Substance Use 8~ Misuse, 34(2), 269-295, 1999
Measurements, Instruments, Scales, and Tests
Characteristics of Inconsistent
Respondents Who Have “Ever Used”
Drugs in a School-Based Sample
Ohidul Siddiqui, Ph.D.,* Joshua A. Mott, Ph.D., Tammy L.
Anderson, Ph.D., and Brian R. Flay, Ph.D.
Prevention Research Center, University of Illinois at Chicago, Chicago, Illinois, USA
ABSTRACTS
This study examines the predictors of inconsistent responses from adolescents to questions about whether they ever used alcohol, cigarettes,
and marijuana. Male adolescents had significantly higher rates of inconsistent responses than female adolescents. Black and Hispanic adolescents had significantly higher rates of inconsistent responses regarding
ever using alcohol and cigarettes (only for Black) than White adolescents. The subjects’ living status and academic achievements were significant predictors of inconsistent responses regarding ever using marijuana. Thus, these results are consistent with the notion that inconsistent
responses may bias the estimation of the prevalence of ever using drugs
in multivariate analyses.
Key words. Random-effects models; Inconsistent responses; Longitudinal study; Smoking prevention
*To whom correspondence should be addressed at Georgetown Medical Center, 2233 Wisconsin Ave.
NW, Suite 440, Washington DC 20007, USA.
269
Copyright 0 1999 by Marcel Dekker, Inc.
www.dekker.com
270
SIDDIQUI ET AL.
INTRODUCTION
.
Longitudinal surveys of adolescent drug use rely heavily on self-report measures. The reliabilities of such measures are therefore of concern to researchers
(Martin and Newman, 1988; Needle et al., 1989; Single et al., 1975). As a result,
a considerable amount of empirical research has been devoted to identifying
sources of adolescents’ inaccurate responses to questions about personal drug use.
One way to do this is to examine the consistency of adolescent drug use selfreports over time.*
Inconsistencies in adolescents’ reporting of drug use over time may result
from a variety of causes including lying, errors in recall, and random errors. When
asked to report on socially disapproved behaviors such as drug use, adolescents’
responses may be influenced by factors such as the type of social image they
desire to project about themselves. For example, adolescents may often conceal
their true actions and underreport drug-use behaviors. Alternatively, some adolescents may “brag” or overreport about engaging in a disapproved behavior even
when they have not. In this regard, the “truthfulness” of adolescent responses to
drug-use questions could be expected to be influenced by their misunderstanding of the purpose of the survey, personal characteristics of the data collector
(gender, race/ethnicity, age), the site of data collection, confidentiality issues, or
the implications of the survey findings.
There are also factors which might increase the chance that adolescents will
unintentionally respond inconsistently over time. Recall bias makes it questionable that adolescents can always accurately remember their past drug-use behaviors. They may also be uncertain of definitions such as “ever using” drugs. For
example, adolescents who have used substances once or twice may not consider
themselves “users” at all. In this regard, the complexity of the responses requested, the time of day when questions are asked, or ambiguity in the wording
of the questionnaire items may be important causes of inconsistent responses.
Single et al. (1975) investigated reasons for the inconsistent reporting of drug
use among adolescents within waves and between waves of a longitudinal survey. They concluded that most of the inconsistencies reflected uncertainty regarding the definition of drug use as well as recall error. The results of Bachman and
O’Malley (198 1) suggested that the tendency to “telescope” historical eventsthat is, to accidentally recall events of the distant past into more recent time periods-may also produce inconsistencies in drug-use data. In particular, they
reported that the frequency of drug use during the past month was roughly three
times larger than would be estimated based on reports of use during the past year.
These and other causes of the misreporting of drug use may have important con*However, while consistency of reporting is a necessary component of accuracy, it does not guarantee it because a subject may report inaccurately all of the time.
INCONSISTENT
sequences for
cused more cl
dividuals, and
responses to s
Luetgert ;
and frequency
did not affect
it did affect tl
the subjects M
and more like:
that among hi
port “ever” 01
consistent wil
quality of dru
nal Survey of
than heavy UE
Users of i
of licit drugs.
likely to unde
results of Ped
among Norwc
use. In this sl
written surve
sponses show
amphetamine
of marijuana
While Si
of experimen
claimed that
able response
likely to con1
responses ova
social accept;
In this regard
experimental
14% of male
males being 1
to marijuana
responses va
ing more inc
by Fendrich
underreporti]
SIDDIQUI ET AL.
,N
rely heavily on self-report mearefore of concern to researchers
Single et al., 1975). As a result,
(as been devoted to identifying
lestions about personal drug use.
.cy of adolescent drug use self? drug use over time may result
recall, and random errors. When
s such as drug use, adolescents’
s the type of social image they
, adolescents may often conceal
.viors. Alternatively, some ado: in a disapproved behavior even
less” of adolescent responses to
.tenced by their misunderstandacteristics of the data collector
ection, confidentiality issues, or
the chance that adolescents will
. Recall bias makes it questionmber their past drug-use behav;uch as “ever using” drugs. For
once or twice may not consider
jmplexity of the responses re:d, or ambiguity in the wording
ses of inconsistent responses.
ie inconsistent reporting of drug
:n waves of a longitudinal ‘suries reflected uncertainty regardor. The results of Bachman and
“telescope” historical events: past into more recent time peIg-use data. In particular, they
: past month was roughly three
sorts of use during the past year.
lg use may have important connponent of accuracy, it does not guar.ime.
.
.-
INCONSISTENT RESPONSE
271
sequences for the validity of self-report data. As a result, other research has focused more closely on the identification of the behavioral characteristics of individuals, and aspects of their environment, that are associated with inconsistent
responses to survey questions about drug use.
Luetgert and Armstrong (1973) examined how anonymity, recency of use,
and frequency of use affected subjects’ self-reported marijuana use. Anonymity
did not affect whether the subjects identified themselves as users or nonusers, but
it did affect the type of use that the subjects reported. As anonymity decreased,
the subjects were less likely to report occasional present use (experimental use)
and more likely to report frequent past use. Bachman and O’Malley (1981) found
that among high school seniors, experimental users were much less likely to report “ever” or “current” use of drugs than extensive users. Their findings were
consistent with the findings of Mensch and Kandel (1988), who examined the
quality of drug data over two waves (1980 and 1984) of the National Longitudinal Survey of Youth (NLSY) and found that light users of drugs were less likely
than heavy users to acknowledge their use.
Users of illicit drugs may also be less likely to report their behavior than users
of licit drugs. According to Mensch and Kandel(l988), adolescents are especially
likely to underreport illicit drug use (other than marijuana). This is similar to the
results of Pedersen (1990) who used a longitudinal study of lifestyle and drug use
among Norwegian adolescents to investigate the reliability of self-reports of drug
use. In this study, 1,936 high school students (aged 12-19 years) completed a
written survey and completed it again 7-8 months later. The legal drug-use responses showed the highest consistency while the reports of hard drug use (e.g.,
amphetamine, heroin) showed the least consistency. The consistency of reports
of marijuana and inhalant use were somewhere in the middle.
While Single et al. (1975) argued that such inconsistencies in the responses
of experimental users were the result of recall error, Mensch and Kandel (1988)
claimed that this was more due to the tendency of some to give socially desirable responses with maturation. If this were the case, then subjects who are more
likely to conform to societal norms with age would be more inconsistent in their
responses over time. This would be due to the fact that their views regarding the
social acceptability of their earlier responses had changed at a later interview date.
In this regard, Mensch and Kandel(1988) found that of the subjects who reported
experimental marijuana use (1-5 times) in 1980, 19% of females compared to
14% of males denied ever using marijuana in 1984. They interpreted this as females being more likely to be concerned with the social desirability of a response
to marijuana use than males. The same study also concluded that inconsistent
responses varied significantly by race/ethnicity, with Blacks and Hispanics being more inconsistent than Whites. Further support for this notion was provided
by Fendrich and Vaughn (1994) who used the same data set to investigate the
underreporting of lifetime marijuana and cocaine use. The results of this study
272
.
SIDDIQLJI ET AL.
also suggested that a high rate of underreporting was evident for both marijuana
and cocaine use, with Blacks and Hispanics displaying higher underreporting than
Whites.
