limited.

Young adulthood is an important period in which to examine relations between emotions and substance use because: (a) risky behaviors and experimentation that started in adolescence may be prevalent at this time; (b) there is independence from parental control, there are life stressors including problems at school, with family, and in extracurricular activities, which may generate emotional responses or coping mechanisms that can lead to adverse resolutions including substance use, violence, and other self-destructive behaviors and; (c) promising findings in the literature indicate that individuals with high emotional intelligence preserve positive mood states and self-esteem attributes that help regulate emotions and counteract some of the perils of everyday life (Brackett, Mayer, & Warner, 2004; Schutte, Malouff, Simunek, McKenley, & Hollander, 2002).

The use of alcohol, marijuana, and tobacco are most prevalent among young adults. Few studies link emotional intelligence with cigarette smoking and alcohol use (Trinidad & Anderson, 2002; Trinidad, Unger, Chou, Azen, & Anderson, 2004; Trinidad, Unger, Chou, & Anderson, 2005) but do not include marijuana use, thus creating a gap in the literature. This study sought to establish the relationship between emotional intelligence and the use of the three most commonly used substances: alcohol, marijuana, and cigarettes among a college student sample.

Purpose of the Study

The purpose of this study was to explore the relationship between emotional intelligence and alcohol, marijuana use, and cigarette use among young adults in a selected college student sample, aged 18 to 20 years. Previous research indicated that individuals with high emotional intelligence are more likely to report greater feelings of well-being (Schutte et al, 2002); exhibit greater performance outcomes (Carmeli & Josman, 2006); higher self-esteem (Fernandez-Berrocal et al., 2006); and increased resistance to the use of alcohol and tobacco (Trinidad & Anderson, 2002). This study examined the following research questions:

1. Is there a relationship between emotional intelligence and high risk behaviors of alcohol abuse, marijuana use, and cigarette smoking among young adults aged 18-20?

2. Which emotional intelligence constructs are most influential in predicting high risk behaviors of substance abuse, alcohol, marijuana, and cigarette smoking among young adults?



A convenience sample of 199 college students in a Massachusetts urban city, ages 18 to 20 years, male and female of mixed ethnicity, participated in this study. The participants were recruited from the Student Success Center at a community college in a medium-sized metropolitan area after all ethical and proper clearances were obtained from the research office at the College. IRB permission was obtained and approved for this study. Participants in the study were given an informed consent form. Participants were informed of the proposed use of the data, benefits and risks of participating in the study, and confidentiality rights. Additionally, participants were informed of their right to obtain personal and collective results, once the data had been analyzed.

The participants received a coded packet that contained the questionnaires and a copy of the informed consent. The students were informed that the questionnaires would take approximately 30 minutes to complete and that the purpose of these tests was to assess emotional intelligence, and to survey the use of alcohol, marijuana, and tobacco intake. All participants completed the questionnaires at the time given and received a $10.00 gift certificate to the college bookstore as a token of their participation in the study.

Data Collection Process

Students were administered the paper and pencil form of the Schutte Self Report Inventory (SSRI), along with the alcohol use disorders identification test (AUDIT), the Fagerstrom Test for nicotine dependence (FTND), and the marijuana use assessment scale (MSI). Demographical data were collected separately using the demographics and the Clinical Trial Network (CTN), a brief questionnaire form given to the participants prior to commencing the battery of tests.

Instruments Schutte Self Report Inventory (SSRI)

The construct of emotional intelligence was measured using the SSRI. The SSRI is comprised of 33 items designed to measure the individual's ability to problem solve situations of emotional charge, using the Salovey and Mayer (1990) model of emotional intelligence. The SSRI is a Likert type scale in which respondents rate themselves regarding emotions, or reactions associated with emotions, on a scale of 1 to 5, with 1 representing "strongly disagree" and 5 representing "strongly agree" (Schutte et al., 1998, p. 169). The SSRI has been deemed both a valid and reliable scale ([alpha] = .93) (Brackett & Mayer, 2003, p. 5); ([alpha] = .90) as well as a valid and reliable overall score by Gardner and Qualter (2010).

