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ASSOCIATION OF SUBSTANCE USE WITH LIFE OUTCOMES AMONG COLLEGE STUDENTS
Kylie E. Mims
The study of deviance involves the analysis of any attitude, behavior, or characteristic
that violates a societal norm. Although people differ in their opinions regarding the label of
“deviant” being applied to these actions or traits, they are often considered to be negative
because of the various potentially harmful physical, mental, and social consequences they may
bring upon the “deviant” individual or others around them. Substance use is one of these
behaviors that may not seem to the user to be detrimental, but may cause issues such as
interpersonal conflict, health problems, or even trouble with the law to arise. The purpose of this
study was to measure the substance use habits of college students and to analyze the effect of
those habits on various arenas of the students’ lives, and to evaluate how negative the
consequences of the “deviant” behavior of substance use really are in the lives of these
individuals
REVIEW OF THE LITERATURE
White and Hingson (2013) analyzed data from the National Survey on Drug Use and
Health, Monitoring the Future, the National Epidemiologic Survey on Alcohol and Related
Conditions, and the Harvard College Alcohol Study to explore the relationship between college
drinking and certain associated physical, mental, and social outcomes. Based on the responses to
these surveys and review of other studies of drinking conducted on college campuses nationwide
over the past fifteen years, these researchers concluded that “Roughly 20 percent of college
students meet the criteria for an alcohol use disorder in a given year (8 percent alcohol abuse, 13
percent alcohol dependence) (209). They also reported that drinking, especially binge drinking,
had a negative effect on the students’ grade point averages, test performance, and class
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attendance (209). Their analysis suggests that college students who regularly drink are at a much
greater risk of participating in and being victims of dangerous actions such as drunk driving,
physical violence, sexual assault, unsafe sex, memory loss, health problems, legal problems,
suicide attempts, and death related to their alcohol use (208-9). Since the age range of traditional
college students is approximately 18-24, the data collected from these surveys most likely
reflects the habits of individuals within that age group without much consideration of older
nontraditional students. Due to the large number of students who fall into the nontraditional
category on the campus at which the current study was conducted, the researcher expects that
many of the issues White and Hingson found to be associated with college drinking will be
present in the student body at hand, but not to such a large degree as this previous analysis found
in their nationwide samples.
A 2012 study by Horton et al implemented modern psychological attachment theory in
attempt to determine the effect of attachment to God, rather than simple religiosity as defined by
church attendance, on college students’ use of alcohol and marijuana in general and their use of
alcohol and/or drugs before the last time they had sex. Researchers surveyed 328 college
students to determine their social support level, attachment to God, and health risk-taking
behaviors (556). Although the data refuted their prediction that secure attachment to God would
be inversely related to use of the substances they measured, they did find an inverse relationship
between religious attendance and alcohol use, marijuana use, and alcohol/drug use before last
sexual experience (563), which supports previous research indicating that religiosity is often a
protective factor for substance use (553). This study was conducted in a college in the
southwestern United States (555); therefore the researcher expects that the current study will also
find religious attendance to be inversely related to substance use since the research at hand was
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conducted in a small southeastern city in what is often known as the “Bible Belt,” where the
social pressure from religious groups is often assumed to be greater than in other parts of the
country.
Randolph et al conducted a study in 2009 to assess gender, ethnic, and age differences in
alcohol use and perceived risk of sexual activity. A sample of 425 sexually active college
students completed a Health Behaviors Survey to provide information about their substance use
and sexual behaviors (81); their data analysis showed that African American women were less
likely to have positive expectations regarding alcohol consumption and drank less frequently
than women of other ethnicities (81), but had a greater number of sexual partners on average
(83). Men as a whole were not different in their responses regarding these issues based on their
ethnicity (81). They also found that older students were less likely to engage in unprotected or
otherwise risky sexual behavior, and that greater alcohol consumption was related to higher
number of sexual partners among men (83). The study count not account for ethnic differences
among men due to the lack of variation in ethnicity among males in their sample (83), and it also
did not mention the effect of students’ age on drinking behaviors, as most students in the sample
were between 18 and 25 (81). The researcher believes that the current study will find a variation
in levels of substance use between men and women, between African Americans and all other
ethnicities, and between respondents of each different age group.