While the level of current use, the type of substance used, anonymity, gender, and race have all been found to be significantly associated with the
underreporting of drug use among adolescents, other studies have not found significant relationships in this regard. For example, Needle et al. (1983) compared
drug-use self-reports taken from an in-school survey of 155 7th11 th grade adolescents, with self-reports taken from the same students while they were in their
homes in the company of their families. Measures of within-wave and acrosswave inconsistencies were used. They found that adolescent self-reports were, in
most cases, reliable and valid, and that the setting in which the respondents completed questionnaires did not, in general, result in any systematic reporting bias.
O’Malley et al. (1983) examined data from a nationwide survey of 2,589 high
school seniors (19761978) and a follow-up survey 1 or 2 years after graduation.
Personal characteristics (gender, race/ethnicity) were not associated with inconsistencies in self-reports of substance use. Bailey et al. (1992) explored the consistencies of self-reports of the frequency of use and age of first use of alcohol
and marijuana in a sample of 5,770 secondary school students. Two waves of data
were collected between 1985 and 1988 using self-administered instruments. In
contrast to the results of Pedersen (1990), results showed that self-reports were
more consistent for lifetime marijuana use than for alcohol use. However, the
reliability of reported age of first use was very low for both substances. Finally,
Simon et al. (1996) examined reports of the onset of smoking taken from 2,597
junior high school students in 7th and 8th grades. They found that students’
intrapersonal characteristics including risk-taking, self-esteem, perceived stress,
perceived susceptibility to social influence, and friends’ smoking were not associated with their misreporting on a smoking onset question.
A summary table for the above literature review is given in the Appendix.
It conveys that the validity of self-reports of drug use remain ambiguous. A number of studies concluded that self-reports of drug use were reliable and valid
(Needle et al., 1983, 1989; O’Malley et al., 1983; Pedersen, 1990; Single et al.,
1975). However, Bachman et al. (1981), Bailey et al. (1992), Fendrich and
Vaughn (1994), Luetgert and Armstrong (1973), and Mensch and Kandel(1988)
concluded that self-reports of drug use were not highly reliable. In these latter
studies the level and type of current drug use (Bachman et al., 1981; Mensch and
Kandel, 1988; Pedersen, 1990), anonymity (Luetgert and Armstrong, 1973),
gender (Mensch and Kandel, 1988), and raceiethnicity (Fendrich and Vaughn,
1994; Mensch and Kandel, 1988) were significant predictors of inconsistent responses.
It can also be seen that existing multivariate research in this area (Fendrich
and Vaughn, 1994; Mensch and Kandel, 1988) is based on the analysis of a single
INCONSISTENI
data set (the b
has used univ
consistent resI
of the variabk
(in a longitud
a multivariate
prehensively c
cents (i.e., ger
religiosity, ant
over time.
While the
ethnicity may
no multivariatl
and risk-takin;
For example,
attendance mz
haviors such a
use at a baseli
in a socially d
lead to a reca
associated wit
at Time 1 ma)
partly reflect :
b) they may bc
when, in fact,
criminality to
tions (Harrisc
extent that de
1985), those v
to inconsisten
could include
ful of researcl
terview, or to 4
For these rest
among adole:
sponses to qu
In these s
as predictors 1
hol, cigarette!
longitudinal c
tors of denyin
earlier wave.
SIDDIQUI ET AL.
vas evident for both marijuana
ring higher underreporting than
>stance used, anonymity, genficantly associated with the
ter studies have not found sigNeedle et al. (1983) compared
ey of 155 7611 th grade adoidents while they were in their
:s of within-wave and acrossdolescent self-reports were, in
.n which the respondents comany systematic reporting bias.
ionwide survey of 2,589 high
,’ 1 or 2 years after graduation.
ere not associated with incon:t al. (1992) explored the conmd age of first use of alcohol
31 students. Two waves of data
‘-administered instruments. In
showed that self-reports were
or alcohol use. However, the
1 for both substances. Finally,
of smoking taken from 2,597
s. They found that students’
self-esteem, perceived stress,
ends’ smoking were not assoquestion.
ew is given in the Appendix.
se remain ambiguous. A num: use were reliable and valid
Pedersen, 1990; Single et al.,
et al. (1992), Fendrich and
rd Mensch and Kandel(1988)
iighly reliable. In these latter
man et al., 198 1; Mensch and
tgert and Armstrong, 1973),
ticity (Fendrich and Vaughn,
predictors of inconsistent reesearch in this area (Fendrich
used on the analysis of a single
1
.
INCONSISTENT RESPONSE
213
data set (the National Longitudinal Survey of Youth). Research on other samples
has used univariate analyses on two waves of data to find the predictors of inconsistent responses. Univariate analysis can not consider the independent effects
of the variables under study or the correlation structure of a variable over time
(in a longitudinal design). As a result, the goal of this current research is to use
a multivariate methodology and a data set from Southern California to more comprehensively examine the associations between several characteristics of adolescents (i.e., gender, race/ethnicity, family structure, risk-taking, perceived stress,
religiosity, and academic achievement) and the inconsistent reporting of drug use
over time.
While the literature review indicates that factors such as gender and race/
ethnicity may be predictors of inconsistent responses to questions about drug use,
no multivariate research has focused on the roles that adolescent religiosity, stress,
and risk-taking tendencies may play as determinants of the same (see Appendix).
For example, adolescents who have strong ties to religion and church/temple
attendance may be less willing to consistently report engaging in “deviant” behaviors such as drug use over time. While they may have indicated personal drug
use at a baseline measurement, perceived pressures to answer drug-use questions
in a socially desirable manner that are associated with increased religiosity could
lead to a recanting of earlier reports of drug use over time. Stress may also be
associated with inconsistency similarly. Adolescent smokers who are under stress
at Time 1 may be more likely to misreport at Time 2 because, a) their stress may
partly reflect perceived negative social responses to their current smoking, and
b) they may be able to reduce some of this stress by saying that they don’t smoke
when, in fact, they do. Finally, some recent literature which links deviance and
criminality to inconsistent reporting on drug use may also inform our expectations (Harrison and Hughes, 1997) concerning adolescent risk-taking. To the
extent that deviant adolescents are more likely to be risk-takers (Elliot et al.,
1985), those who score higher on a risk-taking measure may also be more likely
to inconsistently report their drug use over time. Reasons for this inconsistency
could include the increased tendency of deviant youth to be suspicious or fearful of research interviewers, to perceive a stigmatization of drug use by the interview, or to define current use as high frequency use (Magma and Kang, 1997).
For these reasons we suspect that increased religiosity, stress, and risk-taking
among adolescents may be significant determinants of inconsistencies in responses to questions about drug use.
In these analyses we considered these and other sociodemographic indices.
as predictors of the consistency of adolescents’ self-reports of ever using alcohol, cigarettes, and marijuana. Specifically, we used a multilevel analysis in a
longitudinal cohort study with four waves of data to identify significant predictors of denying ever having used a substance after having admitted using it at an
earlier wave.
274
SlDDIQLJl ET AL.
INCONSISTENT
METHODOLOGY
Subjects
The data set for this study was collected as part of the Television, School and
Family Project (TVSFP), a longitudinal study of adolescent smoking prevention
in Los Angeles and San Diego (Flay et al., 1988, 1995). This project used a longitudinal cohort design with randomization of schools to various treatment conditions. Interventions were delivered to students within classrooms. A cohort of
6,695 students from 287 classrooms within 47 public schools was surveyed with
self-administered questionnaires at four time points. In the cohort, 757 students
were present at only one time point. As a result, they were dropped from the
analysis. The remaining 5,938 children were present at a minimum of two successive time points, and their information is analyzed in this paper. The survey
questionnaire covered demographics, tobacco use, other drug use, and psychosocial correlates of substance use. A detailed description of informed consent as
well as of procedures for assuring the subjects confidentiality can be found in Flay
et al. (1988). The questionnaire was administered by trained data collectors and
took 45 minutes to complete. These data collectors were project staff whom the
students had come to know quite well in group discussions over the course of the
study. The project staff had an average of 10.5 years of classroom teaching experience (including an average of 3 years of junior high school teaching) prior
to the implementation of this curriculum, and the majority had previously taught
a drug education program (Sob01 et al., 1989). Within schools, classrooms were
randomly selected and letters describing the study were sent to the parents of the
children. Although they were made aware of the optional nature of the data collection, less than 8% of the subjects did not participate due to parental or personal
reasons.
