Copyright 1994 by the American Psychological Association, Inc., and
the Division of Health Psychology/0278-6133/94/$3.00
Health Psychology
1994, Vol. 13, No. 1,73-85
Attitudes and Health Behavior in Diverse Populations: Drunk Driving,
Alcohol Use, Binge Eating, Marijuana Use, and Cigarette Use
Alan W. Stacy, Peter M. Rentier, and Brian R. Flay
Five different health behaviors (cigarette use, alcohol use, binge eating, illicit drug use, and drunk
driving) were studied prospectively in 5 different groups of subjects. Associations between attitudes
toward these behaviors and the behaviors themselves were investigated over at least 2 waves of
measurement. Findings revealed that attitudes predicted behavior nonspuriously in 2 instances:
alcohol use and marijuana use. Attitudes did not predict drunk driving, binge eating, or smoking
behaviors. Past behavior predicted attitude in the domains of binge eating and smoking, but not in
the domains of alcohol use, drunk driving, or marijuana use. The results are discussed in terms of
several alternative approaches that have implications for interventions that attempt to influence
health behavior through attitude change.
Key Words: attitudes, health, behavior, longitudinal, expectancy, drugs
Fishbein & Ajzen, 1975; Speckart & Bentler, 1982). However,
comprehensive evaluations of the predictive impact of attitudes on health behavior have been rare, and the case for
attitude as a strong natural precursor to behavior may not be as
strong as some of the basic research would suggest. Our goal
was to investigate the predictive precedence of attitude and
health behavior over time, so that the usefulness of the attitude
construct in health can be more thoroughly examined. First, it
is important to summarize briefly the predominant definitions
of attitude and some of the early research on this construct.
Programs designed to promote changes in health behavior
frequently include components that attempt to change attitudes concerning the targeted behavior. Sometimes the focus
on attitude change is an explicit component, in which an
individual's evaluations of the behavior are targeted. In other
cases, as in many educational media campaigns (e.g., Flay,
1987), several key or "salient" beliefs are the focus of the
strategy, which more implicitly attempts attitude change by
first trying to change beliefs (cf. Ajzen & Fishbein, 1980).
Programs that provide information about the harmful consequences of smoking, the dangers of drunk driving, or the
necessity of a balanced diet often represent, implicitly or
explicitly, an attempt to change behavior through attitude
change. These programs may focus on harmful outcomes of a
negative health behavior, attempting to make personal evaluations (attitude) of the behavior more negative, or on beneficial
outcomes of a positive health behavior, attempting to make
evaluations of the healthy behavior more positive. Because of
the widespread use and potential of attitude concepts in
health-behavior research and interventions, it is important to
document the predictive impact of this construct and to
integrate theories of health behavior with empirical findings.
The rationale for attitudinal approaches in health-behavior
interventions has been strengthened by findings in basic
research documenting the association between attitudes and
behavior (Bentler & Speckart, 1981; Fazio & Williams, 1986;
Attitude Definitions and Behavioral Prediction
In the theory of reasoned action (Ajzen & Fishbein, 1980;
Fishbein & Ajzen, 1975), attitude is thought to represent a
general evaluation of an object, and affect is considered the
most essential aspect of attitude (also see Thurstone, 1931). In
another dominant view, attitude is considered to be multidimensional, composed of correlated but somewhat independent
affective, cognitive, and conative components (Rosenberg &
Hovland, 1960). The affect-based definition of attitude, in
which an overall evaluation of an object or a behavior is
emphasized, is probably the more common one in present
research in social psychology and health, and this is the
definition we use in this article. A focus on this definition of
attitude does not imply that cognitive factors are not important
in health behavior, but instead suggests that cognitive variables
are better construed as separate but related constructs (e.g.,
Ajzen & Fishbein, 1980; Stacy, Widaman, & Marlatt, 1990).
Attitudinal research has frequently focused on the strength
or nature of attitude-behavior relations, attempting to shed
light on the predictive validity of attitude concepts. Although
early research often found poor correlations between attitude
and behavior (e.g., Corey, 1937; LaPiere, 1934; for review, see
Wicker, 1971), Fishbein and Ajzen (1975; Ajzen & Fishbein,
1980) suggested that relations between attitudes and behavior
would be stronger when measurements of these variables
correspond highly in certain properties. Specifically, Ajzen and
Alan W. Stacy, Department of Preventive Medicine, Institute for
Prevention Research, University of Southern California, Peter M.
Bentler, Department of Psychology, University of California, Los
Angeles; Brian R. Flay, Prevention Research Center, School of Public
Health, University of Illinois, Chicago.
Support for this research was provided by Grant DA01070-20 from
the National Institute on Drug Abuse. The views expressed herein are
ours and do not necessarily reflect the opinions of the Public Health
Service.
Correspondence regarding this article should be addressed to Alan
W. Stacy, Institute for Prevention Research, University of Southern
California, 1000 S. Fremont, #641, Alhambra, California 91803-1358.
73
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A. STACY, P. BENTLER, AND B. FLAY
Fishbein's (1977) review of the literature identified strong
attitude-behavior associations across a number of studies in
which at least the target (object) and action (e.g., drinking)
elements of measurement corresponded highly.
Fishbein and Ajzen's (1975) approach is consistent with the
position that attitudes have predictive priority over behavior
(e.g., Bentler & Speckart, 1981). Other positions relevant to
health behavior include the view that behavior causes attitude
(Bern, 1978), attitudes and behavior influence each other
reciprocally (Kelman, 1974), and the influence of attitude on
behavior depends strongly on moderator variables, such as
individual differences, in the accessibility of attitudes or beliefs
from memory (Fazio & Williams, 1986; Stacy, Dent, et al.,
1990) or in personality characteristics (e.g., Snyder, 1974).
Although these latter perspectives have made contributions to
the study of moderators of attitude-behavior relations, most
health-behavior campaigns focus on attitude and behavior
change rather than on attitude-moderator change. Thus, at
least in health-behavior research, it is important to determine
the precedence of attitudes and behavior as direct-effect
predictors of one another. Surprisingly, questions about the
predictive priority of attitudes and behavior have been addressed rarely in health-behavior research.
