EXECUTIVE OFFICE OF THE PRESIDENT

OFFICE OF NATIONAL DRUG CONTROL POLICY

Predicting 

Heavy Drug Use

Results of a Longitudinal Study, Youth Characteristics 

Describing and Predicting Heavy Drug Use by Adults

FEBRUARY 2004
Predicting Heavy Drug Use 

Executive Office of the President 

Office of National Drug Control Policy 

Washington, DC 

20503 

February 2004 
Acknowledgements 

This publication was sponsored by the Executive Office of the President, Office of National Drug 

Control Policy (ONDCP). Sam Schildhaus, Yoku Shaw-Taylor, Steven Pedlow, Michael R. 

Pergamit, and other staff of the National Opinion Research Center (NORC) at the University of 

Chicago produced this publication under a contract to ONDCP, contract number 282-98-0024, Task 

Order number 3. June S. Sivilli served as Project Officer for this study and resulting publication. 

 

Disclaimer 

The opinions expressed herein are the views of the authors and do not necessarily reflect the 

official policy of the Federal Government. 

Public Domain Notice 

All material appearing in this report is in the public domain and may be reproduced without 

permission from ONDCP or the authors. Citation of the source is appreciated. Suggested 

citation:

Office of National Drug Control Policy (2004). Predicting Heavy Drug Use 

Washington, DC: Executive Office of the President (Publication No. NCJ 208382) 

Electronic Access to Publication 

This document can be accessed electronically through the following World Wide Web address: 

http://www.whitehousedrugpolicy.gov

Originating Office 

Executive Office of the President 

Office of National Drug Control Policy 

Washington, DC 20503 

Publication Number NCJ 208382 

Printed in January 2005
Executive Summary 

Dynamic Picture of Marijuana and Cocaine Use from Age 14 to 40

 This study’s main aims are to describe the movement of adolescents and young adults 

into and out of drug use and to predict heavy drug use. The data source is the 

Department of Labor’s National Longitudinal Survey of Youth, which began in 1979 

with a sample of 12,686 adolescents aged 14–21. After 17 rounds and 19 years, the 

response rate in 1998 was 87 percent. 

The Transient Nature of Most Marijuana and Cocaine Use 

 A majority of the sample reported no drug use in the five “index” rounds (questions on 

drug use were asked for five years during the 15-year period between 1984 and 1998). 

Overall, 42 percent of individuals reported marijuana use, 19 percent reported cocaine 

use, and 3 percent reported crack use (crack questions were collected only between 1992 

and 1998). 

 Most of the persons using any drug in a given round never used the drug again or used 

the drug for one additional round. Overall, 19.2 percent used marijuana, 11.7 percent

used cocaine, and 2.7 percent used crack in one round only. 

 Very few individuals reported drug use in all five index rounds: 3.2 percent used 

marijuana and 0.2 percent used cocaine in every round from 1984 to 1998. For crack, 

0.2 percent used in the three rounds between 1992 and 1998. 

 Only 3.9 percent used marijuana and 0.6 percent used cocaine in four rounds; crack was 

used in two rounds by 0.6 percent. 

 Almost half of marijuana users who used in one index round used marijuana in the next 

one. Only one of 16 non-marijuana users in one index period used marijuana in the 

subsequent period. 

 About one-quarter of cocaine users who used in one index round used cocaine in the 

subsequent study period. Only one of 32 non-cocaine users in one index period used

cocaine in the next one. 

Early Predictors of Heavy Cocaine Use 

 All other things being equal, adolescents and young adults who smoked marijuana more

than 50 times at the first contact were six times more likely to become heavy cocaine 

users than those who did not smoke marijuana. This finding supports the suggestion that 

preventing adolescents and young adults from using substantial amounts of marijuana 

may lead to a considerable decrease in the number of future heavy cocaine users. 

 Young male users were almost twice as likely as young female drug users to become

heavy drug users. 

PREDICTING HEAVY DRUG USE: FINAL REPORT i
 Those who had been suspended from school were one and one-half times more likely to 

become heavy cocaine users than those who had not been suspended from school. 

 Adolescents and young adults who reported a significant amount of illegal income at 

round one were more than twice as likely to become heavy cocaine users as those who 

did not. Also, those who sold hard drugs at round one during adolescence were two times

as likely to become heavy cocaine users as those who did not. Directing adolescents and 

young adults from criminal occupations is not only associated with lower future criminal

behavior, it is likely to be associated with substantial decreases in the number of heavy 

cocaine users. 

 Adolescents and young adults who attended religious services at least twice a month were 

one-third less likely to become heavy cocaine users as those who attended no religious

services. Attending infrequently is associated with a one-quarter lower likelihood of 

becoming a heavy cocaine user. Family and community efforts to encourage religious 

attendance by youth may be associated with fewer heavy cocaine users in the future. 

PREDICTING HEAVY DRUG USE: FINAL REPORT ii
Table of Contents

1. INTRODUCTION......................................................................................1

Background and Purpose........................................................................................................1

Organization of this Report .....................................................................................................2

2. METHODOLOGY....................................................................................3

Description of National Longitudinal Survey of Youth.............................................................3

Analytic Techniques................................................................................................................5

Description of Movement into and out of Drug Use.....................................................5

Prediction ....................................................................................................................5

Variables .................................................................................................................................6

Nonresponse Analysis ............................................................................................................9

3. DRUG USE 1984–1998 .........................................................................11

Comparison Between Those Included/Not Included in Analysis...........................................11

Movement Into and Out of Drug Use ....................................................................................14

Marijuana Use in the Past Year.................................................................................14

Cocaine Use in the Past Year ...................................................................................17

Marijuana and Cocaine Use in the Past Year ...........................................................17

Correlates of Number of Years Used Drugs: Gender and 

 Racial/Ethnic Differences .....................................................................................22

Comparisons of Drug Use Over Time .......................................................................23

Comparison Between NLSY and National Household Survey 

 on Drug Abuse (NHSDA) .....................................................................................26

Strengths of Longitudinal Surveys.............................................................................29

Summary...............................................................................................................................29

4. HEAVY DRUG USE..............................................................................31

Introduction ...........................................................................................................................31

Age of First Use ....................................................................................................................31

Prediction of Heavy Cocaine Use .........................................................................................37

Correlates of Number of Years of Drug Use .........................................................................40

Summary...............................................................................................................................42

5. LONGITUDINAL SCREENING.............................................................43

Introduction ...........................................................................................................................43

Screening the Sample...........................................................................................................43

Selecting the Sample ............................................................................................................44

Weighting the Sample...........................................................................................................45

Conclusion ............................................................................................................................45

APPENDIX A .............................................................................................46

PREDICTING HEAVY DRUG USE: FINAL REPORT iii
List of Exhibits

Page 

EXHIBIT 2.1 NLSY79 MODE AND RESPONSE RATES BY YEAR.......................................... 4 

EXHIBIT 2.2 DRUG USE FOR INDIVIDUAL YEARS AND COMBINATIONS OF YEAR .......... 8 

EXHIBIT 2.3 FOUR TESTS OF NON-RANDOM DROPOUT AMONG HEAVY COCAINE 

USERS ............................................................................................................... 10 

EXHIBIT 3.1 COMPARISONS OF SAMPLE RESPONDENTS 1984-1998 AND 

SAMPLE RESPONDENTS 1984-1994............................................................... 12 

EXHIBIT 3.2 COMPARISONS OF STUDY SAMPLE AND SAMPLE EXCLUDED 

FROM STUDY.................................................................................................... 13 

EXHIBIT 3.3 DISTRIBUTION TREE OF RESPONDENTS WHO REPORTED 

SMOKING MARIJUANA IN 1984 ....................................................................... 15 

EXHIBIT 3.4 DISTRIBUTION TREE OF RESPONDENTS WHO REPORTED NO 

MARIJUANA USE IN 1984................................................................................. 16 

EXHIBIT 3.5 DISTRIBUTION TREE OF RESPONDENTS WHO REPORTED USING 

COCAINE IN 1984.............................................................................................. 18 

EXHIBIT 3.6 DISTRIBUTION TREE OF RESPONDENTS WHO REPORTED NO COCAINE 

USE IN 1984....................................................................................................... 19 

EXHIBIT 3.7 DISTRIBUTION TREE OF RESPONDENTS WHO REPORTED 

COCAINE AND MARIJUANA USE IN 1984 ....................................................... 20 

EXHIBIT 3.8 DISTRIBUTION TREE OF RESPONDENTS WHO REPORTED 

NO COCAINE AND MARIJUANA USE IN 1984................................................. 21 

EXHIBIT 3.9 NUMBER OF TIMES USED MARIJUANA BY GENDER.................................... 22 

EXHIBIT 3.10 NUMBER OF TIMES USED COCAINE BY GENDER........................................ 22 

EXHIBIT 3.11 NUMBER OF TIMES USED MARIJUANA BY RACE/ETHNICITY..................... 23 

EXHIBIT 3.12 NUMBER OF TIMES USED COCAINE BY RACE/ETHNICITY ......................... 23 

EXHIBIT 3.13 MARIJUANA USE BETWEEN ADJACENT SURVEY PERIODS ....................... 24 

EXHIBIT 3.14 COCAINE USE BETWEEN ADJACENT SURVEY PERIODS............................ 24 

EXHIBIT 3.15 NUMBER OF YEARS USED DRUG .................................................................. 24 

EXHIBIT 3.16 PERCENTAGE WHO USED MARIJUANA ADDITIONAL YEARS BY 

NUMBER OF YEARS USED MARIJUANA ........................................................ 25 

EXHIBIT 3.17 PERCENTAGE WHO USED COCAINE ADDITIONAL YEARS BY 

NUMBER OF YEARS USED COCAINE............................................................. 25 

EXHIBIT 3.18 NUMBER OF YEARS USED COCAINE BY NUMBER OF YEARS USED 

MARIJUANA....................................................................................................... 27 

PREDICTING HEAVY DRUG USE: FINAL REPORT iv
Page 

EXHIBIT 3.19 COMPARISON BETWEEN NHSDA AND NLSY FOR LIFETIME 

AND PAST YEAR MARIJUANA AND COCAINE USE 1984-1998..................... 28 

EXHIBIT 4.1 HEAVY COCAINE USE AMONG THOSE WHO HAD/HAD NOT SMOKED 

FIRST CIGARETTE BY AGE ............................................................................. 32 

EXHIBIT 4.2 HEAVY COCAINE USE AMONG THOSE WHO HAD/HAD NOT STARTED 

SMOKING DAILY BY AG ................................................................................... 33 

EXHIBIT 4.3 HEAVY COCAINE USE AMONG THOSE WHO HAD/HAD NOT STARTED 

DRINKING 2+ A WEEK BY AGE........................................................................ 34 

EXHIBIT 4.4 HEAVY COCAINE USE AMONG THOSE WHO HAD/HAD NOT USED 

MARIJUANA BY AGE ........................................................................................ 35 

EXHIBIT 4.5 HEAVY COCAINE USE AMONG THOSE WHO HAD/HAD NOT USED 

COCAINE BY AGE............................................................................................. 36 

EXHIBIT 4.6 HEAVY COCAINE USE AMONG THOSE WHO HAD/HAD NOT USED CRACK 

COCAINE BY AGE............................................................................................. 37 

EXHIBIT 4.7 CORRELATES OF HEAVY COCAINE USE: RESULTS OF LOGISTIC 

REGRESSION MODEL...................................................................................... 39 

EXHIBIT 4.8 CORRELATES OF NUMBER OF YEARS USING MARIJUANA, COCAINE, 

CRACK: RESULTS OF OLS REGRESSION MODELS .................................... .41 

PREDICTING HEAVY DRUG USE: FINAL REPORT v
1. INTRODUCTION 

Background and Purpose 

The Office of National Drug Control Policy (ONDCP) has supported several projects to learn about 

heavy drug users. These projects include a major study to estimate the number of “hard core” users1

as well as analyses of the National Household Survey on Drug Abuse (NHSDA) and Drug Use

Forecasting (DUF) system.

2

 Although these analyses have estimated the number of heavy drug

users, they do not predict the correlates that distinguish heavy drug users from the remainder of the 

population. Such correlates would be developed with models using longitudinal data (i.e., multiple 

years of panel data) for characteristics (demographics, attitudes, and behavior) of youth to describe 

and predict heavy drug use by adults. 

Given the lack of longitudinal data to describe trends and infer reasons for changes, drug policy

research has relied on cross-sectional data (e.g., NHSDA, Drug Abuse Warning Network, Arrestee

Drug Abuse Monitoring Program/DUF, Treatment Episode Data Set, Monitoring the Future, Drug

Evaluation Network System). Some cross-sectional studies use calendars or other memory aids to

collect data that describe the respondent’s recollection of an earlier time. Such data appear 

longitudinal and are quite useful. Memory of family characteristics in high school and attitudes, 

beliefs, and behaviors is more accurate when collected prospectively than when collected many years 

after the event (or perception of the event).3

 Examples of factors that influence such retrospective

memory are recall delay and telescoping. Recall delay refers to reductions in the report of behavior 

due to the respondent’s ability to remember events; the concept describes lower reports of remote 

events, characteristics, and behaviors than for more recent events. Telescoping refers to the

allocation of events, characteristics, or behaviors to an earlier or later time period than the one in

which it actually occurred. 

Longitudinal data can inform researchers and policymakers about changes over time. Such data 

contain variables that allow better description and prediction of change, since these variables are 

based on current perceptions and not long-term recall. Possible descriptive questions that could be 

answered with longitudinal data include: What percentage of marijuana users continue to use

marijuana in future years? How well can the movement of individuals between drug use and nonuse 

be described? Analysts of drug policy data have recommended a longitudinal study to describe and

explain changes in drug use and criminal behavior over time.4

 In addition to their usefulness in

describing changes over time, longitudinal data can predict future behavior, using variables for prior

behavior/attitudes without the weaknesses of retrospective memory. 

 1

 R. Simeone, W. Rhodes, D. Hunt, L. Truitt, A Plan for Estimating the Number of “Hardcore” Drug Users in

the United States: Preliminary Findings, Abt Associates, 1997. 2

 W. Rhodes et al., What America’s Users Spend on Illegal Drugs 1988-2000, Office of National Drug Control

Policy, Office of Programs, Budget, Research, and Evaluation, Executive Office of the President, 2001. 3

 R. Tourangeau, L. J. Rips, and K. Rasinski, The Psychology of Survey Response, New York: Cambridge 

University Press, 2000; N Bradburn, L.J. Rips, S. K. Shevell, “Answering Autobiographical Questions: The Impact

of Memory and Inference on Surveys,” Science, v. 236, pp. 157-161, 1987. 4 Informing America’s Policy on Illegal Drugs: What We Do not Know Keeps Hurting Us, Committee on Data 

and Research for Policy on Illegal Drugs, Charles F. Manski, John V. Pepper, and Carol V. Petrie, Editors, 

Committee on Law and Justice and Committee on National Statistics, National Research Council, 2001. 

PREDICTING HEAVY DRUG USE: FINAL REPORT 1
The purpose of this study is to: 

 Describe the movement of a representative sample of adolescents and young adults (14–

21) into and out of drug use; 

 Predict the correlates of “heavy drug use,” using factors of adolescence/early adulthood; 

and 

 Present a preliminary method for screening and weighting a future sample of drug users. 

Organization of this Report 

This chapter provides the background and purpose of this report. Chapter 2 introduces the National 

Longitudinal Survey on Youth (NLSY) and the analytical techniques used in this study. Chapter 3

describes the movement into and out of drug use by adolescents and young adults between 1984

through 1998. Chapter 4 presents an analysis predicting heavy cocaine use. Chapter 5 presents a

discussion on longitudinal screening. 

PREDICTING HEAVY DRUG USE: FINAL REPORT 2
2. METHODOLOGY 

Description of National Longitudinal Survey of Youth 

To describe drug use and to predict who becomes a heavy drug user, this study uses the Department

of Labor’s National Longitudinal Survey of Youth–1979 (NLSY79) cohort, representing 33.6 million 

youth. The NLSY79 is sponsored by the Bureau of Labor Statistics, Department of Labor, to focus 

on the labor market experiences of adolescents moving into the labor market and adulthood in the

United States. For the last 23 years, the NLSY79 has followed a representative sample of about

10,000 youth (aged 14–21 as of December 31, 1978), through their late thirties and early forties by 

2000. The survey oversamples blacks and Hispanics, permitting generalizations about these 

populations. Participants were interviewed annually until 1994 and biennially after 1994. 

The NLSY79 has gathered information about the following factors that might influence the entry of

adolescents/youth into the labor market: 

 Alcohol and substance use; 

 Attitudes and aspirations; 

 Child care; 

 Criminal behavior; 

 Family background and demographic characteristics; 

 Geographic residence; 

 Health conditions, injuries, and insurance coverage; 

 Household composition; 

 Income and assets; 

 Marital and fertility history;

 Military experience; 

 Schooling, school records, and aptitude information; 

 Training investments; and 

 Work and non-work experiences. 