The students were pretested in January 1986 when they were in the seventh
grade (wave A), and completed an immediate postintervention questionnaire in
April 1986 (wave B). A l-year follow-up was given in April 1987 in the eighth
grade (wave C), and a second year follow-up in April 1988 in the ninth grade
(wave D). The pretested students were 49.3% male and 50.7% female. They were
35.2% Hispanic, 32.8%White, 15.2% Black, and 16.8% Asian. By the 2-year
follow-up, 3,155 (47%) of the original sample was present. At that .time, 47.6%
were male, 36.1% were Hispanic, 34.8% were White, 10.3% were Black, and
18.6% were Asian. A breakdown of the overall, consistent, and inconsistent
samples by the independent variables is presented in Table 1. Our precise operational definitions of consistency and inconsistency are described in more detail
in the Univariate Analysis section.
An analysis of the factors affecting attrition in this study (those lost from the
baseline to the 2-year follow-up) suggested that those who dropped out tended
.Th
Sample characteris
Gender:
Male
Female
Race:
African-America
White
Hispanicnatino
Asian
Risktakers:
Not risk takers
Risk takers
Stress during last 3
Did not feel
Felt
Religiosity:
Never go to chur
SometimesIalwa:
Livingstatus:
Living with both
Not living with b
Academic performa
Usual grade A
Usual grade B
Usual grade C
Usual grade D or
‘Subjects who had 1
waves.
bSubjects who had a
across all waves.
to have lower ac
and be more lik
was not a signit
likely than otha
1996). Thus, p
underrepresentel
prevalence of in
As a result, we c
1NCONSlSTENT RESPONSE
SIDDlQUl ET AL.
275
Table 1.
.The Distribution of the Independent Variables in the Sample:
Overall and by Reporting Consistency
elevision, School and
: smoking prevention
is project used a lonuious treatment conrsrooms. A cohort of
Is was surveyed with
cohort, 757 students
re dropped from the
minimum of two sucis paper. The survey
ug use, and psychoinformed consent as
can be found in Flay
1 data collectors and
eject staff whom the
ver the course of the
ssroom teaching exhool teaching) prior
Id previously taught
~ls, classrooms were
to the parents of the
ture of the data colparental or personal
were in the seventh
.on questionnaire in
I 1987 in the eighth
! in the ninth grade
; female. They were
sian. By the 2-year
4t that time, 47.6%
% were Black, and
t, and inconsistent
Our precise operaibed in more detail
(those lost from the
lropped out tended
Sample characteristics
Gender:
Male
Female
Race:
African-American
White
Hispanic/Latin0
Asian
Risk takers:
Not risk takers
Risk takers
Stress during last 30 days:
Did not feel
Felt
Religiosity:
Never goto church
Sometimes/always go to church
Living status:
Living with both parents
Not living with both parents
Academic performance in school:
Usual grade A
Usual grade B
Usual grade C
or F
Usual grade D
.
Consistent reporters’
(N = 4,597)
Inconsistent
reporters
(N= I.3411
Total sample
(N = 5.9381
46.8
53.2
57.5
42.5
49.3
50.7
14.0
33.8
35.2
17.1
19.4
29.6
35.1
15.9
15.2
32.8
35.2
16.8
80.1
19.9
74.5
25.5
79.1
20.9
75.4
24.6
78.4
21.6
75.9
24.1
26.6
73.4
30.4
69.6
27.3
72.7
70.2
29.8
69.0
31.0
69.9
30.1
6.7
42.4
39.1
11.7
4.3
39.1
43.4
13.2
6.2
41.7
40. I
12.1
*Subjects who had consistent responses to ever using alcohol, cigarettes, and marijuana across all
waves.
bsubjects who had at least one inconsistent response to ever using alcohol, cigarettes, and marijuana
across all waves.
to have lower academic achievement, have lower tobacco and health knowledge,
and be more likely to be cigarette smokers and marijuana users. While gender
was not a significant predictor of dropout, African-Americans were also more
likely than other raceiethnicities to have been’lost to follow-up (Siddiqui et al.,
1996). Thus, problem-prone subjects (substance users) may be somewhat
underrepresented in these analyses, and it could be expected that estimates of the
prevalence of inconsistent reporting among these subjects may be conservative.
As a result, we considered attrition as a predictor of inconsistent responses in the
276
SIDDIQUI ET AL.
model to test whether the attriters were more likely to provide inconsistent reporting during the time they were in the study as compared to subjects continually present at all four waves.
Variables of Interest and Their Measures
Measurement of Individual Inconsistency Scores. Individual inconsistency scores for ever using alcohol, cigarettes, and marijuana were calculated
separately at waves B, C, and D. Answers given at one wave were compared to
those given at the preceding wave to measure individual inconsistency scores.
Thus, transitions between any two waves are essentially a 2x2 contingency table
(Table 2). Specifically, the numerator of the inconsistency measure are those
subjects who claimed to have used a given substance at time X, and then claimed
“never” to have used that substance at a time X+ 1 (cell “yes, no” in Table 1).
The denominator of the inconsistency measure is all subjects in each of the four
cells in Table 2. We calculated inconsistency scores for ever using cigarettes,
alcohol, and marijuana in this manner.
Race/Ethnic@ and Gender. Dummy variables representing Black, Hispanic, and Asian racial/ethnic categories (White is the reference group) were
created from the race/ethnicity variable. Gender was coded as 1 = male or 0 =
female.
Risk-Taking. Risk-taking preference was assessed by three items: a) It is
worth getting into trouble to have fun, b) I like to take risks, and c) I enjoy doing things people say shouldn’t be done. The possible responses of each of the
items were 1 = this describes me very well, 2 = fairly well, . . . , 5 = not at all.
Since the three items form a reliable scale (Cronbach alpha = .78), we summed
them together and averaged them to form the risk-taking scale score. For the
univariate analyses, we dichotomized the scale as 0 = I do not like to take risks
(responses: 3,4, and 5) or 1 = I like to take risks (responses: 1 and 2). This measure is described further by Collins et al. (1987) and Sussman et al. (1990).
Table 2.
Possible Responses to “Ever Using” a Drug
in between Two Adjacent Waves
Ever use
Wave I + I
No
Wave I
No
Yes
(no, no)
(yes, no)
Yes
(no, yes)
(yes, yes)
INCONSISTEN-I
Perceive1
four items: a)
thing that hap
unable to cant
have you felt r
angered becat
possible respo
4 = often. The
adapted from
and averaged
(only in the u
or 1 = stress (
Religiosil
asked. The re:
With WI
as 0 = not wit
Academi
assessed by “I
were 1 = A, 2
sured at wave
Dropout
tionnaires we]
to follow-up a
jects who wer
ered as stayer
Statistical P
We cannc
independent, 1
that assume in
surveyed on n
highly likely
ments nested
anced design t
dents within 1
multilevel dat
effects modelr
models (Jenm
SIDDIQUI ET AL.
Ire likely to provide inconsistent retdy as compared to subjects continuwes
ency Scores. Individual inconsistes, and marijuana were calculated
iven at one wave were compared to
re individual inconsistency scores.
essentially a 2x2 contingency table
e inconsistency measure are those
bstance at time X, and then claimed
: X + 1 (cell “yes, no” in Table 1).
.e is all subjects in each of the four
y scores for ever using cigarettes,
variables representing Black, Hishite is the reference group) were
ier was coded as 1 = male or 0 =
LS assessed by three items: a) It is
: to take risks, and c) I enjoy doJossible responses of each of the
= fairly well,. . . , 5 = not at all.
mbach alpha = .78), we summed
risk-taking scale score. For the
as 0 = I do not like to take risks
; (responses: 1 and 2). This mea) and Sussman et al. (1990).
.
‘sing” a Drug
Waves
eI+l
Yes
(no, yes)
(yes, yes)
INCONSISTENT RESPONSE
277
Perceived Stress. The perceived stress of each subject was assessed by
four items: a) In the last month, how often have you been upset because of something that happened unexpectedly? b) In the last month, how often have you felt
unable to control the important things in your life? c) In the last month, how often
have you felt nervous and stressed? d) In the last month, how often have you been
angered because of things that happened that were outside of your control? The
possible responses to each of the items were 1 = never, 2 = rarely, 3 = sometimes,
4 = often. The four items form a reliable scale (Cronbach alpha = .83) which is
adapted from the work of Cohen et al. (1983). We summed the items together
and averaged them to form the perceived stress scale score. We dichotomized
(only in the univariate analyses) the scale as 0 = no stress (responses: 1 and 2)
or 1 = stress (responses: 3 and 4).