Alcohol Use and Drunk Driving
Existing evidence about attitude-behavior relations in alcohol-use research can be considered mixed. Kahle and Herman
(1979) found that attitudes showed predictive predominance
over behavior in a cross-lagged panel correlational investigation of alcohol consumption over a 2-month period (also see
Schlegel, Manske, & d'Avernas, 1985), but this type of statistical analysis has been criticized on several grounds (Rogosa,
1980). In a structural equation analysis, Bentler and Speckart
(1979; also see Stacy, Widaman, & Marlatt, 1990) found
evidence that attitude toward alcohol use predicted drinking
behavior 2 weeks later, although they did not perform an
examination of predictive precedence. In a 3-year prospective
study, Johnson (1988) found no evidence that attitudes about
drinking and drinking behavior predicted one another over
time, in either of the possible directions. No studies have
investigated the predictive precedence of attitudes and behavior regarding alcohol-related problem behaviors, such as
driving under the influence of alcohol (DUI). However, the
call for attitude change regarding alcohol use frequently
accompanies discussions of the social costs of this and other
problems associated with alcohol use.
Illicit Drug Use
Attitudes and Specific Health Behaviors
Smoking
The influence of attitudes on smoking onset and cessation is
important because attitude-change strategies are frequently
used in large mass media programs (Farquhar, 1991; Flay,
1987) and school-based interventions (e.g., Bruvold, 1990).
Although little is known at present about the critical elements
of successful interventions in this area (Flay, 1987; Snow,
Gilchrist, & Schinke, 1985), theoretically sound attitudechange strategies are in principle quite amenable to mass
media efforts and school-based interventions. Because most
adult smokers begin to smoke in adolescence (Flay, d'Avernas,
Best, Kersell, & Ryan, 1983; Leventhal & Cleary, 1980), this
age group seems particularly important for research and
intervention. Charlton and Blair (1989; but see Jarvis, Goddard, & McNeil, 1990) reported evidence that attitudes among
initial nonsmokers predicted adolescent smoking behavior
prospectively, but they did not investigate the predictive
precedence of attitude and behavior over time. In other
instances, attitudes that do not correspond in level of specificity to this behavioral target and action (smoking) have been
studied in smoking research, including attitudes toward self or
toward health (e.g., Brunswick & Messeri, 1984). Consistent
with Ajzen and Fishbein's (1977) predictions and findings, a
lack of correspondence in the target or action element of
attitudes and behavior has led to low attitude-behavior relationships. Despite the widespread use of attitude concepts in both
interventions and theories of adolescent smoking, no studies
have investigated the predictive precedence of attitudes toward smoking and smoking behavior in this population.
As with tobacco and alcohol use, many intervention efforts
attempt some form of direct or indirect attitude change in the
prevention and treatment of drug abuse (e.g., Pentz et al.,
1989). Although attitudes appear to be predictive of certain
types of illicit drug use (e.g., Akers, Krohn, Lanza-Kaduce, &
Radosevich, 1979), few studies have examined whether this
prediction is as strong as the prediction of attitudes by drug
use. If the prediction of attitudes by drug use is much stronger
than the prediction of drug use by attitudes, then it may be the
case that behavior must change before attitude changes. A
predictive predominance of behavior over attitude would have
implications for the focus of interventions.
The only existing evidence concerning the predictive precedence of attitudes and illicit drug use was reported by Johnson
(1988) and by Schlegel et al. (1985). Johnson found that
attitude and marijuana use did not predict one another over
time, and that only the stability (autoregressive) effects of the
same construct on itself over time were significant. Schlegel et
al. reported only preliminary information about predictive
precedence, as evaluated in a cross-lagged correlational design
of marijuana attitudes and behavior.
Binge Eating
During the past two decades, binge eating has appeared to
increase in frequency among young women (Polivy & Herman,
1985). Although the behavior of binge eating may be part of
the larger eating disorder of bulimia, people who binge eat
may vary continuously in their extent of disordered eating
behavior and concomitant symptoms. Research investigating
this behavior is important because of a number of negative
health consequences of the behavior (e.g., Hawkins & Clement, 1980), whether or not a clear, clinically relevant case of
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ATTITUDES AND HEALTH BEHAVIOR
bulimia is evident. Even when binge eating is not apparently
severe, the typical diet associated with binge eating is high in
fat and is likely to cause health consequences if the diet
continues for an extended period of time.
A number of studies have found that cognitive factors more
consistently trigger binge-eating behaviors than do physiological variables, such as caloric intake (e.g., Polivy, Herman,
Olmstead, & Jazwinski, 1984; Spencer & Fremouw, 1979), and
that certain types of attitudes are associated with this behavior.
However, the only types of attitudes that have been investigated are attitudes about one's body (for review, see BenTovim & Walker, 1991). Attitudes toward the target behavior
(e.g., Fishbein & Ajzen, 1975) have not been investigated as a
predictor of binge eating, so the degree to which binge eating is
under attitudinal control is unknown at present. Thus, although attitudinal approaches to primary prevention of binge
eating among adolescents and young adults may be theoretically feasible, the absence of empirical evidence showing a
relation between attitudes and behavior in this domain dampens the potential enthusiasm for such an approach.
Summary
Little is known about the precise relations between attitude
and the health behaviors we address in this article. When
attitudes have been studied, attitude and behavior usually have
not been measured prospectively over time in a way that would
reveal predictive precedence of one variable over the other
(e.g., Bentler & Speckart, 1981). We measured binge eating,
alcohol use, drunk driving, cigarette smoking, and illicit drug
use behaviors and attitudes in five different groups sampled
from populations in which these problem health behaviors are
relevant. Although there are many other variables relevant to
the study of attitude-behavior relations (e.g., Fazio & Williams, 1986; Madden, Ellen, & Ajzen, 1992; Stacy, Widaman,
& Marlatt, 1990), the only relevant variables available across
each of the present studies were measures of attitude and
behavior. As stated earlier, the investigation of the predictive
relations between attitude and health behavior is important in
its own right, especially when a range of different health
behaviors are investigated prospectively. Furthermore, many
health-promotion interventions using attitude-change strategies have assumed implicitly that attitudes have direct effects
on behavior, consistent with the predictive models we evaluated.
