High and low-dose alcohol-related expectancies and the differential associations with drinking in...

Addiction (1997) 92(7), 871 ± 888

RESEARCH REPORT

High- and low-dose alcohol-relatedexpectancies and the differential associationswith drinking in male and female adolescentsand young adults

REINOUT W. WIERS,1,2

KEES-JAN HOOGEVEEN,2

JOSEPH A. SERGEANT 2 & W. BOUDEWIJN GUNNING1

1Academic Medical Centre Amsterdam, the Netherlands, Department of Child and Adolescent

Psychiatry and Amsterdam Institute for Addiction Research (AIAR) &2Department of

Clinical Psychology, University of Amsterdam, The Netherlands

Abstract

A Dutch questionnaire was developed consisting of positive and negative expectancies relating to low and high

doses of alcohol. The associations of these four types of expectancies with current alcohol consumption were

investigated in three samples: secondary school pupils of 11± 15 years old, secondary school pupils of 16 and

older, and university undergraduate student s (total n 5 554). Using restrictive factor analyses, a common

factor-model of the expectancies was shown to ® t adequately across subgroups. W hich expectancies were

associated with current alcohol consumption varied substantially across the subgroups. As expected from

previous research, inclusion of high dose expectancies did not substantially improve the prediction of drinking

in university students . However, positive and negative high-dose expectancies were found to be powerful

predictors of current alcohol consumption in secondary school boys of 16 and older, the subgroup with the

highest average alcohol consumption on each occasion. Possible implications are discussed for future research

and interventions.

Introduction

In the past 15 years, alcohol-related expectancies

have emerged as powerful predictors of alcohol

consumption. Expectancies have consistently

been found to be associated with current alcohol

consumption in college students (e.g. Leigh &

Stacy, 1993), community samples (e.g. Brown et

al., 1980) and adolescents (e.g. Christiansen,

Goldman & Inn, 1982). Expectancies have also

been shown to predict future drinking in adoles-

cents after 1 year (Christiansen et al., 1989), 2

years (Smith et al., 1995) and 9 years (Stacy,

Newcomb & Bentler, 1991). Furthermore, ex-

pectancies have proven to be useful in predicting

treatment-outcome in alcoholics (e.g. Jones &

McMahon, 1994). Although expectancies are

now widely believed to be important in alcohol

research, several issues concerning their

Correspondence: R. W. Wiers, Department of Child and Adolescent Psychiatry, Academic Medical Centre, POBox 22700, 1100 DE Amsterdam, The Netherlands. Tel.: 1 31 20 5256818 /5662230 ; fax: 1 31 206391369- 6971937 ; e-mail:kp [email protected]

Submitted 18th June 1996; initial review completed 15th November 1996; ® nal version accepted 6th January 1997 .

0965 ± 2140/97/070871 ± 18 $9.50 Ó Society for the Study of Addiction to Alcohol and Other Drugs

Carfax Publishing Company

872 Reinout W . Wiers et al.

measurement remain (e.g. Leigh, 1989a; Gold-

man et al., 1991; Leigh & Stacy, 1991, 1993).

It is now well established that people hold

both positive and negative alcohol-related expec-

tancies (e.g. Fromme, Stroot & Kaplan, 1993;

Leigh & Stacy, 1993; Chen et al., 1994; McMa-

hon, Jones & O’ Donnell, 1994). In addition, it

has been shown that expectancies vary with the

dose of alcohol concerned (Southwick et al.,

1981; Connors et al., 1987; Collins et al., 1990;

Earleywine & Martin, 1993). With respect to

dose and valence, four types of expectancies can

be distinguished: positive expectancies for a low

dose, positive expectancies for a high dose, nega-

tive expectancies for a low dose, and negative

expectancies for a high dose.

Previous research ® rst focused on positive ex-

pectancies. The ® rst and the most widely used

instrument in alcohol-expectancy research

(AEQ, Brown et al., 1980) was aimed at: ª the

domain of humans’ expectancies about the rein-

forcing effects of moderate alcohol consump-

tionº (Brown et al., 1980, p. 424). The authors

followed Rotter’ s de® nition of expectancies as

ª the probability held by the individual that a

particular reinforcement will occur as a function

of a speci® c behaviourº (ibid., p. 419). The

instrument contained items concerning a low

dose of alcohol (ª a drink or twoº ) as well as

items about an unspeci® ed dose of alcohol (e.g.

ª Drinking relieves boredomº ), but no items

speci® cally targeted high doses of alcohol. In the

typology de® ned above, all six factors of the

AEQ fall into the category of positive expectan-

cies for a low (to moderate) dose of alcohol.

Negative expectancies were introduced on a

small scale in two modi® ed versions of the AEQ:

the AEQ-A (AEQ modi® ed for adolescents,

Christiansen et al., 1982) contained one negative

scale, one mixed scale and ® ve positive scales. In

the modi® ed AEQ by Rohsenow (1983) two

negative scales were added to the six abridged

positive expectancy-scales of the AEQ. In sub-

sequent years, several instruments were devel-

oped with an approximately equal number of

positive and negative scales (e.g. Young &

Knight, 1989; Fromme et al., 1993; Leigh &

Stacy, 1993). It was shown with various instru-

ments that negative expectancies signi® cantly

improve the prediction of current drinking

(Fromme et al., 1993; Leigh & Stacy, 1993;

Chen et al., 1994; McMahon et al., 1994). The

issue of whether positive or negative expectancies

are better predictors of current drinking is an

unresolved issue: the ® ndings of Stacy, Widaman

& Marlatt (1990) and of Leigh & Stacy (1993)

favour positive expectancies, but those by

Grube, Ames & Delaney (1994) and McMahon

et al. (1994) favour negative expectancies. The

comparison between positive and negative ex-

pectancies is confounded by the fact that the

expected positive effects are more proximal than

the expected negative effects. Negative effects

are expected to occur later in a drinking session;

students expect more positive effects of alcohol

for the rising limb of the blood± alcohol curve

and expect more negative effects for the de-

scending limb of the blood± alcohol curve, which

is in agreement with the actual effects of alcohol

(Earleywine & Martin, 1993). Recently, it has

been found that still more distal negative expec-

tancies (e.g. feeling sick the day after, or losing

one’ s job with continued drinking), are also

strongly associated with current drinking

(McMahon et al., 1994).

Even though it has been recognized in the

literature that expectancies vary with dose of

alcohol (Southwick et al., 1981; Connors et al.,

1987; Collins et al., 1990; Earleywine & Martin,

1993), the assessment of dose-related expectan-

cies has received relatively little attention. One

reason could be that the AES (Southwick et al.,

1981), an early scale which assessed dose-related

expectancies, predicted current alcohol con-

sumption less accurately than two questionnaires

which did not assess high and low dose effects

independently (Leigh, 1989b). The AES was

also criticized in an in¯ uential review of the

expectancy literature for the use of a bipolar

response format (Leigh, 1989a). This could well

be the main reason for the poor association with

current drinking. In a more recent expectancy

questionnaire (Fromme et al., 1993), the relation

between dose and expectancy was investigated

by asking the subjects to indicate the number of

drinks they would need to consume in order to

experience each of the expected alcohol effects.