Religiosity. A question “How often do you attend church or temple” was
asked. The responses were 0 = never or 1 = sometimes or often.
With Whom Subject Lives. The living status of the subjects was coded
as 0 = not with both parents or I = lives with both parents.
Academic Achievement. The academic achievement of each subject was
assessed by “What grades do you usually get in school.” The possible responses
were 1 = A, 2 = B, 3 = C, and 4 = D, F. All of the above variables were measured at waves B, C, and D.
Dropout Status. Subjects who completed each of the four waves of questionnaires were considered as stayers (coded as I), and subjects who were lost
to follow-up at successive waves were considered as dropouts (coded as 0). Subjects who were absent in one wave but present at later waves were also considered as stayers.
Statistical Analysis
We cannot assume that the behaviors of students within each classroom are
independent, hence traditional statistical models, including logistic regression,
that assume independence are not appropriate for this analysis. Each student was
surveyed on multiple occasions, thus the multiple responses of each student were
highly likely to be correlated. The data are multilevel data (repeated measurements nested within students nested within classrooms) and represent an unbalanced design (the repeated measurements within students and the number ofstudents within classrooms are not constant). Statistical analysis of unbalanced
multilevel data has been developed under a variety of names, including randomeffects models (Gibbons et al., 1988; Laird and Ware, 1982), random-coefficients
models (Jennrich and Schlucher, 1986), variance component models (Dempster
278
SIDDlQUl ET AL.
et al., 1981), hierarchical linear models (Bryk and Raudenbush, 1982), multilevel
models (Jennrisch and Schlucher, 1986), two-stage models (Bock, 1989), and
mixed models (Longford, 1987). Advantages of random-effects regression models
(RRM) over the traditional statistical models for the analysis of longitudinal data
have been described (Gibbons et al., 1988), and include an ability to allow for
missing observations, subjects measured at different time points, and estimation
of random person-specific effects. Covariates included in the model can be either time-variant or time-invariant. We used a random-effects ordinal regression
model described by Hedeker and Gibbons ( 1994) and implemented in the MIXOR
computer program (Hedeker, 1993) for the data analysis. Since at present this
procedure has only been developed for two-level data (Hedeker, 1993), our strategy was to tit the two-level model of students within classrooms (treating classrooms as a random effect) and repeated observations within students (treating
students as a random effect). It was always the case that the program was not able
to estimate a nonzero student variance term for the repeated observations within
the student model. We then based our results on the students within classrooms
model. The dependent variables in the models were the inconsistency scores (1
= yes or 0 = no) at the three time points (waves B, C, and D). Among the independent variables, gender and race were time-invariant, and the remaining variables were time-variant.
RESULTS
Univariate Analysis
.
The percentages of students by racelethnicity and gender who had inconsistent responses at each wave on each of the three questions assessing whether they
ever used cigarettes, alcohol, and marijuana are shown in Table 3. The percentages of inconsistent responses of ever using alcohol at times Tl-T2, T2-T3, and
T3-T4 were 6.82, 5.05, and 5.32, respectively. The corresponding percentages
for cigarettes were 4.64,4.29, and 5.61, respectively. The percentages of inconsistent responses of ever using marijuana at times Tl-T2, T2-T3, and T3-T4
were 2.1, 2.5, and 2.8. The percentages of inconsistent responses of ever using
alcohol decreased over time, but for cigarettes and marijuana they increased over
time. Between 8.6 and 10.4% of the respondents gave inconsistent response at
least once to each of the three drug questions between any two time points. About
17% of the subjects who gave inconsistent responses to ever using cigarettes also
gave inconsistent responses to questions concerning ever using alcohol and marijuana. Of the subjects who were present at each of the four time points, 24.3%
had at least one inconsistent response across time.
Male subjects had higher percentages of inconsistent responses than female
subjects for each of the three drug questions at each time point. Blacks and His-
INCONSISTENT
SIDDIQUI ET AL.
Raudenbush, 1982), multilevel
3ge models (Bock, 1989), and
rdom-effects regression models
le analysis of longitudinal data
include an ability to allow for
:nt time points, and estimation
luded in the model can be eijam-effects ordinal regression
rd implemented in the MIXOR
analysis. Since at present this
ata (Hedeker, 1993), our strattin classrooms (treating classions within students (treating
that the program was not able
repeated observations within
le students within classrooms
‘e the inconsistency scores (1
C, and D). Among the indeiant, and the remaining vari-
Id gender who had inconsisitions assessing whether they
wn in Table 3. The percentat times Tl-T2, T2-T3, and
: corresponding percentages
r. The percentages of inconTl-T2, T2-T3, and T3-Ti
ent responses of ever using
arijuana they increased over
ve inconsistent response at
any two time points. About
to ever using cigarettes also
ver using alcohol and marihe four time points, 24.3%
itent responses than female
ime point. Blacks and His-
INCONSISTENT RESPONSE
280
INCONSI:
SIDDIQUI ET AL.
panics had higher percentages of inconsistent responses than Whites and Asians
(the percentages of inconsistencies for Whites and Asians were very close to each
other). Similar patterns of inconsistent responses by gender and race/ethnicity
were also found for each of the drugs and at each time point. The subjects who
liked to take risks had higher percentages of inconsistent responses on each of
the three drugs between time 1 and time 2. Differences in the rates between high
and low risk-takers decreased across waves however. The subjects who score high
on the stress scale had lower inconsistent response rates to each of the three questions than the subjects who scored higher, though the differences were very small.
Subjects who never attended church had higher inconsistency rates of ever using alcohol, cigarettes, and marijuana compared to subjects who attended church.
Subjects who lived with both parents had lower rates of inconsistent responses
for each of the three drugs compared with subjects who did not live with both
parents (these differences also decreased over time). Subjects with higher academic achievement had lower inconsistent response rates than subjects with lower
academic achievement.
The prevalence of ever using alcohol, cigarettes, and marijuana with respect
to the total sample and by race/ethnicity and gender is shown in Table 4. Prevalence rates for alcohol use were the highest (58.7-78.7%), while rates for marijuana were the lowest (I 2.0-25.4%). Prevalence rates for cigarette use were intermediate (42.4-59.1%). The percentages for each of the drugs increased over
time. The prevalence rates for each of the drugs was lowest for Blacks, highest
for Whites, and intermediate for Hispanics and Asians. For each of the drugs,
males had higher prevalence rates than females.
Table 5 shows the prevalence of ever using alcohol, cigarettes, and marijuana
with respect to the subjects who had consistent responses (defined as the consistent group) across waves and those who had at least one inconsistent response
(defined as inconsistent group) in ever using alcohol, cigarettes, or marijuana
across waves. The inconsistent group had higher prevalence rates of ever using
alcohol than the consistent group at wave A, but at waves B, C, and D the prevalence was lower for the inconsistent group than for the consistent group. For ever
using cigarettes and marijuana, the prevalence rates were higher for the inconsistent group at the first three waves, and lower at the fourth wave. These patterns held across race/ethnicity and gender.
Tt
R.
G
‘b
Multivariate Analysis
.,
Table 6 shows the parameter estimates and the P-values in parentheses from
the random-effects regression models for each of.the three drugs. We modeled
the inconsistent responses (yes/no) of ever using a drug (e.g., alcohol) across time,
and as a function of time, demographic variables, intrapersonal variables, and
dropout
observec
not signi
juana. T
response
porting 1
at early 1
marijuar
Blat
cigarette
of incon:
SIDDIQUI ET AL.
ponses than Whites and Asians
I Asians were very close to each
s by gender and racelethnicity
h time point. The subjects who
onsistent responses on each of
ences in the rates between high
‘er. The subjects who score high
: rates to each of the three questhe differences were very small.
inconsistency rates of ever us) subjects who attended church.
rates of inconsistent responses
cts who did not live with both
me). Subjects with higher aca;e rates than subjects with lower
tes, and marijuana with respect
ler is shown in Table 4. Preva-78.7%), while rates for marirates for cigarette use were inch of the drugs increased over
was lowest for Blacks, highest
Asians. For each of the drugs,
:ohol, cigarettes, and marijuana
sponses (defined as the consiseast one inconsistent response
:ohol, cigarettes, or marijuana
prevalence rates of ever using
.t waves B, C, and D the pr&ar the consistent group. For ever
ltes were higher for the inconat the fourth wave. These pat-
e P-values in parentheses from
f the three drugs. We modeled
drug (e.g., alcohol) across time,
s, intrapersonal variables, and
.