General Method
In each study reported below, we measured attitude and behavior
prospectively and used covariance structure analysis (e.g., Bentler,
1989; Bentler & Speckart, 1981; Bollen, 1989) to investigate the
predictive precedence of the two primary variables. We report results
based on maximum likelihood estimation. This form of estimation has
been found to be relatively robust to departures in multivariate
normality (e.g., Huba & Harlow, 1987), but we also evaluated each of
the covariance structure models using at least one other method of
estimation. We evaluated the statistical significance of paths using
chi-square difference tests and critical ratios; the overall goodness of
fit of the models was evaluated primarily through use of the nonnor-
med fit index (NNFI; Bentler & Bonett, 1980), which has been found
to be among the most robust indexes of fit regarding sample-size biases
(Marsh, Balla, & McDonald, 1988). We also report the normed fit
index (NFI; Bentler & Bonett, 1980).
In each of the studies, measures of attitude and behavior corresponded specifically in at least the target (e.g., cigarettes) and action
(e.g., smoking) elements of measurement, which have been found to
be most important in the study of attitude-behavior relations (Ajzen &
Fishbein, 1977). The context element (e.g., place where smoking
occurs) remained quite general in all of the studies, but corresponded
across measures of attitude and behavior. The time element (e.g.,
smoking in next month) corresponded very specifically in the three
short-term studies and more generally in the two longer-term studies.
For each behavioral domain we studied, only subjects who indicated
they had engaged in the relevant behavior were used in the analysis.
Thus, the different samples were equated for having at least minimal
experience with the behavior. Equating the samples in this way
reduced the possibility that attitude-behavior relations differed across
samples because of differences in relevant experience—a likely moderator of attitude effects. A number of findings in basic research show
that this moderator confound is likely, because subjects who differ in
direct experience with an object typically show different strengths of
attitude-behavior relations (for review, see Raden, 1985). In these
studies, the subjects with direct experience generally showed stronger
levels of prediction of behavior by attitudes. Clearly, some type of
control of level of experience is necessary if results from different
samples with varying levels of experience are to be compared. The
subjects in each study were predominantly middle class and White, but
represented several different age and nationality groups.
Study 1: Binge Eating
Method
Subjects. Respondents were 260 undergraduate female college
students who participated in the study for extra credit in introductory
psychology classes. Although 241 subjects completed all three waves of
measurement and provided complete data, only the subjects who
participated at all waves and who indicated they binge ate at some time
in the past year (N = 144) were included in data analysis. We sampled
only women because this problem health behavior generally is much
more frequent in women than in men.
Procedure. The survey was given at three different times, separated
by 2-week intervals. Respondents completed questionnaires at predesignated times in a classroom, group-testing situation. Questionnaire
identification numbers were used to allow for matching of questionnaires across waves; these numbers were self-generated by the subjects
according to a previously pilot-tested protocol that ensures a high level
of matches (95% or more) over a short period. The anonymity of the
questionnaire was emphasized both by the administrator and in
written statements in the questionnaire. Previous research has shown
that if anonymity is guaranteed fully, valid responses to socially
proscribed behaviors are likely even among young adolescents providing self-reports of drug use (Murray & Perry, 1987). Strong assurances
of anonymity appear to be as effective as are bogus pipeline procedures in studies of consummatory behavior (Murray & Perry, 1987).
Measures of behavior. The behavioral measures at each measurement asked respondents to report on their binge eating in the past 2
weeks. Before answering the binge-eating questions, respondents were
asked to read a common definition of binge eating: "Binge eating is the
rapid consumption of a large amount of food in a discrete period of
time, usually less than two hours." For this behavior, 18 activities were
76
A. STACY, P. BENTLER, AND B. FLAY
assessed that characterize the behavior, consistent with criteria from
the Diagnostic and Statistical Manual of Mental Disorders (3rd ed., rev.;
Z>SM-///-rR; American Psychiatric Association, 1987) and frequently
used definitions (Katzman & Wolchik, 1984; Polivy et al., 1984). These
items were consistent with most criteria of binge eating based on these
sources, but DSM-III-R criteria that refer more specifically to bulimia
were omitted (e.g., dieting and purging). Thus, in our study, binge
eating was used as a more general construct than bulimia per se. Most
items were scored from 1 to 7, with frequency items anchored by not at
all (1) and every day (7). The binge items assessed the number of days
respondents: binge ate, ate large amounts of food while feeling
compelled to do so, were out of control in their binge eating, hid binge
eating from others, could not stop eating voluntarily, ate an extreme
amount of a single type of food, consumed more than 1,200 calories
per binge, ate much more than needed beyond satisfying hunger, ate
much more than typical for body size, ate high-caloric and easily
ingestible food, felt depressed after binge eating, kept eating until they
thought they would explode, ate to the point of feeling sick, ate a large
amount of food when not hungry, felt food controlled their life, and ate
much more rapidly than most people ever eat. An additional item
asked respondents how much food they ate very rapidly when they
binge ate, coded from 1 (none) to 7 (a very large amount). A self-rating
item (see Stacy, Widaman, Hays, & DiMatteo, 1985) assessed how
respondents rated themselves regarding their binge eating, from 1
(non-binge eater) to 8 (very heavy binge eater).
To organize the binge items into factors, we composed three
indicators of binge eating by randomly assigning each of the aforementioned binge items to one of the indicators, resulting in three indicators
composed of simple sums of 6 items each. We conducted a preliminary
study to evaluate the construct validity of these measures. In that
study, the binge-eating measures were found to show adequate levels
of item convergence and discrimination when thorough multitraitmultimethod procedures were used (Stacy & Saetermoe, 1987). For
example, binge-eating indicators did not load on other behavioral
factors (dieting and alcohol use), all binge-eating loadings were
substantial (above .81), and none of the potential assessment method
factors appeared to bias the results.
Measures of attitude. Attitude at each time of measurement was
assessed with three indicators that each were simple sums of two items.
The items composing this scale were introduced with the statement
"Your binge eating in the next four weeks is": followed by six 7-point
bipolar adjective items ranging from extremely good to extremely bad,
extremely beneficial to extremely harmful, extremely rewarding to extremely
punishing, extremely pleasant to extremely unpleasant, extremely enjoyable
to extremely unenjoyable, and extremely favorable to extremely unfavorable. These measures are based on the recommendations of Ajzen and
Fishbein (1980).