Positive effects were expected to occur after a

signi® cantly lower dose than negative effects. In

this way, the two most plausible types of expec-

tancies are measured which correspond to the

biphasic response to alcohol (low dose positive

and high dose negative expectancies). However,

the other two types of expectancies (negative low

dose and positive high dose) are not measured by

this response format. This might be an adequate

High- and low-dose expectancies 873

strategy in students but not necessarily in other

populations.

What exactly constitutes a ª positiveº or a

ª negativeº expectancy? One person’ s positive

expectancy might be another person’ s negative

expectancy. Aggression, for example, has been

reported both as a positive expectancy (Brown et

al., 1980) and as a negative expectancy (e.g.

Leigh & Stacy, 1993). Some researchers have

argued that the valence (or ª valueº or

ª evaluationº ) of the expected effect should be

measured on an individual basis (e.g. Fromme et

al., 1993; Grube et al., 1995). However, as ob-

served by Grube (1995), the usually applied

strategy in expectancy research is not to include

valence on an individual basis. In this study

value was not measured on an individual basis,

but this approach holds promise for future

expectancy research and clinical applications

(e.g. Mooney & Corcoran, 1989).

The expectancy questionnaire used in this

study contains items stated in the general format

concerning the expected effects of alcohol on ª an

average personº . This strategy has several advan-

tages: one questionnaire may be used irrespective

of the alcohol experience of the (young) subject,

which makes a direct comparison of the expec-

tancies of drinking and non-drinking subjects in

the youngest subject-group possible. A second

advantage is that it will allow for future compari-

sons with a different subject-group that our re-

search group is studying (children of alcoholics,

see Wiers, Sergeant & Gunning, 1994). The

third advantage is that the results can more easily

be compared with the most widely used ex-

pectancy questionnaire for adolescents which

also contains items in the general format (the

AEQ-A; e.g. Christiansen et al., 1982, 1989;

Smith et al., 1995). It has been argued, however,

that the general format is not optimal with

drinkers (Leigh, 1989a; Young & Knight, 1989).

In direct comparisons it has been found that

subjects expect stronger effects for others than

for themselves (Rohsenow, 1983; Leigh, 1987).

It is not clear, however, which format results in

a better prediction of current drinking. In the

only reported study known to the authors in

which both personal and general expectancies

were included, the correlations of personal ex-

pectancies and general expectancies with current

drinking were judged to be so similar that the

latter were left out of the report (Wood, Nagoshi

& Dennis, 1992). Hence, this paper reports a

study which measured expectancies about the

effects of a high or a low dose of alcohol on an

average person and the associations of these

expectancies with current drinking patterns in

adolescents and adults.

M ethod

M easures

Expectancies. A Dutch expectancy question-

naire was developed to measure the four types of

expectancies described in the introduction. It

consisted of a primary section of 42 items con-

cerning the response of an average person to a

low dose of alcohol, followed by a smaller sec-

ondary (exploratory) section of 14 items regard-

ing the response of an average person to a high

dose of alcohol. An unipolar Likert scale was

used as the response format as in recent expect-

ancy questionnaires, with a ® ve-point scale from

ª don’ t agreeº to ª strongly agreeº . Several

speci® c scales from the AEQ and AEQ-A served

as models for our a priori positive scales. The

general positive scale of these measures was not

included. General positive expectancies are ac-

counted for using the method of ® tting a second

order factor model, as in Leigh & Stacy (1993).

Most negative items were constructed by chang-

ing positive items into the opposite meaning. For

example, the item ª after a few drinks people say

clever thingsº (cognitive/motor enhancement)

became ª after a few drinks people say stupid

thingsº (cognitive/motor impairment). Some

items were translated from the AEQ and the

AEQ-A, others from a questionnaire developed

by Sher et al. (1991). The remaining items were

generated by the authors with the help of several

regular bar visitors.

Alcohol consumption. A self-report measure of

alcohol-consumption was used. Self-report mea-

sures have generally been found valid when used

in research settings with sober subjects who are

given assurances of con® dentiality (Sobell &

Sobell, 1978, 1990; Sobell et al., 1988; Knight &

Godfrey, 1993). These requirements were

ful® lled here.

Subjects were asked to indicate on a checklist

of alcoholic beverages how many they would

typically drink of each beverage on an average

weekday (Monday± Thursday). All alcoholic

consumptions were transformed into ª stan-

dard drinksº (a standard drink contains


approximately 15 ml of pure ethanol). In ad-

dition to the amount consumed on an average

weekday, subjects were asked to estimate the

number of weekdays in a month that they would

drink this quantity (range: 0± 16). The same re-

sponse format was used to ascertain the number

of standard drinks consumed during weekend-

days (Friday± Sunday; range: 0± 12). Subjects

were also asked to indicate on how many days

they drank ® ve or more standard drinks during

the past month. At the end of the questionnaire,

the subject was asked to give an estimate of the

number of standard drinks he or she consumed

during an average week (ª one guessº ). From

these data, an estimate of the subject’ s weekly

alcohol consumption was calculated (abridged

as ª QFº , indicating quantity 3 frequency1), as

well as the number of days a subject had con-

sumed ® ve or more alcoholic consumptions

(ª Dº , indicating number of days intoxicated).

Subjects

Subjects were 554 secondary school pupils and

university students. The secondary school pupils

were drawn from four secondary education lev-

els: low level professional training, medium gen-

eral education, medium level professional

education and high-level general education. The

secondary school pupils were divided into two

groups with respect to age: 11± 15-year-olds

(n 5 216; 104 boys and 112 girls, mean

age 5 13.3 years) and secondary school pupils of

16 and older (n 5 163; 101 boys and 62 girls,

mean age 5 17.0 years). The third group

(ª university studentsº ) consisted of introductory

psychology students (n 5 175; 73 men and 102

women, mean age 5 21.7 years). The second and

third subject-groups showed some overlap in

age: university students were 18 years or older,

and the age of some of the 16 1 group of sec-

ondary school pupils extended into the range of

the university students. The overlap is not large:

when 18 years is used as an overlapping border,

the age of 8% (13/163) of the subjects of group

2 had an age in the range of group 3 (nine

subjects were 19, three subjects were 20, and one

subject was 21 years old). The reason why the

subject-groups were differentiated with respect

to current education level rather than age was

that the subcultures of the subject-groups is

rather different: university students generally live

on their own, in student houses or in private

rooms, whereas the secondary school pupils gen-

erally live with their parents. In addition, the

university students were selected for school

ability: they must have ® nished the highest level

of education in secondary school. The secondary

school pupils, in contrast, are of different

levels of education: only about 25% of them

would eventually qualify for university. Further

details concerning the subgroups can be found in

Table 1.

