Available via license: CC BY 4.0
Content may be subject to copyright.
Substance Use
Emerging Adulthood
2022, Vol. 10(3) 595–608
© 2022 Society for the
Study of Emerging Adulthood
and SAGE Publishing
Article reuse guidelines:
sagepub.com/journals-permissions
DOI: 10.1177/21676968211060945
journals.sagepub.com/home/eax
Friendship Conflict, Drinking to Cope, and
Alcohol-Related Problems: A Longitudinal
Actor-Partner Interdependence Model
Sean P. Mackinnon
1
, Michelle E. Tougas
1
, Ivy-Lee L. Kehayes
1
,
and Sherry H. Stewart
1
Abstract
Drinking to cope with negative affect is a strong predictor of alcohol-related problems. We hypothesized that the association
between friendship conflict and alcohol-related problems would be mediated by coping-with-depression motives in emerging
adults’close friendships. We used a 4-wave, 4-month longitudinal self-report survey design measuring friendship conflict, coping
motives, and alcohol-related problems from 174 same-sex friendship dyads. Participants were recruited from Nova Scotia,
Canada between September 2016 and February 2019. Participants had a mean age of 18.66 (SD = 1.17) and were 66.1% female.
Data were analyzed using multilevel structural equation modeling. Coping-with-depression motives mediated the link between
conflict and alcohol-related problems at the between- and within-subject levels. Unexpectedly, coping-with-anxiety motives
was an additional mediator at the within-subjects level. Interventions for emerging adults’problem drinking should consider the
influence of friendship conflict and its impact on emerging adults’tendencies to drink to cope with both depression and anxiety.
Materials/Syntax: https://osf.io/krs3v/
Keywords
alcohol use/abuse, coping, friendship, transitions to adulthood, peers
Emerging adults (∼18–29 years old; Arnett et al., 2014) have
the highest prevalence of alcohol use, with 82.8% of Canadian
emerging adults drinking in the past year (Canadian Centre on
Substance Use and Addiction, 2017). Among Canadian post-
secondary students, 35% report consuming at least five or
more drinks in one sitting in the last 2 weeks, and 49.5% report
at least one alcohol-related problem over the past 12 months
(American College Health Association, 2019). The most
common alcohol-related problems experienced by post-secondary
students are having a bad time (33.0%), noticing a change in
personality (31.7%), neglecting responsibilities (27.6%), miss-
ing a day (or part-day) of school or work (25.6%), and being
unable to do homework or study for a test (22.1%) (Neal et al.,
2006). The present study tests whether friendship conflict and
drinking to cope predict alcohol-related problems in friendship
dyads using a 4-wave, 4-month longitudinal design.
Drinking Motives Theory
Young people drink alcohol for a variety of motives, some of
which are riskier than others. The motivational model of alcohol
use (Cooper, 1994;seealsoCooper et al., 2016) describes two
underlying dimensions of the consequences that young people
seek from drinking alcohol: (1) positive vs. negative
reinforcement; and (2) internal versus external motivational
sources. More specifically, the desired outcomes of drinking may
involve pursuing a positive outcome (e.g., pleasurable arousal) or
avoiding a negative outcome (e.g., avoiding depression), and
achieving sought after internal (e.g., mood manipulation) or
external (social approval) consequences. Combining these un-
derlying drinking motivation dimensions results in four indi-
vidual drinking motives: social (positive reinforcement, external),
enhancement (positive reinforcement, internal), conformity
(negative reinforcement, external), and coping (negative rein-
forcement, internal) (Cooper, 1994;Cooper et al., 2016).
Each of the four drinking motives are associated with
specific alcohol-related outcomes (see Cooper et al., 2016).
Drinking to cope (i.e., drinking to alleviate negative affect) is
unique in that it is directly related to alcohol-related problems
(e.g., problems with school; Cooper et al., 1995) even after
accounting for alcohol consumption levels. Further research
1
Dalhousie University, Halifax, NS, Canada
Corresponding Author:
Sean P. Mackinnon, Psychology and Neuroscience, Dalhousie University, Life
Sciences Centre, 1355 Oxford St., Halifax, NS B3H 4R2, Canada.