A 2012 study by Geisner, Mallett, and Kilmer examined the relationship between alcohol
use and depressive symptoms in a sample of 869 first-year college students at a university in the
Northeastern United States. Their survey asked students to rate their drinking pattern on a 0 to 5
scale, from “I have not tried alcohol” to “I am a heavy, problem drinker” (282). The used the
Beck Depression Inventory-II to measure the respondents’ depressive symptoms. The study’s
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results showed that the heaviest drinkers had significantly higher levels of depression than all
other categories, although those who fell within each of the four less severe drinking categories
had similar BDI-II scores on average, with no significant gender differences (283). Based on this
information, the researcher expects that results from the current study will find a similar
relationship between those with high substance use scores and higher ratings of mental health
problems, but the relationship may be stronger than the one found by Geisner et al as the study at
hand inquired about mental health problems in general rather than specifically depression, so
respondents who reported that they endured any mental health issue would all fall into the same
category.
In order to assess the validity of the commonly accepted theory that earlier age at first use
of a substance is a predictor of regular use later in life, Stallings et al (1999) conducted twin
studies in a sample of volunteers between the ages of 50 and 96 (410). The researchers mailed
surveys asking respondents how old they were the first time they drank, how old they were the
first time they became intoxicated, and how old they were when they began drinking at least one
time per week; a second set of three questions on the survey asked for the same information
about participants’ cigarette smoking habits and ages (411-2). Results showed that first use and
beginning of regular use of cigarettes was earlier for both men and women than the same
variables for alcohol (413). Those who became regular alcohol users had an age of first use that
was on average two years earlier than those who did not become regular users, and regular
smokers’ age at first use was one year earlier than those who did not become regular smokers
(413). The researchers also reported that the time gap between the age at first use of alcohol and
age at beginning of regular use was ten years on average with males’ time gaps being two years
shorter than females’ (413). This leads the researcher to believe that the current study will find
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discrepancies between genders in severity of substance use, particularly alcohol, at different age
ranges, and that respondents who reported an earlier age at first use of a substance will also
report higher frequencies and severities of substance use at later age ranges.
Kuntsche et al found similar results in their 2013 assessment of the relationship between
early drunkenness and problem behaviors. They used data from the Health Behavior in School-
Aged Children survey to analyze drunkenness prevalence, smoking tobacco in marijuana,
injuries/fights, and low academic performance in 15-year-old students across 38 North American
and European countries (308-9). Their analysis of these data supported their hypothesis that the
students’ age at first drink was a consistent predictor of problem behaviors, and age at first
drunkenness had an even stronger positive relationship with problem behaviors, with these
conclusions being consistent between both genders and across most cultures surveyed (312).
Since the current study aims to examine students aged 18 and older, the results from this study
may differ from the one conducted by Kuntsche et al in that the length of time between the
respondents’ age at first drink and their current report of problem behaviors associated with
substance use will be much longer than in the previous study; therefore it is possible that there
could be a substantial change in substance use habits due to the older age of respondents, or the
researcher could find a more substantial frequency and severity of substance use among the older
participants who reported early ages at first drink because of the longer time period they had for
potential problem behaviors to form.
A 2008 University of Kentucky study by Miller, Danner, and Staton examined the
relationship between college students’ number of hours worked while in school, their academic
progress, and certain health behaviors. To obtain the data, they mailed surveys to a random
sample of 1,700 students; survey questions requested that students report their employment,
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GPA, class standing, sleep habits, substance use, physical activity, and sexual behaviors (676).