281
INCONSISTENT RESPONSE
Table 4.
Percentages of Respondents Ever Using Alcohol,
Cigarettes, and Marijuana at Different Waves:
For the Total Sample and by Race/Ethnicity and Gender=
Wave A
Wave B
Wave C
Wave D
A
C
M
58.7
42.4
12.0
62.1
46.8
14.2
71.7
55.1
20.2
78.7
59.1
25.4
A
C
M
A
C
M
A
C
M
A
C
M
43.7
33.1
9.0
70.8
46.1
14.3
57.0
45.1
12.0
54.4
38.3
10.3
47.8
41.6
12.3
74.3
49.9
16.6
61.1
48.8
13.8
58.4
41.9
11.0
61.0
45.9
19.0
82.8
58.1
23.3
72.2
58.3
19.3
70.0
51.6
15.0
66.8
52.7
27.2
86.4
60.9
29.5
77.7
61.7
24.1
76.6
54.6
20.2
A
C
M
A
C
M
55.8
39.8
8.3
61.6
45.0
15.6
61.3
43.8
9.9
64.9
50.2
18.4
74.0
54.9
16.7
73.6
55.9
23.3
81.2
60.5
23.5
77.4
57.7
27.7
Total sample:
Race:
Blacks
Whites
Hispanics
Asians
Gender:
Females
Males
-~
‘A: alcohol, C: cigarettes, M: marijuana.
-
\
dropout status. The intrapersonal variables were time-varying, that is, they were
observed at the time we measured the inconsistent responses. The time effect was
not significant for inconsistent responses regarding ever using alcohol and marijuana. The time effect was significant and had a positive effect on inconsistent
responses regarding ever using cigarettes. This means that the subjects were reporting more inconsistent responses to ever using cigarettes at later waves than
at early waves. This was not the case with respect to the reporting of alcohol and
..
marijuana use.
Blacks provided inconsistent responses regarding ever using alcohol and
cigarettes significantly more than Whites. Hispanics had significantly higher rates
of inconsistent responses regarding ever using alcohol than Whites, but their rates
282
INCON:
SIDDIQUI ET AL.
Table 5.
Percentages of Respondents Ever Using Alcohol, Cigarettes, and Marduana at Different
Waves: By Reporting Consistency, Race/Ethnic& and Gender’
Group1 (subjects who had consistent
responses in ever using alcohol,
cigarette, and marijuana across the
waves: N = 4,597)
Group 2 (subjects who had at least one
inconsistent response in ever using
alcohol, cigarette, and marijuana
across the waves; N = I ,34 1)
Race:
Blacks:
Group I
Group 2
Wave A
Wave B
Wave C
Wave D
A
C
M
56.8
36.1
6.6
60.0
42.7
9.0
66.4
52.1
16.0
73.7
57.5
23.4
A
C
M
76.0
45.8
18.8
52.0
47.9
16.8
63.0
53.2
19.2
58.5
51.1
16.6
Male:
‘A: alcc
A
C
M
A
C
M
38.6
25.9
2.5
69.9
35.3
17.5
45.0
36.1
5.4
45.6
39.4
16.0
60.4
43.3
13.9
54.7
41.5
12.6
72.3
49.0
24.8
48.5
45.8
22.2
A
C
M
A
C
M
68.8
37.8
7.5
81.9
49.7
21.5
78.1
43.4
10.0
53.6
52.0
18.7
86.5
54.2
18.3
67.6
58.5
24.6
90.7
61.4
30.6
67.5
51.8
14.9
A
C
M
A
C
M
52.9
36.8
6.9
79.2
50.4
19.9
62.9
45.0
9.5
49.4
47.6
14.8
72.7
55. I
15.9
67.1
57.7
19.4
82.2
60.7
23.7
60.3
56.3
19.6
A
C
M
A
C
M
50.1
37.0
6.6
68.6
36.6
12.5
59.2
40.9
7.6
52.7
41.2
11.5
71.9
50.4
13.0
53.9
44.7
15.0
79.5
57.0
20.8
57.0
43.7
11.8
for ci!
White
the ral
sisten
cantly
sisten
not lil
using
better
to eve
nifica
cigarc
of the
F
ceive,
spans
mode
nal ri
(attrit
not a
the ti
Whites:
Group 1
Group 2
Hispanics:
Group I
Group 2
Asians:
Group 1
Group 2
.,
Gender:
Females:
Group 1
A
C
M
54.3
35.6
5.5
64.9
40.9
7.1
77.5
52.6
13.9
85.0
60.9
23.6
(conrimed)
1
perce
from
SIDDIQUI ET AL.
INCONSISTENT RESPONSE
283
Table 5. Continued
t, Cigarettes, and Manjuana at Different
?ace/Ethnicity, and Gender a
ave A
Wave B
Wave C
Wave D
6.1
6.6
60.0
42.7
9.0
66.4
52.7
16.0
73.7
57.5
23.4
5.0
i.8
.8
52.0
47.9
16.8
63.0
53.2
19.2
58.5
51.1
16.6
6
9
5
3
I
45.0
36.1
5.4
45.6
39.4
16.0
60.4
43.3
13.9
54.7
41.5
12.6
72.3
49.0
24.8
48.5
45.8
22.2
78.1
43.4
10.0
53.6
52.0
18.7
86.5
54.2
18.3
67.6
58.5
24.6
90.7
61.4
30.6
67.5
51.8
14.9
62.9
45.0
9.5
49.4
47.6
14.8
72.7
55.1
15.9
67.1
57.7
19.4
82.2
60.7
23.7
60.3
56.3
19.6
.
59.2
40.9
7.6
52.7
41.2
11.5
71.9
50.4
13.0
53.9
44.7
15.0
79.5
57.0
20.8
57.0
43.7
11.8
6.8
Wave A
Wave B
Wave C
Wave D
Group 2
A
C
M
74.1
42.1
12.9
46.7
43.3
10.1
59.4
52.4
15.5
61.1
52.2
13.5
Group 1
A
C
M
A
C
M
59.8
36.8
8.0
77.4
48.3
23.1
67.3
45.1
11.3
53.1
50.7
19.9
75.0
53.0
18.6
65. I
54.5
21.4
82.3
56.7
28.4
59.5
52.0
20.0
Males:
Group 2
‘A: alcohol, C: cigarettes, M: marijuana.
for cigarettes and marijuana were not significantly different from the rates for
Whites. The inconsistency rates for Asians were not significantly different from
the rates for Whites with regard to each of the three drug questions. The inconsistent rates of ever using marijuana for Blacks and Hispanics were not significantly different from the rates for Whites. Males gave significantly more inconsistent responses than females to each of the three questions. Subjects who did
not live with both parents gave significantly more inconsistent responses to ever
using marijuana than the subjects who lived with both parents. Subjects with
better academic achievement also had significantly fewer inconsistent responses
to ever using marijuana. Living status and academic achievement were not significant predictors of inconsistent responses of ever using alcohol and smoking
cigarettes. No three-way or two-way interaction effects were significant in any
of the three models.
We also considered several intrapersonal variables such as risk-taking, perceived stress, and church attendance as possible predictors of inconsistent responses of ever using alcohol, cigarettes, and marijuana in the random-effects
models. None of the variables was significant, so we dropped them from our final random-effects regression models. We also considered the dropout status
(attrition) of the subjects as a predictor of inconsistency responses. Attrition was
not a significant predictor in any of the three models, so it was also dropped from
the final models.
DISCUSSION
64.9
40.9
7.1
77.5
52.6
13.9
85.0
60.9
23.6
(continued)
These analyses suggest several interesting findings. In the overall sample the
percentage of inconsistent responses in the reporting of ever using alcohol ranged
from 5.05 to 6.82% between any two time points (Table 3). For cigarettes this
Table 6.
Significant Predictors of Inconsistent Responses to Ever Using Alcohol, Cigarettes, and Marijuana: Final Multivariate
Model
Dependent measures
Predictors
Time
Race:
Black
Hispanic
Asian
Gender
Living status
Academic achievement
In alcohol parameter (P-value)
.679(001)
.208(.028)
.178(.112)
.289(<.001)
-
In cigarette parameter (P-value)
.010(.022)
.435(<.001)
.112(.310)
.089(.454)
.382(<.001)
-
In marijuana parameter (P-value)
.041 (.884)
.029(.890)
-.340(.191)
.736(<.001)
,598 (.009)
.612 (C.001)
SIDDIQUI ET AL.