Table 1. Characteristics of the six subgroups de® ned by cohort and gender

Number ofMean standard Mean standard intoxications

Mean age % drinks/week drinks/occasion past monthSubgroup Gender n ( 6 SD) abstainers QF

1( 6 SD) Q

1( 6 SD) D

1( 6 SD)

15 or younger boys 104 13.4 46 2.0 2.1 0.8( 6 1.0) ( 6 4.0) ( 6 2.8) ( 6 2.6)

girls 112 13.3 60 1.5 1.3 0.5( 6 1.0) ( 6 3.6) ( 6 1.9) ( 6 1.6)

Adolescents16 or older boys 101 17.1 6 15.3 6.5 4.7

( 6 1.1) ( 6 13.8) ( 6 4.0) ( 6 4.5)girls 62 16.8 15 6.5 3.8 2.3

( 6 0.8) ( 6 7.0) ( 6 2.7) ( 6 3.4)

Students men 73 22.2 3 15.9 5.3 5.8( 6 5.0) ( 6 11.7) ( 6 3.5) ( 6 6.7)

women 102 21.4 10 9.5 3.5 3.2( 6 3.8) ( 6 9.6) ( 6 2.4) ( 6 4.5)

1The formulae of the drinking variables are given in Note 1.

1.41.00.8

It is arousing to dancePeople talk about sex more easilyPeople kiss more readily

After a few drinks ... (low dose expectancies)

0.60.71.0

SEX+low-dose sexualenhancement

0.50.70.90.8

People get good ideasPeople can write poems more easilyPeople can ride their bike fastPeople are good at pinball

0.60.50.50.5

CM+ low-dosecognitive and motorenhancement

1.11.01.2

People find a dinner party festivePeople enjoy watching tv togetherOne is more readily accepted by a group

0.70.60.7

CEL+ low-dosecelebrationgroup-acceptance

0.60.50.60.60.8

People do not feel like making lovePeople feel unattractivePeople feel insecurePeople become gloomy about the futureA party becomes annoying

0.40.50.50.50.5

INH ± low-doseinhibition/negative mood

1.01.60.8

People have difficulties expressing themselvesPeople become bad at snookerPeople cannot think clearly

CM ± low-dosecognitive and motorimpairment

0.70.60.9

1.00.80.61.0

It is exciting to drink with friendsPeople enjoy a partyPeople get good ideasPeople become romantic

0.60.70.40.5

HD+ high-dosepositive

After many drinks ... (high dose expectancies)

0.90.71.71.4

People want to fightPeople say stupid thingsPeople dance badlyPeople become gloomy

0.80.70.60.8

HD ± high-dosenegative


Figure 1. Restrictive factor analysis model with ® rst-order factors as ® t to all subgroups. The rectangles contain the items

of the latent factors. Small ellipses contain the error components, the numbers above the arrows are the common factor scores

obtained in the multi-group analyses (see Appendix).

Procedure

In a pilot study (Hoogeveen, 1994), two versions

of the expectancy questionnaire were compared

in a university student sample (n 5 370). One

version contained positive and negative expec-

tancies, as described above. The other version

consisted of only positive a priori scales for a low

and for a high dose of alcohol. The version

which included negative expectancies resulted in

a better prediction of current drinking. It was

then decided to continue the research with ado-

lescents with the version that included negative

items. The general instruction and the wording

of some items were changed to make them more

suitable for secondary school pupils. The items

in the ® nal common factor model presented

in Fig. 1 were identical across all subjects. All

questionnaires were completed in a surveyed

assembly setting (the secondary school pupils in

a regular classroom, and the university students

as part of a questionnaire battery for freshmen).

Con® dentiality was assured verbally and in

writing.

Statistical analyses

The main aim of the statistical analyses was to

compare the endorsement of the different expec-

tancies and the prediction of current drinking

across the three subject-groups and gender.


Before these comparisons could be done in

meaningful way, it had to be shown ® rst that one

measurement model could be used across the

subject-groups. Because the present study used a

new expectancy instrument, the analyses were

started with two exploratory factor analyses

(PCAs), to get a rough idea of the factor struc-

ture in the secondary school pupils and in the

university students. Then a series of multi-group

restrictive factor analyses were conducted to test

whether one common factor model held up

across the subgroups. This procedure is not

strictly con® rmatory (the exploratory analyses

were performed on the same sample, see

JoÈ reskog, 1993), but not trivial because it is

explicitly tested that one common model can be

used to compare the subject groups. This test is

more stringent than the standard Box test, be-

cause the covariances of two groups can be of the

same order of magnitude while the same factor

structure does not hold across groups.

After it was shown that one measurement

model could indeed be used across the sub-

groups, regression analyses were performed for

each subgroup in which it was investigated which

expectancies predicted current drinking. Finally,

the endorsement of the different expectancies

across subgroups was analysed using multivariate

analyses of variance (MANOVA).

Results

Drinking variables

The two measures of the average number of

standard drinks consumed in a week, QF (QF is

quantity 3 frequency1) were highly correlated

(0.85). In all three subject groups the ª one

guessº measure was signi® cantly lower than the

estimate based upon an average weekday and an

average day at the weekend (paired t-tests: group

1 t(215 df) 5 2.9, p , 0.01, group 2 t(162

df) 5 8.4, p , 0.01, group 3 t(174 df) 5 6.7,

p , 0.01). In subject-group 3 (university stu-

dents), QF could be compared with a detailed

measure of drinking during the past week. QF

correlated 0.77 with the number of standard

drinks consumed during the past week, but was

again signi® cantly lower (12.3 vs. 14.6; t(183

df) 5 2.70, p , 0.01).

The second drinking variable which was as-

sessed in the entire subject-group was the num-

ber of days in the past month on which ® ve or

more alcoholic standard drinks were consumed

(D). This drinking-variable correlated 0.75 with

QF. For each of the subject groups further sub-

divided with respect to gender, means and stan-

dard deviations of the variables concerning

self-reported drinking can be found in Table 1.

A MANOVA of the drinking variables showed

a signi® cant main effect for gender F (2,547

df) 5 22.5, p , 0.001) and subject group

(F 5 22.5; 4,1094 df, p , 0.001), and a

signi® cant interaction between subject group and

gender F (4,1094 df) 5 5.8, p , 0.001). Follow-

up univariate tests showed that the two main

effects and the interaction were signi® cant for

both drinking variables (both p , 0.01). Pairwise

comparisons of the harmonic means (adjusted

for unequal group sizes, Stevens, 1992, p. 204)

with the Student± Newman± Keuls method

( a 5 0.05) showed that males in groups 2 and 3

drink signi® cantly more standard drinks each

week than girls of the same groups. The

youngest boys and girls consumed signi® cantly

less than the older subjects. On the number of

drinks on each occasion (Q), the pattern of

results showed one difference: boys of group 2

drink signi® cantly more on each occasion than

male university students (and all other sub-

groups).

