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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

20

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

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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