The NLSY began in 1979 with a sample of 12,686 youth and continues to have a high response rate 

(see Exhibit 2.1). In 1980, 12,141 individuals were interviewed with a response rate of 96 percent.

By 1990, the response rate was 91 percent. In 1998, the last year included in this study, the response

rate was 87 percent, after 17 rounds and 19 years. By 2000, the age of the sample population ranged

from 35–42. 

This study uses the battery of questions on drug use that were administered in 1984, 1988, 1992, 

1994, and 1998. Comparable questions were asked regarding lifetime and past-year use of 

marijuana, cocaine (between 1984 and 1998), and crack (between 1992 and 1998). 

PREDICTING HEAVY DRUG USE: FINAL REPORT 3
Exhibit 2.1: NLSY79 Mode and Response Rates, by Year

YEAR MODE SAMPLE SIZE RESPONSE RATE 

1979 Personal/PAPI 12,686 — 

1980 Personal/PAPI 12,141 96% 

1981 Personal/PAPI 12,195 96% 

1982 Personal/PAPI 12,123 96% 

1983 Personal/PAPI 12,221 97% 

1984 Personal/PAPI 12,069 96% 

1985 Personal/PAPI 10,8945 95% 

1986 Personal/PAPI 10,655 93% 

1987 Telephone/PAPI 10,485 91% 

1988 Personal/PAPI 10,465 91% 

1989 Personal/PAPI /CAPI 10,605 93% 

1990 Personal/PAPI/CAPI 10,436 91% 

1991 Personal/PAPI 9,0186 92% 

1992 Personal/PAPI 9,015 92% 

1993 Personal/CAPI 9,011 92% 

1994 Personal/CAPI 8,891 91% 

1996 Personal/CAPI 8,636 89% 

1998 Personal/CAPI 8,399 87% 

2000 Personal/CAPI 8,033 83% 

SOURCE: U.S. Department of Labor, Bureau of Labor Statistics, NLS Handbook, 2000 and 2001. 

 5

 A total of 201 military respondents were retained from the original sample of 1,280; 186 of the 201 

participated in the 1985 interview. The total number of NLSY79 civilian and military respondents eligible for 

interview (including deceased respondents) beginning in 1985 was 11,607. 6

 The 1,643 economically disadvantaged non-black/non-Hispanic male and female members of the

supplemental subsample were not eligible for interview in the 1991 survey year. The total number of NLSY civilian

and military respondents eligible for interview (including deceased respondents) beginning in 1991 was 9,964. 

PREDICTING HEAVY DRUG USE: FINAL REPORT 4
Analytic Techniques 

This study focuses on two general areas of interest: (1) the movement of individuals into and out of 

drug use and (2) the prediction of heavy drug use. Each will be presented in turn. 

DESCRIPTION OF MOVEMENT INTO AND OUT OF DRUG USE 

In order to depict movement into and out of drug use, we developed “distribution trees” for the five

index years during the 15-year period between 1984 and 1998. The distribution trees are based on

frequency counts of use starting with 1984 and conditional frequency counts for subsequent years.

Frequency counts for 1988 are based on the condition of use or nonuse for 1984, and frequency

counts for 1992 are based on condition of use or nonuse for the 1984 and 1988 survey years. 

Frequency counts for 1994 are in turn based on condition of use or nonuse for the earlier index years. 

For 1998, the counts are based on condition of use or nonuse for all previous study periods. For 

example, the distribution of those who used in 1988 is based on use versus nonuse in 1984. For 

marijuana, cocaine, and a combination of both, two distribution trees were developed—one for those

who reported use and another for those who did not report use, starting with the 1984 period. 

The trees are also based on crosstabulations (2*2 tables) that yielded distributions of use and nonuse 

for two adjacent study periods. Crosstabulations were used to verify the conditional frequency 

counts developed for the distribution trees. One additional advantage is that these trees permit 

recalculation of change by combining each of the component cells. 

PREDICTION 

The prediction of heavy drug use primarily employs logistic regressions and odds ratios. No claim is

made for a direct causal relationship among the variables used in the analysis. Rather, we seek to

explain whether certain behaviors or characteristics tend to coincide with the presence of heavy drug

use to a greater or lesser degree than do other variables or characteristics. 

Logistic regression is often used when the dependent variable is composed of two values, such as the 

presence and absence of an activity (e.g., was a heavy drug user or was not a heavy drug user). The

measure of strength of association is r

2 . 

To illustrate the model, let H denote the measurement of the dichotomous outcome, heavy drug use. 

Then H=0 if the individual was not a heavy drug user and H=1 if the individual was a heavy drug

user. Variables W through Z symbolize additional explanatory variables, which may be either 

dichotomous or continuous. (NOTE: The three explanatory variables are presented for illustrative 

purposes only; additional explanatory variables do not change the form of the model.). The unified 

model for dichotomous outcomes is: 

Logit (H) = b0 + b1*W + b2*X + b3*Z 

Logistic regression generates odds ratio estimates for each predictor. Such estimates are readily

interpretable probabilities that indicate how much more likely it is that an outcome would be 

observed if, all other elements being the same, the predictor occurs compared to when the predictor

PREDICTING HEAVY DRUG USE: FINAL REPORT 5
does not occur. For example, all other things being equal, an odds ratio would estimate how much

more or less likely a man than a woman is to become a heavy drug user. An odds ratio above 1.0

means that the activity becomes more likely, while an odds ratio below 1.0 means that this activity

becomes less likely. For continuous variables, we used ordinary least-squares models. 

Variables 

Chapter 3 describes drug use for five index years between 1984 and 1998. To portray the movement 

into and out of drug use, the variables presented are marijuana use, cocaine use, and both marijuana 

and cocaine use in the past year. 

Chapter 4 focuses on predicting drug use. The dependent variable, heavy drug use, is the first focus.

There are many ways that the concepts and measures related to heavy drug use have been

operationally defined. One operational measure that the Substance Abuse and Mental Health

Services Administration (SAMHSA) used in its National Household Survey on Drug Abuse 

(NHSDA) to define and count individuals who are dependent on drugs is problematic drug use. This 

measure is based on criteria established by the American Psychiatric Association in its Diagnostic

and Statistical Manual of Mental Disorders versions three (revised) and four (DSM-III-R, DSM-IV). 

From 1994 to 2000, SAMHSA applied the DSM-IV definition that a person was dependent if s/he

met three out of seven dependence criteria7

 (for substances with a withdrawal criterion) or three out 

of six criteria (for substances without a withdrawal criterion) based on responses to questions in the 

NHSDA. These questions were revised for the 2000 survey, and questions were added, regarding 

respondents’ potential abuse of drugs (again, based on DSM-IV criteria). 

Chronic drug use is a concept used in ONDCP’s publication, What America’s Users Spend on Illegal 

Drugs (2001). The study defined chronic users by drug use behaviors rather than DSM criteria. The 

report’s authors analyzed the data collected by the National Institute of Justice’s Drug Use 

Forecasting program and operationally defined the concept of chronic drug use as “those who 

admitted using cocaine or heroin on more than 10 days during the month before being arrested.”8

 

Obviously, the differences among these measures of heavy drug use are substantial, ranging from a 

combination of psychological variables (i.e., the desire for drugs, the difficulty of stopping use, and 

withdrawal when attempting to end use) as opposed to measures of actual use. The NLSY questions

on illicit drugs focus on behaviors regarding drug use, rather than on dependence or abuse. In 

addition, the categories used in the NLSY to measure level of drug use (e.g., marijuana, cocaine,

crack) are high, but capped at 100 or more lifetime uses.9

 That volume of drug use is substantial, but 

does not contain information on attempts at withdrawal from drug use or on the difficulty of 

withdrawal. 

 7

 The operational definitions before and after 2000 are described in: Substance Abuse and Mental Health

Administration, National and State Estimates of the Drug Abuse Treatment Gap: 2000 National Household Survey 

on Drug Abuse, Appendix A, DHHS, 2002. 8

 W. Rhodes, ibid. 9

 There is one exception. In 1984, the measure was capped at 1,000 or more uses. 

PREDICTING HEAVY DRUG USE: FINAL REPORT 6
Eighteen rounds of the NLSY were conducted between 1979 and 1998.10 Questions on drug use were

administered for five of these rounds between 1984 and 1998. Based on data from these years, we 

developed an operational definition for heavy drug use based on the following criteria: 

 Include drugs generally associated with very heavy drug use (cocaine, crack, heroin) as 

feasible; and 

 Select operational definitions that provide a robust number of cases for analysis. 

The operational definition of heavy drug use for the analysis in Chapter 4 is both (1) self-reported 

lifetime use of cocaine or crack on 100 or more occasions, and (2) cocaine or crack use in the year of 

the interview (it is important to note that not all individuals in the sample responded each year). 

Heavy cocaine users in any of the five survey years were retained, even if they were nonrespondents 

to any of the first four rounds (e.g., a “heavy” cocaine user during 1984 was kept in the analyses even

if s/he was a nonrespondent after 1984). As presented in Exhibit 2.2, a lenient measure—report of

lifetime cocaine or crack use on 100 or more occasions—results in 1,447 heavy drug users. The 

more stringent measure (e.g., 100 or more lifetime uses of cocaine or crack plus use on 3–4 days per 

week of cocaine/crack) results in only 127 heavy drug users. We selected a measure that provides 

enough cases for a robust analysis and also contains a population that used a substantial amount of 

drugs. This measure—self-reported use of cocaine or crack 100 or more times in one’s lifetime plus

cocaine or crack use in the past year— results in 551 individuals defined as heavy drug users. 

Other drugs were not included in the operational definition of heavy drug use for several reasons.

Heroin and injection drug use were excluded, as the NLSY asked only whether or not the respondent

had ever injected drugs. Such knowledge about use without indication of frequency of use did not

seem a priori heavy drug use. In addition, lifetime use of marijuana was not included as a measure 

of lifetime heavy drug use, as the operational definition (100 or more times) seemed too low a

standard. (NOTE: The range of individuals with 100 or more lifetime uses of marijuana ranges from

2,093 in 1984, to 1,141 in 1992, and 1,330 in 1998. The numbers of individuals who used marijuana 

in the year prior to each NLSY round are as follows: 3,812 in 1984, 2,213 in 1988, 1,084 in 1992, 

1,173 in 1994, and 814 in 1998). 

 10 The NLSY was also conducted in 2000 and 2002, and another round will be conducted in 2004; none 

included questions on drug use. 

PREDICTING HEAVY DRUG USE: FINAL REPORT 7
Exhibit 2.2: Drug Use for Individual Years and Combinations of Year

1984 1988 1992 1994 1998 ANY of the 5 

Cocaine—Lifetime use 100+ times 240 273 583 315 294 1,335

Cocaine—Used in last year 302 990 323 302 184 2,085

Cocaine—Lifetime use 100+ and used 

cocaine in past year 

191 165 39 93 72 484

Cocaine—Lifetime use 100+ and used 

cocaine 1–2 days or more per week

82 32 8 26 16 156

Cocaine—Lifetime use 100+ and used 

cocaine 3–4 days or more per week. 

50 20 8 16 12 103

Cocaine or crack—Lifetime use 100+ 

and used cocaine or crack in past year 

191* 165* 71 118 106 551

Cocaine or crack—Lifetime use 100+ 

and used cocaine/crack 1–2 days or 

more per week

82* 32* 17 43 29 189

Cocaine or crack—Lifetime use 100+ 

and used cocaine/crack 3–4 days or 

more per week

50* 20* 16 24 22 127

Heroin—Lifetime use 119 n/a 126 199 170 416

Injection drug use (without doctor’s 

permission)—Lifetime use 

n/a 117** n/a 206 198 382

Cocaine or crack, Lifetime 100+ 240* 273* 678 361 349 1,447

Cocaine or crack 100+ or heroin 329 273* 785 462 430 1,613

Cocaine or crack 100+ or heroin or

injection drug use 

329 340 785 544 500 1,723

Cocaine or crack. lifetime use 100+, 

heroin or IV drug use, and used

cocaine/crack in past year 

222 165 103 153 115 615

Cocaine or crack, Lifetime use 100+

Heroin or IV drug use, and used 

cocaine/crack 1–2 days or more per 

week

84 32 32 48 30 209

Cocaine or crack, Lifetime use 100+, 

Heroin or IV drug use, and used 

cocaine/crack 3–4 days or more per 

week

51 20 30 28 23 145

* Powder cocaine only; **Cocaine injectors 

PREDICTING HEAVY DRUG USE: FINAL REPORT 8
As cocaine and crack are the operational measures, we refined the dependent variable to heavy 

cocaine use. Cocaine and crack were extensively used in the 1980s and 1990s, the period analyzed

in this study.

Nonresponse Analysis 

As with any longitudinal survey, dropouts occur over time. In a study concerned with drug use, it is 

reasonable to determine if heavy cocaine users (defined above) were more likely to drop out. If, for 

example, heavy cocaine users were more likely to drop out, the data set may not be representative of

all heavy cocaine users who were 14–21 years old on December 31, 1978. 

Fortunately, we can test the hypothesis of whether heavy cocaine users were more likely to drop out

of the NLSY79 than other youth, using the method of Diggle.11 Since we have five study periods 

(1984, 1988, 1992, 1994, and 1998) with data on drug use, this method calls for a separate test on

each of the first four time periods. 

The first test is whether those who drop out after 1984 (they completed the 1984 interview, but they 

were not respondents in the 1988, 1992, 1994, or 1998 interviews) are more likely to be heavy

cocaine users than those who are respondents in 1984. Of the 12,069 respondents in 1984, 10,700

responded in at least one of 1988, 1992, 1994, and 1998, while 1,369 were nonrespondents to all four 

as shown in Exhibit 2.3. Among the 10,700 non-dropouts, 1.57 percent satisfied the heavy cocaine

user condition in 1984 (100 or more lifetime uses of cocaine or crack, plus use of either cocaine or 

crack in the last year). It is important that we only use 1984 data to determine the heavy cocaine 

users, since we only have data beyond 1984 for the non-dropouts. The percentage of heavy cocaine

users was 1.57 percent among the non-dropouts and 1.68 percent for the dropouts; this difference was 

not statistically significant (p=.76). 

The second test for those who responded in 1988 is very similar. We split this subset into dropouts 

(nonrespondents in 1992, 1994, and 1998) and nondropouts, and determined the percentages in each 

who satisfied the heavy cocaine use condition in 1984 or 1988. This test also returned a nonsignificant p value (p=.43). The third (1992) and fourth (1994) tests were conducted in similar

fashion, and the differences were not statistically significant. No test is appropriate for 1998, of 

course, since this is the most recent year drug-related questions were asked in the NLS79. 

In conclusion, NLSY79 dropouts were not more likely to have been heavy cocaine users before

dropping out of the survey than were non-dropouts. 

 11 P. J. Diggle, K. Y. Liang, and S. L. Zeger, The Analysis of Longitudinal Data, Oxford, England: Oxford 

University Press, 1994. 

PREDICTING HEAVY DRUG USE: FINAL REPORT 9
Exhibit 2.3: Four Tests of Non-Random Dropout among Heavy Cocaine Users

Dropout? 

Yes No 

Number of cases 1,369 10,700 

Percent heavy cocaine users 1.68% 1.57% 

Chi-square statistic 0.0942 1 d.f. 

1984 

p value 0.76 Not significant 

Number of cases 1,704 8,761 

Percent heavy cocaine users 3.11% 2.76% 

Chi-square statistic 0.6309 1 d.f. 

1988 

p value 0.43 Not significant 

Number of cases 216 8,800 

Percent heavy cocaine users 2.31% 3.41% 

Chi-square statistic 0.7724 1 d.f. 

1992 

p value 0.38 Not significant 

Number of cases 695 8,196 

Percent heavy cocaine users 5.32% 4.34% 

Chi-square statistic 1.4569 1 d.f. 

1994 

p value 0.23 Not significant 

PREDICTING HEAVY DRUG USE: FINAL REPORT 10
3. DRUG USE 1984–1998 

This section compares the sample analyzed with the sample not included in the analysis, describes 

the movement in and out of drug use, and then compares the NLSY with another national survey, the 

National Household Survey on Drug Abuse (NHSDA). 

Comparison Between Those Included/Not Included in Analysis 

In Exhibits 3.1–3.8, we present tables that depict use and nonuse of illicit drugs by individuals 

between 1984 and 1998. In order to determine movement in and out of drug use, we selected 

individuals who had consistently responded in survey years 1984, 1988, 1992, 1994, and 1998 of the 

NLSY. By selecting individuals who had participated in each round of the survey, we avoided

developing a database with gaps due to nonresponse in some of the survey years. The analyses 

presented in this chapter are based on the individuals who responded to all five surveys with drug use 

modules between 1984–1998, and some who responded to every year except 1998 (participation in

1998 was not a condition for inclusion in the analysis)12. In all, 7,597 individuals responded to all 

five rounds, and an additional 534 responded to only the first four rounds (8,131 in total). 