Cross-lagged structural models. All of the models we tested used the
same measurement model, in which the scale of measurement was
determined by fixing Tl factor variances at 1.0 and imposing equality
constraints on loadings of identical indicators of the same construct
measured at the three different times. In addition, the measurement
model specified covariances among unique scores of identical indicators measured across the three times. These procedures are consistent
with those of Joreskog (1979).
Results
The initial, structurally saturated model specified all four
possible predictive paths from Tl attitude and binge factors to
T2 attitude and binge factors. An analogous set of four paths
was estimated among T2 and T3 attitude and binge factors and
among Tl and T3 attitude and binge factors. Covariances
between same-wave factors at Tl and same-wave residual
factor covariances at T2 and T3 also were freely estimated. We
used the chi-square value for this initial model (x2 [110,
N = 144] = 144.98,;? = .01) in model comparisons with morerestricted models. Thus, the paths in the initial model were
tested for statistical significance by deleting paths one at a time
from the initial model and evaluating the decrease in fit from
the initial model in single-degree-of-freedom chi-square difference tests (cf. Bentler & Speckart, 1981). Using this procedure, all nonsignificant (p < .05) paths, factor covariances,
and factor residual covariances were deleted from the initial
model, deriving the final model. This final model (x2 [118,
N = 144] = 154.97,;? = .01) fit the data well regarding practical indices of fit (NNFI = .99; NFI = .96). An alternative
estimator appropriate for relatively small samples, using elliptical distribution theory (Shapiro & Browne, 1987), yielded
identical results regarding significance tests of alternative
paths.
No single path, factor covariance, or factor residual covariance could be added to this final model to increase its fit
significantly, nor could any single estimate in the final model be
omitted without resulting in a significant decrease in model fit.
In a more global test, the final model was not significantly
different in fit from the initial model (x2 [8, N = 144] = 9.98,
p > .05). The only significant cross-lagged path in the final
model was from Tl behavior to T2 attitude. In addition,
covariances between Tl attitude and behavior factors and
between T3 attitude and behavior factor residuals were nonsignificant. The final model is shown in Figure 1, which presents
the standardized structural parameter estimates and residual
variances. Standardized factor loadings in the measurement
model all were statistically significant (allps < .001) and were
of adequate size, ranging from .71 to .96 for attitude and from
.96 to .98 for binge-eating. These moderate to large loadings
indicated that these indicators were measured with good
internal consistency; they were well above the minimum
loading sizes recommended prior to interpretation in factor
analysis (e.g., Gorsuch, 1983).
Study 2: College Alcohol Use
Method
Subjects. Respondents were 356 male and female undergraduate
college students enrolled at a small West Coast university. Of the total,
334 respondents provided complete data and completed both waves of
measurement. A subsample of respondents who indicated that they
had drunk at least some alcohol in the previous year was selected for
analysis (N = 270). The students received extra credit in an introductory-level psychology course for their participation in the study. These
data were part of a larger study on alcohol use (Stacy, Widaman, &
Marlatt, 1990). In the previous study, we did not examine a model in
which behavior could predict attitude over time. Because the present
cross-lagged structural model, which is parallel to the other models
analyzed in this article, was not analyzed in the previous study, it is
appropriate to report these analyses here.
Procedure. The procedure was identical to the one reported in
Study 1, except that we assessed each subject at two different times,
separated by a 4-week interval. Anonymity of responses was again
77
ATTITUDES AND HEALTH BEHAVIOR
.28***
Binge
Eating
Time 1
Attitude
Time 1
»>(
Binge
Eating
Time 3
Binge
Eating
Time 2
Attitude
.76***
/ Attitude
Time 3
Figure 1. Final structural equation model and standardized maximum likelihood estimates for binge
eating. (Large circles designate latent constructs. Small circles depict residual variances of factors. Path
coefficients are depicted with single-headed arrows. Correlations between factors or factor residuals are
depicted with double-headed arrows. Significance levels are based on critical ratios on unstandardized
maximum likelihood estimates [*p < .05; **p < .01; ***p < .001].)
thoroughly emphasized. Three indicators were developed for each
latent variable.
Measures. Measures of attitude were identical to those outlined in
Study 1, but were worded in terms of alcohol use. That is, subjects
responded to identical semantic differential attitude items but reported attitudes in terms of "your drinking alcohol in the next four
weeks." Alcohol-use measures were constructed to assess the general
extent (i.e., both quantity and frequency) of alcohol use during the past
4 weeks. The first indicator of alcohol use was the sum of two 7-point
scales, closely modeled after scales used by Bentler and Speckart
(1979,1981). Both scales assessed frequency of use; one had endpoints
of every day and not at all, and the second scale had endpoints of
extremely frequently and never. The second indicator used an 8-point
rating scale on which each respondent was instructed to classify
himself or herself into 1 of 8 drinker categories (very heavy drinker,
heavy drinker, fairly heavy drinker, moderate drinker, fairly light
drinker, light drinker, very light drinker, and nondrinker). The third
indicator was a quantity-frequency, or QF, index in which quantity and
frequency of typical use (number of drinks consumed in previous
month, based on typical quantity consumed on days the subject drank)
was summed with greatest use (greatest number of drinks the subject
consumed multiplied by the number of days the subject drank this
greatest amount). A thorough multitrait-multimethod assessment of
similar alcohol indicators has demonstrated their convergent and
discriminant validity (Stacy et ah, 1985). In addition, Stacy et al. found
evidence that method effects, such as general self-report biases, were
not strong influences on this type of data.
Cross-lagged structural models. The model construction and evaluation in this study were identical to that outlined in Study 1, except that
this model involved a two-wave design.