Scores on the SSRI were calculated by adding up all scores for items 1 through 33 of the scale. Items 5, 28, and 33 were scored by reversed coding. To reverse code these items, the scores 1 to 5 were changed to 5 to 1; 4 to 2, and 2 to 4. These items indicated lower EI competency. Simply stated, a 5 response to an item meant the opposite of a 5 response to the remaining items on the scale. Scores ranged from a minimum of 33 to a maximum of 165, with the higher score indicating distinctiveness of emotional intelligence. Calculated means and standard deviations from the SSRI in multiple samples of participants provide information of central tendency and distribution for the various groups. In this way, the means from other samples on the total scale score can be compared. Alcohol Use Disorders Identification Test (AUDIT)

The AUDIT is a self-report questionnaire developed by the World Health Organization (WHO) for establishing a standardized tool for the assessment and screening of excessive use of alcohol. There are 10 questions composing three domains: hazardous alcohol use; dependence symptoms; and harmful alcohol use (Babor et al., 2001). Each question has a possibility for five responses ranging from 0 to 4, indicating "never," "less than monthly," " monthly," and "weekly" (p. 31). Scores on the AUDIT can be obtained by entering a number (based on the participant's response) for each question to the far right column. A total score of 8 or more will indicate hazardous and harmful alcohol use, and possible dependence. The AUDIT internal consistency reliability was tested using the Cronbach's alpha against the seven constructs and was determined to be 0.81.

The Marijuana Screening Inventory (MSI)

The MSI is a 31-item marijuana specific screening, paper and pencil scale developed to conform to the Diagnostic and Statistical Manual of Mental Disorders-IV (DSM-IV). The scale consists of self-report questions that require "yes" or "no" answers (Alexander & Leung, 2004, p. 328). All "yes" responses are added for all 31 items for a total individual score. The MSI internal consistency reliability was tested using Cronbach's alpha against the seven constructs and determined to be 0.94.

The Fagerstrom Test for Nicotine Dependence (FTND)

The FTND (Heatherton et al., 1991) is a self-reported six-item test designed to assess patterns of nicotine use and dependence. Construct validity of the FTND in the literature shows a Cronbach's alpha established at 0.61 and varying in other studies from 0.56 to 0.67 (Richardson & Ratner, 2005, p. 698). The scale has been used for tobacco treatment planning purposes and for prognostic decision-making (Heatherton et al., 1991). The FTND construct validity in this study was .407, well below the acceptable cutoff of .70. Since the construct for this measure has been problematic, historically, and it is still widely used in the literature, it was retained for all inferential analyses in this study.

Methods of Data Analysis

This study utilized a multiple regression analysis using the SPSS v 15.0 to determine if there were significant relationships between EI scores and scores on the substance use and abuse measures. A simultaneous regression was used to determine if there were significant relationships between EI scores on the SSRI, and scores on the AUDIT alcohol use scale. Thus, the scores on the clusters of the SSRI were regressed against the total score on the AUDIT. The same statistical procedure was utilized in determining correlations between EI scores on the SSRI and scores on the Marijuana Screening Inventory (MSI) and between the scores on the FTND, the nicotine dependence scale. Bivariate correlations were computed through the Pearson product moment correlation coefficient.


Demographic data of the study sample revealed that the majority of the sample was male (119, 59.8%). Eighty-one participants (40.7%) were 18 years of age, 63 participants (31.7%) were 19 years of age, and 54 participants (27.1%) were 20 years of age. Thirty participants (15.1%) indicated ethnicity of Latino (of Spanish) origin. Thirty-seven participants (18.6%) indicated ethnicity as Latino (not of Spanish) origin. Because of the complexity in classifying the diverse identifications within the Hispanic/Latino population living in the U.S. (Amaro & Zambrana, 2000), this category of ethnicity has been combined as one Latino group in this study. Additional demographic characteristics were reported under the classification of race, and included participants from American Indian or Alaskan Native, Asian, Black or African American, Native Hawaiian or Pacific Islander descent, and White.

Cronbach's alphas to check the internal consistency reliability of the seven variable constructs were used. Cronbach's alpha values for each variable construct are as follows: (a) SSRI-perceive = 0.70, (b) SSRI-utilize = 0.66, (c) SSRI-regulate = 0.82, (d) SSRI-manage = 0.68, (e)AUDIT = 0.81, (f) MSI = 0.94 and (g) FTND = -0.52. A Cronbach's alpha value of 0.70 or above was considered acceptable in this study. SSRI-Utilize and SSRI-Manage were close in value to the .70 cutoff and were not of concern for violation of construct reliability in this study. Table 1 summarizes the descriptive statistics for latent variable constructs derived from study instrumentation.