In order to examine the effect of this decision, we conducted two analyses in order to learn the 

following: (1) Are the individuals who did not respond in 1998, but responded in 1984, 1988, 1992 

and 1994 (n=534), different from those who responded every year including 1998 (n=7,597)? (2) Is 

the sample included in the analysis (7,597+534) different from those we excluded because they did

not respond to the NLSY in one or more years between 1984 and 1994? 

With regard to question 1, Exhibit 3.1 presents a comparison along 17 variables between respondents 

in 1984–1998 and respondents in 1984–1994. The results indicate few differences between the two

groups. More females were in the 1984–1998 group, and there was a significantly higher percentage 

of whites in the 1984–1998 group (79.2 percent vs. 75.7 percent). Significantly more individuals in 

the 1984–1994 group had used marijuana or hashish in 1984, had used cocaine in survey years 1984,

1992, and 1994, and had been suspended from school. For 12 variables, these two groups were not 

statistically different: There were no significant differences for Hispanic and black race/ethnicity;

high school education; and whether the respondent sold hard drugs, gained income from illegal 

activities in the last year, smoked cigarettes in the past 30 days, smoked marijuana or hashish in 

1988–1994, and used cocaine in 1988. Results suggest that these two groups do not differ markedly, 

but enough that it is desirable to include the 1984-1994 respondents in our analyses. 

Exhibit 3.2 compares the sample used for our study with those we excluded. The study sample 

includes the group who consistently responded to all five rounds (1984–1998) and four rounds 

(1984–1994); this group is labeled the “in study group.” Nonrespondents (or those excluded from 

the sample) are those who did not respond in one or more years between 1984 and 1994; this group is

labeled “out of study.” As in the previous analysis, these groups were compared along 17

characteristics. 

 12 1998 is the last survey year used in the analysis and nonresponse in that year would not affect comparisons to 

subsequent survey years. 

PREDICTING HEAVY DRUG USE: FINAL REPORT 11
Exhibit 3.1: Comparisons of Sample Respondents 1984–1998 And Sample Respondents 

1984–1994

Respondents To All 

5 Rounds: 1984–1998 

(Base n max=7,597) 

Respondents To 4 

Rounds: 1984–1994 

(Base n max=534) 

Percentages % N % N 

Female* 51.9 7,597 38.9 534 

Hispanic 6.3 7,597 7.5 534

White (non-Hispanic)* 79.2 7,597 75.7 534 

Black (non-Hispanic) 14.5 7,597 16.9 534

At least high school education 38.9 7,597 36.6 534 

Smoked marijuana or hashish in 1984* 33.1 7,597 40.0 534

Smoked marijuana or hashish in 1988 22.7 7,597 22.6 534 

Smoked marijuana or hashish in 1992 13.7 7,597 14.6 534

Smoked marijuana or hashish in 1994 14.3 7,597 14.3 534 

Used cocaine in 1984* 11.4 7,597 14.2 534

Used cocaine in 1988 10.6 7,597 10.4 534 

Used cocaine in 1992* 3.6 7,597 5.2 534

Used cocaine in 1994* 3.0 7,597 5.5 534 

Ever been suspended from school (1980)* 20.8 7,404 25.1 516

Sold hard drugs in past year (1980) 2.4 7,201 3.0 500 

Illegal income in past year (1980) 16.1 7,032 16.9 476

Smoked cigarettes in past 30 days (1984) 40.7 7,570 42.6 533 

Note: Significant differences between percentages were based on chi-square tests of independence. 

* Difference between respondents and nonrespondents is significant at p < 0.05.

Results show that these groups differ significantly for eight characteristics. Hispanics were more

likely to be in the out of study group, and individuals in the in-study group were more likely to be 

female. Individuals in the out of study group were more likely to have a high school education (42.5

percent vs. 38.7 percent). 

PREDICTING HEAVY DRUG USE: FINAL REPORT 12
Exhibit 3.2 Comparisons of Study Sample and Sample Excluded from Study

In-Study Group (Base

n max=8,131)

Out of Study Group 

(Base n max=1,855) 

Percentages % n % n 

Female* 51.1 8,131 43.4 1,855 

Hispanic* 6.3 8,131 8.3 1,855

White (non-Hispanic) 79.0 8,131 77.6 1,855 

Black (non-Hispanic) 14.7 8,131 14.1 1,855

At least high school education* 38.7 8,131 42.5 1,855 

Smoked marijuana or hashish in 1984* 33.5 8,131 30.4 1,376

Smoked marijuana or hashish in 1988* 22.7 8,131 17.9 848 

Smoked marijuana or hashish in 1992* 13.8 8,131 10.3 885

Smoked marijuana or hashish in 1994* 14.3 8,131 10.8 760 

Used cocaine in 1984* 11.6 8,131 9.3 1,376

Used cocaine in 1988 10.5 8,131 8.7 848 

Used cocaine in 1992 3.7 8,131 3.4 885

Used cocaine in 1994 3.2 8,131 3.2 760 

Ever been suspended from school (1980) 21.1 7,920 22.0 1,650

Sold hard drugs in past year (1980) 2.4 7,701 2.5 1,562 

Illegal income in past year (1980) 16.1 7,508 14.7 1,522

Smoked cigarettes in past 30 days (1984) 40.8 8,103 39.8 1,368 

Note: Significant differences between percentages were based on chi-square tests of independence. 

* Difference between respondents and nonrespondents is significant at p < 0.05.

Respondents in the study were more likely than those out of the study to have smoked marijuana in

1984, 1988, 1992, and 1994, and to have used cocaine in 1984. The group included in this analysis

had higher rates of drug use than those excluded from the analysis, leaving us less concerned about

the possible diminution of drug users from the analysis. 

These results show that the respondents included in the study share more in common with

respondents excluded from the study; that is, these two groups are more alike than dissimilar, 

especially because there were no significant differences between them for a majority of the variables

along which they were compared. Although there are notable differences, particularly for the

behavioral variables regarding drug use, these differences are in the right direction; for instance,

there are higher proportions of individuals in the in-study group who have smoked marijuana, which

is useful for this study. The usual concern is that those excluded from the analysis are less likely to

engage in the analyzed behavior than those included. For this analysis of the movement into and out 

PREDICTING HEAVY DRUG USE: FINAL REPORT 13
of drug use, those included are somewhat more likely than those in the out-of-study group to have

smoked marijuana. Otherwise, the differences between the groups are small. 

Movement Into and Out of Drug Use 

MARIJUANA USE IN THE PAST YEAR 

Exhibit 3.3 depicts the distribution “tree” of respondents who reported marijuana use in the past year, 

in 1984; Exhibit 3.4 depicts the distribution tree of respondents who stated they did not use marijuana 

in 1984. 

The tree describes the distribution of individuals who reported use or nonuse of drugs in each of the

five index years from 1984 to 1998. There are two distribution trees for each drug. The first is based

on respondents who reported marijuana use in 1984, and the second, on respondents who reported no

marijuana use in 1984. Subsequent years after 1984 describe the distributions of respondents based 

on whether they reported marijuana use or no marijuana use. For instance, the 1988 column in 

Exhibit 3.3 describes the distribution of respondents who reported marijuana use (“yes”) or no

marijuana use in that year (“no”) after reporting drug use in 1984. In 1992, the distribution of 

respondents is expanded to capture drug use or non-use based on the distribution in 1988. In 1992, 

there are two binomial distributions (i.e. four alternatives); one is based on those who reported drug

use and the other based on those who did not report drug use in 1988. In 1994, there are four

binomial distributions based on responses in 1992. And in 1998, there are eight binomial 

distributions based on responses in 1994. In this way, the distribution tree presents movement in and

out of drug use for the five index years (1984, 1988, 1992, 1994, and 1998) over the 15-year study 

period based on the initial response to questions on drug use in 1984. Weighted percentages are used

in all the exhibits. 

Over the first 5-year timespan (1984–1988), half of all individuals who used marijuana in 1984 did

not use again in 1988 (see Exhibit 3.3). Over the next 5-year span (1988–1992), almost half of those

who had used in 1988 reported use again in 1992, and almost 87 percent of those who did not use in 

1988 did not use again in 1992. From 1992 to 1998, one-half to two-thirds of individuals who used 

in one index year also used in the subsequent index year, if they initiated use in 1984 or 1988. 

Overall, only 3 percent (244/8,131) used marijuana in every one of the five interview years. 

Exhibit 3.4 shows that two-thirds (66.5 percent) of respondents did not use marijuana in 1984, and 91

percent (5,062/5,542) did not report use in the subsequent study year. Of those who used in 1988, 

three-quarters reported no use in 1992. Overall, almost all those who did not use marijuana in 1984 

did not use in 1998 (96 percent, or 4,990/5,198). These results suggest that youth who do not initiate 

marijuana use in the early years are unlikely to ever start marijuana use. 

PREDICTING HEAVY DRUG USE: FINAL REPORT 14
 Exhibit 3.3: Distribution Tree of Respondents Who Reported Smoking Marijuana in 1984

1984 1988 1992 1994 1998*

Yes 66.3% 244

Yes

 70.7% 393 No 33.7% 130

Yes

49.7% 563 Yes 32.5% 45 

No

 29.3% 170 No 67.5% 111 

Yes

 49.9% 1,224 Yes 35.8% 65 

Yes

 30.0% 193 No 64.2% 111

No Yes 13.7% 68 

 50.3% 661 No

 70.0% 468 No 86.3% 370

Yes

33.5% Yes 36.7% 29

2,589 Yes

Yes 49.7% 84 No 63.3% 48

 13.5% 183

Yes 16.2% 16

No

 50.3% 99 No 83.8% 73

No

 50.1% 1,365 Yes 33.3% 32 

Yes

8.9% 114 No 66.7% 73

No

86.5% 1,182 Yes 4.4% 43

No

 91.1% 1,068 No 95.6% 941

Source: National Longitudinal Study of Youth, 1979 cohort, 1984-1998* There were 534 individuals who responded to the 1984, 1988, 1992, and 1994 surveys, but not to 1998. These individuals were included in 1984-1994, but not in 1998.

PREDICTING HEAVY DRUG USE: FINAL REPORT 
 Exhibit 3.4: Distribution Tree of Respondents Who Reported No Marijuana Use in 1984

1984 1988 1992 1994 1998*

Yes 50.6% 28

Yes

53.1% 60 No 49.4% 28

Yes

24.3% 109 Yes 15.1% 9 

No

46.9% 49 No 84.9% 39

Yes

9.0% 480 Yes 31.7% 15

Yes

13.2% 52 No 68.3% 35

No Yes 7.2% 26

75.7% 371 No

86.8% 319 No 92.8% 276 

No

66.5% Yes 44.3% 15

5,542 Yes

Yes 33.2% 44 No 55.7% 25

2.9% 133

Yes 19.9% 16

No

 66.8% 89 No 80.1% 65

No

91.0% 5,062 Yes 79.8% 31

Yes

 2.7% 150 No 20.2% 109

No

97.1% 4,929 Yes 1.3% 68

No

97.3% 4,779 No 98.7% 4,413

Source: National Longitudinal Study of Youth, 1979 cohort, 1984-1998* There were 534 individuals who responded to the 1984, 1988, 1992, and 1994 surveys,

 but not to 1998. These individuals were included in 1984-1994, but not in 1998.

PREDICTING HEAVY DRUG USE: FINAL REPORT 
These results also suggest that few adolescents who initiate marijuana use continue such behavior 

consistently over the long run. In general, most of those who did not use marijuana in 1984 did not

use over the five index years during the 15-year period. However, many adolescents also reported 

intermittent marijuana use over the 15-year period. 

COCAINE USE IN THE PAST YEAR 

Exhibits 3.5 and 3.6 present data on cocaine use during the interview years of 1984 to 1998. 

Whereas cocaine users are more prevalent than marijuana users, there are similar patterns of use for

marijuana and cocaine. 

When cocaine use is examined over the first 5-year timespan (1984–1988), we see that two-fifths

who initiated cocaine use in 1984 used again in 1988. Over the next five year span (1988–1992), a

quarter of those who had used in 1984 and 1988 used again in 1992, and only 1 in 10 individuals who 

had not used in 1988 used in 1992. Overall, less than two-tenths of one percent used cocaine in

every year over the study period, and of those who reported cocaine use in 1984, 93 percent

(692/748) did not use in 1998. 

Exhibit 3.6 shows that about seven-eights (88.4 percent) of interviewees did not report cocaine use in

1984, and over nine-tenths of these individuals (93.7 percent) did not use in 1988, the subsequent

study year. Of the few (6.7 percent) individuals who used cocaine in 1988, more than four-fifths 

(85.6 percent) did not use in 1992. Almost all respondents who did not use cocaine in 1984 did not

use in 1998 (98 percent, or 6,737/6849). 

MARIJUANA AND COCAINE USE IN THE PAST YEAR 

Ten percent of respondents reported use of both marijuana and cocaine in 1984 (see Exhibit 3.7). Of 

these individuals, 35.7 percent used both drugs again in 1988 (3.6 percent of interviewees). In 1992, 

almost one quarter (23.8 percent) of those who used in 1988 and 1984 used both drugs again. 

Exhibit 3.8 shows that nine-tenths of respondents (89.8 percent) did not use marijuana and cocaine in 

1984. Only 5 percent of those who did not use both drugs in 1984 used both drugs in 1988. Of the 5

percent who reported use of marijuana and cocaine in 1988, approximately 9 out of 10 did not use

both again in 1992. Overall, almost all non-users of marijuana and cocaine in 1984 did not report use

in the subsequent interview years from 1988 to 1998 (98.9 percent, or 6,872/6,948). These data from

combined marijuana and cocaine use paint a similar story to the results presented in Exhibits 3.3 to

3.6. Though most respondents do not report initiating drug use, there are others who initiate and use 

these drugs intermittently. 

Even though the relationship between initiation of drug use and the continuation of such behavior is 

not clear from this analysis, the results clearly depict individuals moving in and out of drug use. In

general, these data suggest that the majority of youth abstained from drug use during the study 

period, and only a few individuals consistently used drugs in every interview or index year (3.2

percent for marijuana and 0.2 percent for cocaine). Further, varying proportions of individuals who 

initiated drug use became intermittent users or regular users. What these data do not tell us are the 

intervening factors that influence movement in and out of drug use. 

PREDICTING HEAVY DRUG USE: FINAL REPORT 17
 Exhibit 3.5: Distribution Tree of Respondents Who Reported Using Cocaine in 1984

1984 1988 1992 1994 1998*

Yes 32.3% 13 

Yes

 44.6% 38 No 67.7% 24 

Yes

25.7% 80 Yes 19.4% 7 

No

 55.4% 42 No 80.6% 28 

Yes

 40.0% 317 Yes 18.0% 4 

Yes

 10.7% 29 No 82.0% 20

No Yes 3.5% 9 

 74.3% 237 No

 89.3% 208 No 96.5% 191 

Yes

11.6% Yes 17.1% 1 

811 Yes

Yes 29.5% 11 No 82.9% 5 

 8.9% 45

Yes 12.2% 5

No

 70.5% 34 No 87.8% 26

No

 60.0% 494 Yes 0% 0

Yes

 3.7% 19 No 100% 19

No

 91.1% 449 Yes 3.8% 17

No

96.3% 430 No 96.2% 379

Source: National Longitudinal Study of Youth, 1979 cohort, 1984-1998* There were 534 individuals who responded to the 1984, 1988, 1992, and 1994 surveys, but not to 1998. These individuals were included in 1984-1994, but not in 1998.

PREDICTING HEAVY DRUG USE: FINAL REPORT 

 Exhibit 3.5: Distribution Tree of Respondents Who Reported Using Cocaine in 1984

1984 1988 1992 1994 1998*

Yes 32.3% 13 

Yes

 44.6% 38 No 67.7% 24 

Yes

25.7% 80 Yes 19.4% 7 

No

 55.4% 42 No 80.6% 28 

Yes

 40.0% 317 Yes 18.0% 4 

Yes

 10.7% 29 No 82.0% 20

No Yes 3.5% 9 

 74.3% 237 No

 89.3% 208 No 96.5% 191 

Yes

11.6% Yes 17.1% 1 

811 Yes

Yes 29.5% 11 No 82.9% 5 

 8.9% 45

Yes 12.2% 5

No

 70.5% 34 No 87.8% 26

No

 60.0% 494 Yes 0% 0

Yes

 3.7% 19 No 100% 19

No

 91.1% 449 Yes 3.8% 17

No

96.3% 430 No 96.2% 379

Source: National Longitudinal Study of Youth, 1979 cohort, 1984-1998* There were 534 individuals who responded to the 1984, 1988, 1992, and 1994 surveys, but not to 1998. These individuals were included in 1984-1994, but not in 1998.

