PROFILING THE DRUGGIE LIFESTYLE: CHARACTERISTICS RELATED TO SOUTHERN COLLEGE STUDENTS' USE OF ILLICIT DRUGS

Abstract

Drawing on self-report survey data from a sample of 1,218 Southern college/university students collected in 1998, this study examines the relationship of demographics, family and background statuses, peer influences, experiences of alcohol and tobacco use, and academic activities as they influence the use of illicit drugs. Separate examinations are conducted to construct the profile of individuals who use marijuana only and those who use harder (i.e., cocaine, stimulants, LSD, opiates, ecstasy) drugs. Results reveal that marijuana-only users received little/inconsistent supervision as children, are members of fewer social clubs/organizations, are more likely to skip class, smoke, party with friends, get drunk often, and get drunk in public. Harder drug users report little/inconsistent supervision as children, getting drunk frequently and in public, are less far along in their schooling, spend their leisure time partying at friends’ homes or bars where they are regulars, and/or going to concerts, and/or attending club functions, and are tobacco smokers.

Notes

¹Participating institutions are located in Alabama, Florida, Georgia, Kentucky, North Carolina, South Carolina, Tennessee, and Virginia.

²No data is reported on students' majors. Items to assess these measures were not included on the instrument, as one author's institutional review board believed such information would unnecessarily compromise respondents' anonymity.

³We utilize a dichotomous variable for this measure rather than an ordinal one because most students who had used harder drugs had used only one type of harder drug (92.4%).

*p ≤ .10 (variable carried forward).

**S.E. = Standard Error.

⁴While we are aware that tests of significance are less meaningful on a nonrandom sample, we utilize them throughout this research as aids in interpretation.

⁵Factor analysis is another frequently used method for variable reduction. We do not use it here because factor analysis is only appropriate for ratio or interval variables, and our data is primarily nominal and ordinal. Additionally, factor analysis is not an efficient method of data reduction when the categories of the variables are not evenly distributed (SPSS, Inc. 1997); this is the case with our data. Further, factor analysis is a method of identifying which variables measure like concepts. The richness of these data is that there is immense specificity; something that would be minimized when grouped with similar concepts.

⁶The equation, χ2₁ − χ2₂, df1 − df2 can be used to compare two similar models with the same sample and indicate which is a more parsimonious fit to the data (Lottes, Adler, and SAS 1990; DeMaris 1996).

* p ≤ .05

** p ≤ .10.

χ² = 92.774 (p = .001).

df = 7.

N = 1061.

* p ≤ .05

** p ≤ .10.

χ² = 240.369 (p = .001).

df = 11.

N = 1001.

⁷Several statistics are presented in the regression tables to follow. The chi-square statistic provides an indication of the overall fit of the data to the model. A significant chi-square indicates that the variables, as a group, contribute significantly to the dependent variable. In addition, the tables report the logistic coefficients and their standard errors (S.E.).The unstandardized logistic coefficient (B) can be interpreted as the change in log odds for a one-unit change in the predictor. We also report Exp (b) which is an odds ratio. Variables are regarded as significant if α ≤ .05 or .1 using a two-tailed test.

⁸Again, this variable measures the recent use of more serious drugs (cocaine, stimulants, LSD/acid, opiates, ecstasy in combination with marijuana or not) rather than the frequent use of any drugs.