Table 2 summarizes the results for the AUDIT scores regressed on the four constructs of the SSRI. One of the constructs SSRI manage is significant. Table 3 summarizes the multiple regression results for MSI scores regressed on independent predictors of SSRI-perceive, SSRI-utilize, SSRI-regulate, and SSRI-manage.

Regression analysis found that SSRI-perceive was not a statistically significant predictor of the AUDIT score. Low scores on the SSRI-perceive were associated with high-risk behaviors of marijuana use (r = -. 172). Likewise, findings indicated that low scores on the SSRI-regulate were associated with high risk behaviors of marijuana (r = -.268) and alcohol (r = -.216) use. Low scores on the SSRI-utilize were associated with high-risk behaviors of marijuana (r = -.333) and alcohol (r = -.258) use. Similarly, low scores on the SSRI-manage were associated with high-risk behaviors of marijuana (r = -.265) and alcohol (r = -.311) use.


The results demonstrated bivariate correlations between EI ability to perceive and manage emotions in the self; the ability to manage emotions in self and others, and the ability to regulate emotions, indicating a strong association with the use of alcohol and marijuana. EI is composed of four emotional abilities: (a) perceive, (b) utilize, (c) regulate, and (d) manage. High scores on EI are associated with abilities to cope with life stressors, and with fewer probabilities of consuming alcohol and smoking cigarettes. The data supported an association between scores on the SSRI's ability to manage one's own emotions and scores on the AUDIT (t (4) = -2.36, p = 0.20) for alcohol use. Data supported an association between the SSRI's ability to utilize emotion scores and scores on the MSI (t (4) = -2.79, p = .006) for marijuana use.

EI has been credited with successful leadership performance in the workplace, academic achievement, effective communication, assertiveness, and positive relationships that can lead to effective coping. This study found strong positive and negative correlations between scores of the SSRI as predictors of the scores on alcohol and marijuana use, indicating that high scores on the EI scale correlated with less use of these substances. Although this finding offers an insight to the factors contributing to substance use, other factors to consider include, but are not limited to: socioeconomic status, age, genetics, family history, environment, mental health, and education, as reported in the literature.

Tobacco use

The correlational analysis between the SSRI construct and the FTND was not significant. In contrast to Qualter and Gardner et al. (2007), Trinidad and Anderson (2002), and Trinidad et al. (2005), an association between high scores on EI and decreased tendency to smoke among adolescents was not supported by this study. SAMHSA (2005) reported the cigarette-smoking rate among the 18 to 25 year old group at 39.5%, contrasting this study in which 20% of the participants reported cigarette smoking to any extent. Based on these find ings, the researchers propose that the use of an instrument with higher reliability attributes in future studies, to assess cigarette use, may have implications of statistical significance for this outcome. Furthermore, the researchers attest that the absence of statistical significance between EI assessed with the SSRI, and cigarette smoke assessed with the FTND, in this study does not constitute its nonexistence. Hence, there is still a need for further research assessing EI and the relationship to tobacco smoke in young adults with further recommendations for the utilization of a different nicotine use assessment tool.

Marijuana Use

There was a negative correlation between El constructs with the MSI posing as a significant predictor of marijuana use. Thus, an inverse relationship between the ability to manage, regulate, and to utilize emotions, surfaced with marijuana use. This negative relationship emerged between low scores on EI constructs and high scores on the use of marijuana among the college student sample. Current literature informs that nearly 60% of students report using marijuana (Johnston et al., 2007), marijuana with alcohol, and other substance use (Shillington & Clapp, 2001). Lower EI scores are associated with problematic behavior related to increased use of alcohol and illegal substances (Brackett et al., 2004). The results of this study showed that 55% of the participants reported the use of marijuana at some point in their lives, and from these, 59% reported using marijuana within the last month. Two percent of the participants reported first time use of marijuana at 10 years of age or younger, and 82% of the participants who use marijuana reported first time use between the ages of 14 and 16. Additionally, of those reporting the use of marijuana, 31% reported risky use as evidenced by scores of 7 or more on the MSI. Although marijuana use and other aberrant behaviors have been attributed to multiple etiologies of socio-environmental nature, this study adds to the existing body of knowledge regarding EI and its correlation to alcohol and marijuana use.