PREDICTING HEAVY DRUG USE: FINAL REPORT 
PREDICTING HEAVY DRUG USE: FINAL REPORT 

 Exhibit 3.6: Distribution Tree of Respondents Who Reported No Cocaine Use in 1984

1984 1988 1992 1994 1998*

Yes 29.0% 9 

Yes

 41.2% 28 No 71.0% 18

Yes

14.4% 65 Yes 14.2% 6

No

58.8% 37 No 85.8% 31 

Yes

6.7% 468 Yes 11.0% 4 

Yes

 6.7% 33 No 89.0% 27

No Yes 4.0% 19

85.6% 403 No

93.3% 370 No 96.0% 328

No

88.4% Yes 32.0% 7

7,320 Yes

Yes 30.8% 27 No 68.0% 15

 1.3% 101

Yes 17.4% 11

No

 69.2% 74 No 82.6% 59

No

 93.7% 6,852 Yes 10.9% 7

Yes

 1.1% 89 No 89.1% 74

No

98.7% 6,751 Yes 0.6% 49

No

98.9% 6,662 No 99.4% 6,18

Source: National Longitudinal Study of Youth, 1979 cohort, 1984-1998* There were 534 individuals who responded to the 1984, 1988, 1992, and 1994 surveys,

 but not to 1998. These individuals were included in 1984-1994, but not in 1998.

 Exhibit 3.6: Distribution Tree of Respondents Who Reported No Cocaine Use in 1984

1984 1988 1992 1994 1998*

Yes 29.0% 9 

Yes

 41.2% 28 No 71.0% 18

Yes

14.4% 65 Yes 14.2% 6

No

58.8% 37 No 85.8% 31 

Yes

6.7% 468 Yes 11.0% 4 

Yes

 6.7% 33 No 89.0% 27

No Yes 4.0% 19

85.6% 403 No

93.3% 370 No 96.0% 328

No

88.4% Yes 32.0% 7

7,320 Yes

Yes 30.8% 27 No 68.0% 15

 1.3% 101

Yes 17.4% 11

No

 69.2% 74 No 82.6% 59

No

 93.7% 6,852 Yes 10.9% 7

Yes

 1.1% 89 No 89.1% 74

No

98.7% 6,751 Yes 0.6% 49

No

98.9% 6,662 No 99.4% 6,18

Source: National Longitudinal Study of Youth, 1979 cohort, 1984-1998* There were 534 individuals who responded to the 1984, 1988, 1992, and 1994 surveys,

 but not to 1998. These individuals were included in 1984-1994, but not in 1998.

PREDICTING HEAVY DRUG USE: FINAL REPORT 
 Exhibit 3.7: Distribution Tree of Respondents Who Reported Cocaine and Marijuana Use in 1984

1984 1988 1992 1994 1998*

Yes 25.6% 7

Yes

40.9% 25 No 74.4% 17

Yes

 23.8% 56 Yes 16.7% 6

No

59.1% 31 No 83.3% 20

Yes

 35.7% 244 Yes 27.5% 4

Yes

10.1% 20 No 72.5% 14

No Yes 4.7% 8

 76.2% 188 No

89.9% 168 No 95.3% 151

Yes

10.2% Yes 17.3% 1

706 Yes

Yes 37.3% 9 No 82.7% 5

 6.7% 29

Yes 17.0% 4

No

 62.7% 20 No 83.0% 14

No

 64.3% 462 Yes 0% 0

Yes

 3.0% 18 No 100% 18

No

93.3% 433 Yes 3.7% 11

No

 97.0% 415 No 96.3% 369

Source: National Longitudinal Study of Youth, 1979 cohort, 1984-1998* There were 534 individuals who responded to the 1984, 1988, 1992, and 1994 surveys,

but not to 1998. These individuals were included in 1984-1994, but not in 1998.

PREDICTING HEAVY DRUG USE: FINAL REPORT 
Exhibit 3.8: Distribution Tree of Respondents Who Reported No Cocaine and Marijuana Use in 19

1984 1988 1992 1994 1998

Yes 35.9% 

Yes

 33.9% 14 No 64.1% 

Yes

 12.5% 40 Yes 17.8% 

No

 66.1% 26 No 82.2% 

Yes

 5.1% 358 Yes 9.5% 

Yes

6.6% 25 No 90.5% 

No Yes 2.7% 

 87.5% 318 No

93.4% 293 No 97.3% 

No

89.8% Yes 30.5% 

7,425 Yes

Yes 26.8% 17 No 69.5% 

 0.9% 66

Yes 15.9% 

No

 73.2% 49 No 84.1% 

No

 94.9% 7,067 Yes 11.2% 

Yes

8.8% 71 No 88.8% 

No

99.1% 7,001 Yes 0.5% 

No

99.2% 6,930 No 99.5% 

Source: National Longitudinal Study of Youth, 1979 cohort, 1984-1998* There were 534 individuals who responded to the 1984, 1988, 1992, and 1994 surveys, but not to 1998. These individuals were included in 1984-1994, but not in 1998.

Exhibit 3.8: Distribution Tree of Respondents Who Reported No Cocaine and Marijuana Use in 19

1984 1988 1992 1994 1998

Yes 35.9% 

Yes

 33.9% 14 No 64.1% 

Yes

 12.5% 40 Yes 17.8% 

No

 66.1% 26 No 82.2% 

Yes

 5.1% 358 Yes 9.5% 

Yes

6.6% 25 No 90.5% 

No Yes 2.7% 

 87.5% 318 No

93.4% 293 No 97.3% 

No

89.8% Yes 30.5% 

7,425 Yes

Yes 26.8% 17 No 69.5% 

 0.9% 66

Yes 15.9% 

No

 73.2% 49 No 84.1% 

No

 94.9% 7,067 Yes 11.2% 

Yes

8.8% 71 No 88.8% 

No

99.1% 7,001 Yes 0.5% 

No

99.2% 6,930 No 99.5% 

Source: National Longitudinal Study of Youth, 1979 cohort, 1984-1998* There were 534 individuals who responded to the 1984, 1988, 1992, and 1994 surveys, but not to 1998. These individuals were included in 1984-1994, but not in 1998.

PREDICTING HEAVY DRUG USE: FINAL REPORT 
PREDICTING HEAVY DRUG USE: FINAL REPORT 22

CORRELATES OF NUMBER OF YEARS USED DRUGS: GENDER AND RACIAL/ETHNIC 

DIFFERENCES 

Exhibits 3.9 and 3.10 display the association between demographic characteristics and the number of 

index years respondents used marijuana and cocaine. Higher percentages of females than males 

abstain from marijuana and cocaine use. For both marijuana and cocaine, males are about twice as 

likely as females to have used in 3–5 index years. 

Exhibit 3.9: Number of Times Used Marijuana By Gender*

Number of Index Years Male Female 

Used Marijuana 

0 49.4% 66.2% 

1 21.4% 17.5% 

2 11.8% 7.7% 

3–5 17.4% 8.7% 

*p < 0.05 

Exhibit 3.10: Number of Times Used Cocaine By Gender

Number of Index Years Male Female 

Used Cocaine 

0 75.7% 86.8% 

1 14.9% 8.8% 

2 6.3% 3.1% 

3–5 3.2% 1.3% 

*p < 0.05 

In Exhibits 3.11 and 3.12, the analyses are repeated using race/ethnicity as the classification variable. 

The distribution in Exhibit 3.11 is statistically significant and shows that there were more Hispanics 

than blacks or whites who abstained from marijuana use in any of the index years. Interestingly, 

while use in one or two index years by Blacks and whites is slightly higher than for Hispanics, the 

largest differences are in the 3-5 year category, where Blacks and especially whites have much 

higher use rates than Hispanics. In Exhibit 3.12, there is no statistically significant racial/ethnic 

difference for the number of index years of cocaine use. 
Exhibit 3.11: Number of Times Used Marijuana By Race/Ethnicity*

Number of Index Years Hispanic Black White 

Used Marijuana 

0 63.8% 57.3% 56.2% 

1 18.3% 20.3% 19.3% 

2 9.4% 10.4% 9.3% 

3–5 8.4% 12.1% 15.2% 

*p < 0.05

Exhibit 3.12: Number of Times Used Cocaine By Race/Ethnicity

Number of Index Years Hispanic Black White 

Used Cocaine 

0 82.3 83.2 79.9 

1 11.3 10.9 12.5 

2 4.5 4.1 5.1 

3–5 2.0 1.7 2.6 

COMPARISONS OF DRUG U

Exhibits 3.13 through 3.18 present summary data about ber rs of drug use, and the 

additional years that drugs were used. hibits 3.13 and 3. ent between drug use

for adjacent study years. When marijuana use is considered over the entire study period, 53 percent 

of those who reported marijuana use one time period t us e following study year. 

Further, 94 percent of those who did se marijuana in ior s

following study year. 

 Only a small minority of 

individuals reported drug use in all five years (3.2 percent for marijuana, 0.2 percent for cocaine, and

0.2 percent for crack). Note that data on crack use were collected in 1992, 1994, and 1998 only. 

SE OVER TIME

the num of yea

Ex 14 pres comparisons 

in did no e in th

not u the pr tudy period did not use in the

For cocaine, almost three-quarters (71 percent) of those who used in the prior time period did not use

in the subsequent study year. In a similar pattern to marijuana, 97 percent of those who did not use

cocaine in the prior time period did not use in the following study period. It seems that abstinence 

from drugs for one study year may be indicative of a substantial longer term effect. 

Over the entire study period, the majority of respondents reported no drug use, as presented in 

Exhibit 3.15. Overall, 58.1 percent, 81.4 percent, and 96.6 percent of respondents reported no 

marijuana, cocaine, or crack use, respectively, in the five reporting periods. 

PREDICTING HEAVY DRUG USE: FINAL REPORT 23
Exhibit 3.13: Marijuana Use Between Adjacent Survey Periods 

First Time Period

 Used Marijuana 

Did Not Use 

Marijuana 

Following Used 

Time Marijuana 46.6% 6.2% 

Period Did Not Use 

Marijuana 53.4% 93.8% 

Exhibit 3.14: Cocaine Use Between Adjacent Survey Periods

 First Time Period

 Used Cocaine 

Did Not Use

Cocaine 

Following Used 

Time Co e 28. cain 6% 3.0% 

Period Did Not Use 

Cocaine 71.4% 97.1% 

Exhibit 3.15: Number of Years Used Drug

ver,

23 percent of individuals who used marijuana in one study year used in a second study year. Twofifths (42 percent) of those who used marijuana in two study years did not report additional use. 

Number of Years 

Used Marijuana Cocaine Crack* 

 0 58.1% 4,413 81.4% 6,185 96.6% 7,335 

 1 19.2% 1,459 11.7% 889 2.7% 203 

2 9.6% 731 4.8% 363 0.6% 46 

3 6.0% 453 1.3% 102 0.2% 13 

 *Data for use of crack are available only for 1992, 1994 and 1998.

 4 3.9% 297 0.6% 45 NA 

 5 3.2% 244 0.2% 13 NA 

Total 100% 7,597 100% 7,597 100% 7,597

 Note: Percentages may not sum to 100 due to rounding. 

 

Exhibits 3.16 and 3.17 display the number of additional years that drug users continue their use of 

drugs, given drug use in one study year. Among those who reported marijuana use in any one year 

(Exhibit 3.16), almost half (46 percent) never again reported use in the other study years. Howe

PREDICTING HEAVY DRUG USE: FINAL REPORT 24
Exhibit 3.17 presents the percentage of cocaine users who used cocaine additional y

two-thirds (63 percent) of individuals who reported cocaine use in

ears. Almost 

one study year did not report 

cocaine use in other study years. About a quarter (26 percent) of those who used cocaine in one 

Exhibit 3.16: Percentage Who Used Marijuana Additional Years by Number of Years Used 

Marijuana

Additional Number of Years Used

Years Used 1 2 3 4 

0 46% 42% 46% 55%

1 23% 26% 30% 45% 

2 14% 17% 25% NA 

3 9% 14% NA NA 

4 8% NA NA NA 

Total 100% 99% 101% 100%

Note: The numb

1984, 1988

er of years used is computed by adding each year of use for surve years 

, 1992, 199 98. 

ges m to 100 due to rou

y

4 and 19

Note: Percenta ay not sum nding.

Exhibit 3.17: Percentage Who Used Cocaine Additional Years by 

Number of Years Used Cocaine 

Additional Number of Years Used

Years Used 1 2 3 4 

0 63% 69% 64% 78% 

1 26% 20% 28% 22%

2 7% 9% 8% NA

3 3% 2% NA NA

4 1% NA NA NA 

Total 100% 100% 100% 100% 

Note: The nu r of years u pute ding e us survey years 1

1988, 199 and 1998.

mbe sed is com d by ad ach year of e for 984, 

2, 1994,

PREDICTING HEAVY DRUG USE: FINAL REPORT 25
study year used in a second year. Two-thirds (69 percent) of those who used in two study years did

not report additional use. 

Exhibit 3.18 shows that almost all (96.6 percent) of respondents who did not use marijuana did not

d

COMPARISON BETW Y AND NATIONA VEY

ON DRUG ABUSE (

The discussion of drug use over time using a longitudinal survey leads to a different picture of drug 

use than is usually seen sing a cross-s al su Eac vey t rovides crucial

information. While the c s-sectional surv ovide exce napsh a specific time

period, without the burden interviewing an tervie the s dividu over many years, 

longitudinal data permit inferences regarding c e ove as the information about each survey

participant is sequenced along the years these individuals were interviewed. 

This section of the repor ares and con o p e data sets: the National Longitudinal 

Survey on Youth (the data used in this report) and the National Household Survey on Drug Abuse, 

sponsored by t e Department of

Health and Hu arison between these surveys. Except for 

985, we compare the relevant NLSY and NHSDA during the same year. When the NLSY was 

conducted in 1984, the NHSDA was not conducted that year. Instead, we used the 1985 NHSDA,

SY. To compare the surveys,

The bulk of differences between the two nationall were not statistically

significant. The only differences were reports of higher rates of cocaine use (both lifetime and last 

year report) in the 1985 NHSDA and higher reports of lifetime marijuana use in the 1994 and 1998 

NLSY than the NHSDA.

The NLSY data provide information on a cohort of 12,686 individuals and therefore allows us to 

investigate the following: (1) who uses drugs years); (2) who becomes a 

heavy drug user, and what variables predict such a phenomenon; (3) which individuals abstain or 

never use drugs, and what variables might hel lain t henom n; and hich individuals 

use drugs intermittently y. The longitudinal data set also enables us to investigate the

lationship between drug use in one period and a later period. In this way, longitudinal data provide 

advantages

use cocaine, while more than half of those who used marijuana three or more study years also use

cocaine. 

EEN NLS

NHSDA) 

L HOUSEHOLD SUR

u ection rvey. h sur ype p

ros ey pr s an llent s ot of

of d rein wing ame in als

hang r time,

t comp trasts tw remier

he Substance Abuse and Mental Health Services Administration of th

man Services. Exhibit 3.19 presents a comp

1

the most proximate survey year, as the comparison with the 1984 NL

we used the same age cohorts for each. 

y representative surveys

over extended periods (15 

p exp his p eno (4) w

, and wh

re

over cross-sectional data. 

PREDICTING HEAVY DRUG USE: FINAL REPORT 26
REDICTING HEAVY DRUG USE: FINAL REPORT 

Number of Years Used Cocaine By Number of Years Used Marijuana*

Numbe Number of Years Used Marijuana r of Years 

Used Cocaine 0 1 2 3 4 

0 96.6% 4,261 77.5% 1,130 56.8% 415 44.4% 201 35.0% 104 1 2.8% 125 17.6% 256 30.0% 219 30.7% 139 31.0% 92 2 0.4% 19 4.2% 61 11.4% 83 17.7% 80 22.2% 66 3 + 0.7% 10 + 5.7% 26 6.7% 20 4 + + + + 3.7% 11 5 + + + + + 

Total 99.8% 4,413 100% 1,459 98.2% 731 98.5% 453 98.6% 297 

* Survey years 1984, 1988, 1992, 1994, 1+ Fewer than 10 cases. 

 Note: Percentages may not sum to 100 d

998.

ue to rounding. 

PExhibit 3.18: 
Exhibit 3.19: Comparison between NHSDA and NLSY for Lifetime and Past Year Marijuana and Cocaine Use 

NHSDA NATIONAL ESTIMATE (%) NLSY NATIONAL EST

Age Group 

NHSDA

Year 

Lifetime

Marijuana 

Pa Lifetime

Cocaine 

Past-Year

Cocaine 

NLSY

Year 

Lifetime

Marijuana 

Past-Year

Marijuana 

L

C

st-Year

Marijuana 

20–27 1985 63.1 34.4 26.2 15.6 1984 61.0 31.8 23–30 1988 63.8 20.9 26.7 10.4 1988 62.9 21.0 27–34 1992 58.8 14.1 25.5 4.9 1992 55.1 12.2 29–36 1994 53.7 11.0 23.3 3.1 1994 61.4 13.4 33–40 1998 51.3 9.3 22.4 2.5 1998 56.3 9.2 

NOTES: Due to changes in the questionnaire design in 1994, data from previous survey years are not directly comparable to 1994 estimates and su

The N mate is deve ugh of t tion d i ter ( os h de on 1992, 1994, almost alw

are nonsignificant at the 0.05 level, except lifetime marijuana use in 1994 and 1998 (NLSY is significantly 

se in 1985 (NHSDA is significantly higher). 