Expectancies: analyses of the measurement model

Preliminary expectancy scales were constructed

separately for the university students (group 3)

and the secondary school pupils (groups 1 and 2)

by using exploratory factor analysis with oblimin

rotation. Items measuring low and high dose

expectancies were analysed separately. The fac-

tor structures in the students and secondary

school pupils appeared to be quite similar. To

enable a test of a common factor model for the

different subject groups (multi-group restrictive

analysis), a second exploratory factor analysis

was done excluding those items which had been

slightly changed in wording for the adolescents.

The ® rst six exploratory factors on this reduced

item set explained 54% of the variance. These

factors were used to construct six preliminary

scales concerning a low dose of alcohol. Three

scales concerned positive effects of a low dose of

alcohol and three concerned negative effects of a

low dose of alcohol. Following the same pro-

cedure, two preliminary common scales were

constructed concerning a high dose of alcohol:

high-dose positive and high-dose negative.


Unfortunately, the small number of the high-

dose items did not allow for speci® c subscales.

Restrictive factor analyses were used to investi-

gate whether the preliminary common factor

structure ® tted the expectancy data for each of

the subject groups and across gender (with

LISREL VIII, JoÈ reskog & SoÈ rbom, 1993). In

all structural equation models that follow,

maximum likelihood estimation was used.

The covariance matrix of the latent factors of

the preliminary model ( F, see Appendix) was not

positive de® nite in every subgroup. As a solution

to this problem, the model was reduced to seven

factors. The aggression scale was removed be-

cause a preliminary multiple regression analysis

showed that it did not correlate signi® cantly with

alcohol consumption in any of the subgroups.

The ® nal model consisted of three low-dose

positive expectancy scales (cognitive± motor en-

hancement, sexual enhancement, celebration±

group acceptance), two low-dose negative

expectancy scales (sexual± social inhibition and

cognitive± motor impairment), a scale of high-

dose positive expectancies and a scale of high-

dose negative expectancies. The ® nal model with

all remaining items is shown in Fig. 1. The ® t of

this model was adequate in the adolescent sam-

ple: F 2 (278 df) 5 531 (n 5 379), with ® t indices:

RMSEA 5 0.049, AGFI 5 0.88, NNFI 5 0.82

(for an explanation of the ® t indices see note

[2]). The ® t for the student sample was reason-

able: F 2 (278 df) 5 421 (n 5 175), RM-

SEA 5 0.054, AGFI 5 0.81, NNFI 5 0.80.

Does one common factor model hold across sub-

groups? The question addressed in this section is

whether the same factor structure for the expec-

tancies holds in the six subgroups (details con-

cerning the multi-group model may be found in

the Appendix). Fitting the common model to all

six subgroups in one restrictive multi-group

analysis was not possible, because this would

have required a larger number of subjects in each

subgroup. The ® rst multi-group analysis con-

cerned the question whether the same factor

structure held for students and adolescents. The

® t was reasonable: F 2 (575 df) 5 991 (n1 5 175,

n2 5 379), with ® t indices: RMSEA 5 0.051,

GFI 5 0.84, NNFI 5 0.81. Within the ado-

lescent group, the comparison between subject

group 1 (maximum age 15) and subject group 2

(secondary school pupils 16 1 ) also resulted in

a moderate ® t, F 2 (575 df) 5 901, (n1 5 163,

n2 5 216), RMSEA 5 0.055, GFI 5 0.83,

NNFI 5 0.77. This is interesting, because in the

youngest age-group the majority did not drink

alcohol (see Table 1). When comparing all male

and female subjects the ® t was also found to

be reasonable: F 2 (575 df) 5 999 (n1 5 278,

n2 5 276), RMSEA 5 0.052, GFI 5 0.88,

NNFI 5 0.81. Note that for all the multi-group

results, the informal ® t indices indicated a mod-

erate to adequate ® t (RMSEA between 0.050

and 0.0552), suggesting that a common factor

model was acceptable for further comparisons

across groups.

Second-order factors. Restrictive factor analysis

was performed on a model consisting of the four

types of expectancies as second order factors (see

Fig. 2). This resulted in a reasonable ® t in ado-

lescents, F 2 (288 df) 5 587 (n 5 379), with ® t

indices RMSEA 5 0.052, AGFI 5 0.87, NNFI 50.80 and again a modest ® t for students: F 2 (288

df) 5 472 (n 5 175) RMSEA 5 0.061, AGFI 50.79, NNFI 5 0.75. The ® t indices of the model

with second-order factors indicated a somewhat

poorer ® t to the data than the model with only

® rst-order factors. Nevertheless, the model con-

taining second-order factors was used to predict

drinking in addition to the model containing

only the ® rst-order factors. The reason is that

under certain circumstances, second-order fac-

tors may predict drinking better than ® rst-order

factors. The reason for this difference is that the

® rst-order factors contain a residual error com-

ponent that the second-order factors do not con-

tain (see Fig. 2). The presence of the residual

variance of the ® rst-order factors has the same

effect as error in the predictors: it results in a

(downward) bias of the estimates of the re-

gression coef® cients and a reduction of explained

variance. This is not the case when the residual

error is ® xed to zero, in the case of second-order

factors with only one ® rst-order indicator (here

the two high-dose expectancies and age). A simi-

lar model (with two higher-order factors: low-

dose positive and low-dose negative) was used in

students in a study by Leigh & Stacy (1993). An

advantage of this procedure is that no general

scale is introduced at the level of the ® rst-order

factors (which correlate highly with other predic-

tors) while the general effect of a type of expect-

ancy can still be investigated (Leigh, 1989; Leigh

& Stacy, 1993).

Low-dose cognitiveand motorimpairment

Low-dose sexualenhancement

Low-dose cognitiveand motorenhancement

Low-dosecelebrationgroup-acceptance

Low-dose inhibition/negative mood

High-dose positive

High-dose negative

Age (withinsubject-group)

Low-dose positiveexpectancies

High-dose positiveexpectancies

High-dose negativeexpectancies

Age (withinsubject-group)

Alcohol use

Low-dose negativeexpectancies

1

1

1

+

±

±

+


Figure 2. Regression model with second-order latent factors as predictors. The small ellipses in the left column contain the

error components. The bigger ellipses in the second column depict the ® rst-order expectanc y factors (as in Fig. 1) and age within

group. The five ellipses in the third column are the second-order factors used to predict drinking. Note that the high-dose factors

and age are equivalent at the ® rst- and second-order levels. The result is that prediction of drinking is not improved for these

predictors due to the introduction of the higher-order factors. The prediction of drinking can be improved by the introduction

of the second-order factors of the low-dose expectancie s (see Appendix).