The ability to regulate emotions, and the ability to manage emotions, was significantly correlated with marijuana use. The researchers concluded that individuals with high El and ability for self-control and emotional management allow them better decision-making regarding risky behaviors, and coping and dealing with stressors. Furthermore, individuals with higher El lowered their odds of engaging in the harmful use of marijuana. However, it is necessary to validate these findings in future research.

Alcohol Use

Correlational analysis of the EI constructs and the use of alcohol were significantly and negatively correlated, establishing lower EI as a key predictor of alcohol use. This finding is consistent with similar EI research. Qualter and Gardner et al. (2007) discussed studies of youth linking high EI with decreased probabilities of involvement of alcohol and cigarette smoke. High EI is associated with better coping skills, which allow individuals to deal better with stress. Although research has established the association of alcohol use to a number of etiologies, the prevalence of the use of alcohol in college youth must be noted. The NIAAA (2006) reported that 12.3% of the college student population meets the DSM-IV criteria for diagnosis of alcohol abuse. In this study, 11% of the college sample scored 8 or greater on the AUDIT scale indicating harmful use of alcohol. The participants in this study were provided with an informational pamphlet about alcohol and drug use from the Substance Abuse and Mental Health Services Administration, SAMHSA. As with the use of marijuana, there was a significant correlation with the ability to regulate emotions, and the ability for emotional management, and alcohol use. Therefore, an inference can be made about individuals with high EI and their abilities for self-control and emotional management lead to positive coping, which affords them better decision-making and, thus, lowers the likelihood of them engaging in harmful drinking.

This study revealed significant associations between emotional intelligence, the ability to perceive, the ability to manage emotions in self and in others, and the ability to utilize emotions with the use of alcohol and marijuana. Also, to a limited extent, to the use of tobacco among college students. Thus, the researchers concluded that college students with higher levels of emotional intelligence could use self-control and regulation regarding decisions affecting their behavior. The researchers further concluded that the ability to perceive and utilize emotions in others might be a protective factor when dealing with peer pressure, or deviant group norms. Therefore, understanding how emotional intelligence can influence factors associated with risky behaviors among young adults may help mitigate the harmful consequences associated with such risks.

Parents, educators, counselors, youth leaders, and health professionals are in an optimal position to implement interventions that help youth recognize, utilize and manage their own emotions and the emotions in others. These interventions can start early in childhood, and can include modeling empathy, compassion for others, and encouraging youth to express their feelings, and to assist them in understanding their emotions so that they are channeled positively and productively. The college setting is the most likely place to unleash the use of alcohol and marijuana (Gillespie et al., 2007). Therefore, the need to intervene much earlier cannot be over emphasized. College-wide activities that foster a harm reduction approach can be developed to promote safety and guard against harmful substance use. Similarly, college curricula can be tailored to include formal and informal training such as regular workshops or seminars and peer-to-peer mentoring to include skill-building activities of intangibles such as self-awareness. Self awareness includes: identifying emotions in the self, empathy (recognizing and validating emotions in others), communication (ability to express feelings), and conflict resolution (managing emotions in self and others).

Although specific causes of substance abuse remain unclear, prevention and early intervention are promising strategies identified as objectives of Healthy People 2020. Health professionals and counselors (e.g., school, vocational, or substance abuse counselors) should be attuned to assessment, problem identification, and referral for students, as well as working in partnerships with the students, the colleges, and the communities, with the goal of promoting a college-wide drug-free environment as the norm. Parents and other stakeholders have the capacity for modeling, teaching, communicating, practicing, and nurturing EI skills, and should emphasize the need to strengthen those abilities as they are vital to social well-being.


Although the population of interest for this study was college students, much of the existing literature related to risk behaviors of substance use pertains to adolescents in general. This study encompassed an overlapping period between late adolescence (18 years of age) and young adulthood (age 20). The population sample is relatively small at 199 participants, and it is restricted to college students at a pre-selected community college in an urban city of approximately 100,000. This restriction poses limitations to the generalizability of the findings of this study to other cohorts, including similar populations at other colleges or universities in other metropolitan areas. Despite this limitation, the importance of the health related issues in this population provides the critical need for psychosocial research. This study collected data utilizing self-report tools. Self-report responses are subject to subjective interpretation (delivery of distorted responses in favor of positive impressions). Self-report items for data collection of this nature are a limitation of this study.