LSY esti

and 

loped thro

ays 1998). 

analysis he popula describe n this chap i.e., th e individuals w o respon d to every e of t

All differences between NLSY and NHSDApast-year cocaine u

PREDICTING HEAVY DRUG USE: FINAL REPORT 
P

STRENGTHS OF LONGITUDINAL 

REDICTING HEAVY DRUG USE: FINAL REPORT 29

S

d

UR

scribe events over tim

V

Survey data can be co e e through both prospective panel 

(longitudinal) surveys or tio eans. Prospective data (e.g., NLYS79) are

collected by taking repeated measures in a set of p o -ups. Retrospective data are generally 

collected in a single in w ar) methods to assist memory. The 

strengths of longitudinal survey he order of 

events as they occur. Longitudinal data collection provides more accurate data than cross-sectional 

data for long-term asures sub t r o e such things as recall and telescoping 

A problem with retrospe c s c n ata collection is the issue of recall accuracy.

13 Also,

given time lim permits 

com y less detail on timing and change ov e than is available from several interviews 

from l data provides better answers than cross-sectional

data to some questions, ation on the order 

of events. For example: 

What is the relation tw n u im ovement from one to the 

other?

Who become avy drug user? Wha drug use?

Who never uses drugs? Who uses drugs a fe es? Can we predict either? 

To what extent is drug u iated with use at another time? What 

explains the association?

What is the relationship between exposure to prevention messages and drug use/criminal 

behavior?

Wha the t drug use and what is the duration of 

use?

What is the relationship between unemp ent and drug use? What explains the 

movem

Wha e com ed or getting married or divorced) 

on initiation, ? a 

single slice in tim is less likely to learn about the p int when events occur relative to 

changes in beh

If imp ant e variable, the regression 

estimates will be biased. Longitudinal d ates through 

the use of fix ects models that “net out” characteristics such as personality traits. 

Summary

Respondents ud n e s e on with respondents excluded 

from the study. However, respondents included in the study report more drug use than 

those excluded

 

EYS 

llected to

retrospective cross-sec

hnson, D. R. Gerstein, K. A. Rasinski, ey Estimates for Response Bias: An 

App on to s in Alcohol and Marijuana Use,” Public Opinion Quart , v. 62, no.3, 1998. 

13 R. A. Jo

licati

nal m

anel f

ry (calend

o measure transitions, changes, and t

r caused by

er tim

and cr

t variables predict heavy

only

loym

ing unem

ata allow us to obtain unbiased estim

more in co

llow

e and the m

w tim

ploy

 Cross-sectional data collection from

o

mm

tervie , u

s a

sing

re t

ev

heir

ent

abi

histo

lity t

me

itations, the brief interview period of a single cross-sectional survey

y. As a result, longitudinal pane

jec

ros

to

-se

esp

tio

ns

al d

erro

ctive

parativel

 a panel stud





















Trend

particularly because of lower recall error and better inform

ship be ee dr gs

s a he

se at one time period assoc

t are

ent fr

t is th

ort

transi ions (initiation and cessation) of 

om on

effect o

 cessation, or changes in drug use

e 

e to

f ev

the o

ents

the

(e.g

r? W

., be

hich is prior? 

avio

unobserved

ed eff

r.

 characteristics influence the outcom

incl ed i th study har

from the study.

“Adjusting Surv

erly
 A majority of individuals reported no drug use during the five study periods. Overall, 

reported no marijuana use, 81.4 percent reported 

 in one year. Among those who reported marijuana use in any one year, 

almost half (46 percent) never reported use again. Among cocaine users, almost two-

.

 males than males reported no marijuana or cocaine use and more males than 

re 

r

was no 1984 NHSDA), almost no significant differences were found in lifetime and 

nd 1998 NLSY showed significantly

58.1 percent of interviewed individuals

no cocaine use, and 96.6 percent reported no crack use (crack was only collected for the 

three study periods from 1992 to 1998). 

 Very few individuals consistently reported drug use in all five years: for marijuana and 

cocaine, 3.2 percent and 0.2 percent reported use from 1984 to 1998, respectively. For 

crack, 0.2 percent reported use from 1992 to 1998. 

 About half of marijuana users who used in one study period reported marijuana use in the 

subsequent study period. Only one of 16 who did not use marijuana in one study period 

used marijuana in the subsequent period. 

 About one-quarter of cocaine users who used in one study period reported cocaine use in 

the subsequent study period. Only one of 32 who did not use cocaine in one study period

used cocaine in the subsequent period.

 Of those who used in one study period, most did not use again. Overall, 19.2 percent, 

11.7 percent, and 2.7 percent of individuals reported marijuana, cocaine or crack use, 

respectively, only

thirds (63 percent) of those who reported use in any one year never reported use again

More fe

females reported marijuana or cocaine use during one or more of the index years. Mo

Hispanics reported no marijuana use and more blacks reported marijuana use in one o

two index years, whereas more whites reported marijuana use in three to five of the index

years. 

 In terms of poly-drug use, almost all of the interviewed individuals who did not report 

marijuana use did not report cocaine use (96.6 percent). More than half of those who 

reported marijuana use in three or more years also used cocaine. 

 When compared to the similar age group in the same year of NHSDA data (except that 

there

last-year use of marijuana and cocaine. The 1985 NHSDA had significantly higher 

lifetime and past-year cocaine use, while the 1994 a

higher lifetime marijuana use. 

PREDICTING HEAVY DRUG USE: FINAL REPORT 30
4. HEAVY DRUG USE 

Introd c

In this chap to

the followin







Age o F

Our analys

particular,

daily, starte

cocaine o eavy 

cocaine e f

heavy coca e

presented in

Exhibit of

these 1 6 is

compares to heir

first ciga et

first cig t

smoke their

by the decl

NO column

hance that user. For example, even if a youth has not started 

oking by age 11, there is still a 5 percent chance that s/he will become a heavy cocaine user. This 

sk is less than 3 percent if the youth has not started smoking by age 17. 

The ratio of percentages is also a measure of how much smoking cigarettes is associated with heavy

cocaine use. Youth who have started smoking by age 10 are about 55 percent more likely to become

heavy cocaine users as those who did not start smoking by age 10 (8.90 %/5.73% = 1.55). This ratio 

steadily increases, such that youth who have started smoking by age 17 are 2.5 times as likely (150

percent more likely) to become heavy cocaine users as those who have not started smoking by age 

17. The increasing ratios are really just a facet of this particular presentation, which is designed to 

allow a comparison at any age cut-off. While the table shows that those who have started smoking

by age 18 still have a 7.25 percent chance of becoming a heavy drug user, those who start at age 18

only have a 4.07 percent chance (15/369) of becoming a heavy drug user, which is closer to the risk

of those who have not smoked by age 18 (2.78 percent) than it is to those who started by age 16 (7.62

percent). 

u tion 

ter, we analyze drug use, specifically heavy cocaine use. Our analysis is separated in

g components: 

Age of first use 

Prediction of heavy cocaine use 

 Correlates of number of years of drug use 

Summary 

f irst Use

is also explored the relationships of heavy cocaine use and substance use by age. In

we examined the ages at which NLSY youth first smoked a cigarette, started smoking

d drinking twice a week, first smoked marijuana, first tried cocaine, and first tried crack

. F r a set of age ranges for which we have sufficient data, we report the percentage of h

us rs among those who tried the drug by each particular age compared to the percentage o

ine users among those who had not tried the drug by that particular age. Our results ar

Exhibits 4.1 through 4.6. 

4.1 shows that 1,236 youth had smoked their first cigarette by age 10 (7,469 had not). Out

,23 youth, 110 (8.90 percent) satisfied our conditions as a heavy cocaine user. Th

a heavy cocaine user rate of only 5.73 percent among those who had not smoked t

r te by age 10. The highest rate of heavy cocaine users was among those who smoked their

are te at age 11 (43 out of 298 = 14.1 percent). After age 11, the longer a youth waited to

first cigarette, the less likely they were to become a heavy cocaine user. This is shown

ining percentages in the YES column as age increases. The declining percentages in the

 also show that the longer a youth waits to smoke their first cigarette, the lower the 

c the youth will become a heavy cocaine 

sm

ri

PREDICTING HEAVY DRUG USE: FINAL REPORT 31
Exhibit 4.1: Heavy Cocaine Use among those who had/had not smoked first cigarette by given 

age

YES NO 

Used by age 10? 8.90% (110/1236) 5.73% (428/7469) 

Used by age 11? 9.97% (153/1534) 5.37% (385/7171) 

Used by age 12? 9.82% (241/2454) 4.75% (297/6251) 

Used by age 13? 9.12% (301/3301) 4.39% (237/5404) 

Used by age 14? 8.56% (356/4160) 4.00% (182/4545) 

Used by age 15? 7.93% (397/5008) 3.81% (141/3697) 

Used by age 16? 7.62% (444/5826) 3.27% (94/2879) 

Used by age 17? 7.44% (465/6253) 2.98% (73/2452) 

Used by age 18? 7.25% (480/6622) 2.78% (58/2083) 

Ever Reported? 7.08% (494/6982) 2.55% (44/1723) 

Exhibit 4.2 shows that 464 youth had started smoking daily by age 12 (8,188 had not). Out of these

464 youth, 62 (13.36 percent) satisfied our conditions as heavy cocaine users. This compares to a 

heavy cocaine user rate of only 4.96 percent among those who had not started smoking daily by age

12. The highest rate of heavy cocaine users was among those who started smoking daily by age 12. 

% = 2.69). This ratio stays 

constant until it rises consistently after age 16, such that youth who have started smoking daily by

After age 12, the decreasing percentages in the YES column as age increases indicates that the longer 

a youth waits to start smoking daily, the less likely s/he is to become a heavy cocaine user (though it 

does seem to stop decreasing after age 18). The declining percentages in the NO column also show

that the longer a youth waits to smoke daily, the lower the chance that the youth will become a heavy

cocaine user. For example, even if a youth has not started smoking daily by age 12, there is still a 5

percent chance that s/he will become a heavy cocaine user. This risk is less than 3 percent if the

youth has not started smoking daily by age 18. 

The ratio of percentages is also a measure of the extent to which smoking cigarettes daily is 

associated with heavy cocaine use. Youth who start smoking daily by age 12 are about 2.7 times as 

likely to become heavy cocaine users as those who have not (13.36%/4.96

age 21 are almost four times as likely (8.85%/2.27% = 3.90 times as likely) to become heavy cocaine 

users as those who have not started smoking daily by age 21. In this case, the increasing ratios are

associated with a decrease in the NO column percentages, while the YES column percentages stay 

roughly constant (just under 9 percent). Those who start smoking daily at the age of 19, 20, or 21

still have a 8.15 percent (49/601) chance of becoming a heavy cocaine user, which is quite close to

the percentage for those who started by age 18 (8.97 percent), and is much higher than those who 

have not started by age 21 (2.27 percent). 

PREDICTING HEAVY DRUG USE: FINAL REPORT 32
Exhibit 4.2: Heavy Cocaine Use among those who had/had not 

started smoking daily by given age

YES NO 

Used by age 12? 13.36% (62/464) 4.96% (406/8188)

Used by age 13? 12.56% (100/796) 4.68% (368/7856)

Used by age 14? 11.01% (136/1235) 4.48% (332/7417)

Used by age 15? 10.58% (196/1853) 4.00% (272/6799)

Used by age 16? 9.81% (252/2570) 3.55% (216/6082)

Used by age 17? 9.48% (286/3018) 3.23% (182/5634) 

Used by age 18? 8.97% (316/3522) 2.96% (152/5130) 

Used by age 19? 8.78% (331/3768) 2.81% (137/4884) 

Used by age 20? 8.83% (349/3953) 2.53% (119/4699) 

Used by age 21? 8.85% (365/4123) 2.27% (103/4529) 

Ever Reported? 8.56% (392/4582) 1.87% (76/4070) 

Exhibit 4.3 shows that there are 170 youth in our sample who had started drinking alcohol twice a 

week by age 12 (8,618 had not). Out of these 170 youth, 30 (17.65 percent) satisfied our conditions 

as a heavy cocaine user. This compares to a heavy cocaine user rate of only 5.96 percent among

those who had not started drinking alcohol twice a week by age 12. The highest rate of heavy 

cocaine users was among those who started drinking alcohol twice a week by age 12, since after age 

12, the percentages in YES column decrease as age increases, indicating that the longer a youth

waited to start drinking alcohol twice a week, the less likely they were to become a heavy cocaine 

user. The declining percentages in the NO column show that the longer a youth waits to start

ge 21. The increasing ratios are really just a facet of this

particular presentation, which is designed to allow a comparison at any age cut-off. While the table 

shows that those who have started drinking alcohol twice a week by age 18 still have a 7.98 percent

chance of becoming a heavy drug user, those who start at age 18 only have a 4.35 percent chance 

(81/1863) of becoming a heavy cocaine user, which is closer to the risk of those who have not started 

drinking alcohol twice a week, the lower the chance that the youth will become a heavy cocaine user. 

For example, even if a youth has not started drinking alcohol twice a week by age 12, there is still an 

almost 6 percent chance that s/he will become a heavy cocaine user. This risk is less than 2 percent if 

the youth has not started drinking alcohol twice a week by age 21. 

The ratio of percentages is also a measure of the extent to which drinking alcohol twice a week leads 

to heavy cocaine use. Youth who have started drinking alcohol twice a week by age 12 are about 3.0

times as likely to become heavy cocaine users as those who have not started drinking alcohol twice a 

week by age 12 (17.65%/5.96% = 2.96). This ratio stays constant until it rises consistently after age 

18, such that youth who have started drinking alcohol twice a week by age 21 are almost four times

as likely (7.32%/1.95% = 3.75 times as likely) to become heavy cocaine users as those who have not 

started drinking alcohol twice a week by a

PREDICTING HEAVY DRUG USE: FINAL REPORT 33
drinking twice or more per week by age 18 (2.60 percent) than it is to thos

(11.58 percent). 

e who started by age 16

Exhibit 4.3: Heavy Cocaine Use among those who had/had not started drinking 2+/week by 

given age

YES NO 

Used by age 12? 17.65% (30/170) 5.96% (514/8618) 

Used by age 13? 16.76% (57/340) 5.76% (487/8448) 

Used by age 14? 15.94% (99/621) 5.45% (445/8167) 

Used by age 15? 14.96% (190/1270) 4.71% (354/7518) 

Used by age 16? 11.58% (303/2617) 3.91% (241/6171) 

Used by age 17? 9.67% (387/4003) 3.28% (157/4785) 

Used by age 18? 7.98% (468/5866) 2.60% (76/2922) 

Used by age 19? 7.65% (492/6434) 2.21% (52/2354) 

Used by age 20? 7.46% (502/6728) 2.04% (42/2060) 

Used by age 21? 7.32% (508/6942) 1.95% (36/1846) 

Ever Reported? 7.24% (510/7045) 1.95% (34/1743) 

Exhibit 4.4 shows that there are 358 youth had started smoking marijuana by age 12 (8,321 had not).

Out of these 358 youth, 84 (23.46 percent) satisfied our conditions as a heavy cocaine user. This

compares to a heavy cocaine user rate of only 5.34 percent among those who had not started smoking

marijuana by age 12. The highest rate of heavy cocaine users was among those who started smoking 

marijuana by age 12, since after age 12, the decreasing percentages in YES column as age increases 

indicate that the longer a youth waited to start smoking marijuana, the less likely s/he was to become 

a heavy cocaine user. The declining percentages in the NO column also show that the longer a youth

waits to start smoking marijuana, the lower the chance that the youth will become a heavy cocaine 

user. For example, even if a youth has not started smoking marijuana by age 12, there is still a 5 

percent chance that s/he will become a heavy cocaine user. This risk is just over 1 percent if the

youth has not started smoking marijuana by age 21. 

The ratio of percentages is also a measure of the extent to which smoking marijuana is associated 

with heavy cocaine use. Youth who have started smoking marijuana by age 12 are about 4.4 times as

likely to become heavy cocaine users as those who have not started smoking marijuana by age 12

(23.46%/5.34% = 4.39). This ratio varies around 4.5 until it eventually rises consistently after age

17, such that youth who have started smoking marijuana by age 21 are almost eight times as likely

(8.11%/1.05% = 7.72 times) to become heavy cocaine users as those who have not started smoking 

marijuana by age 21. The increasing ratios are really just a facet of this particular presentation, 

which is designed to allow a comparison at any age cut-off. While the table shows that those who 

have started smoking marijuana by age 18 still have a 8.88 percent chance of becoming a heavy

cocaine user, those who start at age 18 only have a 3.81 percent chance (34/892) of becoming a 

PREDICTING HEAVY DRUG USE: FINAL REPORT 34
heavy cocaine user, which is closer to the risk of those who have not smoked marijuana by age 18

(1.81 percent) than it is to those who started by age 16 (11.37 percent). 