Email: mackinnon.sean@dal.ca
exploring the four-factor model of alcohol-use motives found
that subdividing coping motives into two distinct factors to
create a five-factor model was a better fit for assessing
drinking motivations in emerging adults (Grant, Stewart,
O’Connor, et al., 2007). To create the five-factor model,
coping motives were divided into coping with anxiety motives
(CAM) and coping with depression motives (CDM), which
both uniquely predicted distinct alcohol-related outcomes.
Coping with anxiety motives showed cross sectional and CDM
longitudinal associations with alcohol-related problems (Grant,
Stewart, O’Connor, et al., 2007). Further exploration is needed
of factors, such as friendship conflict, that may lead to each of
these coping motives and in turn to alcohol-related problems.
Friendship Conflict and Alcohol
Conflict within friendships may be an important trigger of both
negative emotions and of drinking in young people given that
friendships are central to the lives of emerging adults (McNamara
Barry et al., 2015). In a large cross-sectional study of 1074
emerging adult friend dyads, Boman et al. (2013) found that
friendship dyads where both friends engage in binge drinking
and cannabis use were characterized by increased conflict. One
possible explanation for this link is that friendship conflict may
trigger heavy drinking in both friendship dyad members. Studies
of young friendship dyads have examined whether friendship
conflict leads to negative emotions. Schwartz–Mette et al.
(2021) found that positive friendship quality longitudinally
predicted lower depression in cross-lagged panel models for
adolescent friend dyads; however, friendship conflict was
generally unrelated to depressive symptoms in this sample. In
contrast, Chow et al. (2015) found that friendship discord was
associated with increased depressive symptoms in university
student friendship dyads. One possible reason for the discrepant
results is the difference in developmental stage (adolescence vs.
emerging adulthood). The more transitory nature of friendships
during emerging adulthood (Laursen & Bukowski, 1997)and
the tendency in this developmental stage to shift focus from
friendships to romantic relationships (McNamara Barry et al.,
2009) might give rise to increased friendship conflict in the
transition from adolescence to emerging adulthood (Camirand
& Poulin, 2019) and to result in negative emotions that could
trigger coping drinking. Thus, we focused our investigation on
emerging adulthood. Another difference between Chow et al.
(2015) and Schwartz–Mette et al. (2021) pertains to the op-
erationalization of conflict—the item content of Chow et al.
(2015) focused much more on criticism, dominance, and ex-
clusion than the measure used by Schwartz–Mette et al. (2021).
The present study utilized measures that tap friendship conflict
in a manner consistent with Chow et al. (2015).
In the present study, we conceptualized conflict as a dyadic
variable—for example, mutually expressed anger, hostility, and/
or communicative disengagement that happens within a given
relationship. This contrasts with research on social negativity,
which conceptualizes anger, hostility, and criticism as an intra-
individual variable (Ibarra–Rovillard & Kuiper, 2011). Thus, our
operationalization of conflict measures it as an inherently in-
terpersonal process (i.e., relational conflict between people,
rather than a measurement of intra-individual hostility), as we
believe it is dyadic conflict between friends, not the act of being
hostile toward a friend per se, that predicts drinking to cope.
Replication Target
Using a 4-wave, 4-week longitudinal design with 100 romantic
dyads, Lambe et al. (2015) found that the relationship between
conflict and alcohol-related problems was mediated by coping
motives. Specifically, only CDM (and not CAM) motives me-
diated the conflict-to-alcohol problems association at the within-
subjects level when both motives were entered as mediators in a
single model (Lambe et al., 2015). These results were found for
actor effects (how an individual influences their own behavior),
but not for partner effects (how a partner influences an indi-
vidual’s behavior). Therefore, increases in conflict within a ro-
mantic relationship predicted increases in CDM drinking motives
which, in turn, predicted increases in alcohol-related problems in
the same individual.
Lambe et al. (2015) has important implications for the
treatment of drinking problems. Specifically, it implies that
romantic relationship conflict might be a distal predictor of
alcohol problems—thus, addressing relationship conflict could
have downstream improvements for alcohol-related problems.
Nonetheless, romantic relationships represent only one facet of
an emerging adult’s social network and potential relationship
conflicts. Thus, we wanted to see if the findings of Lambe et al.