Results
The initial, structurally saturated model specified all four
possible predictive paths from Tl attitude and alcohol factors
to T2 attitude and alcohol factors. Covariances between
same-wave factors at Tl and same-wave residual factor covariances at T2 also were freely estimated. We used the chi-square
value for this initial model (x2 [46, N = 270] = 73.40, p < .01)
in model comparisons with more-restricted models, using the
same model testing and parameter trimming procedures as
reported in Study 1. Only one path, from Tl behavior to T2
attitude, was nonsignificant using this procedure, and this path
was eliminated to derive the final model. The final model (x2
[47, N = 270] = 74.98, p < .01) fit the data well according to
practical indices of fit (NNFI = .98; NFI = .97). Elliptical
theory and arbitrary distribution theory estimation (Bentler,
1989) led to the same final model. The final model did not
differ significantly from the initial model. The standardized
structural parameter estimates for the final model are provided in Figure 2. Standardized factor loadings ranged from
78
A. STACY, P. BENTLER, AND B. FLAY
.25'
Figure 2. Final structural equation model and standardized maximum likelihood estimates for college alcohol use. (Large circles
designate latent constructs. Small circles depict residual variances of
factors. Path coefficients are depicted with single-headed arrows.
Correlations between factors or factor residuals are depicted with
double-headed arrows. Significance levels are based on critical ratios
on unstandardized maximum likelihood estimates [*p < .05;**/> < .01;
"**p < .001].)
.82 to .86 for attitude and from .8 I to .93 for alcohol use (all
ps < .001).
Study 3: Alcohol Use and Drunk Driving
Among DUI Offenders
Method
Subjects. Respondents were 175 men who had been convicted of
driving under the influence (DUI) of alcohol and who were participating in an educational program mandated by the state of California.
Because all of the subjects drank alcohol and had engaged in DUI in
the recent past, no subjects were removed from the analysis. Out of
this sample, 164 respondents completed both waves of measurement.
Respondents were paid $5.00 to complete the questionnaire, which
was completely voluntary and anonymous.
Procedure. The procedure was identical to the one described in
Study 2. Subjects again were assessed at two different times, separated
by a 4-week interval. No subjects completed the program before the
final assessment. Respondents completed questionnaires on a voluntary basis after regular driver-education program meetings, in a
classroom setting. Anonymity of responses was again thoroughly
emphasized, but respondents were further assured that their participation in the study had nothing to do with the education program and
that no one, including the program staff, had any way to discover their
responses.
Measures of alcohol use behavior and attitude. Three indicators
were developed for each latent variable. Measures of attitude toward
alcohol use were identical to those outlined in Study 2, except that one
of three attitude indicators contained only one item because only five
attitude items were used in the questionnaire. Alcohol-use measures
included the three indicators used in Study 2, using the same
frequency, rating, and QF methods of measurement; similar alcoholuse indicators were validated by Stacy et at. (1985).
Measures of DUI behavior and DUI attitude. Attitude toward
driving under the influence of alcohol was assessed with a single
bipolar item that asked respondents to answer the following question
on a scale coded from 1 (extremely unfavorable) to 7 (extremely
favorable): "Your attitude toward or feeling about your driving after
drinking in the next month is?" This item is in the form of one of the
alternative methods of measuring attitude suggested by Ajzen and
Fishbein (1980). DUI behavior was measured by three items. One item
asked respondents how often they drove after drinking alcohol in the
past month, coded from 1 (not at all) to 7 (every day). A second item
asked respondents how often they drank at least three drinks right
before driving in the past month, coded on the same 7-point scale. The
third item asked respondents to rate what kind of drinker they were
when they drove after drinking in the last month, coded from 1
(nondrinker) to 8 (very heavy drinker).
Cross-lagged structural models. Two different domains of attitudebehavior relations (DUI and alcohol use per se) were examined in two
different series of models. For the alcohol-use domain, the models we
tested used the same model construction and evaluation procedures as
in Studies 1 and 2. For the DUI domain, the behavior factor at Tl and
T2 represented three indicators, as with the aforementioned alcohol
factor, but the attitude factors at both times were composed of a single
indicator, consistent with a standard path-analytic specification (e.g.,
Bentler, 1989). The model-evaluation procedure for the DUI domain
was the same as the procedure used in the alcohol-use domain and in
Studies 1 and 2.
Results
Alcohol Use. The chi-square values for the initial, saturated structural model (x2 [46, N = 164] = 53.44, p = .21)
were employed in model comparisons with more-restricted
models, using the same model-testing and parameter-trimming
procedures as reported in the previous studies. The two
cross-lagged paths and the covariance between the factor
residuals at T2 were found to be nonsignificant. These three
estimates were omitted to derive the final model. The final
model (x2 [49, N = 164] = 57.68,p = .19) fit the data very well
using practical indices of fit (NNFI = .99; NFI = .97). The
final model did not differ significantly from the initial model in
terms of model fit, and the significance of paths did not change
when we evaluated the models with alternative (elliptical and
arbitrary distribution) estimators. Standardized structural parameter estimates for the final model are reported in Figure 3.
Standardized factor loadings ranged from .81 to .97 for
attitude and from .89 to .96 for alcohol use (all/?s < .001).
Driving under the influence of alcohol. The initial model was
structurally saturated, as in the initial model for alcohol use.
The chi-square value for this initial model (x2 [15,
N = 164] = 50.00, p < .001) was to be used in model comparisons with more-restricted models, but more-restricted models
could not be estimated, because of linear dependencies in
estimation. Even when a model is mathematically identified,
such dependencies can be a product of "empirical
underidentification" (resulting from various peculiarities in
the data; Rindskopf, 1984). However, the structurally saturated model did not contain any dependencies or estimation
problems, and the statistical significance of parameter estimates therefore could be evaluated on the basis of z-tests from
this model (instead of model-trimming procedures). Neither of
the cross-lagged paths, from attitude to behavior or from
behavior to attitude, was significant at p < .05 according to
these tests. Standardized structural parameter estimates from
this model are provided in Figure 4. Standardized factor
loadings on DUI behavior ranged from .70 to .95 and each was
79
ATTITUDES AND HEALTH BEHAVIOR
statistically significant (all ps < .001); because it was a single
indicator, DUI attitude had no factor loadings. This model fit
the data somewhat less well than did the preceding final
models (NNFI = .92; NFI = .94). Use of the alternative estimators did not change the pattern of significance of structural
paths.
DUI-Offender
Drinking
Driving
Time 1
.60*
DUI-Offender
DrinkingDrlvlng
Time 2
Study 4: Adolescent Smoking
.21'
Method
Subjects. Respondents were 199 male and female llth-grade
Canadian high-school students who indicated they had smoked at least
once in their lives. These students constituted a subsample of a larger
sample of students involved in a longitudinal study of adolescent
smoking described by Flay et al. (1985).