INCONSISTENT RESPONSE
285
percentage varied from 4.29 to 5.6 1, and for marijuana use it varied from 2.1 to
2.8. We calculated these percentages based on the subjects who responded at time
“X” that they had ever used drugs but responded at time “X + 1” that they had
never used drugs.
We found that the percentages of inconsistent responses for ever using cigarettes were less than the percentages for ever using alcohol at early time points.
At later time points these differences were diminished, however. The percentage
of inconsistent responses for marijuana use was the lowest of all the drugs. This
is consistent with the findings of Bailey et al. (1992) who found greater inconsistencies in self-reports of alcohol as compared to marijuana use. It is possible
that subjects may be more likely to change their reports over time, or be more
susceptible to recall error, when asked to report on past licit drug use (which
occurs with relatively high frequency) as compared to past marijuana use. However, the notion that marijuana use is a “rarer” statistical event relative to cigarette or alcohol use may also, in part, explain the reduced number of inconsistencies associated with its reporting. That is, from a statistical perspective one
might anticipate that the rarer an event, the less the likelihood of inconsistencies
appears. Everything else being equal, the greater the percent never using a substance at time X, the less the possibility of an inconsistent response (defined as
it is in this and most other studies) from time X to time X + 1.
When inconsistency rates were considered by gender and race, several trends
were notable. The Black and Hispanic adolescents were more inconsistent in
responding to ever using drugs than the White adolescents. Mensch and Kandel
(1988) and Fendrich and Vaughn (1994) also found this to be the case. Such
differences by race/ethnicity might be the result of culturally-based confusion of
definitions of ever using drugs, intentional erroneous reporting, or some other
sources. The effects of raceiethnicity might also be confounded with other factors. Socioeconomic status (SES), for instance, might be linked to inconsistencies in self-reports of drug use and to the race/ethnicity of a subject. In this regard, the definition of ever use might differ across ethnic or SES groups such that
experimental or lower-level users might classify themselves as nonusers, and this
classification criterion might vary systematically by ethnic and socioeconomic
standing.
Male adolescents also gave more inconsistent responses than the female
adolescents in all cases. In the multivariate analysis, gender remained a significant predictor of inconsistency for each of the three drug use questions. Therefore, we can conclude that the female adolescents reported their drug use more
reliably than the male adolescents. This stands in contrast to Mensch and KandEl’s
(1988) findings that female adolescents were more inconsistent than male adolescents in answering questions about ever using marijuana. These differences
might be due to the different ages of the two study cohorts. Our cohort was
SIDDIQUI ET AL.
286
younger (12-15 years old) than the cohort ( 1927 years old) used by Mensch and
Kandel (1988). In addition, Mensch and Kandel (1988) relied on a univariate
analysis of the gender difference while our findings are based on both univariate
and multivariate analyses.
Finally, coming from a “broken” family structure and getting poorer grades
in school were significant predictors of inconsistent responses to ever using
marijuana. However, these factors were not significant predictors of inconsistent
responses to the questions regarding alcohol and cigarette use. The affirmative
reporting of marijuana use reflects the subjects’ participation in an activity which
is illegal for adults, whereas the reporting of cigarette and alcohol use does not.
In addition, living status and academic achievement may serve as proxies for a
subjects’ social class. Thus, these findings are consistent with the notion that
youth in lower socioeconomic circumstances may feel a greater need to “lie” in
response to questioning about illegal activities as they grow older. This would
reflect an increasing lack of willingness to acknowledge personal participation
in illegal activities to adults, or those in “authority,” over time.
LIMITATIONS
.
While these analyses extend existing research in this area in several important ways, they also have some limitations of their own. First, the rate of attrition in this longitudinal sample is not insignificant. Largely because of the transfer
from junior to senior high-school, half of the original subjects were lost from the
sample by the final wave of the study. In many important respects (i.e., gender,
perceived stress, risk-taking, and religiosity) the attriters were not different from
those subjects who remained in the study at the final wave. However, the subjects who dropped out did report greater lifetime use of cigarettes and marijuana.
Thus, as in other longitudinal studies, it remains a possibility that drug-use behaviors among inner-city high-school students are somewhat underrepresented
in this sample. However, we also expect that this underrepresentation of problem-prone youth would conservatively bias our prevalence estimates of inconsistent reporting among high school adolescents (Magura and Kang, 1997). Second, these data are drawn from an urban sample of children from Southern
California. As many of the existing health and social problems confronting adolescents are often concentrated among inner-city youth, this is a very relevant
sample for these analyses. At the same time, these results require replication in
a sample which reflects the geographic and socioeconomic characteristics of the
United States as a whole (and, indeed, other countries as well) before wider generalizations can be made. Third, these analyses included no control for the socioeconomic situation of the adolescents. As has been suggested above, many of
the associations between raceiethnicity, family structure, academic achievement,
and inconsistent reporting may reflect underlying linkages between socioeco-
INCONSISTEN
nomic status
be structured
proxy for SE:
analyses, 0th
For example,
at time “r tl
had never us
“X” might he
consistency,
baseline, ma:
jects use dru
definition the
which this is
between the
responses mi
complexity 1
(Lorenz, 199
ing; howevet
be addressed
These li
useful pract3
of substance
using multiv
to study incc
appropriate :
are indepenc
correct for tl
adolescents
drug-use quc
move beyor
sponses. For
sistent respo
levels of risk
not signitica
Confoundin.
meaningful
In long:
sponses are
indicate tha
SIDDIQUI ET AL.
s old) used by Mensch and
88) relied on a univariate
e based on both univariate
nomic status and the consistency of drug-use self-reports. Further analyses should
be structured to test our assertions about the extent to which these factors may
proxy for SES effects. Fourth, in addition to the inconsistencies measured in these
analyses, other types of inconsistent responses are possible but not measurable.
For example, we defined inconsistent reporters as those subjects who responded
at time “X’ that they had ever used drugs but responded at time ‘X+ 1” that they
had never used drugs. However, some of those who had, in reality, used at time
“X’ might have indicated that they had never used at that time. This type of inconsistency, where subjects hide the fact that they have ever used drugs at the
baseline, may in all likelihood occur as frequently as inconsistencies where subjects use drugs and then “change their minds” at later time points. However, by
definition the censoring of the data does not allow us to determine the extent to
which this is the case. Finally, all of our analyses presume linear relationships
between the dependent and independent variables. The debate on inconsistent
responses might benefit from newer artificial paradigms (chaos, uncertainty, or
complexity theories) that do not presume linear systems of cause and effect
(Lorenz, 1993; Casti, 1994). Analysis strategies of these types are very promising; however, they are beyond the scope of the present investigation and should
be addressed in future research.
and getting poorer grades
: responses to ever using
predictors of inconsistent
rette use. The affirmative
bation in an activity which
and alcohol use does not.
ray serve as proxies for a
tent with the notion that
a greater need to “lie” in
grow older. This would
ge personal participation
ler time.
is area in several imporI. First, the rate of attrily because of the transfer
ejects were lost from the
nt respects (i.e., gender,
were not different from
ave. However, the subigarettes and marijuana.
ibility that drug-use be:what underrepresented
representation of probnce estimates of incon1 and Kang, 1997). Sec‘lildren from Southern
blems confronting adothis is a very relevant
s require replication in
ic characteristics of the
Yell) before wider genno control for the sogested above, many of
academic achievement,
:es between socioeco-
281
INCONSISTENT RESPONSE
CONCLUSION
.
These limitations not withstanding, the results of this study offer several
useful practical and theoretical implications for empirical researchers in the field
of substance use. Methodologically, these findings suggest the importance of
using multivariate methodologies which can account for multiple levels of data
to study inconsistent responses. To this point, prior analyses have made the inappropriate assumption that the behaviors of students nested within classrooms
are independent of each other. In these analyses we used multilevel modeling to
correct for this assumption and found that several demographic characteristics of
adolescents remained significant predictors of their inconsistent responses to
drug-use questions over time. These findings also suggest that it is important to
move beyond the univariate analyses of the determinants of inconsistent responses. For example, although our descriptive analyses revealed that the inconsistent response rates for ever using alcohol and cigarettes were different by the
levels of risk-taking, perceived stress, and church attendance, these variables were
not significant predictors of inconsistent responses in tbe multivariate analyses.
Confounding effects such as these can be misleading to those looking to draw
meaningful theoretical conclusions from uncontrolled analyses.