Exhibit 4.4: Heavy Cocaine Use among those who had/had not used marijuana by given age

YES NO 

Used by age 12? 23.46% (84/358) 5.34% (444/8321) 

Used by age 13? 19.74% (149/755) 4.78% (379/7924) 

Used by age 14? 16.12% (211/1309) 4.30% (317/7370) 

Used by age 15? 13.78% (295/2141) 3.56% (233/6538) 

Used by age 16? 11.37% (385/3385) 2.70% (143/5294) 

Used by age 17? 9.92% (432/4354) 2.22% (96/4325) 

Used by age 18? 8.88% (466/5246) 1.81% (62/3433) 

Used by age 19? 8.47% (481/5679) 1.57% (47/3000) 

Used by age 20? 8.23% (492/5980) 1.33% (36/2699) 

Used by age 21? 8.11% (502/6191) 1.05% (26/2488) 

Ever Reported? 7.76% (513/6607) 0.72% (15/2072) 

xhibit 4.5 shows that 137 youth had started using cocaine by age 15 (8,392 had not). Out of these 

37 youth, 48 (35.04 percent) satisfied our conditions as a heavy cocaine user. This compares to a 

ost six times as likely to become heavy 

E

1

heavy cocaine user rate of only 5.93 percent among those who had not started using cocaine by age

15. The highest rate of heavy cocaine users was among those who started using cocaine by age 15, 

since after age 15, the decreasing percentages in the YES column as age increases indicate that the 

longer a youth waited to start using cocaine, the less likely s/he was to become a heavy cocaine user. 

The declining percentages in the NO column also show that the longer a youth waits to start using

cocaine, the lower the chance that the youth will become a heavy cocaine user. For example, even if

a youth has not started using cocaine by age 15, there is still an almost 6 percent chance that s/he will

become a heavy cocaine user. This risk is under 2 percent if the youth has not started using cocaine 

by age 24. 

The ratio of percentages is also a measure of how much using cocaine is associated with heavy 

cocaine use. Youth who used cocaine by age 15 are alm

cocaine users as those who have not started using cocaine by age 15 (35.04%/5.93% = 5.91). This 

ratio actually decreases to about 5 until it starts increasing after age 19, such that youth who have

started using cocaine by age 25 are almost eleven times as likely (15.61%/1.46% = 10.69 times as 

likely) to become heavy cocaine users as those who have not started using cocaine by age 25. In this 

case, the increasing ratios are associated with a decrease in the NO column percentages, while the

YES column percentages stay more constant (around 16 percent). Those who start using cocaine at 

the age of 22, 23, 24, or 25 still have a 10.93 percent (99/906) chance of becoming a heavy cocaine

user, which is closer to the percentage for those who started by age 21 (17.66 percent) than those

who have not started by age 21 (2.79 percent). 

PREDICTING HEAVY DRUG USE: FINAL REPORT 35
Exhibit 4.5: Heavy Cocaine Use among those who had/had not used cocaine by given age

YES NO 

Used by age 15? 35.04% (48/137) 5.93% (498/8392) 

Used by age 16? 25.57% (79/309) 5.68% (467/8220) 

Used by age 17? 25.58%(132/516) 5.17% (414/8013) 

Used by age 18? 21.72% (199/916) 4.56% (347/7613) 

Used by age 19? 20.32% (252/1240) 4.03% (294/7289) 

Used by age 20? 18.50% (314/1697) 3.40% (232/6832) 

Used by age 21? 17.66% (366/2073) 2.79% (180/6456) 

Used by age 22? 16.67% (393/2357) 2.48% (153/6172) 

Used by age 23? 16.29% (416/2554) 2.18% (130/5975) 

Used by age 24? 15.67% (431/2750) 1.99% (115/5779) 

Used by age 25? 15.61% (465/2979) 1.46% (81/5550) 

Ever Reported? 15.44% (521/3374) 0.48% (25/5155) 

Exhibit 4 re were 107 y ed using c ,204 had not). 

ut of these 107 youth, 25 (23.36 percent) satisfied our conditions as a heavy cocaine user. This

 Youth who have started using crack by age 18 are 4.4 times as likely to become heavy

.6 shows that the outh who had start rack by age 18 (8

O

compares to a heavy cocaine user rate of only 5.28 percent among those who had not started using

crack by age 18. Unlike the other variables, the percentages in the YES column do not monotonely

decrease as age increases. The highest rate of heavy cocaine users was among those who started 

using crack between the ages of 18 and 21 (46/127 = 36.22 percent), but the percentage of crack 

users who become heavy cocaine users stays around 30 percent, even for those who start after age 30. 

The declining percentages in the NO column also show that the longer a person waits to start using 

crack, the lower the chance that the youth will become a heavy cocaine user. For example, even if a 

youth has not started using crack by age 18, there is still a 5 percent chance that s/he will become a 

heavy cocaine user. This risk is only about 3 percent if one does not start using crack by age 30. 

The ratio of percentages is also a measure of how much smoking crack is associated with heavy 

cocaine use. 

cocaine users as those who have not started using crack by age 18 (23.36%/5.28% = 4.42). This ratio

steadily increases, such that youth who have started using crack by age 30 are 9.3 times as likely

(28.72%/3.08% = 9.32) to become heavy cocaine users as those who have not started using crack by

age 30. In this case, the increasing ratios are associated with a decrease in the NO column

percentages, while the YES column percentages stay roughly constant (around 30 percent). Those 

who start using crack have a 30 percent chance of becoming heavy cocaine users no matter what age 

they start, while those who do not start by a certain age have their likelihood of becoming a heavy

cocaine user decrease steadily with age. 

PREDICTING HEAVY DRUG USE: FINAL REPORT 36
Exhibit 4.6: Heavy Cocaine Use among those who had/had not used crack cocaine by given 

age

YES NO 

Used by age 18? 23.36% (25/107) 5.28% (431/8204) 

Used by age 21? 30.34% (71/234) 4.79% (385/8032) 

Used by age 24? 30.19%(112/371) 4.36% (344/7895) 

Used by age 27? 27.84% (169/607) 3.75% (287/7659) 

Used by age 30? 28.72% (226/787) 3.08% (230/7479) 

Ever Reported? 29.12% (272/934) 2.51% (184/7332) 

It is not surp heavy c ers are five or

more time ers) are amo 4.5) and crack cocaine use (Exhibit 4.6), 

since thes a for our hea However are generally

about fou as likely to aine user ave not tried 

arijuana at a young age (Exhibit 4.4). Those drinking alcohol twice a week by a certain age are 

ity that y=1: 

 

rising that the largest differentials (rates of future ocaine use for us

s those for non-us ng cocaine (Exhibit

e form the criteri vy cocaine users. , marijuana users

r times or more become heavy coc s as those who h

m

generally about three times or more as likely to become heavy cocaine users as those who have not

started drinking at least twice a week (Exhibit 4.3). Interestingly, smoking one cigarette by a certain

age seems to at least double one’s odds of becoming a heavy cocaine user (Exhibit 4.2), and smoking

every day seems to triple the odds of one becoming a heavy cocaine user (Exhibit 4.1). 

One key result in Exhibits 4.1–4.6 is that the longer youth wait to use any of these drugs, the less

likely they are to become heavy cocaine users. This is shown by the “NO” column percentages 

decreasing uniformly through all six exhibits. However, this decrease is most significant for 

smoking the first cigarette, drinking alcohol, and smoking marijuana. For these three, later starters 

have rates of later heavy cocaine use closer to non-starters than to early starters. For smoking daily,

using cocaine, or using crack, even late starters have significantly higher rates of later heavy cocaine 

use than non-starters. 

Prediction of Heavy Cocaine Use 

Regression is a useful tool to model data. One of our main goals is to find variables that can predict 

future heavy cocaine use. In order to stress the prediction, our emphasis for explanatory variables 

was on early round variables (generally in 1979 and 1980, once in 1984) so that we could test their

predictive power on heavy cocaine use. Whether or not the youth is a heavy cocaine user is a binary

variable, so we use logistic regression. Logistic regression is similar to ordinary linear regression,

except that the dependent variable is dichotomous rather than continuous. For example, if y is 1 for a

heavy cocaine user and 0 otherwise, then a logistic regression model posits the relationship between 

the explanatory variables and the probabil

( )

( ) i i x

y

y Log ⎥ = + ∑ ⎦

⎤ ⎢

⎣

⎡

=

= β 0 β Pr 0

Pr 1

PREDICTING HEAVY DRUG USE: FINAL REPORT 37
The probability itself cannot be used as the dependent variable because it is bounded by 0 and 1, 

he 

re 

ar regression can be used for logistic regression. 

In interpreting our logistic regression results, no is made for a direct causal relationship among 

the variables used in the proposed analysi dy seeks to explain covariation among the 

variables certain behavi ics tend to presence of a 

particular outcom cocaine use) to a greater or lesser degree t

characteri

Logistic regression generates “odds ratio ch predic s are readily 

interpretable probabilities that indicate how much more likely it is that an outcome would be 

observed ents being the r occurs the predictor 

oes not occur. For example, all other things being equal, an odds ratio would estimate how much 

mber (which 

are sorted by questionnaire year). Ninety-eight of these variables had a significant p value of less 

ght of these 26 variables. 

f only 0.08. However, logistic regression does not 

 are more likely to be heavy cocaine users as 

odel also shows that those who smoked marijuana at least monthly (11–50 times) in 

980 were more than three times as likely to be heavy cocaine users than those who did not smoke 

whereas the right side of the equation is not. The log odds ratio is used because it is unbounded. T

only difference from linear regression is the dependent variable. Any explanatory variables that we

used for line

claim

s. Rather, the stu

to assess whether ors or characterist coincide with the

e (i.e., heavy

stics. Our goal is prediction.

han do other variables or

” estimates for ea tor. Such estimate

if, all other elem same, the predicto compared to when

d

more (or less) likely a man than a woman is to become a heavy cocaine user. An odds ratio above 1.0

means that the activity becomes more likely, while an odds ratio below 1.0 means that this activity

becomes less likely. 

Our modeling procedure followed a very programmatic path. We first selected 140 variables of 

possible interest from the NLSY79 data. All variables were treated as, or converted into categorical 

variables (where a goal was to have only a few categories). We examined the bivariate relationship

between each of these variables and our heavy cocaine use dependent variable through chi-square

tests of independence. These 140 items are presented in Appendix A sorted by item nu

than 0.05, including 79 with a p value of less than 0.0001. 

Clearly, these are too many independent variables to put into a logistic regression model, but we

selected 26 variables for both their significance and their coverage of the important topics among the 

140 variables. We ran several logistic regression stepwise procedures to determine the best model, 

using the SUrvey DAta ANalysis (SUDAAN) software package. The best model shown below in 

Exhibit 4.7 included ei

The model in Exhibit 4.7 shows an R-square o

allow the full range of R-squares from zero to one. Instead, the R-square can be constrained quite 

strongly. Rescaling the R-square for logistic regression gives a fairer comparison to R-squares from

linear regression. Cox and Snell’s

14 max-rescaled R-square is 0.24, which indicates a moderate 

explanatory model. 

The odds ratios in Exhibit 4.7 are easily interpretable. The strongest factor is that those who smoked 

marijuana or hashish more than 50 times in the last year (in 1980) are 6.7 times as likely to be heavy 

cocaine users many years later than those who did not smoke any marijuana or hashish. This model 

shows that those who are smoking marijuana as youth

adults. This m

1

 14 D. R. Cox and E. J. Snell, Analysis of Binary Data, 2nd Edition, London: Chapman and Hall, 1989. 

PREDICTING HEAVY DRUG USE: FINAL REPORT 38
any marijuana or hashish. Even occasional marijuana/hashish smokers (once or twice a year) were at 

least 50 percent more likely to become heavy cocaine users. 

Exhibit 4.7: Correlates of Heavy Cocaine Use: Results of Logistic Regression Model

Observations used in analysis 8,033 

R-square 0.08 

Max-rescaled R-square 0.24 

Independent Variables (Variables are significant at p<0.05 level) Odds Ratios 

Smoked marijuana/hashish 51+ times in past year vs. none (1980) 6.67 

 Smoked marijuana/hashish 11–50 times in past year vs. none 3.23 

 Smoked marijuana/hashish 3–10 times in past year vs. none 2.38 

 Smoked marijuana/hashish 1–2 times in past year vs. none 1.54 

Male vs. female (1979) 1.87 

Had cigarette less than six months ago vs. never (1984) 1.41 

 Had cigarette more than six months ago vs. never 0.48 

Has been suspended from school vs. has not (1980) 1.56 

Significant amount of illegal income in last year vs. none (1980) 2.63 

 A little illegal income in last year vs. none 1.37 

Attends religious services at least once a month vs. does not attend (1979) 0.67 

 Attends religious services infrequently vs. does not attend 0.76 

Has sold hard drugs in past year vs. has not (1980) 2.00 

Hispanic vs. non-black, non-Hispanic (1979) 1.59 

 Non-Hispanic black vs. non-black, non-Hispanic 1.61 

Smoking cigarettes as young adults also has a strong relationship with heavy cocaine use. Current 

smokers in 1984 were 41 percent more likely to be heavy cocaine users than those who had never 

smoked. Interestingly, former smokers (those who had gone at least six months without smoking) 

were only half as likely to become heavy cocaine users as those who had never started smoking

The final significant variable involves attendance at religious services. Those youth who attended

reli e heavy 

cigarettes. 

Demographics also play a role. Males were 87 percent more likely than females to become heavy

cocaine users, while non-Hispanic blacks and Hispanics were 61 percent and 59 percent,

respectively, more likely than non-Hispanic, non-blacks to become heavy cocaine users. 

Juvenile delinquent behavior is another valuable predictor of future heavy cocaine use. Youth who

are suspended from school have a 56 percent greater chance in being a heavy cocaine user than those 

never suspended. Youth who received a substantial amount of illegal income were more than twice 

as likely to become heavy cocaine users. Those who reported “a little” illegal income were 37 

percent more likely to become heavy cocaine users. Finally, those who sold hard drugs were twice as 

likely to become heavy cocaine users.

gious services at least once per month in 1979 were 33 percent less likely to becom

PREDICTING HEAVY DRUG USE: FINAL REPORT 39
cocaine users than those who did not attend religious services in 1979. Even those who attended 

religious services infrequently were 24 percent less likely to become heavy cocaine users than those

who did not attend at all. 

collected 

app n average of 8 years) before the data were collected hich 

we classified aine users. This suggests that it is possible to predict future 

hea use several years in advance. 

Co

We ears reported using juana, 

coc analyses were not intended to be 

exh ships that could be as from 

this e same 26 variables we used in the 

hea regression analysis to explore these three variables. Results for all three 

mod

The d cocaine than for crack. We be this is 

due to the fact that there is m odeled. While the weighted mean 

for of crack use is only 0.03, which is 

very as the max-rescaled R-square for heavy e use 

(abo

Som models. These are the same variables that are 

mos : smoking of marijuana/hashish, smoking status, and 

sell are less likely to be heavy cocaine users) do 

nd re cocaine (β = 0.00) or crack (β = –0.01). 

Also significant is whether youth expect to be married within five years (possibly a proxy for 

Race/ethnicity differences are not significant for cocaine, but African-Americans tend to use 

nly one of the regressions. With regard to marijuana, males 

and those who had been suspended from school were users for more years, while for cocaine, those 

What is most striking about these relationships is that the explanatory variables were

roximately five or more years (a

outh as heavy coc

from w

 the y

vy cocaine

rrelates of Number of Years of Drug Use

also ran linear regression analyses on the number of study y mari

aine, and crack cocaine. The variables used in these regression

austive of the data set or of other potentially meaningful relation sessed

rich data resource. Instead, we used stepwise models with th

vy cocaine users logistic

els are shown in Exhibit 4.8. 

modeling is much more effective for marijuana an lieve

uch more marijuana use that can be m

marijuana is 1.15 years of use, the weighted mean for years

small. None of the R-squares is as large cocain

ve), but marijuana is very close. 

e variables are important contributors to all three

t important in predicting heavy cocaine use

ing of hard drugs. Interestingly, ex-smokers (who

te to smoke more marijuana (β = + 0.15), but not mo

Several variables were important for two of the three models. Those who had at least one episode of 

“binge drinking” (at least 5 alcoholic drinks at one time) in the last month were heavier users of 

marijuana and cocaine but not crack. Attending religious services is also significant and inversely

related to number of years of marijuana and cocaine use, but not related to crack use. 

whether the youth is in a committed relationship) and whether youth are satisfied with themselves; 

both are inversely related to number of years of drug use. 

marijuana and crack for more years. However, Hispanics use marijuana for fewer years, and crack 

for about the same number of years as non-Hispanic whites. Youth who were referred to courtrelated counseling used cocaine and crack more years than those who were not referred to courtrelated counseling. 

Several variables were significant in o

who expect to go to college are likely to use for slightly fewer years, but still use for more years than

those who expect to get only a high school diploma (as expected, those who do not expect to finish 

PREDICTING HEAVY DRUG USE: FINAL REPORT 40
high school have the highest usage of cocaine). Finally, those who had at least one drink in the 

previous month and those with illegal income were likely to use cocaine for more years. 