Correspondence concerning this article should be addressed to: Edith Claros, Ph.D., MSN, RN, Associate Professor, School of Nursing, Massachusetts College of Pharmacy & Health Sciences, Boston, MA 02115, Phone: (617) 274-3347; Fax: (617) 879-5089; Email:


Adrian, M. (2001). Do treatments and other interventions work? Some critical issues. Substance Use and Misuse, 36(13), 1759-1781

Afifi, T., Cox, B., & Katz, L. (2007). The associations between health risk behaviors and suicidal ideation and attempts in a nationally representative sample of young adolescents. La Revue Canadienne de Psychiatrie, 52, 666-676.

Alexander, D., & Leung, P. (2004). The Marijuana Screening Inventory (MSI-X): Reliability, factor structure, and scoring criteria with a clinical sample. The American Journal of Drug and Alcohol Abuse, 30, 321-351.

Amaro, H., & Zambrana, R. E. (2000). Indigena, white, or black? The US Hispanic/Latino population and multiple responses in the 2000 census. American Journal of Public Health, 90(11), 1724-1727.

Babor, T., Higgins-Biddle, J., Saunders, J., & Monteiro, M. (2001). AUDIT: The Alcohol Use Disorders Identification Test--Guidelines for use in primary care (2nd ed.). WHO/MSD/MSB/01.6a. Geneva, Switzerland: World Health Organization.

Bar-On, R. (2006). The Bar-On model of emotional-social intelligence (ESI) Psicothema, 18, 13-25.

Brackett, M., Mayer, J., & Warner, R. (2004). Emotional intelligence and its relation to everyday behavior. Personality and Individual Differences, 36(6), 1387-1402.

Burrow-Sanchez, J. (2006). Understanding adolescent substance abuse: Prevalence, risk factors, and clinical implications. Journal of Counseling and Development, 84(3), 283-291.

Carmeli, A. & Josman, Z. (2006). The relationship among emotional intelligence, task performance, and organizational citizen behaviors. Human Behaviors, 19(4), 403-419.

Center for Disease Control and Prevention [CDC]. (2005). WISQAS: Web-based injury statistics query and reporting system. Retrieved on June 5, 2008 from

Cooper, K., May, D., Soderstrom, I., & Jarjoura, G. R. (2009). Examining theoretical predictors of substance use among a sample of incarcerated youth. Journal of Offender Rehabilitation, 48(8), 669-695.

Diala, C., Muntaner, C., & Walrath, C. (2004). Gender, occupational, and socioeconomic correlates of alcohol and drug abuse among U. S. rural, metropolitan, and urban residents. The American Journal of Drug and Alcohol Abuse, 30(2), 409-428.

Dooley, D., & Prause, J. (2006). Predictors of early alcohol drinking onset. Journal of Child and Adolescent Substance Abuse, 16(2), 1-29.

Dube, S., Feliti, V., Dong, M., Chapman, D., Giles, W., & Anda, R. (2003). Childhood abuse, neglect, and household dysfunction and the risk of illicit drug use: The adverse childhood experiences study. Pediatrics, 111(3), 564-572.

Fernandez-Berrocal, P., Alcaide, R., Extremera, N. & Pizarro, D. (2006). The role of emotional intelligence and depression among adolescents. Individual Differences Research, 4(1), 16-27.

Gardner, K. J., & Qualter, P. (2010). Concurrent and incremental validity of three trait emotional intelligence measures. Australian Journal of Psychology, 62(1), 5-13.

Gillespie, W., Holt, J., & Blackwell, R. (2007). Measuring outcomes of alcohol, marijuana, and cocaine use among college students: A preliminary test of the Shortened Inventory of Problems--Alcohol and Drugs (SIP-AD). Journal of Drug Issues, 37, 549-567.

Goleman, D. (1995). Emotional intelligence. New York: Bantam.

Gordon, M., Kinlock, T., & Battjes, R. (2004). Correlates of early substance use and crime among adolescents entering outpatient substance abuse treatment. The American Journal of Drug and Alcohol Abuse, 30(1), 39-59.