In longitudinal smoking prevention studies, subjects with inconsistent responses are often overlooked, deleted, or ignored. The results from this study
indicate that this may lead to important misrepresentations of substance use
288
.
SIDDIQUI ET AL.
among high school adolescents. About 24.3% of the subjects gave at least one
inconsistent response to questions about ever using drugs across time. In addition, there was a tendency for subjects who reported inconsistently with regard
to one type of drug to misreport on other types of substance use as well. These
inconsistent responses did not occur randomly. Significant predictors of inconsistent responses to ever using alcohol and cigarettes were gender and race. Gender, living status, and academic achievement were significant predictors of inconsistent responses to ever using marijuana. Perhaps more importantly, the
inconsistent group had differential prevalence rates of each of the three drugs over
time compared to the rates for the consistent group. Thus, this study reveals that
reporting a) the percentages of inconsistencies in self-reports of substance use and
b) the methods used to handle such inconsistencies are essential for any longitudinal analyses of lifetime substance use among adolescents. Simon et al. (in press)
made a recommendation that future longitudinal researchers identify misreporters
and omit them from their analyses. However, the results of this study suggest that
such a deletion of inconsistent responders might result in biased prevalence estimates of ever using drugs. For this reason, researchers need to be cautions in
calculating the prevalence rates of lifetime substance use based on adolescent selfreports in longitudinal studies.
APPENDIX: ANALYSES OF INCONSISTENT RESPONSES
TO ADOLESCENT SUBSTANCE USE
A summary table of the existing literature is presented on the next two pages.
SIDDIQUI ET AL.
of the subjects gave at least one
tsing drugs across time. In addiborted inconsistently tvith regard
of substance use as well. These
Significant predictors of inconettes were gender and race. Genrere significant predictors of inPerhaps more importantly, the
es of each of the three drugs over
ntp. Thus, this study reveals that
self-reports of substance use and
ies are essential for any longitulolescents. Simon et al. (in press)
researchers identify misreporters
results of this study suggest that
t result in biased prevalence essearchers need to be cautions in
nce use based on adolescent self-
YSTENT RESPONSES
TANCE USE
presented on the next two pages.
.
.. ^
___-
c
Authors and year
Data source
Number of subjects
Needle et al., 1983
Survey conducted by the
authors. spring 1980, USA
I.55 juniorlsenior high school
students and their families
O’Malley et al..
1983.
Monitoring the Future Project,
a study of high school
seniors conducted by the
Institute for Social Research. 1976 through 1978.
USA
Two wwcs of data were
collected by the author.
1985 and 1988. USA
2.589 high school seniors
Bailey et al.. 1992
Aim of study
Method of data collection
Type of analysis
RCSUitS
To test for differences in
adolescents responses on a
substance we questionnaire
as a function of social
setting
To measure the frequency of
drug use and related
attitudes
Self-reported in classroom and
self-reported at home with
their families participating
Univariate analysis
Adolescent self-reports are, in
most cases. reliable and valid
Self-reported in classroom
Correlation analysis between
time points of responses
5.770 secondary school
sludcnts
To explore the consistency of
self-reports of the frequency
of “se and age of tint use of
alcohol and marijuana
Self-reported in classroom
Univariate analysis
Gender and race were not
associated with inconsistencies
in self-reports of substance
use. Discrepant reports of
marijuana and alcohol use
WcrC COrrelatcd within time
Self-reports were nwre consistent for lifetime and marijuana
UK thsn alc0h01 use. Reliability of reported age of first use
was w-y low for both
substances
The consistency dropped over
time. The legal drug-use
r~spotts~ showed the highest
WnSiStCnCy: hsrd d”I8S
showed the lowest consistency
Students intrapersonal
characteristics were not
associated with their
misreporting of the smoking
onset question
Pedersen. 1990
Two WBWS of data were
collected by the author.
1989. Norway
1.936 high school students
(age 12-l 9 years)
To measure the reliability of
self-reports of drug use
Self-reported in classroom
Univariate analysis
Simon et al.. 1996
Cigarette smoking assessment
project. 1989 and 1990.
USA
2.597 junior high school
students
To measure the prevalence of
SttIOking
Self-reported in classrwm
Univariate analysis
m
P
I-
INCONSISTENT RESPONSE
291
ACKNOWLEDGMENTS
Collection of data for this research was supported by Grant ROl-DA0348.
The analyses reported here were completed with support from Grants ROlDA06307 and ROl -DA 10306 from the National Institute on Drug Abuse.
REFERENCES
BACHMAN, J. G., and O’MALLEY, P. M. (1981). When four months equal a year: lnconsistenties in student reports of drug use. Public Opin. Q. 45: 536-548.
BAILEY, S. L., FLEWELLING, R. L., and RACHAL, J. V. (1992). The characteristics of inconsistencies in self-reports of alcohol and marijuana use in a longitudinal study of adolescents.
J. Stud. Alcohol 53: 636-647.
BENSON, P., and DONAHUE, M. (1989). Ten-year trends in at-risk behavior: A national study of
African-Americans adolescents. J. Adolesc. Res. 4: 125-139.
BOCK, R. D. (1989). Measurement of human variation: A two stage model. In R. D. Bock (Ed.),
Multilevel Analysis o/Educational Data. New York, NY: Academic Press.
BROOK, J. S. (1993). lnteractional theory: Its utility in explaining drug use behavior among African-American and Puerto Rican youth. Drug abuse among minority youth. In M. R. De La Rosa
and J. L. Recio Adrados (Eds.), Methodological Issues andRecent Research Advances (NIDA
Research Monograph 130). Rockville, MD: National Institute of Drug Abuse.
BRYK, A. S., and RAUDENBUSH (I 982). Hierarchical Linear Models: Applications and Data
Analysis Models. Newbury Park, CA: Sage.
CASTI, J. L. (1994). Complexijication: Explaining a Paradoxical World through the Science of
Surprise. New York, NY: Harper Collins.
COHEN, S., KARARCK, T., and MERMELSTEM, R. (1983). A global measure of perceived stress,
J. Health Sot. Behav. 24: 38%396.
COLLINS, L. M., SUSSMAN, S., RAUCH, J. M., DENT, C. W., JOHNSON, C. A., HANSEN, W.
B., and FLAY, B. R. (1987). Psychosocial predictors of young adolescent cigarette smoking:
A sixteen month, three wave longitudinal study. J. Appl. Sot. Psychol. 17: 554-573.
DEMPSTER, A. P., RUBIN, D. B., and TSUTAKAWA, R. K. (1981). Estimation in covariance
components models. J. Am. Sfat. Assoc. 76: 341-353.
ELLIOT, D. S., HUIZMGA, D., and AGETON, S. S. (1985). Explaining Delinquency and Drag Use.
Beverly Hills, CA: Sage.
FENDRICH, M., and VAUGHN, C. M. (1994). Diminished lifetime substance use over time. Pubtic Opin. Q. 58: 96-123.
FLAY, B. R., BRANNON, B. R., JOHNSON, C. A.,HANSEN, W. B., ULENE, A. L., WHITNEYSALTIEL, D. A., GLEASON, L. R., SUSSMAN, S., GAVIN, M. D., GLOWACZ, K. M.,
SOBOL, D. F., and SPIEGAL, D. C. (1988). The television, school and family smoking cessation and prevention project: 1. Theoretical basis and program development. Prev. Med. 17:
585-607.
FLAY, B. R., MILLER, T. Q., HEDEKER, D., SIDDIQUI, O., BRITTON, C. F., BRANNON, B.
R., JOHNSON, C. A., HANSEN, W. B., SUSSMAN, S., and DENT, C. (I 995). The television,
school and family smoking prevention and cessation project: VIII. Student outcomes &id mediating variables. Prev. Med. 24: 29-40.
FIORE, M., NOVOTNY, T., PIERCE, J., HATZIANREU, E., PATEL, K., and DAVIS, R. (1990).
Trends in cigarette smoking in the United States: The changing influence of gender and race.
JAMA 261: 4!%55.
292
SIDDIQUI ET AL.
GIBBONS, R. D., EATERNAUX, C., HEDEKER, D., and DAVIS, J. M. (1988). Random regression models: A comprehensive approach to the analysis of longitudinal psychiatric data.
Psychopharmacol. Bull. 27: 73-77.