They found that less than half of the students who reported working 20 or more hours per week
also reported having good grades, which the researchers defined as maintaining at least a 3.0
grade point average; these students were also 1.56 times more likely to engage in frequent binge
drinking (677). Because the population in the current study contains many students who hold
full-time or multiple part-time jobs while taking a full course load, the researcher expects that
this survey data will show a similar relationship between number of hours worked and GPA, as
well as between number of hours worked and frequency and severity of substance use scores.
Huang, DeJong, Towvim, and Schneider (2008) surveyed 4,798 college students who
abstained from alcohol to evaluate their psychosocial and behavioral characteristics since many
studies focus on the relationships between those traits and substance use among that population
(395). They used the Survey of College Alcohol Norms and Behavior to obtain data about
students’ alcohol consumption, peer and family alcohol consumption, tobacco and other drug
use, attitudes toward alcohol, academic and extracurricular activities, and perceptions of campus
norms and attitudes (398). Students who did not use any other substance were more likely to
abstain from drinking than students who had used another substance within the past 30 days, and
those who abstained from alcohol consumption during high school were more likely to continue
their abstinence into college (401). Those who participated in community service or religious
group activities were significantly less likely to consume alcohol than students who did not
engage in these activities, and those who did not work while in school were less likely to drink
than those who did work (402). Men were found to be more likely to abstain than women, and
those under 21 were more likely to abstain than those at older ranges (403). About 1/5 of the
students surveyed identified themselves as complete abstainers (403). The researcher expects that
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the results of the current survey will find similar results among traditional students, since the
previous study surveyed mostly students aged 21 and younger, but the results as a whole will
likely vary since the current study’s population consists of a moderate number of nontraditional
students aged 25 and older.
METHODS
The pencil-and-paper survey consisted of a cover page with 15 demographic and life
experience questions and a 32-question rating scale to measure the frequency and severity of the
participants’ substance use. The demographic variables and areas of students’ personal lives
about which the study inquired included their race/ethnicity, age, gender, household income,
grade point average, absenteeism from school and work, number of semesters in college to earn
their degree, number of hours worked per week, age at first sexual experience, religious service
attendance, and physical, mental, and legal problems associated with their substance use For the
purposes of this study, the term “substance use” included the students’ use of any kind of
tobacco products, alcohol, marijuana, prescription medication, and caffeine, as previous studies
suggested that these were the most commonly used substances among the surveyed population.
The rating scale asked about the frequency and age ranges at which each respondent
experienced certain factors that are commonly associated with substance use; these included guilt
or worry about using substances, arguments with family or close friends about their substance
use, perceived control of substance use, behavior while using a substance, and attempts to
change substance use. Each question was followed by four blanks for the participants to answer
about the frequency of the experience mentioned in the question at four different age ranges:
under 18, 18-25, 26-30, and 31 and older. This was done to obtain information about the
potential change in substance use habits throughout the respondents’ lifespans, especially for
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nontraditional students. The frequency scale created for the participants to use for each question
on the rating scale was as follows: Never = 0; Rarely = 1; Sometimes = 2; Often = 3; and Always
= 4. In order to calculate a frequency of substance use score, the researcher added the responses
from questions one through five on the rating scale, awarding a number of points equivalent to
the number each participant used to rate the frequency of his or her use of that substance at each
age (zero points for each “Never” response, one point for each “Rarely,” etc.). In order to
calculate a substance use severity score, the researcher followed the same point system, but
included responses from all 32 questions on the rating scale so that the frequency of use and the
number and severity of symptoms associated with their substance use both contributed to the
severity score. Content for the rating scale questions was obtained from the DSM-V criteria for
substance use and substance dependence disorders, as well as from the five-question Severity of
Dependence Scale, which contains questions about the participants’ own perception of their
substance use.