(2015) generalized to another important type of relationship in
the lives of emerging adults, namely friendships. We sought to
conceptually replicate and extend Lambe et al. (2015) with
three primary methodological changes. The first change was to
recruit same-sex friends instead of romantic couples. At the
emerging adult developmental stage, young people increasingly
turn to peers for social interactions and social support, with
these friendships influencing many areas of their lives (Buote
et al., 2007;Lewis et al., 2015). Ruptures in these friendships
may have significant consequences on feelings of anxiety and
depression (Chow et al., 2015, which may trigger coping-
motivated drinking and in turn alcohol-related problems.
Thus, the current study investigated the mediational role of each
of the coping motives (CDM and CAM) in explaining the
relationship between friendship conflict and alcohol-related
problems in both emerging adult actors and their friends.
The second methodological change was to change from a
4-wave, 4-week design to a 4-wave, 4-month design. A pri-
mary difficulty in longitudinal research is to find the right time
lag for the causal processes under study. A weakness of the
Lambe et al. (2015) study was that the 1-week measurement
occasions were short, and did not necessarily allow sufficient
time for conflict, motives, and alcohol problems to change
over time. Moreover, short time lags can result in restricted
variance for comparatively rare events, such as conflict and
596 Emerging Adulthood 10(3)
alcohol problems. Thus, we increased the lag between mea-
surement occasions to increase the variation in our studied
constructs. The third change was a move from in-person data
collection to online data collection. This change was for ef-
ficiency and pragmatics. Otherwise, the methods of the present
study were virtually identical to Lambe et al. (2015).
Objectives
Using a longitudinal, 4-month, 4-wave design, this study ex-
amined the association between friendship conflict and alcohol-
related problems. This association was further explored with
CDM and CAM motives as potential mediators of the asso-
ciation between friendship conflict and alcohol-related prob-
lems. Moreover, we investigated actor and partner effects.
Partner effects may be expressed directly, where a friend’s
drinking motive may impact an individual’s own alcohol-related
problems. Partner effects may also be expressed indirectly,
wherein a friend’s drinking motive predicts an individual’sown
alcohol-related problems, which in turn may influence the in-
dividual’s own alcohol-related problems. Given the longitudinal
nature of the study, data were analyzed using multilevel
structural equation modeling (SEM) (Preacher et al., 2010).
Structural equation modeling explores between-subject vari-
ance, the portion of variance that stays consistent across the
4 months, as well as within-subject variance, the portion of
variance that changes from month to month.
Given that this research aimed to replicate and extend the
work of Lambe et al. (2015), the hypotheses and research
questions were formulated from their findings on the rela-
tionship between conflict, coping motives, and alcohol-related
problems in romantic couples. Our hypotheses were also
informed by prior findings linking friendship conflict to de-
pression (Chow et al., 2015) and conflict to alcohol outcomes
(Boman et al., 2013) in emerging adult friendship dyads. In
our confirmatory model replicating Lambe et al. (2015),we
tested the same model, and derived our predictions from the
findings of that study. Avisual depiction of the model and our
predictions for the direction of relationships derived from
Lambe et al. (2015) can be found in Figure 1. These pre-
dictions can be summarized as follows:
1) Friendship conflict would have an indirect effect on
alcohol-related problems through CDM motives,
meaning that friendship conflict would lead to drinking
to cope with depression, which in turn would lead to
alcohol-related problems. Specifically, we expected
these findings to hold for actor effects at the within-
subjects level.
2) Friendship conflict would lead to drinking to cope with
anxiety at both the between- and within-subject levels.
Other paths in Lambe et al. (2015) were inconclusive.
Thus, for other paths in Figure 1, we did not have a priori
predictions, and these tests were considered exploratory.
1) Would friendship conflict have an indirect effect on
alcohol-related problems through CDM motives for
partner effects and/or at the between-subjects level?
2) Would friendship conflict have an indirect effect on
alcohol-related problems through CAM motives for
actor and/or partner effects at either the between- and/
or within-subject levels?
Method
Participants
Participants were required to be same-sex friends who con-
sume alcohol together and had known each other for a year or
less. Participants were included in the study if they consumed
12 or more alcoholic drinks in the past year, were between the
ages of 18–25 years old, and at least one of the friends was a
first-year undergraduate student. These requirements were
listed in our recruitment materials. All participants met these
criteria when screened via email prior to arrival at the lab. All
participants were recruited from Nova Scotia, Canada. These
data were previously analyzed in three prior studies, one
examining the relationship between drinking motives and
alcohol quantity/frequency (Kehayes et al., 2021), another on
extraversion and drinking similarity (Nogueira–Arjona et al.,
2019), and a third on the validity of informant-reported drinking
motives (Kim et al., in press); our study made secondary use of
these data.