Guiao, I., Blakemore, N., & Boswell-Wise, A. (2004). Predictors of teen substance use and risky sexual behaviors: Implications for advanced nursing practice. Clinical Excellence for Nurse Practitioners, 8 (1), 52-59.

Hampson, S. E., Andrews., J. A., & Barckley, M. (2008). Childhood predictors of adolescent marijuana use: Early sensation-seeking, deviant peer affiliation, and social images. Addictive Behaviors, 33(3), 1140-1147.

Heatherton, T., Kozlowski, L., Frecker, R., Fagerstrom, K. (1991). The Fagerstrom Test for Nicotine Dependence: A revision of the Fagerstrom Tolerance Questionnaire. British Journal of Addictions, 86, 1119-1127.

Hingson, R., Heeren, T., Winter, M., & Weschler, H. (2005). Magnitude of alcohol-related mortality and morbidity among U. S. college students ages 18-24. Annual Review of Public Health, 26, 259-279.

Hollist, D. R. & McBroom, W. H. (2006). Family structure, family tension, and self-reported marijuana use: A research finding of research behaviour among youths. Journal of Drug Issues, 36(4), 975-998.

Johnston, L., O'Malley, P., Bachman, J., & Schulenberg, J. (2007). Monitoring the future national results on adolescent drug use 1975-2006: Volume 1, secondary school students (NIH publication no. 07-6205). Bethesda, MD: National Institute on Drug Abuse.

King, K., Meehan, B. T., Trim, R., S., & Chassin, L. (2006). Marker or mediator? The effects of adolescent substance use on young adult educational attainment. Addiction, 101(12), 1730-1740.

Mayer, J., & Salovey, P. (1997). What is emotional intelligence? In P. Salovey & D. Sluyter (Eds.), Emotional development and emotional intelligence: Educational implications. New York: Basic Books.

Mayer, J., Salovey, P., & Caruso, D. (2004). Emotional intelligence: Theory, findings, and implications. Psychological Inquiry, 15(3), 197-215.

Miller, J., Naimi, T., Brewer, R., & Everett-Jones, S. (2007). Binge drinking and associated health risk behaviors among high school students. Pediatrics, 119(1), 76-85.

Nanda, S., & Konnur, N. (2006). Adolescent drug and alcohol use in the 21st century. Psychiatric Annals, 36, 706-712.

National Adolescent Health Information Center [NAHIC] (2007). Fact sheet on substance use: Adolescent and young adults. San Francisco, CA: Author, University of California, San Francisco.

National Drug Intelligence Center (2011). The economic impact of illicit drug use on American Society. Washington D.C.: United States Department of Justice. Publication No. 2011-Q0317-002. Available online

National Survey on Drug Use and Health (2006). Serious psychological distress and substance use among young and adult males. The NSDUH report, Issue 3. Retrieved on May 23, 2008 from

Petrides, K., Sangareau, Y., Furnham, A. & Frederickson, N. (2006). Trait emotional intelligence and children's peer relations at school. SociaI Development, 15(3), 538-550.

Qualter, R, Gardner, K. & Whiteley, H. (2007, March). Emotional Intelligence: Review of research and educational implications. Pastoral Care, 11-22.

Salovey, R, & Mayer, J. (1990). Emotional intelligence. Imagination, Cognition, and Personality, 9(3), 185-211.

Salovey, P. & Grewal, D. (2005). The science of emotional intelligence. Current Directions in Psychological Science, 14, 281-325.

Schutte, N., Malouff, J., Hall, L., Haggerty, D., Cooper, J., Golden, C., & Dornheim, L. (1998). Development and validation of a measure of emotional intelligence. Personality and Individual Differences, 25(2), 167-177.

Schutte, N., Malouff, J., Simunek, M., McKenley, J. & Hollander, S. (2002). Characteristic emotional intelligence and emotional well-being. Cognition and Emotion, 16(6), 769-785.

Shillington, A., Clapp, J. (2001). Substance use problems reported by college students: Combined marijuana and alcohol use versus alcohol-only use. Substance Use and Misuse, 36(5), 663-672.

Simons-Morton, B. (2007). Social influences on adolescent substance use. American Journal of Health Behavior, 31(6), 672-684.

Substance Abuse and Mental Health Services Administration [SAMHSA] (2005). Results for the 2004 national survey on drug use and health: National findings. Retrieved on June 19, 2008 from

Trinidad, D., & Anderson, C. (2002). The association between emotional intelligence and early adolescent tobacco and alcohol use. Personality and Individual Differences, 32(1), 95-105.