Prediction of alcohol consumption

The prediction of current alcohol consumption

with the latent expectancy variables was also

performed within the framework of structural

equation modelling (LISREL VIII, JoÈ reskog &

SoÈ rbom, 1993). The regression with ® rst-order

factors only is equivalent to a conventional re-

gression analysis. In this procedure non-

normality of the dependent variables is undesir-

able. Therefore, both indices of alcohol con-

sumption used (QF and D) were log

transformed.1 Prediction of the number of stan-

dard drinks consumed in an average week (QF)

by the ® rst-order expectancy scales and within-

group age were conducted for each of the six

subgroups separately. In all analyses the same

procedure was followed. All regression analyses

were carried out using the scores on the scales


derived from the common factor model. First, all

expectancies and age were entered as predictors

and QF as dependent variable. The estimates of

the regression coef® cients were judged against

their standard errors to determine their

signi® cance. On the basis of these results,

signi® cant predictors were identi® ed and re-

tained in a second regression analysis. The effect

of removing the non-signi® cant predictors was

evaluated by inspecting the c 2 goodness-of- ® t

indices. Using ® rst-order factors as predictors,

removal of the non-signi® cant predictors did not

result in a signi® cant decrease in goodness-of- ® t

in any instance. None of the analyses were sub-

stantially in¯ uenced by outliers; regression with-

out the outliers resulted in virtually identical

results. Regression analyses for all six subgroups

with ® rst-order latent factors are displayed in

Fig. 3a± f.

Boys 11± 15 years (Fig. 3a). Signi® cant predic-

tors were two low-dose positive expectancies

(sexual enhancement and cognitive± motor en-

hancement), the low-dose negative expectancy of

sexual and social inhibition, the high-dose nega-

tive scale and age. Together these predictors

accounted for 27% of the variance in current

drinking. Expectancies alone (without age) ac-

counted for 23% of the variance. Because of the

large percentage of non-drinkers in the youngest

subject group, drinking boys were analysed sepa-

rately (n 5 56). Interestingly, apart from age,

only positive expectancies signi® cantly explained

drinking for this group: cognitive± motor en-

hancement, celebration± tension reduction and

the high-dose positive scale. Including age as

predictor 41% of the variance was accounted for,

and without age 29%.

Girls 11± 15 years (Fig. 3b). Signi® cant predic-

tors were one low-dose negative expectancy scale

(sexual and social inhibition) and age, account-

ing for 26% of the variance in current drinking.

Expectancy alone (without age) accounted for

14% of the variance. The analysis of the sub-

group of drinking girls (n 5 45) showed that,

besides age, one expectancy scale signi® cantly

predicted drinking: cognitive± motor enhance-

ment. It is noteworthy that the regression

coef® cient of this positive expectancy was nega-

tive in this subgroup.

Adolescent boys 16 1 (Fig. 3c). High-dose

expectancies in this group appeared to be of

primary importance in the prediction of current

drinking; all other predictors were not signi® cant

(including age). Positive and negative high-dose

expectancies together accounted for 24% of the

variance in current drinking.

Adolescent girls 16 1 (Fig. 3d). Signi® cant pre-

dictors were the two negative expectancies for a

low dose of alcohol (sexual and social inhibition

and cognitive± motor impairment), one positive

scale (sexual enhancement) and age. Together

these variables accounted for 29% of the vari-

ance in current drinking. Expectancies alone

(without age) accounted for 23% of the variance.

Male student s (Fig. 3e). Signi® cant predictors

were cognitive± motor enhancement, cognitive±

motor impairment and the high-dose negative

expectancies, together accounting for 25% of the

variance in current drinking.

Female student s (Fig. 3f). Signi® cant predictors

were sexual enhancement, cognitive± motor im-

pairment and the high-dose negative scale, to-

gether accounting for 22% of the variance in

current drinking.

Prediction of the second drinking variable, the

number of times subjects drank more than ® ve

alcoholic beverages on one occasion during the

past month (D), was analysed in the same way as

QF. The results were very similar in most sub-

groups. Only those subgroups where different

predictors were found in comparison with the

prediction of QF are reported. D was explained

by other expectancies for boys in the youngest

subject group. Signi® cant predictors were high-

dose positive expectancies and high-dose nega-

tive expectancies. In the subgroup of drinking

young boys, high-dose positive expectancies and

the low-dose expectancy of celebration± tension

reduction signi® cantly predicted drinking. In

girls in the youngest subject group, D was

signi® cantly predicted by the expectancy of sex-

ual enhancement (like QF in the other female

subgroups). For the boys in the second subject

group, D was not only signi® cantly explained by

the two high-dose expectancies (like QF), but

also by the low-dose expectancy of cognitive±

motor enhancement. In the other subgroups the

signi® cant predictors of D were the same as

those for QF.

Finally, the second-order factors were used to

SEX+SEX+

CM+

CEL+

CM ±

INH ±

HD+

HD ±

AGE

QF

(a) Boys 11-15 years

± 0.16

± 0.23

0.1927% variance QFexplainedwithout age: 23%

SEX+SEX+

CM+

CEL+

CM ±

INH ±

HD+

HD ±

AGE

QF

(c) Boys 16+ years

± 0.26

24% variance QFexplained

SEX+SEX+

CM+

CEL+

CM ±

INH ±

HD+

HD ±

AGE

QF

(e) Male students

± 0.24

± 0.31


SEX+SEX+

CM+

CEL+

CM ±

INH ±

HD+

HD ±

AGE

QF

(b) Girls 11-15 years

± 0.34


SEX+SEX+

CM+

CEL+

CM ±

INH ±

HD+

HD ±

AGE

QF

(d) Girls 16+ years

± 0.23


SEX+SEX+

CM+

CEL+

CM ±

INH ±

HD+

HD ±

AGE

QF

(f) Female students

± 0.20

± 0.30


0.27

0.17

0.43

0.23

± 0.35

0.29

0.37


Figure 3a ± f. Regression models for each of the six subgroups as de® ned by gender and subject group (see Table 1). In each

of the six ® gures, the small ellipses in the left column denote the residuals. The bigger ellipses in the second column depict the

® rst-order expectanc y factors and age (within the subgroup). Legend for the expectancie s (as in Fig. 1): sex 1 5 sexual

enhancement for a low dose of alcohol; cm 1 5 cognitive± motor enhancement for a low dose of alcohol, cel 1 5 celebration±

group acceptanc e for a low dose of alcohol, cm 2 5 cognitive± motor impairment for a low dose of alcohol, inh 2 5 sexual and

social inhibition for a low dose of alcohol, hd 1 5 positive expectanci es for a high dose of alcohol, hd 2 5 negative expectancie s

for a high dose of alcohol. The dependen t variable is QF: the number of standard alcoholic consumptions consumed per week

(log-transformed1), with the small ellipse depicting the residual. Only signi® cant predictors of QF are depicted with the arrows

from the expectanci es and age. Details of the analyses are given in the text and in the Appendix.


predict drinking (QF) in all subgroups. In the

regression of the second-order factors upon QF,

the same scale scores were used as above, but the

second-order factors were de® ned as predictors

in the LISREL model (see Fig. 2). Note that

there is a 1 ;1 relationship between three ® rst-or-

der factors and their second-order equivalents:

the two high-dose expectancies and age. As indi-

cated above, the explained variance will not be

increased for these variables by using the second-

order factors. The results were dif® cult to com-

pare across the different subgroups for two

reasons. First, in the smallest subgroup (girls of

subject group 2), the model did not converge.