Participants were 348 undergraduate students from 174
same-sex friendship dyads. Individual participants had an av-
erage age of 18.66 years (SD = 1.17), were 66.1% female,
79.3% Caucasian, and 84.8% were university students. They
were recruited from Dalhousie University and the surrounding
community. Participants reported hearing about the study
through flyers (37.9%), word-of-mouth (25.0%), class website
(20.7%), classroom announcements (17.0%), the Psychology
Department participant pool (10.1%), or other sources (15.8%),
with some participants reporting more than one source. At
Wave 1, the initial point of contact with the lab, the dyads re-
ported an average friendship length of 4.05 months (SD =2.21),
with an average face-to-face contact of 19.75 days/month (SD =
7.60). At Wave 1, a total of 21.0% friends reported cohabitating
together for an average of 2.88 months (SD =1.56).AtWave1,
85.1% reported experiencing at least one alcohol-related problem
in the past month (M=4.14,SD =3.60,range=0–18).
Procedure
Data collection for the first wave started in September 2016
and ended February 2019
1
. Data were collected during the Fall
and Winter semesters. Participants were recruited using the
psychology subject pool, online ads, flyers, and in-class an-
nouncements. Interested participants contacted the study ad-
ministrators via email and were screened for eligibility. Dyads
arrived together at the lab and were again screened for
Mackinnon et al. 597
eligibility. Participants reviewed the consent form and gave
informed consent prior to participation. During the same visit,
each participant completed the Wave 1 questionnaire battery
online. For three monthly follow-ups, participants were
emailed the same questionnaire batteries for Waves 2–4 at 30-
day intervals. If participants did not complete a questionnaire
the day it was mailed, they were emailed reminders daily for
7 days, with three additional reminders until the end of the 30-
day period. Reminders ceased after completion of the ques-
tionnaires. The make-up questionnaires evaluated the same
30-day period as the original to ensure that responses would be
for the same 30-day period, regardless of when completed. If
Figure 1. Figure depicting confirmatory hypotheses (solid lines) and exploratory hypotheses (dotted lines). Note. Rectangles indicate
manifest variables. Single-headed arrows indicate paths. Residual covariances are omitted on this conceptual diagram to reduce clutter.
Black lines indicate hypothesized paths based on Lambe et al. (2015) and the expected direction (+, or positive). Dotted lines represent
exploratory research questions for other paths included in the model.
598 Emerging Adulthood 10(3)
participants did not complete one of the waves, their data was
counted as missing for that wave only. Skip logic was em-
ployed, where participants were not asked to complete the
drinking motives questionnaire for a given wave if they ab-
stained from alcohol during that month (i.e., an individual
cannot have a motive for drinking if they did not drink).
Participants were compensated CAD$10.00 or one credit
point for each wave that they completed on-time (within a
week of the questionnaire being sent). For questionnaires that
were completed 8–30 days after originally being sent, par-
ticipants were compensated with CAD$5.00 or ½ credit point.
All participants were debriefed at completion of participation.
Figure 2. Tested multilevel structural equation model including constraints. Note. Pathways that share a color and label (e.g., wa2) were
constrained to equality due to indistinguishable dyads. Paths were not constrained to equality across levels (e.g., wa2 and ba2 are not equal
to one another). Variance partitioned into between and within levels using latent mean centering. Actor effects are paths wb1, wb2, bb1, and
bb2. Partner effects are paths wp1, wp2, bp1, and bp2. Double-headed arrows are correlated residuals to account for non-independence.
Mackinnon et al. 599
Materials
Demographics and friendship. At Wave 1, each participant
completed questions about their demographic characteristics
(age, sex, and ethnicity) and information about their friendship
(friendship length, amount of face-to-face contact, and
cohabitation).