Exhibit 4.8: Correlates of Number of Years Using Marijuana, Cocaine, and Crack: Results of 

Ordinary Least Squares Regression Models

Observations used in analysis 

R-square 

Marijuana 

4,490 

0.23

Cocaine 

4,391 

0.18

Crack 

8,293 

0.04

Independent Variables (significant at p<0.05 level) valuesa

MEAN IN YEARS 1.15 0.40 0.03 

Smoked marijuana/hashish more than 50 times in past year vs. 

none—(1980) 1.28 0.52 0.06 

Smoked marijuana/hashish 11–50 times in past year vs. none 0.87 0.41 0.01 

Smoked marijuana/hashish 3–10 times in past year vs. none 0.48 0.21 0.02 

Smoked marijuana/hashish 1–2 times in past year vs. none 0.32 0.11 0.01 

Had cigarette less than six months ago vs. never (1984) 0.53 0.14 0.03 

Had cigarette more than six months ago vs. never 0.15 0.00 –0.01 

Has sold hard drugs in past year vs. has not (1980) 0.39 0.26 0.05b

H

l

ad at least five alcoholic drinks at one time in 

ast month vs. did not (1983) 0.36 0.20 

Attends religious services at least once a 

month vs. does not attend (1979) –0.14 –0.07 

Attends religious services infrequently vs. does not attend –0.08 –0.04 

Hispanic vs. non-black, non-Hispanic (1979) –0.06b 0.01

non-Hispanic black vs. non-black, non-Hispanic 0.11b 0.07

Has been referred to court-related counseling vs. has not (1980) 0.23 0.06 

E

e

xpects to be married in next five years vs. 

xpects to not be married (1979) –0.30 –0.12 

Respondent is very satisfied with self vs. not satisfied (1980) –0.11b –0.09 

R b espondent is satisfied with self vs. not satisfied –0.01 0.01

Expects to go to college vs. expects to 

not finish high school (1979) –0.02b

 –0.10 Expects highest grade to be HS diploma vs. b

 expects to not finish HS 

Had at least one alcoholic drink in the last 

month vs. had not (1980) 0.09 

Has been suspended from school vs. has not (1980) 0.13 

Male vs. female (1979) 0.19 

Significant amount of illegal income in last year vs. none (1980) 0.29 

A little illegal income in last year vs. none 0.12 

a This effect is the additional years of drug use attributable to this level of the variable. Blank cells indicate variable

was not significant in that regression. b Only significant at p < 0.10.

PREDICTING HEAVY DRUG USE: FINAL REPORT 41
Summary

r 

y cocaine users. 

 Youth who first used cigarettes, alcohol, and marijuana late more closely resemble nonr heavy cocaine use. 

However, those youth who started smoking cigarettes da ing cocaine, or using crack 

late have heavy cocaine use percentages more like early than non-users. 

 50 times as adolescents are mo es 

ly to become heavy cocaine users as those who did not smoke ana as 

a use in adolescence decreases. 

oung male drug users are almost twice as likely as female drug users to become heavy 

d one imes m kely to 

t suspended from school. 

f illegal inco adoles are mo

ocaine users tha se who o illeg

ices at least twice a month are one third less likely

caine users as those who did not attend religious services. Attending 

th a one-quarter lower likelihood of becoming a heavy 

 twice a to be eavy cocaine 

rugs as adolescents. 

 Youth who wait longer before their first use of alcohol, cigarettes, marijuana, cocaine, o

crack are less likely to become heav

users with regard to thei

 ily, us

users

 Those who smoked marijuana more than re than six tim

as like mariju

adolescents. The odds ratio decreases as marijuan

 Y

cocaine users. 

 Those who were suspended from school are one an -half t ore li

become heavy cocaine users as those who were no

 Individuals reporting a significant amount o me as cents re than 

two times more likely to become heavy c n tho had n al

income as adolescents. 

 Those who attended religious serv

become heavy co

 to 

infrequently is associated wi

cocaine user. 

 Those selling hard drugs during adolescence are s likely come h

users as those who did not sell d

PREDICTING HEAVY DRUG USE: FINAL REPORT 42
5. LONGITUDINAL SCREENING 

Introd c

The fin l o

predict tu t

questions th

then be p

user. It is eavy drug 

users. 

representati e more 

oversampli stimates

using t w

weights). M ing, of course, also should lead to a larger sample of heavy cocaine users.

Screenin

One goal w

includes ap ld be

designed w ith the responses input to predict the likelihood of becoming a 

heavy d g d

likelihood o a

(roughly) d ted at

twice the ra

It is import y that would study heavy

rug use. The age of the youth need to be determined to balance the benefits and costs of starting 

with younger children who could develop into heavy drug users versus older children (e.g., teenagers 

and college-age) who could already be using heavy drugs. According to our analyses of age of first

use, 14.2 percent of the NLSY sample had smoked a cigarette by age 10, and 10.7 percent had used 

cocaine by age 18. 

While it will be difficult to ask about marijuana and other sensitive topics during a screening 

interview, there are several possible approaches. One straightforward approach is to have a

screening interview only to determine age-eligible children and performing a short interview to ask 

the more sensitive questions as part of a further screener. 

This would involve a lot of screening, so another suggestion is to try to “piggy-back” on another 

large screening sample. In this way, screening money could be saved if permission could be granted 

to simply target age-eligible children found by another project’s large screening operation. The 1997

Profiles of American Youth (PAY97) sponsored by the Department of Defense did just this in

partnership with the 1997 National Longitudinal Survey of Youth (NLSY97) sponsored by the

Bureau of Labor Statistics. NLSY97 (the newer cohort to NLSY79) screened a national sample of

90,000 housing units for 12- to 16-year-old youth. At the same time, PAY97 screened for 18- to 23-

year-old youth and 10th- to 12th-grade students. 

u tion 

al ogistic regression model for heavy drug use above can be used as a screening tool t

fu re heavy cocaine use. The variables in this final logistic regression model point ou

at could be used in future surveys or general population applications. The answers can 

in ut into this model to predict the likelihood of each respondent becoming a heavy drug

also possible to use this likelihood to oversample the “most” likely future h

As long as this is done on a probability basis, we can use weights to get a nationally

ve and unbiased sample no matter how strong the oversampling is. Of course, th

ng there is, the more variability we would have in our nationally representative e

he eights (precision of the estimates is mathematically related to the variation in the

ore oversampl

g the Sample

as to develop a logistic regression model to help screen for a longitudinal survey that

propriate numbers of likely heavy drug users. As above, the screener questions wou

ith the model in mind, w

ru user. The sample could be selected with a probability proportional to this estimate

f becoming a heavy drug user. For example, since the odds ratio for males indicates

oubled risk of becoming heavy drug users versus females, then males will be selec

te of females. 

ant to realize that age is a critical factor in any longitudinal surve

d

PREDICTING HEAVY DRUG USE: FINAL REPORT 43
Selecting the Sa

Let us assume that screener data are available for all Number of times 

smoked marijuana/hashish in the past year, gender, race/ethnicity, smoking status (most recent 

ol suspension history, amount of illegal income in last year, selling of hard drugs, 

 selected 87 percent more males (34.84 percent female), we would 

 heavy cocaine users) and 5,234 males (468 future heavy cocaine 

of 8.94 percent (which would consist of a 100 percent male sample). 

ters in Exhibit 4.7 suggest that the rates of future heavy cocaine use are 20.22 

percent (more than 50 uses), 9.79 percent (11–50 uses), 7.21 percent (3–10 uses), 4.67 percent (1–2 

ses (33 future heavy cocaine users), and 

1,887 with 0 uses (57 future heavy cocaine users). This sample results in 980 future heavy cocaine 

selection of the sample. 

mple

the variables in Exhibit 4.7: 

cigarette), any scho

and religious attendance. Taking the idea expressed above, a sample proportional to their predicted

probability of becoming a heavy cocaine user can be selected. 

In order to simplify this discussion, we use unweighted NLSY79 data. The rate of future heavy drug 

users among the NLSY79 respondents was 551/8,033 = 6.86 percent. If we used an equal probability

sample of 8,033 screener respondents (including the variables in Exhibit 4.7), we would then expect 

to have 551 future heavy cocaine users in our sample. However, by selecting our sample based on 

the screener variables shown to be related to future heavy cocaine use, we can increase our yield in a 

sample of 8,033. For example, the odds ratio for gender is 1.87, which suggests that the rate of 

future heavy cocaine users is 4.78 percent for females and 1.87 * 4.78 = 8.94 percent for males 

(please note that the average of 4.78 percent and 8.94 percent is 6.86 percent). If instead of a 50/50

sample of males and females, we

obtain 2,799 females (134 future

users). This would result in 468 + 134 = 602 future heavy cocaine users (7.49 percent), an increase 

of over 9 percent over a gender-balanced sample. This assumes, of course, that the screening data 

contains at least 5,234 males. 

A key idea is that the number of future heavy cocaine users can be increased further by increasing the 

oversampling of males. However, using only gender, we could not expect to exceed a percentage of

future heavy cocaine users

Similar to gender, we could repeat the above analysis for all other screener variables. The most 

productive of these would surely be the number of times a respondent smoked marijuana or hashish

in the last year, as shown by the odds ratio of 6.67 in Exhibit 4.7 for those who smoked marijuana or

hashish at least 50 times in the last year. Examining this variable shows that 1,815 reported more 

than 50 uses in the last year, 817 reported 11–50 uses, 1,359 reported 3–10 uses, and 6,229 reported 

no use. The parame

uses), and 3.03 percent (no use). Selecting the sample proportional to these predicted probabilities 

would result in a sample of (total sample of 8,033) 3,667 sample members with more than 50 uses 

(742 future heavy cocaine users), 799 with 11–50 uses (78 future heavy cocaine users), 980 with 3–

10 uses (71 future heavy cocaine users), 701 with 1–2 u

users (an increase of 78 percent)—using only the one variable. 

Using selection probabilities based on using all of the variables in Exhibit 4.7 will obviously lead to 

the possibility of even more future heavy cocaine users in the sample. Calculations, however, are 

highly dependent on the distribution of the screening sample across all cells for these variables, and 

are therefore not shown here. However, the above example for marijuana use shows that the rate of

future heavy cocaine users can easily be increased from the NLSY97 rate of 6.86 percent to more

than 10 percent using only this one variable in the

PREDICTING HEAVY DRUG USE: FINAL REPORT 44
Weighting the Sample 

tor is simply a weighted mean where 

the weight is the reciprocal of the selection probability: 

It should be noted that the survey need not be restricted to “likely” heavy drug users; “likely” heavy 

drug users would simply be overrepresented. For example, the above examples still include females

and non-marijuana smokers in the sample. Therefore, this sample would still be nationally

representative with the proper weights. Weights are commonly used to adjust for differential 

probabilities of selection. In fact, a Horvitz-Thompson15 estima

= ∑

i

i

p

y

N y

1 ˆ , 

where N is the population size, yi is the observation, and pi is the selection probability. HorvitzThompson estimators have been studied extensively, but their main benefit is that they are unbiased. 

In this case, this unbiased property results in nationally representative estimates. 

Taking our example samples selected above, if males were selected with a probability (pi) 87 percent

greater than females, the base weight (1/pi) for females would be 87 percent greater. Taking a very

simple example, let’s assume that the sample of 2,799 females and 5,234 males was taken from a 

population of 25,000 females and 25,000 males. The selection probabilities in this case would be

) in

2,799/25,000 = 11.20 percent for the females and 5,234/25,000 = 20.94 percent for the males. The 

base weight would be 1/0.1120 = 8.93 for the females and 1/0.2094 = 4.78 for the males. Since we 

would then expect 134 female and 468 male future heavy drug users, the above formula would imply

our estimate of the future heavy drug user percentage (where 1= future heavy drug user, 0 = not

the entire population of 50,000 males and females is: 

.0686 .2094

468*1

.1120

134 *1

50,000

1 ˆ ⎟ =

⎠

⎞ ⎜

⎝

⎛

y = +

This estimate of 6.86 percent matches our NLSY97 (unweighted) data, and implies 3,431 future 

heavy drug users in the theoretical population of 50,000 youth. 

Conclusion 

A longitudinal study would fill a currently large gap in our knowledge base of drug use. Currently,

most data on drug use comes from cross-sectional surveys, from which it is very difficult to learn the 

temporal order of the factors that lead to drug use and abuse.

 15 W. G. Cochran, Sampling Techniques, 3rd edition, New York: Wiley, 259-261, 1977. 

PREDICTING HEAVY DRUG USE: FINAL REPORT 45
APPENDIX A 

Selected Variables of Interest from NLSY79 Data* 

m # Question Year CHI^2 Df 

P 

value

N 

(12686

30 LIVED IN PRESENT RESIDENCE SINCE BIRTH? 79 INT 

Ite ) 