Secondly, the ® t indices indicated an inadequate

® t of this model to the data in some subgroups.

As a solution, group-speci® c alterations to the

model had to be introduced. These alterations

will be explained.

In the youngest male subject group, 48% of

the variance in current drinking was accounted

for by two signi® cant second-order predictors:

low-dose positive and low-dose negative expec-

tancies; model ® t: c 2 (18 df) 5 20, p 5 NS. Note

that a signi® cant p denotes a failure of the model

to ® t the data (see JoÈ reskog, 1993). In girls of the

youngest subject group 29% of the variance in

current drinking was accounted for by the se-

cond-order low-dose negative expectancies and

age, model ® t: c 2 (19 df) 5 30.6, p 5 0.045. The

LISREL VIII output indicated (ª Modi® cation

Indicesº , JoÈ reskog & SoÈ rbom, 1993) that the

speci® c alteration to the model required in this

subgroup was a direct path from the residual of

sexual± social inhibition to QF (a similar alter-

ation as described in Leigh & Stacy, 1993). For

boys in the second subject group, prediction of

current drinking was increased to 30%. Two

predictors were signi® cant: high-dose positive

expectancies and the second-order low-dose

negative expectancies (adequate ® t: c 2 [19 df] 529.9, p 5 NS). In male students the ® t of the

second-order model was inadequate ( c 2 [19

df] 5 41.1, p , 0.01). Modi® cation indices indi-

cated that the residual errors of two ® rst-order

factors (sexual enhancement and sexual± social

inhibition) should be allowed to correlate. When

the model was modi® ed in this way, the ® t was

adequate ( c 2 [18 df] 5 25.4, p 5 NS) and the

variance in current drinking accounted for was

32% with low-dose positive and low-dose nega-

tive expectancies as signi® cant predictors. In fe-

male students the original second-order model

also indicated an inadequate ® t ( c 2 [16 df] 532.6, p , 0.01). Modi® cation indices indicated

that in this subgroup the residual error of age

was correlated with the residual errors of sexual

enhancement, sexual± social inhibition and cog-

nitive± motor impairment. When the model was

modi® ed in this way, the ® t was good ( c 2 [15

df] 5 15.7, p 5 NS). Low-dose positive, low-dose

negative and high-dose negative expectancies

were the signi® cant second-order predictors that

together accounted for 42% of the variance in

current drinking.

Differences in the endorsement of the four types of

expectancies between subject groups

Given that a common factor model ® tted ade-

quately across subgroups and that different types

of expectancies associated with drinking in these

groups, the next issue concerned differences be-

tween subject groups in the endorsement of the

expectancy scales. Multivariate analyses of vari-

ance were performed on the four types of expec-

tancies (the second-order factors) for the three

groups by gender (3 3 2 MANOVA). Both main

effects were signi® cant: the expectancies differed

between males and females F(4, 545 df) 5 6.8,

p , 0.001, and between the groups

F(8, 1090) 5 20.2, p , 0.001, with a signi® cant

interaction F(8, 1090) 5 3.6, p , 0.001. Pairwise

comparisons of the harmonic means were con-

ducted with the Student± Newman± Keuls

method ( c 5 0.05).

Low-dose positive expectancies. No signi® cant

gender differences were found on low-dose posi-

tive expectancies in the youngest group, nor in

university students. In contrast, boys of group 2

more often endorsed low-dose positive expectan-

cies than girls of the same group. These expec-

tancies were mostly endorsed by university

students and boys of group 2, followed by girls of

the second group. The youngest group showed

the lowest endorsement of positive expectancies

for low dose of alcohol.

Low-dose negative expectancies. The opposite

pattern was found for low-dose negative expec-

tancies: they were most endorsed by boys and

girls of group 1 (no gender difference), followed

by girls of group 2. The lowest level of endorse-

ment was found in boys of group 2 and univer-

sity students (male and female).


High-dose positive expectancies. Boys of group 2

endorsed high-dose positive expectancies

signi® cantly more than females of group 2 and 3.

No further signi® cant differences were found.

High-dose negative expectancies. Boys and girls

of group 2 endorsed high-dose negative expec-

tancies signi® cantly less in comparison with all

other subgroups. No further signi® cant

differences were found.

High-dose expectancies of boys of group 2. The

pattern of results suggested that boys of the

second subject group showed an extreme pattern

of expectancy endorsement, with the negative

expectancies endorsed least and positive expec-

tancies most in comparison with other groups.

This suggestion was tested with a contrast com-

paring boys of group 2 with the other three

subgroups with somewhat overlapping age-

ranges: girls of the same group, male and female

university students. It was found that boys of

group 2 showed the highest level of endorsement

of positive expectancies for a high-dose and the

lowest level of endorsement of negative expec-

tancies for a high dose (p , 0.001). This is illus-

trated in Fig. 4. The same contrast was also

signi® cant for low-dose positive expectancies

(p 5 0.020), but not for low-dose negative expec-

tancies. The extreme endorsement of the high-

dose expectancies in boys of group 2 paralleled

their high average number of drinks on each

drinking occasion (Q in Table 1), which was also

signi® cantly higher for boys of the second subject

group in comparison with these other three

subgroups (same contrast, p , 0.001).

Differences with alcohol consumption as a

covariate. The general pattern of results was that

a group with a higher alcohol consumption more

endorsed positive expectancies and less endorsed

negative expectancies in comparison with groups

with a lower alcohol consumption. Further

analyses showed, however, that not all differ-

ences in expectancies between the subgroups

could be explained by current drinking pattern.

When current weekly alcohol consumption was

introduced as a covariate, the main effect for

subject group was still signi® cant F(8,1088 df) 514.6, p , 0.001, the main effect for gender was

no longer signi® cant F(4,544) 5 2.2, p 5 NS, and

the interaction between gender and subject

group was still signi® cant F(1088) 5 2.3,

p 5 0.019. The pattern of results remained the

same when average alcohol consumption on each

occasion (Q) was introduced as a covariate.

Differences in the endorsement of expectancies of

young drinkers and non-drinkers. Because approxi-

mately half the subjects in the youngest subject

group reported experience with alcohol (n 5 115)

and the other half did not (n 5 101), the differ-

ences in the endorsement of the four types of

expectancies between drinkers and non-drinkers

in the youngest group was tested. Because the

non-drinkers were found to be signi® cantly

younger, t(214 df) 5 3.4, p , 0.01, age was

introduced as a covariate in the analysis (2 3 2

MANCOVA). The main effect for gender was

not signi® cant F(4,208 df) 5 0.14, p 5 NS, nor

was the interaction between gender and drinking

status F(4,208 df) 5 1.4, p 5 NS. The main ef-

fect for drinking status was signi® cant, F(4,208

df) 5 6.8, p , 0.001. Univariate follow-up tests

indicated that the non-drinkers signi® cantly

more endorsed negative expectancies for a low

dose of alcohol, F(1,211 df) 5 21.1, p , 0.001,

and negative expectancies for a high dose of

alcohol F(1,211 df) 5 5.2, p 5 0.023.