Procedure. We assessed each subject at two different times, separated by approximately 12 months. Respondents completed questionnaires in regular high school classrooms. Confidentiality, but not
anonymity, of responses was emphasized. A number of studies have
indicated that self-reports of cigarette use among adolescents are
reasonably valid as long as certain safeguards are present during
questionnaire administration (e.g., Bauman & Dent, 1982; Evans,
Hansen, & Mittelmark, 1977; Murray & Perry, 1987; Pechacek et al.,
1984). For example, Murray and Perry recommended that pipeline
methods (cf. Jones & Sigall, 1971), which involve obtaining biological
specimens, should be used to increase the validity of self-reports in
adolescents when anonymity cannot be guaranteed. Thus, we used a
pipeline procedure in which carbon monoxide (CO) measures were
taken from each student to increase the validity of self-reports of
cigarette use. Stacy, Flay, et al. (1990) found that this procedure
yielded an adequate level of convergent validity between self-reported
cigarette use and CO measurement in adolescents from the same
population of subjects as sampled in this study.
DUI-Offender
Drinking
Time 1
DUI-OTfender
Drinking
Time 2
.70'
Figure 3. Final structural equation model and standardized maximum likelihood estimates for driving under the influence (DTJI)offender alcohol use. (Large circles designate latent constructs. Small
circles depict residual variances of factors. Path coefficients are
depicted with single-headed arrows. Correlations between factors or
factor residuals are depicted with double-headed arrows. Significance
levels are based on critical ratios on unstandardized maximum
likelihood estimates ['p < .05; **p < .01; ***p < .001].)
Attitude
Time 1
.33*"
Attitude
Time 2
Figure 4. Final structural equation model and standardized maximum likelihood estimates for DUI-offender drinking-driving. (Large
circles designate latent constructs. Rectangles represent measuredvariable, or single-indicator, constructs. Small circles depict residual
variances of constructs. Path coefficients are depicted with singleheaded arrows. Correlations between constructs or their residuals are
shown with double-headed arrows. Significance levels are based on
critical ratios on unstandardized maximum likelihood estimates
["p < .05; "p < .01; ***p < .001].)
Measures of attitude. Measures of attitude were quite similar to the
ones used in the previous studies. Attitude was assessed with three
indicators that were each simple sums of two items. The items
composing this scale were introduced with the statement "Smoking
is": followed by six 5-point bipolar adjective items ranging from very
beautiful to very ugly, very good to very bad, very clean to very dirty, very
safe to very dangerous, very pleasant to very unpleasant, and very nice to
very awful.
Measures of smoking. Three different indicators of smoking were
used. The first indicator was the smoking index used by Biglan,
Gallison, Ary, and Thompson (1985), which is computed on the basis
of the average of two items, including open-ended scales assessing last
24-hour use and past-7-day use. The other two indicators were
proposed by the National Cancer Institute (NCI, 1986). One of these
indicators was a closed-ended, 10-point scale of current smoking that
combines frequency and quantity, asking respondents "How much do
you currently smoke?" (I have never smoked = 1, Not at all in the last
12 months = 2, A few times in the last 12 months = 3, Usually once a
month = 4, A few times each month = 5, Usually once a week = 6, A
few times each week = 7, A few times most days = 8, About half a
pack each day = 9, A pack or more each day = 10.). The second NCI
indicator of smoking was a closed-ended item with 7 points, asking
respondents "How frequently have you smoked cigarettes during the
past 30 days?" (Not at all = 1, Less than one cigarette per day = 2,
One to five cigarettes per day = 3, About one-half pack per day = 4,
About one pack per day = 5, About one and a half packs per day = 6,
two packs or more per day = 7.). Previous research has demonstrated
the convergent and discriminant validity of these types of measures
when used as continuous indexes (Stacy et al., 1985; Stacy, Flay, et al.,
1990).
Cross-lagged structural models. The model construction and evaluation procedures of Studies 1 and 2 were used in this study for the initial
and subsequent models.
Results
We used the initial, structurally saturated model (x2 [46,
N = 199] = 132.42,p < .001) in model comparisons with more-
80
A. STACY, P. BENTLER, AND B. FLAY
restricted models, using the procedures reported in the previous studies. One cross-lagged path, from Tl attitude to T2
smoking, was found to be nonsignificant; this path was omitted
to derive the final model. The final model (x2 [47,
N = 199] = 132.50, p < .001) fit the data well according to
practical indices of fit (NNFI = .96; NFI = .95). The final
model did not differ significantly from the initial model in
terms of model fit. Standardized structural parameter estimates for the final model are reported in Figure 5. Standardized factor loadings ranged from .84 to .91 for attitude and
from .90 to .97 for smoking (all/js < .001). Model evaluations
using alternative (elliptical and arbitrary distribution) estimators showed the same pattern of significance of structural
paths.
Study 5: Illicit Drug Use
Method
Subjects. Respondents were 706 male and female young adults
who were participants in a larger study on drug abuse etiology and
consequences. The predominantly White, middle-class sample had an
average age of 17.9 years at the first time of measurement used in this
study, which was the third wave of assessment in the larger study
(Newcomb & Bentler, 1988). The subjects were recruited for participa-
Adolescent
Smoking
Time 1
tion in the larger study from representative junior high schools from
Los Angeles County.
Procedure. We assessed each subject at two different times, separated by approximately five years. Respondents completed a selfadministered questionnaire at both times of measurement. Although
the data were not anonymous, the complete confidentiality of responses was emphasized to respondents, who were informed that their
responses were protected by a federal certificate of confidentiality.
Measures of attitude and behavior. The two measures of attitude
asked respondents to indicate what they thought about smoking
marijuana or taking PCP, on scales from 1 (terrible idea) to 5 (great
idea). These measures were found to show an adequate degree of
reliability in previous research as shown in high loadings on their
respective factors (Bentler & Speckart, 1979). The measures of illicit
drug-use behavior assessed the frequency of use of a variety of
different drugs over the past 6 months on scales from 1 (never) to 7
(more than once a day). Although we assessed use of a variety of
different drugs, we restricted the primary analyses to marijuana use
because of results from some preliminary analyses described below.