79 0.08 1 0.77 12685 

65 HIGHEST GRADE COMPLETED BY R'S MOTHER 79 INT 79 12.47 6 0.05 12656 

8 DID MOTHER/STEPMOTHER WORK FOR PAY? 1978 79 3.77 2 0.15 12030 

0 DID MOTHER/STEPMOTHER WORK > 35 HOURS PER WEEK IN 1978? 79 1.94 1 0.16 7147 

79 HIGHEST GRADE COMPLETED BY R'S FATHER 79 INT 79 16.89 6 0.01 12453 

84 DID FATHER/STEPFATHER WORK > 35 HOURS PER WEEK IN 1978? 79 0.02 1 0.88 9124 

NUMBER OF SIBLINGS 79 INT 79 5.34 6 0.50 12668 

HIGHEST GRADE COMPLETED BY OLDEST SIBLING 79 INT 79 8.91 6 0.18 9694 

24 

86 3 0.41 5730 

6 

9 

0 

6 

5 

9 

6 

6 

9 

1 

5 

79 40.48 5 0.00 12676 

175 IS CURRENT OR LAST SCHOOL GRADES 1–12 PUBLIC 79 1.11 1 0.29 12665 

INT CHECK 79—IS R CURRENTLY ENROLLED IN GRADES 9–12? 79 0.09 1 0.76 12644 

196 TYPE OF CURRENT OR LAST SCHOOL CURRICULUM IN GRADES 9–12 79 12.94 3 0.00 11526 

234 HIGHEST GRADE COMPLETED R WOULD LIKE 79 INT 79 10.32 4 0.04 12624 

235 HIGHEST GRADE COMPLETED R EXPECTS 79 INT 79 32.30 4 0.00 12565 

HIGHEST GRADE COMPLETED R'S CLOSEST FRIEND WOULD LIKE 79 79 22.38 4 0.00 12366 

5 

8 

79 0.11 1 0.74 10054 

273 G A GOOD JOB—SEX DISCRIMINATION? 79 79 0.02 1 0.89 10057 

ETTING A GOOD JOB—AGE DISCRIMINATION? 79 79 15.05 1 0.00 10063 

ETTING A GOOD JOB—ENGLISH IS A FOREIGN LANG 79 0.38 1 0.54 10059 

5 

1 

1

1 FAMILY 79 5.01 1 0.03 12560 

1710 WORK COMMITMENT—GO ON WELFARE IF UNABLE TO SUPPORT FAMILY 79 2.50 1 0.11 12423 

1711 WORK COMMITMENT—ENTER TRAINING IF UNABLE TO SUPPORT FAMILY 79 0.14 1 0.71 12571 

1712 WORK COMMITMENT—APPLY 4 FOOD STAMPS IF UNABLE TO SUP FAM 79 3.99 1 0.05 12432 

1713 WORK COMMITMENT—SHOPLIFT IF UNABLE TO SUPPORT FAMILY 79 31.97 1 0.00 12574 

1714 WORK COMMITMENT—WOULD WORK IF COULD LIVE COMFORTABLY 79 1.15 1 0.28 12646 

1716 MARITAL STATUS EXPECTATIONS—MARRIED OR NOT IN 5 YEARS 79 4.37 1 0.04 10453 

1718 EDUCATIONAL EXPECTATIONS—IN SCHOOL OR NOT IN 5 YEARS 79 0.05 1 0.81 12237 

1719 EMPLOYMENT EXPECTATIONS—WORKING OR NOT IN 5 YEARS 79 79 3.47 1 0.06 12413 

1720 13 0.01 12686 

6

7

91 

95 

96 1ST OR ONLY RACIAL/ETHNIC ORIGIN 79 INT 79 27.27 14 0.02 125

105 FREQUENCY OF R'S RELIGIOUS ATTENDANCE 79 INT 79 61.58 5 0.00 12669

153 TOTAL NUMBER OF CHILDREN R EXPECTS TO HAVE 79 INT 79 18.88 5 0.00 12460 

159 CURRENT SCHOOL ATTITUDE—FRIENDS CAN EASILY BE MADE 79 2.

160 CURRENT SCHOOL ATTITUDE—TEACHERS WILLING TO HELP 79 0.81 3 0.85 572

161 CURRENT SCHOOL ATTITUDE—CLASSES ARE BORING 79 INT 79 2.56 3 0.46 572

162 CURRENT SCHOOL ATTITUDE—R DOES NOT FEEL SAFE 79 INT 79 5.37 3 0.15 573

163 CURRENT SCHOOL ATTITUDE—TEACHERS KNOW THEIR SUBJECTS 79 2.06 3 0.56 572

164 CURRENT SCHOOL ATTITUDE—STUDENTS CAN "GET AWAY WITH" 79 19.47 3 0.00 572

165 CURRENT SCHOOL ATTITUDE—SCHOOL WORK REQUIRES THOUGHT 79 13.20 3 0.00 572

166 CURRENT SCHOOL ATTITUDE—FREEDOM TO PURSUE INTERESTS 79 3.51 3 0.32 572

167 CURRENT SCHOOL ATTITUDE—SCHOOL OFFERS GOOD JOB 79 9.81 3 0.02 568

168 ATTITUDE TOWARD CURRENT SCHOOL 79 INT 79 9.25 3 0.03 570

171 REASON R LEFT SCHOOL (NOT ENROLLED) 79 INT 79 68.03 11 0.00 523

172 HIGHEST GRADE ATTENDED 79 INT 79 46.98 5 0.00 1267

173 HIGHEST GRADE COMPLETED 79 INT 

186 

236 

270 PROBLEMS IN GETTING A GOOD JOB—TRANSPORTATION? 79 INT 79 2.56 1 0.11 1006

271 PROBLEMS IN GETTING A GOOD JOB—RACE DISCRIMINATION? 79 1.50 1 0.22 1005

272 PROBLEMS IN GETTING A GOOD JOB—NATIONALITY DISRIMINATION?

PROBLEMS IN GETTIN

274 PROBLEMS IN G

275 PROBLEMS IN G

434 ACTIVITY MOST OF SURVEY WEEK CPS ITEM 79 INT 79 43.46 6 0.00 1146

507 R'S OPINION OF DEGREE OF DIFFICULTY IN FINDING A NEW JOB 79 0.43 2 0.81 630

451 INT CHECK 79—DOES R HAVE HEALTH LIMITATIONS—ITEMS 2A 79 0.00 1 0.94 12465 

709 WORK COMMITMENT—SEEK MORE EDUC IF UNABLE TO SUPPORT

OCCUPATION EXPECTATIONS IN 5 YEARS (CENSUS 3 DIGIT) 79 79 28.99 

PREDICTING HEAVY DRUG USE: FINAL REPORT 46
2147 R'S RACIAL/ETHNIC COHORT FROM SCR 79 30.51 2 0.00 12686 

Item # Question Year CHI^2 Df 

P 

value

N 

(12686) 

2148 SEX OF R 79 INT 79 100.19 1 0.00 12686 

2165 AGE O 12686 

2167 HIGHE 12680 

2314 EVER BEEN SU 0.00 12140 

MES R HAS BEEN SUSPENDED FROM SCHOOL 80 INT 

INT 

30 ALITIES 

30

30

30

30

30 1

30 80

30

3 OOD"

3 PAST YEAR 

3 T YEAR 1

3 ST YR 10

3 PAST YR 15 1

3 K PAST YR 15 1

3 21 1

3 1 1

3 1 1

3 YR 1

3 ST YR 9 1

3 ARM PAST YR 9 1

3 ANA/HASHISH PAST YR 4 1

3 CALS PAST YR 4

3 MARIJUANA/HASHISH PAST YR 4

3 IN PAST YR 1

3 R 9

3

3 1

3 /HELD PAST YR 1

3

3 3

3 13

3 19

3 FIC) 14

3 1

3 ) 2

3 IC) 2

3 OR TRAF) 2

14

16

20

1

4

H 15

10

1

EENER 79 INT 

F R AT INTERVIEW DATE 79 INT 79 14.67 8 0.07 

ST GRADE COMPLETED AS OF MAY 1 SURVEY YEAR 79 44.31 6 0.00 

SPENDED FROM SCHOOL? 80 INT 80 276.20 1 

2315 

30

NUMBER OF TI 80 351.73 6 0.00 12106 

35 SELF-ESTEEM 80 INT—I AM A PERSON OF WORTH 80 80 3.89 2 0.14 12120 

36 SELF-ESTEEM 80 INT—I HAVE A NUMBER OF GOOD QU 80 1.02 2 0.60 12117 

37 SELF-ESTEEM 80 INT—I FEEL THAT I AM A FAILURE 80 5.76 2 0.06 12108 

38 SELF-ESTEEM 80 INT—I AM AS CAPABLE AS OTHERS 80 INT 1

OUD

80 1.60 2 0.45 2116

39 SELF-ESTEEM 80 INT—I DO NOT HAVE MUCH TO BE PR 80 5.71 2 0.06 12104 

40 SELF-ESTEEM 80 INT—I HAVE A POSITIVE ATTITUDE 80 INT 1

TISFIED WITH MYSELF 80 INT 

80 0.04 2 0.98 2083

41 SELF-ESTEEM 80 INT—I AM SA 80 3.84 2 0.00 12103 

42 SELF-ESTEEM 80 INT—I WISH I HAD MORE SELF-RESPECT 80 2.46 3 0.48 12091

43 SELF-ESTEEM 80 INT—I FEEL USELESS AT TIMES 80 INT 80 1.85 3 0.60 12114 

044 SELF-ESTEEM 80 INT—I SOMETIMES THINK I AM "NO G 80 3.98 3 0.26 12113 

046 ILLEGAL ACTIVITY 80 INT—TIMES RUN AWAY FROM HOME IN 80 58.58 

0

4 0.00 12141 

047 ILLEGAL ACTIVITY 80 INT—TIMES SKIPPED SCHOOL DAY IN PAS 80 0.08 6 0.00 3896 

048 ILLEGAL ACTIVITY —TIMES DRANK ALCOHOLIC BEVERAGES PA 80 3.26 6 0.00 3886 

049 ILLEGAL ACTIVITY 80 INT—TIMES INTENTIONALLY DAMAGED IN 80 0.90 4 0.00 1734

050 ILLEGAL ACTIVITY 80 INT—TIMES FOUGHT AT SCHOOL OR WOR 80 5.51 4 0.00 1800

051 ILLEGAL ACTIVITY 80 INT—TIMES SHOPLIFTED IN PAST YEAR

R

80 7.08 4 0.00 1788

052 ILLEGAL ACTIVITY 80 INT—TIMES STOLEN OTHER'S < $50 IN PAST Y 80 14.03 4 0.00 1788

053 ILLEGAL ACTIVITY 80 INT—TIMES STOLEN OTHER'S > $50 IN PAST YR

T

80 95.29

3

3 0.00 1776

054 ILLEGAL ACTIVITY —TIMES USED FORCE TO OBTAIN THINGS IN PAS

IT PA

80 7.60 1 0.00 1794

055 ILLEGAL ACTIVITY —TIMES SERIOUSLY THREATENED TO HIT/H

/INTENT TO H

80 7.55 4 0.00 1785

056 ILLEGAL ACTIVITY 80 INT—TIMES ATTACKED W 80 4.50 1 0.00 1792

057 ILLEGAL ACTIVITY 80 INT—TIMES SMOKED MARIJU

ER DRUGS/CHEMI

80 51.98 6 0.00 1722

058 ILLEGAL ACTIVITY —TIMES USED OTH 80 52.39 6 0.00 11698 

059 ILLEGAL ACTIVITY 80 INT—TIMES SOLD 80 08.35

8

6 0.00 11693 

060 ILLEGAL ACTIVITY 80 INT—TIMES SOLD HARD DRUGS 80 2.66 1 0.00 11717 

061 ILLEGAL ACTIVITY 80 INT—TIMES ATTEMPTED TO "CON" PAST Y 80 9.96 4 0.00 11721 

062 ILLEGAL ACTIVITY 80 INT—TIMES TAKEN AUTO W/OUT OWNER PAST YR 

ILDING PAST YR

80 60.60 1 0.00 11752 

063 ILLEGAL ACTIVITY 80 INT—TIMES BROKEN INTO A BU

LD

80 16.16 1 0.00 11748 

064 ILLEGAL ACTIVITY 80 INT—TIMES KNOWINGLY SO 80 68.04 1 0.00 11749 

065 ILLEGAL ACTIVITY 80 INT—TIMES AIDED IN GAMBLING PAST YR 

R

80 35.25 

1

1 0.00 11737 

066 ILLEGAL ACTIVITY —AMT OF TOTAL ILLEGAL INCOME IN PAST YEA 80 5.99 2 0.00 11447 

067 EVER "STOPPED" BY POLICE FOR OTHER THAN MINOR TRAFFIC 80 9.20 1 0.00 12129 

068 TIMES "STOPPED" BY POLICE 80 INT (EXC MINOR TRAFFIC)

AF

80 4.83 6 0.00 12103 

069 TIMES "STOPPED" BY POLICE IN PAST YEAR 80 INT (EXC MINOR TR 80 7.02 3 0.00 12111 

070 AGE OF R 1ST TIME "STOPPED" BY POLICE 80 INT 80 81.96

0

6 0.00 12111 

071 EVER CHARGED WITH ILLEGAL ACTIVITY? 80 INT (EXC MINOR TRAFFIC

MINOR TRAFF

80 9.95 1 0.00 12136 

072 TIMES CHARGED WITH ILLEGAL ACTIVITY 80 INT (EXC 80 33.89

2

3 0.00 12132 

076 AGE AT TIME OF 1ST ILLEGAL ACTIVITY CHARGE 80 INT (EXC MIN 80 6.92 7 0.00 12129

3077 EVER CHARGED WITH ANY ILLEGAL ACTIVITY IN ADULT COURT? 80 0.88 1 0.35 1322 

3078 EVER CONVICTED ON CHARGES OTHER THAN MINOR TRAFFIC 80 3.08 1 0.00 12130 

3101 EVER BEEN REFERRED TO COURT-RELATED COUNSELING? 80 INT 80 7.70 1 0.00 12121 

3105 INT CHECK 80—R EVER BEEN STOPPED, BOOKED/CHARGED, 80 8.59 1 0.00 12124 

3109 EVER BEEN SENTENCED TO ANY TYPE OF CORRECTIONAL FACILITY 80 6.90 1 0.01 2749 

7801 ALCOHOL USE—EVER HAD A DRINK? 82 INT 82 16.71 

0

1 0.00 12122 

7802 ALCOHOL USE—AGE WHEN STARTED DRINKING 82 INT 

H?

82 2.41 6 0.00 11111 

7803 ALCOHOL USE—HAD ANY ALCOHOLIC BEVERAGES IN LAST MONT

T

82 9.62 1 0.00 12121 

7804 ALCOHOL USE—FREQUENCY OF 6 OR MORE DRINKS IN LAST MON 82 9.22 6 0.00 7885 

7805 ALCOHOL USE—FREQUENCY OF GOING TO BARS IN LAST MONTH 82 0.78 5 0.00 7887 

7806 ALCOHOL USE—# OF DAYS DRANK ALCOHOL IN LAST WEEK 82 82 20.90 7 0.00 7882 

PREDICTING HEAVY DRUG USE: FINAL REPORT 47
7807 ALCOHOL USE—# OF CANS/BOTTLES OF BEER CONSUMED IN LAST WEEK

OF GLASSES OF WINE CONSUMED IN LAST WEEK 

OR CONSUMED IN LAST WEEK 

L

H JOB? 82

1 1

1 23

1 AT LEAST WEEKLY 17 1

1 ONTH? 6

1 NTH 17

1 29 1

1 T 1

1 T 11

1 83 INT 13

1 INT 22

1

1 1

1

1

1

1 AST WEEK

1

1

1

1

1

1 1

1

1

3

3 1

5 1

5

6

6 ) 1

i

82 82.31 3 0.00 6147 

Item # Question Year CHI^2 Df

P 

value

N 

(12686) 

7808 ALCOHOL USE—# 82 5.05 2 0.08 6153 

7809 ALCOHOL USE—# OF DRINKS WITH LIQU 82 53.41 2 0.00 6154 

7810 ALCOHOL USE—DRINKING EVER INTERFERED WITH SCHOO 82 12.95 1 0.00 11087 

7811 ALCOHOL USE—DRINKING EVER INTERFERED WIT 82 49.43 1 0.00 11071 

7812 ALCOHOL USE—INT CHECK 83—ANYONE ELSE PRESENT WHEN 82 0.46 1 0.50 11042 

0216 ALCOHOL USE—EVER HAD A DRINK? 83 INT 83 0.08 1 0.00 12209 

0217 ALCOHOL USE—AGE WHEN STARTED DRINKING 83 INT 83 6.59 9 0.00 11407 

0218 ALCOHOL USE—AGE WHEN 1ST STARTED DRINKING 83 6.90 1 0.00 9613 

0219 ALCOHOL USE—HAD ANY ALCOHOLIC BEVERAGES IN LAST M 83 7.60 1 0.00 11417 

0220 ALCOHOL USE—FREQUENCY OF 6 OR MORE DRINKS LAST MO 83 6.56 6 0.00 8055 

0221 ALCOHOL USE—# OF DAYS DRANK ALCOHOL IN LAST MONTH 83 83 4.96 2 0.00 12206 

0222 ALCOHOL USE—# OF DAYS HAD 1 DRINK IN LAST MONTH 83 IN 83 24.04 7 0.00 12203 

0223 ALCOHOL USE—# OF DAYS HAD 2 DRINKS IN LAST MONTH 83 IN 83 2.54 7 0.00 12203 

0224 ALCOHOL USE—# OF DAYS HAD 3 DRINKS IN LAST MONTH 83 4.12 7 0.00 12203 

0225 ALCOHOL USE—# OF DAYS HAD 4 DRINKS IN LAST MONTH 83 83 5.57 7 0.00 12203 

0226 ALCOHOL USE—# OF DAYS HAD 5 DRINKS IN LAST MONTH 83 INT 83 92.54 2 0.00 12203 

0227 ALCOHOL USE—# OF DAYS HAD 6 OR MORE DRINKS IN LAST MONTH 83 227.13 8 0.00 2203

0229 ALCOHOL USE—FREQUENCY OF GOING TO BARS IN LAST MONTH 83 83 75.13 5 0.00 8054 

0230 ALCOHOL USE—# OF DAYS HAD HANGOVER IN LAST MONTH 83 INT 83 162.94 4 0.00 12210 

0231 ALCOHOL USE—# OF DAYS DRANK ALCOHOL IN LAST WEEK 83 INT 83 182.61 7 0.00 8061 

0232 ALCOHOL USE—# OF CANS/BOTTLES OF BEER CONSUMED IN L 83 257.65 7 0.00 12204 

0233 ALCOHOL USE—# OF GLASSES OF WINE CONSUMED IN LAST WEEK 83 148.82 3 0.00 12202 

0234 ALCOHOL USE—# OF DRINKS WITH LIQUOR CONSUMED IN LAST WEEK 83 231.79 4 0.00 12202 

0235 ALCOHOL USE—DRINKING EVER INTERFERED WITH SCHOOL WORK? 83 16.26 1 0.00 11343 

0236 ALCOHOL USE—DRINKING EVER INTERFERED WITH JOB? 83 INT 83 45.90 1 0.00 11343 

0237 ALCOHOL USE—INT CHECK 83—ANYONE ELSE PRESENT DURING SEC 12 83 3.87 2 0.14 12197 

3948 DRUG USE—AGE WHEN 1ST TRIED A CIGARETTE? 84 INT 84 89.39 3 0.00 12030 

3949 DRUG USE—MOST RECENT TIME HAD A CIGARETTE? 84 INT 84 122.73 3 0.00 12045 

3950 DRUG USE—# OF CIGARETTES SMOKED PER DAY IN PAST 30 DAYS 84 8.62 5 0.13 5161 

9142 DRUG USE—R SMOKED AT LEAST 100 CIGARETTES IN ENTIRE LIFE? 92 149.54 1 0.00 8575 

9147 DRUG USE—# CIGARETTES SMOKED PER DAY (R SMOKES DAILY) 92 38.61 4 0.00 8929 

0524 DRUG USE—R SMOKED AT LEAST 100 CIGARETTES IN ENTIRE LIFE? 94 84.19 1 0.00 8826 

0530 DRUG USE—# CIGARETTES SMOKED PER DAY (R SMOKES DAILY) 94 191.55 6 0.00 8869 

4297 DRUG USE—R SMOKED AT LEAST 100 CIGARETTES IN ENTIRE LIFE? 98 155.43 1 0.00 8333 

4303 DRUG USE—# CIGARETTES SMOKED PER DAY (R SMOKES DAILY 98 79.43 7 0.00 8387 

*Var ables in bold were used in regression modeling.

PREDICTING HEAVY DRUG USE: FINAL REPORT 48
EXECUTIVE OFFICE OF THE PRESIDENT

OFFICE OF NATIONAL DRUG CONTROL POLICY

WASHINGTON, DC 20503