The sample was obtained by randomly approaching students in various buildings on the
Macon campus of Middle Georgia State College and asking them to complete the survey. Sixty-
two students complied, with two of those students’ results being excluded from the analysis due
to one participant writing her full name on the survey and another only completing the cover
page of the survey and therefore not providing data for the researcher to computer substance use
frequency or severity scores for that individual. Thirty-one of the respondents were female and
twenty-nine were male, which varies slightly from the demographics of the school’s population,
as the data from Fall 2013 semester report that 59.3% of the students are male and 40.7% are
female (Quick Facts 2014); however, those statistics represent the entire student population for
all campuses, so the variance between that data and the study’s data should be greater than the
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variance between the study’s data and the data for only the Macon campus where the research
was conducted. The average age of survey respondents was approximately representative of the
average age of enrolled students overall, with the participant average age being 23.1 and the
enrolled student average age being 25.2. The sample was also representative of the racial
diversity of the student body, as illustrated in the table below.
Race/Ethnicity Percentage of Respondents Percentage of School Population
White/Caucasian 56.7 56.3
Black/African American 23.3 33.8
Hispanic/Latino 8.3 3.4
Asian 6.0 2.6
Other (Pacific Islander, multiracial, American Indian,
unknown)
5.7 3.9
In order to assure anonymity to protect the respondents’ right to privacy, the researcher
began the survey with a paragraph explaining the purpose of the study and asking the
participants not to write their names on the surveys, but rather to write the date in a blank space
following the paragraph to give their consent for the researcher to use their responses in the
study. One individual wrote her full name in this blank rather than writing the date as requested,
so the researcher excluded her responses from the data to protect her anonymity. Another
respondent completed only the cover page; his responses were also not recorded since the
researcher was unable to compute any kind of substance use score for that participant. The
researcher avoided using the term “substance abuse” on the survey in order to ensure that the
survey questions remained without bias toward or against the use of any particular substance.
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Participation in the study was completely voluntary; no reward was offered for completing the
survey, nor was anyone coerced into completing the survey against their will.
RESULTS
Bivariate correlations conducted on the survey data showed a significant relationship
between the respondents’ age and the frequency of their substance use overall (r=.441, p < .01).
High school GPA and current cumulative GPA were also found to be strongly correlated
regardless of substance use habits, with an effect size of .442 (p <.01). Positive relationships
were also found between household income and both high school and current college GPAs (r
= .377, P < .01 and r - .330, p < .05 respectively). Number of hours worked per week had a very
small relationship with the students’ current grade point averages, with an effect size of
only .145. Age at first use of a substance was found to have a significant inverse relationship
with frequency of use of that substance under age 18 for all substances studied, and strong
negative correlations were also found between age of first use and use of that substance at or
over age 31 for alcohol and tobacco; however, age at first marijuana use had a strong positive
relationship with use of that substance at or over age 31. These effect sizes can be seen in tables
1 through 6 in the appendix.
The presence of mental health issues at each different age range had only small,
statistically insignificant relationships with the participants’ frequency and severity of substance
use scores, as seen in table 7 in the appendix. The reported frequency of alcohol consumption
under age 18 and between ages 18 and 25 were significantly correlated with dangerous behaviors
like risky sexual activity, driving under the influence of alcohol, and physical violence at those
same age ranges (table 8). Frequency of attendance at religious or spiritual services at ages 18-25
and 26-30 was significantly negatively correlated with the participants’ substance use severity
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scores (r = -.347, p<.05 and r = -.662, p<.05 respectively, table 9). Drunkenness and violent
behavior were also significantly correlated at those same age ranges, with effect sizes of .345 and
.472 respectively (table 10).
The data from the independent samples test in table 11 in the appendix shows that there
were no significant differences in responses between males and females. Table 12 shows that
there were large differences in the average frequency and severity of substance use scores among
different races, with those who reported their race as “Other” having much higher scores than
other races. Black respondents reported the lowest frequency of substance use, and Asian
participants reported the lowest severity of symptoms associated with substance use.