Cross-lagged structural models. The model-evaluation procedures
were the same as those used in the previous studies. However, the
construction of the primary model was different because of some
results from a preliminary analysis. As outlined above, attitude
measures were available only for marijuana and PCP use. In the
preliminary analysis, we attempted to model attitude-behavior relations between a general factor of drug-use attitudes, representing the
Adolescent
Smoking
Time 2
.40***
Figure 5. Final structural equation model and standardized maximum likelihood estimates for adolescent smoking. (Large circles designate latent constructs. Small circles depict residual variances of factors.
Path coefficients are depicted with single-headed arrows. Correlations between factors or factor residuals
are depicted with double-headed arrows. Significance levels are based on critical ratios on unstandardized
maximum likelihood estimates [*p < .05; **/> < .01; ***p < .001].)
81
ATTITUDES AND HEALTH BEHAVIOR
common variance of marijuana and PCP attitudes, and more general
drag-use behavior (a variety of different drugs). However, on the PCP
measures only a few respondents indicated values above the minimum.
In addition, the preliminary model did not yield any significant paths
between the general factors, although it did support the significance of
some paths between the marijuana indicators of these factors. Because
only marijuana indicators had significant effects, and because the
measurement of PCP was problematic, we only report the analysis of a
model that was restricted to the associations between marijuana
behavior and attitude indicators over time. In this primary model,
attitude toward marijuana use is represented by a single indicator,
rather than as a factor, and marijuana behavior also is represented by a
single indicator. Otherwise, the model was constructed similarly to the
models in the previous studies. To maintain compatibility with the
other studies, only subjects who had smoked marijuana at least once at
the initial time of measurement were used in the analysis (N = 357).
Results
The primary path model just described for marijuana use
estimated paths from Tl attitude and behavior indicators to T2
attitude and behavior indicators, as well as a covariance
between the Tl indicators and a covariance between the T2
residuals of the predicted indicators. Because this initial model
was completely saturated (i.e., no additional parameter estimates could be made in the model), it fit the data perfectly (x2
[0, N = 357] = 0). This model was used in model comparisons
with more-restricted models, using the procedures reported in
the previous studies. One cross-lagged path, from Tl marijuana use to T2 attitude toward marijuana, was found to be
nonsignificant; this path was omitted to derive the final model.
Adolescent
Marijuana Use
Time 1
The final model (x2 [1, N = 357] = .66, p = .42) fit the data
well according to practical indices of fit (NNFI = 1.00;
NFI = 1.00). The final model did not differ significantly from
the initial model in terms of model fit. Standardized structural
parameter estimates for the final model are reported in Figure
6. Because the model used only single indicators rather than
factors, there were no factor loadings. Model evaluations using
alternative (elliptical and arbitrary distribution) estimators
showed the same pattern of significance of structural paths. In
several supplementary analyses, adjustments of the assumed
amount of error variance in the single-indicator constructs did
not change these results.
Discussion
Many intervention programs include attitude change as an
important element of health-behavior change. In addition,
previous research, as well as most of the present studies, has
shown significant cross-sectional correlations between attitudes and behavior, suggesting that attitudes may be important
precursors of health-related behavior. However, the most
general conclusion from the present longitudinal results is
that, when stabilities and cross-sectional correlations were
taken into account, attitude was neither a consistent nor strong
predictor of the health behaviors we assessed. Although
attitude did not have general or strong effects, it did predict
certain specific health behaviors in two of the six models we
analyzed, suggesting that attitude still may be of some importance.
Adult
Marijuana Use
Time 2
.55***
60
Attitude
Time 1
Attitude
Time 2
Figure 6. Final structural equation model and standardized maximum likelihood estimates for marijuana
use. (Rectangles represent measured-variable, or single-indicator, constructs. Small circles depict residual
variances of constructs. Path coefficients are depicted with single-headed arrows. Correlations between
constructs or construct residuals are depicted with double-headed arrows. Significance levels are based on
critical ratios on unstandardized maximum likelihood estimates [*p < .05;**p < .01; ***p < .001].)
82
A. STACY, P. BENTLER, AND B. FLAY
The failure of attitude to be consistently predictive among
subjects who have a minimal degree of behavioral experience
does not imply that affective or cognitive approaches are not
important in health behavior. On the contrary, the lack of
direct effects of attitude in most of our analyses suggests that
other approaches involving affective or cognitive constructs
should be considered as viable alternatives to the direct-effect
attitude model we investigated. We now outline several of the
possible reasons why attitude may not be a consistent, directeffect predictor of health behavior.
First, it is possible that attitude toward health behavior is
useful empirically, theoretically, and practically only in the
context of a more general theory than that implied by our
investigation of direct-effect paths from attitude to behavior.
More general theories, such as reasoned action (Fishbein &
Ajzen, 1975), planned behavior (Ajzen & Madden, 1986;
Madden et al, 1992), attitude accessibility (Fazio & Williams,
1986), or social-cognitive approaches to attitude (e.g., Wyer &
Srull, 1989) contain more complex predictions than the simple,
direct-effect paths we were able to investigate. Future research
on attitudes toward health behavior may find attitude more
generally explanatory in the context of one of these formal
theories. If so, health-behavior interventions may be improved
by an acknowledgment of the complexity of attitude effects on
behavior as well as by the explicit use of propositions from
these theories in programs of change. Although some healthbehavior interventions use empirically supported, theoretically
driven programs of attitude-behavior change, many others do
not go beyond a simple direct-effect model of attitude effects.