Discussion

The ® rst important ® nding of this study was that

the factor structure of the alcohol-related expec-

tancies was similar across different subject

groups and gender. With multi-group restrictive

factor analyses, it was shown that one model

with seven ® rst-order factors ® tted reasonably

well across subgroups. A second model with four

second-order expectancy factors also ® tted

across subgroups. These second-order factors

were the four types of expectancies, dis-

tinguished by valence (positive or negative) and

dose of alcohol (a few drinks or many drinks).

This allowed for further comparisons of the pre-

diction of current alcohol consumption and of

the differential endorsement of the four types of

expectancies across subgroups.

The main ® nding of this study was that differ-

ent expectancies predicted current drinking

across the subgroups. Positive expectancies for a

high dose of alcohol, for example, did not

signi® cantly predict current drinking in students,

which is in agreement with several studies (e.g.

Fromme et al., 1993; Leigh & Stacy, 1993).

1 2

(a)

3.25

3.5Pos

itive

exp

ecta

ncie

s fo

r a

high

dos

e

3Group

3.75

4.0

4.25

4.5

1 2

(b)

8.0

8.5Neg

ativ

e ex

pect

anci

es fo

r a

high

dos

e

3Group

9.0

9.5

10.0

11.0

10.5


Figure 4. Differences in (a) high-dose positive and (b) high-dose negative expectancie s between subject-groups and gender

( e males; h females).

However, it was found that these ª least plaus-

ibleº expectancies were the strongest predictor

of drinking in secondary school boys of 16

and older. In addition, these expectancies

signi® cantly predicted drinking in secondary

school boys of the youngest subject-group who

had initiated drinking. A comparison between

the older secondary school pupils and students


suggested that male and female university stu-

dents and secondary school girls of 16 and older

maintained a pattern of social drinking due to

their relatively strong negative expectancies for a

high dose and their relatively weak positive ex-

pectancies for a high-dose of alcohol. The boys

in the second subject group, on the other hand,

showed the reverse pattern of high-dose expect-

ancy endorsement (highest on positive expectan-

cies for a high dose and lowest on negative

expectancies for a high dose of alcohol). This

paralleled their current drinking pattern: they

reported the highest average alcohol consump-

tion on each occasion (6.5 standard drinks).

The comparison of the expectancies of

drinkers and non-drinkers in the youngest sub-

ject group showed that non-drinkers scored

signi® cantly higher on negative expectancies (for

a low and a high dose), but not on positive

expectancies. Interestingly, in those boys of the

youngest group who had initiated drinking, posi-

tive expectancies predicted the amount of alcohol

they consumed. This pattern of results suggested

that the relevant positive expectancies were ac-

quired before alcohol consumption actually com-

menced, which is in agreement with the ® ndings

of Christiansen et al., (1989) and Miller, Smith

and Goldman (1990). Negative expectancies ap-

peared to be moderated as a result of drinking

experience. A similar pattern was found for the

comparison between the younger boys in sec-

ondary education and the older boys in second-

ary education: the younger boys held stronger

negative expectancies than the older boys, both

for a high and for a low dose of alcohol. In

contrast, no signi® cant difference was found be-

tween these two subgroups on the positive high

dose expectancies that most strongly predicted

drinking in boys of group 2. Again, the pattern of

results suggested that the most relevant positive

expectancies did not alter much as a result of

increased drinking, but the negative expectancies

did.

There are several limitations in the present

study. First, high-dose expectancies were intro-

duced on an exploratory basis. Therefore, it was

not possible to construct speci® c high dose scales

beyond ª positiveº and ª negativeº high-dose ex-

pectancies. The introduction of more speci® c

scales is important for two reasons: to arrive at a

more precise understanding of these expectan-

cies in those groups where they are important

predictors of current risky drinking patterns. In

addition, the introduction of speci® c high-dose

scales would make the second-order factor struc-

ture more powerful and would enable a more

accurate comparison of the predictive power of

high- and low-dose expectancies in different

samples.

A second limitation of the present study con-

cerns the format of the expectancy question-

naire, which measures the effects of alcohol on

an average person. It has been argued that this

may not be the most appropriate way to measure

expectancies in subgroups primarily consisting of

experienced drinkers (here the older secondary

school pupils and the university students). How-

ever, in a direct comparison between the two

formats in college students no difference was

found (Wood et al., 1992). The advantage of the

general format was that (young) non-drinkers

could be included in the study and that, there-

fore, the results could be compared across sub-

groups, as well as with results obtained with the

widely used AEQ-A (Christiansen et al., 1982).

Nevertheless, a replication study with a question-

naire measuring the four types of expectancies in

a personal format would be desirable. This es-

pecially concerns the ® ndings on the importance

of high-dose expectancies for predicting

ª weekend-bingeingº in secondary school boys of

16 and older. Another possible extension of the

present questionnaire is to measure separately

expected effects and the desirability of the

effects. This might be an interesting way to

proceed. However, in the present set-up subjects

would have to ® ll in some items four times: for

a low dose and for a high dose of alcohol,

concerning expectancy and desirability. This

might be a rather taxing procedure for younger

subjects.

The results of this study have implications for

the prevention of problem drinking in adoles-

cents. Challenging positive expectancies would

seem a better prevention strategy than further

strengthening negative expectancies which ap-

pear to be already unrealistically high before

drinking has started. Darkes & Goldman (1993)

showed that an experimental challenge of (low-

dose) positive expectancies in male problem

drinkers (students) was effective. Present results

suggest that the prevention of problem drinking

in adolescent boys could be improved by chal-

lenging their positive high-dose expectancies. The

more established strategy of enhancing negative

(high-dose) expectancies could also be of


relevance in adolescents at risk. These expectan-

cies appear to moderate drinking successfully in

students and are relatively weak in adolescent

boys with a high alcohol consumption on each

drinking occasion. Learning to distinguish for

dose effects could be a helpful strategy in preven-

tion of problem drinking. An application of the

ª extended balanced placebo designº in a chal-

lenge could be helpful in this respect (Lapp et al.,

1994).