DISCUSSION AND CONCLUSIONS
The purpose of this study was to evaluate the negativity of the life outcomes often
associated with the “deviant” activity of substance use. The strong positive relationship between
age and substance use was likely due to people of older ages having more time throughout their
lives to use various substances and does not necessarily indicate that those individual are active
users while enrolled in college. The researcher expected a much larger relationship to be found
between the number of hours worked per week and the students’ reported GPAs; the reasoning
for such a small effect may be due to the large number of nontraditional students who work more
hours than many traditional students, but are also likely to be more invested in their education
because of their greater maturity and the likelihood that they are funding their own education at
this point in their lives. The researcher also expected substance use and mental health issues to
be strongly correlated; the reason for this lack of the expected relationship could possibly be
because of participants’ responding according to what they perceive to be socially desirable, or it
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could simply be because the potential mental health issues associated with heavy substance use
have not appeared yet in the surveyed individuals.
The strong effect sizes between age of first use of a substance and continued use of that
substance support previous research findings that suggest a consistently strong, positive
correlation between those two variables. The presence of dangerous behaviors among those who
reported high frequencies of drunkenness at younger ages support previous research as well,
although the reason for the lack of a relationship between those variables at older ages could
simply be due to the greater maturity and self-control of the older individuals, whether they
frequently become drunk or not. Based on the data collected, religiosity seemed to be somewhat
of a protective factor against substance use, but only among students in the older age categories.
substance use seemed to be most frequent and to be associated with the most symptoms at
younger ages, regardless of the substance, religiosity, gender, race, or any other factor. African
American ethnicity appeared to be a protective factor for frequent use of substances in general,
and Asian respondents reported the lowest frequency and severity of symptoms associated with
substance use in addition to reporting low frequency of use in general.
LIMITATIONS AND FUTURE RESEARCH
Limitations of this study included the difficulty the researcher had in constructing the
survey; the first administration, which was conducted for a separate class project, was largely
unsuccessful as many participants did not understand how to complete the rating scale and
therefore lacked valid severity of substance use scores. The second administration, from which
the data for this study was collected, went much more smoothly, with all but 2 participants
appropriately completing the survey. A second issue with the study was that, due to the same
data set being used for three separate studies, the survey was difficult to streamline and simplify
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while still ensuring that the researcher would obtain the data needed for all three studies. Also,
due to the large number of nontraditional students in the surveyed population, the researcher
should have taken into account the fact that age could have confounded each variable in different
ways, potentially raising the GPA, frequency, and severity scores overall due to longer lifespans,
and potentially lowering engagement in dangerous behaviors overall because of their
theoretically higher level of maturity. Future surveys should include fewer variables, but more
detailed questions about each variable so that stronger, more reliable results can be found
regarding the relationships between each of the variables studied. An additional variable the
researcher would like to consider is the type of extracurricular activities students are involved in
and the relationship between involvement in those types of groups and the students’ substance
use patterns, as much literature has been written regarding substance use and participation in
school organizations such as Greek life and athletic programs.
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References
Geisner, I., Mallett, K., & Kilmer, J. (2012). An Examination of Depressive Symptoms and
Drinking Patterns in First Year College Students. Issues in Mental Health Nursing,
33(5), 280-287. Retrieved December 10, 2014, from CINAHL Complete.
Horton, K., Ellison, C., Loukas, A., Downey, D., & Barrett, J. (2012). Examining Attachment to
God and Health Risk-Taking Behaviors in College Students. Journal of Religion and
Health, 51(2), 552-566. Retrieved December 10, 2014, from Advanced Placement
Source.
Huang, J., DeJong, W., Towvim, L., & Schneider, S. (2009). Sociodemographic And
Psychobehavioral Characteristics Of US College Students Who Abstain From Alcohol.
Journal of American College Health, 57(4), 395-410. Retrieved December 10, 2014,
from CINAHL Complete.