HARRISON, L., and HUGHES, A. (Eds.) (1997). The Validity ofSe[f-ReportedDrug Use: Improving the Accuracy o/Survey Esrimafes (NIDA Research Monograph 167). Rockville, MD: National Institute of Drug Abuse.
HEDEKER, D. (1993). MIXOR: A Fortran Program for Mixed-Effects OrdinalProbir and Logistic
Regression. Technical Report. Chicago, IL: Prevention Research Center, School of Public
Health, University of Illinois at Chicago.
HEDEKER, D., and GIBBONS, R. D. (I 994). A random-effects ordinal regression model for multilevel analysis. Biometrics 50: 933-944.
JENNRICH, R. I., and SCHLUCHER, M. D. (1986). Unbalanced repeated-measures models with
structured covariance matrices. Biometrics 42: 805-820.
LAIRD, N. M., and WARE, J. H. (1982). Random effects models for longitudinal data. Biometrics
38: 963-974.
LONGFORD, N. T. (1987). A fast scoring algorithm for maximum likelihood estimation in unbalanced mixed models with nested random effects. Biometriha 74: 817-827.
LORENZ, E. N. (1993). The Essence of Chaos. Seattle, WA: University of Washington Press.
LUETGERT, M. L., and ARMSTRONG, A. H. (1973). Methodological issues in drug usage surveys:
Anonymity, recency, and frequency. Inr. J. Addict. 8: 683-689.
MAGURA, S., and KANG, S. (1997). The validity of self-reported cocaine use in two high risk
populations. In L. Harrison and A. Hughes (Eds.), The Validiv of Self-Reported Drug Use:
Improving the Accuracy of Survey Estimates (NIDA Research Monograph 167, pp. 227-246).
Rockville, MD: National Institute of Drug Abuse.
MARTIN, G. L., and NEWMAN, I. M. (I 988). Assessing the validity of self-reported adolescent
cigarette smoking. J. Drug Educ. 18: 275-284.
MENSCH, B. S., and KANDEL, D. B. (1988). Underreporting of substance use in a national longitudinal youth cohort: Individual and interviewer effects. Public Opin. Q. 52: 100-124.
NEEDLE, R. H., JOU, S., and SU, S. S. (I 989). The impact of changing methods of data collection
on the reliability of self-reported drug use of adolescents. Am. .I. Drug Alcohol Abuse 15: 275289.
NEEDLE, R. H., McCUBBIN, H., LORENCE, J., and HOCHHAUSER, M. (1983). Reliability and
validity of adolescent self-reported drug use in a family-based study: A methodological report.
Int. J. Addict. 18: 901-912.
O’MALLEY, P. M., BACHMAN, J. G., and JOHNSTON, L. D. (1983). Reliability and consistency
in self-reports of drug use. Inr. J. Addict. 18: 805-824.
PEDERSEN, W. (1990). Reliability of drug use responses in a longitudinal study. &and. J. Psycho/.
31: 28-33.
PULKKINEN, L. (1993). Youthful smoking and drinking in a longitudinal perspective. J. Youth
Adolesc. 12: 253-283.
SIDDIQUI, O., FLAY, B. R., and HU, F. B. (1996). Factors affecting attrition in a longitudinal
smoking prevention study. Prev. Med. 25: 554-560.
SIMON, T. R., SUSSMAN, S., DENT, C. W., and STACY, A. W. (1996). Predictors of misreporting
cigarette smoking initiation among adolescents. Eval. Rev. 20(S): 552-568.
SINGLE, E., KANDEL, D., and JOHNSON, B. (1975). The reliability and validity of drug use responses in a large scale longitudinal survey. J. Drug Issues 5: 426-443.
SOBOL, D. F., ROHRBACH, L. A., DENT, C. W., GLEASON, L., BRANNON, B. R., JOHNSON,
C. A., and FLAY, B. R. (1989). The integrity of a smoking prevention curriculum delivery.
Health Educ. Res. 4: 5967.
.
INCON:
sUSSM
CE
UNITE
To
mc
Prl
Sn
WINDL
tot
99
Este 1
adoles
y mari
de inc
lo laq
signifi
alcohb
vivieo
incon:
coinci
de la :
Cette
tions
adole
sexe
cigan
des I
concc
les nl
const
l’utili
rep01
l’util
SIDDIQUI ET AL.
D., and DAVIS, J. M. (1988). Random regresthe analysis of longitudinal psychiatric data.
le Validity of Self-Reported Drug Use: Improv-
esearch Monograph 167). Rockville, MD: Nafor Mixed-Effects Ordinal Probit and Logistic
revention Research Center, School of Public
lom-effects ordinal regression model for mulUnbalanced repeated-measures models with
.05-820.
‘ects models for longitudinal data. Biometrics
for maximum likelihood estimation in unbalBiomelrika 74: 8 1 l-827.
e, WA: University of Washington Press.
Methodological issues in drug usage surveys:
:I. 8: 683-689.
f self-reported cocaine use in two high risk
.), The Validity of Self-Reported Drug Use:
)A Research Monograph 167, pp. 227-246).
sing the validity of self-reported adolescent
porting of substance use in a national longiffects. Public Opin. Q. 52: 1 O&1 24.
pact of changing methods of data collection
scents. Am. J. Drug Alcohol Abuse IS: 275IOCHHAUSER, M. (1983). Reliability and
mily-based study: A methodological report.
‘I, L. D. (I 983). Reliability and consistency
324.
s in a longitudinal study. &and. J. Psycho/.
.
ng in a longitudinal perspective. J. Youth
actors affecting attrition in a longitudinal
(, A. W. (1996). Predictors of misreporting
rl. Rev. 20(5): 552-568.
The reliability and validity of drug use re: Issues 5: 426-443.
SON, I-., BRANNON, B. R., JOHNSON,
smoking prevention curriculum delivery.
INCONSISTENT RESPONSE
293
SUSSMAN, S., DENT, C. W., STACY, A. W., BUCIAGA, C., RAYNOR, A., TURNER, G. E.,
CHARLIN, V., CRAIG, S., HANSEN, W. B., BURTON, D., and FLAY, B. R. (1990). Peer
group association and adolescent tobacco use. J. Abnorm. Psychol. 99: 349-352.
UNITED STATES DEPARTMENT OF HEALTH AND HUMAN SERVICES (I 994). Preventing
Tobacco Use among Young People. A Report ofthe Surgeon General. Atlanta, GA: US Department of Health and Human Services, Public Health Service, Centers for Disease Control and
Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on
Smoking and Health.
WINDLE, M. A. (I 990). Longitudinal study of antisocial behaviors in early adolescence as predictors of late adolescent substance use: Gender and ethnic group differences. J. Abnorm. Psychol.
99: S&91.
RESUMEN
Este estudio examina 10s “predictores” de respuestas inconsistentes de
adolescentes a preguntas acerca de si algun vez han probado alcohol, cigarrillos,
y marijuana. Adolescentes masculiones a tuvierren significamente altos niveles
de inconsistencia en respuestas a acerca de1 alcohol, cigarillos, y marijuana. A
lo largo que las adolescentes femeninas. Negros y Hispanos (adolesc.) tuveron
significamente altos niveles de respuestas inconsistentes acerca de haber usado
alcohol y cigarillos (solo negros) que adolescentes blancos. El “estatus de
vivienda” y hazarias academicas jueron predictores signiticantes de respuestas
inconsistentes acerca de haber usado marijuana. En si, (asique) 10s resultados
coincidan con la notion que respuestas inconsistentes puedan “bias” la estimation
de la prevalencia de haber usado drogas en un analyses multivariados.
RliSUMl!
Cette etude examine les “predicteurs” des responses des adolescents aux questions au sujet de l’utilisation de l’alcool des cigarettes, et de la marijuana. Les
adolescents du sexe masculin avaient des taux pies eleves que les adolescents du
sexe feminin concernant l’utilisation pendant un certain temps de l’alcool, eds
cigaretes et de la marijuana. Les adolescents noirs et hispaniques avaient des taux
des reponses inconsistantes considerablement plus eleves que les blancs
concernant si ils avaient jamais utilises de l’alcool et des cigarettes (seulment pour
les noirs). Les conditions de vie de ces sujets et leur reussities academiques
constituaient des signes “predicteurs” des reponses inconsistant au sujet de
l’utilisation de la marijuana. Ainsi ces resultats sont d’accord avec l’idee que les
reponses inconsistantes peuvent influencer l’opinion de la frequence de
l’utilisation constant de la drogue dans les analyses “multivariates.”
P