The results for the older adolescent boys ob-

tained in this study can be related to models of

risks for alcoholism (Zucker, 1987). The devel-

opment of an individual’ s high dose expectancies

could be an important factor in determining the

outcome of a period of high alcohol consump-

tion in late adolescence: returning to moderate

levels of consumption without professional help

(a ª developmentally limitedº alcohol problem,

Zucker, 1987) or developing a more serious al-

cohol problem. The interaction of expectancies

with other risk factors seems to be of great

importance in understanding vulnerability for al-

coholism (Sher, 1991), and constitutes a main

goal in our ongoing study with young children of

alcoholics (Wiers et al., 1994). In this line of

research the recent ® ndings of Deckel, Hessel-

brock & Bauer (1995) are particularly interest-

ing: neuropsychological measures of frontal

cortex functioning predicted positive expectan-

cies in young-adult children of alcoholics. Im-

paired functioning of the frontal cortex has been

related to the genetic vulnerability for alcoholism

(e.g. Pihl, Petersen & Finn, 1990), and might

in¯ uence the development of high-dose expec-

tancies. Another important risk factor that could

be related to the development of (high-dose)

expectancies concerns the subjective response to

an alcohol challenge in early adulthood which

was found to be a strong predictor of future

alcoholism (Schuckit & Smith 1996). Longitudi-

nal evidence for the reciprocal in¯ uence of al-

cohol experience and expectancies has been

found (Smith et al., 1995).

The main conclusion of this study is that the

measurement of dose-related alcohol expectan-

cies is a promising addition in future research

with alcohol-related expectancies, especially

when other populations than university students

are studied. Including the ª implausibleº positive

expectancies for a high dose of alcohol could be

important in relation to the prevention of alcohol

problems. Further longitudinal studies are re-

quired including the four types of expectancies

and other individual risk factors.

Acknowledgements

Special thanks to C. Dolan for help with struc-

tural equation modelling, and to T. Neve for

data collection. We further gratefully acknowl-

edge the helpful comments of K. Sher, J.

Elshout, S. Huismans, H. Vorst, W. van den

Brink, and those of two anonymous reviewers.

We would also like to thank M. Goldman for

promptly sending us requested materials and

SGO for funding the research.

Notes[1] The following variables are de® ned:

NWD 5 the number of weekdays permonth (Monday± Thursday) onwhich alcohol is consumed (0 ±16).

DRINKSWD 5 the number of standard drinkson an average weekday on whichalcohol isconsumed (as summedfrom the beverage checklist).

NWND 5 the number of weekend-days permonth (Friday± Sunday) onwhich alcohol is consumed (0 ±12).

DRINWND 5 the number of standard drinkson an average weekend-day onwhich alcohol is consumed (assummed from the beveragechecklist).

WEEKGSS 5 the number of standard drinks asubject reports to drink in an av-erage week.

NDDrunk2w 5 the number of days in the past 2weeks on which the subjectdrank ® ve or more standardalcoholic drinks on one occasion

The estimate of weekly alcohol consumption(QF, or quantity 3 frequency) was calculated asfollows:(1) QF 5 [{(NWD*DRINKSWD)

1 (NWND*DRINWND)/4}1 WEEKGSS]/2.

The estimate of the average number of drinks perdrinking occasion was calculated as follows:(2) Q 5 QF/Fwhere F denotes the frequency of drinking:(3) F 5 {(NWD 1 NWND)/4}/2.For non-drinkers (F 5 0), Q was set to zero.The number of days intoxicated in the pastmonth was calculated as follows:(4) D 5 2* NDDrunk2w.All alcohol consumption variables were log trans-formed in the following way:


(5) lnDrinkVar 5 ln(DrinkVar 1 1),where one is added to make the inclusion ofnon-drinkers possible.

A problematic aspect of a transformation isthat it might obscure the interpretation of theoutcomes (Stevens, 1992). Here, however, theinterpretation is fairly straightforward: ln(-drinks 1 1) equals zero indicates non-drinkers,ln(drinks 1 1) equals one indicates about threedrinks a week, ln(drinks 1 1) equals about sevendrinks a week, ln(drinks 1 1) equals about 19drinks a week, and ln(drinks 1 1) equals about 50drinks a week, or seven drinks a day.

[2] To assess the model-® t, several indices can beused. However, it is known that ª statistical good-ness-of-® t tests are often more a re¯ ection on thesize of the sample than on the adequacy of themodelº (Browne & Cudeck, 1993, p. 137). It istherefore recommended to concentrate on errorsof approximation such as the root mean squareerror of approximation, or RMSEA (Browne &Cudeck, 1993; JoÈ reskog, 1993). A RMSEA ofbelow 0.05 indicates a close ® t of the model inrelation to the degrees of freedom, which is ª noless subjective than the choice of 5% as asigni® cance levelº (Browne & Cudeck, 1993, p.144 ± 147). To facilitate comparison with otherstructural equation models of expectancy, theadjusted goodness of ® t index (AGFI, as inFromme et al., 1993) is given and the NNFI(non-normed ® t index, as in Leigh & Stacy,1993). In multi-group analyses the GFI is giveninstead of the AGFI. In the multi-group analysesthe corrected RM SEA is given, which is equalto the RMSEA given by LISREL 8.12 timesthe square root of the number of groups (S.Gregorich, personal communication onSM N ET , SE M N ET @ U A1V M .UA .ED U . ,February 13, 1997).

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AppendixA. Multi-group restrictive ® rst order factor analyses in

LISRELIn the multi-group restrictive ® rst order factor analysis,the following model was speci® ed in each of thesubject-groups used in that analysis:

S i 5 SS i S t 1 H i (1)

Here S is a matrix of factor loadings, S is the covari-ance matrix of the (zero mean) factors, and H i is thediagonal covariance matrix of the (zero mean) errors,with the subscript i indicating the subject-group. Thestructure of S was speci® ed a priori, because each itemwas assigned to a predetermined factor (in contrast toexploratory factor analysis) Note that S has no group-indicator because it was constrained to be invariantacross groups. For reasons of identi® cation, one indi-


cator of each factor was ® xed to 1 in all subgroups.The errors in H i were allowed to vary across subject-groups, as was the covariance matrix Y i. All freeparameters in Y, Y i and H i were estimated using theLISREL program.

B. Second-order (multi-group) factor analysis in LISRELThe object of second order factor analysis is to explainthe covariance of the ® rst order factors by postulatinga smaller number of second order factors. The modelused to this end is:

Y i 5 Y ( YY i Y t 1 Y i) Y t 1 H i (2)

Here Y contains factor loadings on ® rst order factors,Y contains factor loadings of ® rst order factors on

second order factors, Y i is the covariance matrix of thesecond order factors, Y i is the diagonal covariancematrix containing the residual variances, i.e. variancesof the ® rst order factors that are not explained by thesecond order factors, and H i is again the diagonalcovariance matrix of the errors. The covariance matrixof the ® rst order factors is now modelled as( YY i Y t 1 Y i). Note that in this analysis both the ® rstand second order factor loadings ( Y and Y) wereestimated under the restriction that these parameterswere invariant across the subject-groups. The parame-ters in Y i, Y i and H i were allowed to vary over thesubject-groups. For reasons of identi® cation, again oneindicator of each ® rst order factor was ® xed to 1 in allsubgroups, and Y i and Y i were standardized in onesubject-group.

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