Kuntsche, E., Rossow, I., Simons-Morton, B., Bogt, T., Kokkevi, A., & Godeau, E. (2013). Not
Early Drinking but Early Drunkenness Is a Risk Factor for Problem Behaviors Among
Adolescents from 38 European and North American Countries. Alcoholism: Clinical and
Experimental Research, 37(2), 308-314. Retrieved December 10, 2014, from
MEDLINE.
Miller, K., Danner, F., & Staten, R. (2008). Relationship Of Work Hours With Selected Health
Behaviors And Academic Progress Among A College Student Cohort. Journal of
American College Health, 56(6), 675-679. Retrieved December 10, 2014, from
CINAHL Complete.
Randolph, M., Torres, H., Gore-Felton, C., Lloyd, B., & McGarvey, E. (2009). Alcohol Use And
Sexual Risk Behavior Among College Students: Understanding Gender And Ethnic
Differences. The American Journal of Drug and Alcohol Abuse, 35(2), 80-84. Retrieved
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December 10, 2014, from EBSCOHost.
Stallings, M., Hewitt, J., Beresford, T., Heath, A., & Eaves, L. (1999). A Twin Study of Drinking
and Smoking Onset and Latencies from First Use to Regular Use. Behavior Genetics,
29(6), 409-21. Retrieved December 10, 2014.
White, A., & Hingson, R. (2013). The Burden of Alcohol Use Excessive Alcohol Consumption
and Related Consequences Among College Students. Alcohol Research: Current
Reviews, 5(2), 201-18. Retrieved December 10, 2014, from MEDLINE.
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APPENDIX
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18
19
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Table 8: Risky Behaviors and Frequency of Alcohol Use
frequency
of alcohol
use under
18
frequency
of alcohol
use 18-25
frequency
of alcohol
use 26-30
frequency
of alcohol
use 31+
been
violent
under
influence
under 18
been
violent
under
influence
18-25
been
violent
under
influence
26-30
frequency of
alcohol use under
18
Pearson
Correlation1 .530** -.334 .b .318* .475** -.075
Sig. (2-tailed) .000 .265 .000 .016 .000 .818
N 59 54 13 6 57 53 12
frequency of
alcohol use 18-25
Pearson
Correlation.530** 1 .073 .508 .146 .294* .233
Sig. (2-tailed) .000 .812 .304 .300 .035 .467
N 54 54 13 6 52 52 12
frequency of
alcohol use 26-30
Pearson
Correlation-.334 .073 1 1.000** .b -.196 .322
Sig. (2-tailed) .265 .812 .000 .000 .564 .307
N 13 13 13 6 11 11 12
frequency of
alcohol use 31+
Pearson
Correlation.b .508 1.000** 1 .b .b .b
Sig. (2-tailed) .000 .304 .000 .000 .000 .000
N 6 6 6 7 6 6 6
been violent
under influence
under 18
Pearson
Correlation.318* .146 .b .b 1 .722** .b
Sig. (2-tailed) .016 .300 .000 .000 .000 .000
N 57 52 11 6 58 54 12
been violent
under influence
18-25
Pearson
Correlation.475** .294* -.196 .b .722** 1 .380
Sig. (2-tailed) .000 .035 .564 .000 .000 .223
N 53 52 11 6 54 54 12
been violent
under influence
26-30
Pearson
Correlation-.075 .233 .322 .b .b .380 1
Sig. (2-tailed) .818 .467 .307 .000 .000 .223
N 12 12 12 6 12 12 13
been violent
under influence
31+
Pearson
Correlation.b .b .b .b .b .b .b
Sig. (2-tailed) . . . . . . .