Trinidad, D., Unger, J., Chou, C., Azen, S., & Anderson, C. (2004). Emotional intelligence and smoking risk factors in adolescents: Interactions on smoking intentions. Journal of Adolescent Health, 34(1), 46-55.

Trinidad, D., Unger, J., Chou, C., & Anderson, C. (2005). Emotional intelligence and acculturation to the United States: Interactions on the perceived social consequences of smoking in early adolescents. Substance Use and Misuse, 40(11), 1697-1706.

Wechsler, H., Lee, J., Kuo, M., Seibring, M., Nelson, T., & Lee, H. (2002). Trends in college binge drinking during a period of increased prevention efforts. Findings from 4 Harvard School of Public Health College Alcohol Study surveys: 1993-2001. Journal of American College Health, 50(5), 203-217.

Edith Claros, Ph.D., MSN, RN

School of Nursing

Massachusetts College of Pharmacy & Health Sciences


Manoj Sharma, MBBS, MCHES, Ph.D.

University of Cincinnati
Descriptive Statistics for Latent Variable Constructs Derived
from Study Instrumentation (n=199)

Variable Associated Item Responses M SD Mdn

SSRI-Total Score All items 127.79 15.14 129
SSRI-perceive 5,9,15,18,22,25,29,32,33 33.04 4.60 33
SSRI-utilize 6,7,8,17,20,27 23.84 3.38 24
SSRI-regulate 2,3,10,12,14,21,23,28,31 36.18 5.3 37
SSRI-manage 1,4,11,13,16,24,26,30 31.02 4.01 31
AUDIT All items 4.67 4.38 3.5
MSI All items 5.08 6.63 1.5
FTND All items 6.88 1.30 7

Variable Sample Range Possible Range

SSRI-Total Score 68-164 33-165
SSRI-perceive 18-45 9-45
SSRI-utilize 12-30 6-30
SSRI-regulate 18-45 9-45
SSRI-manage 15-40 8-40
AUDIT 1 -24 0-40
MSI 0-31 0-36
FTND 4-10 3-13

Note. M = Mean; SD = Standard Deviation; Mdn = Median. SSRI =
Schutte Self Report Inventory; A UDIT = The Alcohol Use
Disorders Identification Test; MSI= Marijuana Use Inventory; FTND =
Fagerstrom Test for Nicotine Dependence.

Multiple Regression Results for AUDIT Score Regressed on
Independent Predictors of SSRI perceive, SSRI-utilize,
SSRI-regulate, and SSRI-manage (n = 118)

Variable B SE B [beta] t Sig. VIF

SSRI-perceive 0.115 0.116 .121 0.993 .323 1.90
SSRI-utilize -.198 .160 -.153 -1.240 .218 1.95
SSRI-regulate .014 .110 .170 .126 .900 2.26
SSRI-manage .339 .144 -.310 -2.360 .020 2.21

F = 3.64

Adjusted [R.sup.2] = .083

Note. Sig. = significance; VIF = Variance Inflation Factor.

Multiple Regression Results for MSI Score Regressed on
Independent Predictors of SSRI perceive, SSRI-utilize,
SRI-regulate, and SSRI-manage (n = 194)

Variable B SE B [beta] t Sig. VIF

SSRI-perceive .126 .135 .088 0.935 .351 1.90

SSRI-utilize -.520 .186 -.265 -2.791 .006 1.95

SSRI-regulate -.079 .128 -.063 -.617 .538 2.26

SSRI-manage -.207 .168 -.125 -1.233 .219 2.21

F = 6.64

Adjusted [R.sup.2] = .105

Note. Sig. = significance. VIF = Variance Inflation Factor.
COPYRIGHT 2012 American Alcohol & Drug Information Foundation
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2012 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Author:Claros, Edith; Sharma, Manoj
Publication:Journal of Alcohol & Drug Education
Date:Jun 1, 2012
Previous Article:Information-Motivation-Behavior Skills (IMB) Model: need for utilization in alcohol and drug education.
Next Article:Implications for college students posting pictures of themselves drinking alcohol on Facebook.

Terms of use | Copyright © 2017 Farlex, Inc. | Feedback | For webmasters