There are other available interpretations. Our study used
one of the most typical definitions of attitude, which was
represented as a single bipolar affective predisposition toward
performing a behavior. In this approach, a person cannot hold
strong positive and negative attitudes simultaneously without
responding to the attitude measure in the middle range of the
single attitude continuum. This approach assumes that positive
and negative affect toward a behavior converge or balance out
in a type of summary of affective tendencies, which then
influences behavior (or a mediator of behavior, e.g., intention;
Fishbein & Ajzen, 1975). One example of such an approach
regarding health behavior has been outlined by Akers (1992),
in which the stronger affective tendencies win out in the overall
attitudinal predisposition toward a behavior. Obviously, people
can answer attitude questions in this way, because the bipolar
construction of attitude scales is likely to force some consideration of both positive and negative affect when answering the
questions. However, this does not imply necessarily that
attitude is a unitary entity that is represented in this way in the
mind. In some perspectives, affect is not a unitary summary of
positive and negative qualities of a behavior or object, but is
instead two separate dimensions of positive and negative
affect. These perspectives are in agreement with findings
showing that affect often appears as two dimensions of positive
and negative (e.g., Diener & Emmons, 1984; Gotlib & Meyer,
1986; Watson, 1988) and with evidence that each of these
dimensions has different neurophysiological substrates (Baker,
Morse, & Sherman, 1987; Wise, 1988).
The rwo-dimensionai view of affect can be translated into a
two-dimensional view of affective predispositions in health
behavior. In some health-behavior research, a two-dimensional view has been used to construct separate positive and
negative value x expectancy scales representing both belief
and affective elements of behavioral tendencies (Stacy, Dent,
et al., 1990; Stacy, Widaman, & Marlatt, 1990). Although these
constructs have been referred to as expectancies, they could be
considered to be quasi-attitudinal constructs because they
include affective components. However, instead of merely
predicting attitude, in accord with traditional attitude perspectives (e.g., Fishbein & Ajzen, 1975), positive expectancies had
a unique and superior predictive effect on behavioral intentions (Stacy, Widaman, & Marlatt, 1990). In addition, positive
and negative expectancies equally and strongly predicted
attitude, yet positive and negative expectancies were only
modestly intercorrelated. Other recent studies, using more
traditional assessments of outcome expectancies, also have
found that general factors of expectancies about positive and
negative outcomes were correlated only weakly, or nonsignificantly (Leigh & Stacy, 1993; Stacy, MacKinnon, & Pentz,
1993), providing further evidence that these constructs are not
simply cognitive elements of opposite poles of attitude.
Empirical support of the two-dimensional views of affect
and expectancies suggests that traditional attitude constructs
may need to be complemented, if not replaced, by alternative
affective and cognitive constructs in some areas of healthbehavior research. It is possible that attitudes are merely
judgments that people sometimes make but that do not
represent the underlying source of judgments or behavior. This
source may be somewhat less unitary or abstract than attitude
and may be better represented by sets of anticipated outcomes
or expectancies. If so, then attitude, as traditionally defined
and measured in the present studies, would not be expected to
be a strong predictor of behavior. Even though this perspective
is only one of several different ways to interpret the lack of
strong, general effects of attitude on health behavior, the
previous success of expectancies as long-term predictors of
some health behaviors (Stacy, Newcomb, & Bentler, 1991)
suggests further that the expectancy framework may be a
viable complement (or alternative) to traditional attitude
frameworks.
Although alternatives to attitude may be needed to more
adequately reflect affective and cognitive influences on health
behavior, attitude was predictive of health behavior in two of
the models we analyzed (college alcohol use and community
marijuana use). Past behavior was a nonspurious predictor of
attitude in two other models (adolescent smoking and college
binge eating). A third interpretation of the pattern of results is
that the nature of the specific behaviors, the population
sampled, or the time interval used in these models may have
contributed to the obtained differences. In other words,
attitudes may be more generally important than we found, but
something correlated with the particular analyzed model
moderated the predictive effects of attitude on health behavior.
Although it is unclear what specific aspects of the populations
or the health behaviors could have acted as moderators of
attitude-behavior relations in the present studies, we offer
some speculations about potential moderators. It is possible
83
ATTITUDES AND HEALTH BEHAVIOR
that alcohol and marijuana use in mostly nonproblematic
populations (college and community populations) are under
greater volitional control; for most people sampled from these
populations, these behaviors probably are not particularly
addictive or compulsive. On the other hand, the literature on
binge eating and smoking suggests that these behaviors are
often quite compulsive or addictive in quality, even among
otherwise nonproblematic populations (e.g., college or high
school students). Alcohol use in groups of drunk-driving
offenders is by definition more problematic, and is likely to be
more compulsive or addictive than alcohol use among college
students. This framework would suggest that, among more
problematic samples or behaviors, previous behavior (as an
indicator of habit) would be a very strong predictor of future
behavior, overriding any potential effects of attitudes. In fact,
in the present models, past behavior was a stronger predictor
of future behavior in the four instances in which attitude was
not predictive, compared with the two instances in which
attitude was predictive of health behavior. It also is possible
that when behavior is particularly stable over time, it has a
greater potential to influence attitudes, as found in our binge
eating and smoking models. This effect could occur through
stable and repeated self-perceptions (Bern, 1978) or other
processes (Stacy et al., 1991).
Another feature of the models we analyzed that may
moderate the predictive effects of attitude is the time interval
used to evaluate attitude-behavior relations. In several theories, such as self-perception, cognitive dissonance, and attitude
accessibility, the influence of attitudes and behavior on one
another may be quite temporary and may not be captured by
the moderate to extremely long time intervals assessed in our
studies. However, there was no apparent correlation between
strength of attitude-behavior relations in our studies and time
interval. In fact, one of the only models to show a significant
prediction of health behavior by attitudes was in the longest
study, in which marijuana attitudes predicted marijuana use
over a 9-year interval. Nevertheless, it is possible that much
shorter time intervals (e.g., days instead of weeks, months, or
years) would show a quite different pattern of findings.
Although findings from very short interval studies would be of
great theoretical interest, their applied importance is not
readily apparent. Most attitude-change interventions in health
behavior seek to make long-term changes in behavior.
Further research may delineate more specifically what
features of the population and specific behavior are responsible for differences in attitude-behavior relations in health.
The potential moderators mentioned earlier should be considered for use in future research on attitudes in health behavior.
The investigation of such moderators may reveal that attitudes
have a relevant degree of predictive utility under certain sets of
specific conditions. If so, then developers of prevention programs may be better able to determine the conditions under
which an attitude-change program may be most successful. On
the other hand, future research may confirm the alternative
view that attitudes are too limited in their predictive effects to
be useful in health-behavior interventions. Such findings
would provide further motivation for the use of alternative
affective and cognitive constructs.
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