N 5 5 5 6 6 6 6
21
risky sex under
influence under
18
Pearson
Correlation.259* .114 .534 .524 .188 .142 -.174
Sig. (2-tailed) .050 .418 .074 .228 .157 .306 .588
N 58 53 12 7 58 54 12
risky sex under
influence 18-25
Pearson
Correlation-.023 .152 .593* .622 .119 .165 .248
Sig. (2-tailed) .868 .276 .042 .136 .392 .233 .437
N 54 53 12 7 54 54 12
risky sex under
influence 26-30
Pearson
Correlation-.095 .090 -.120 -.135 .b -.176 -.116
Sig. (2-tailed) .757 .770 .697 .772 .000 .583 .707
N 13 13 13 7 12 12 13
risky sex under
influence 31+
Pearson
Correlation.b .489 -.162 -.135 .b .b .b
Sig. (2-tailed) .000 .325 .760 .772 .000 .000 .000
N 6 6 6 7 6 6 6
operated vehicle
under influence
under 18
Pearson
Correlation.192 -.007 .106 -.135 .216 .016 -.135
Sig. (2-tailed) .149 .963 .743 .772 .103 .910 .676
N 58 53 12 7 58 54 12
operated vehicle
under inlfluence
18-25
Pearson
Correlation.340* .285* -.287 -.015 .088 .349** .000
Sig. (2-tailed) .012 .038 .367 .974 .527 .010 1.000
N 54 53 12 7 54 54 12
operated vehicle
under influence
26-30
Pearson
Correlation.642* .341 -.443 -.135 .b .663* -.122
Sig. (2-tailed) .018 .254 .129 .772 .000 .019 .692
N 13 13 13 7 12 12 13
operated vehicle
under influence
31+
Pearson
Correlation.b .489 -.162 -.135 .b .b .b
Sig. (2-tailed) .000 .325 .760 .772 .000 .000 .000
N 6 6 6 7 6 6 6
22
Table 8: Risky Behaviors and Frequency of Alcohol Use
operated vehicle
under influence 26-30
operated vehicle
under influence 31+
frequency of alcohol use under 18 Pearson Correlation .642* .b
Sig. (2-tailed) .018 .000
N 13 6
frequency of alcohol use 18-25 Pearson Correlation .341 .489
Sig. (2-tailed) .254 .325
N 13 6
23
frequency of alcohol use 26-30 Pearson Correlation -.443 -.162
Sig. (2-tailed) .129 .760
N 13 6
frequency of alcohol use 31+ Pearson Correlation -.135 -.135
Sig. (2-tailed) .772 .772
N 7 7
been violent under influence under
18
Pearson Correlation .b .b
Sig. (2-tailed) .000 .000
N 12 6
been violent under influence 18-25 Pearson Correlation .663* .b
Sig. (2-tailed) .019 .000
N 12 6
been violent under influence 26-30 Pearson Correlation -.122 .b
Sig. (2-tailed) .692 .000
N 13 6
been violent under influence 31+ Pearson Correlation .b .b
Sig. (2-tailed) . .
N 6 6
risky sex under influence under 18 Pearson Correlation -.231 -.258
Sig. (2-tailed) .448 .576
N 13 7
risky sex under influence 18-25 Pearson Correlation -.086 .354
Sig. (2-tailed) .779 .437
N 13 7
risky sex under influence 26-30 Pearson Correlation .465 1.000**
Sig. (2-tailed) .094 .000
N 14 7
risky sex under influence 31+ Pearson Correlation 1.000** 1.000**
Sig. (2-tailed) .000 .000
N 7 7
operated vehicle under influence
under 18
Pearson Correlation -.180 -.167
Sig. (2-tailed) .557 .721
N 13 7
operated vehicle under inlfluence 18-
25
Pearson Correlation .925** .906**
Sig. (2-tailed) .000 .005
N 13 7
operated vehicle under influence 26-
30
Pearson Correlation 1 1.000**
Sig. (2-tailed) .000
24
N 14 7
operated vehicle under influence 31+ Pearson Correlation 1.000** 1
Sig. (2-tailed) .000
N 7 7
25
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