2
Conflict. Friendship conflict was analyzed as a composite
variable that consisted of the Social Conflict Scale (Abbey &
Andrews, 1985), the Partner-Specific Rejection Behaviors Scale
(Murray et al., 2003), and the Interpersonal Qualities Scale
(Oishi & Sullivan, 2006). Each scale measured friendship
conflict in the past month. The Social Conflict Scale consisted
of five items (e.g., “Got on your friend’snerves”) rated on a
scale ranging from 1 (not at all)to5(a great deal). The Partner-
Specific Rejection Behaviors Scale consisted of seven items
(e.g., “Iinsultedmyfriend”) rated on a scale ranging from 1
(strongly disagree)to9(strongly agree). The Interpersonal
Qualities Scale consisted of five items that rated interpersonal
characteristics when in the presence of the study friend (e.g.,
“moody/irritable”), ranging from 1 (not at all characteristic)to
9(completely characteristic). In previous work, each individual
scale has exceeded acceptable levels of internal consistency
(alphas ranging from 0.75–0.84; Lambe et al., 2015). Averaged
subscale totals for each of the three scales were used for de-
scriptive statistics. For analyses, each subscale total was con-
verted to standardized Z scores, which were then summed to
create a single conflict composite index score. This composite
measure has been shown to possess acceptable psychometric
properties with factor analysis showing a single factor with
loadings from each of the 17 conflict items ranging from 0.47 to
0.81 (Lambe et al., 2015). This established factorial validity
indicated that combining the conflict items into a single factor
was appropriate. For the current study, conflict was assessed as
a dyadic variable with equal contributions from each member of
the friendship (Lambe et al., 2015;Mackinnon et al., 2012).
Modified drinking motives questionnaire—Revised. Coping with
depression motives and CAM were derived from the 30-day
version of the Modified Drinking Motives Questionnaire-
Revised (DMQ-R; Grant, Stewart, O’Connor, et al., 2007),
amodified five-factor version of the original four-factor
drinking motives scale (Cooper, 1994). The DMQ-R is a re-
liable and valid 28-item measure of five drinking motives:
enhancement, social, conformity, CDM, and CAM. As the main
focus of this study was to replicate and extend the work of
Lambe et al. (2015) with friendship dyads, only CDM and
CAM were used in analyses. The CDM scale is composed of
nine items (e.g., “to numb my pain”), and the CAM scale is
composed of four items (e.g., “to reduce my anxiety”). Par-
ticipants rated how often they drank for each reason on a scale
from 1 (almost never/never)to5(almost always). The Modified
DMQ-R has been found to have good to excellent test-retest
reliability (intraclass correlation coefficients from 0.61–0.78),
adequate to excellent internal consistency (αsfrom0.66to
0.91), and strong factorial validity (Grant, Stewart, O’Connor,
et al., 2007).
Rutgers alcohol problem index. The 30-day version of the
Rutgers Alcohol Problem Index (RAPI) was used to measure
alcohol-related problems (White & Labouvie, 1989). The
RAPI is a 23-item measure asking about the experience of
specific negative consequences (e.g., “caused shame or em-
barrassment to someone”) due to drinking. Participants rated
how often each problem occurred in the specified 30-day
timeframe on a scale from 0 (never)to4(4 or more times).
The RAPI has been found to have strong test-retest reliability (r
= .83), and high internal consistency (α= .92) for the total RAPI
score (Grant, Stewart, O’Connor, et al., 2007). The individual
RAPI items were dichotomized (i.e., 1 = presence, 0 = absence
of alcohol-related problem). The dichotomized items were then
summed into a single value (possible range 0–23) for analysis
(Martens et al., 2007). The dichotomized RAPI shows good
psychometric properties including good internal consistency
with αs ranging from 0.74 to 0.83 (Martens et al., 2007).
Self-administered Timeline Follow-Back
Using a self-reported timeline follow-back procedure (Collins
et al., 2008), participants reported on their past 30 days of
alcohol consumption. Participants reported on the number of
alcoholic drinks they consumed each day, with a standard drink
defined as 12oz of beer, 5oz of wine, 3oz of fortified wine, or
1.5oz of hard liquor. Total volume of consumption was cal-
culated by summing the total number of drinks consumed over
the past 30 days. The Self-administered Timeline Follow-Back
(STLFB) provides similar results to more traditional quantity-
frequency measures of consumption (Collins et al., 2008).
Data Analytic Strategy
Data were analyzed using Mplus 7.0 software using multilevel
structural equation modeling (Preacher et al., 2010), com-
bining elements of traditional mediation models and Actor
Partner Interdependence models (Kenny & Ledermann,
2010). The tested model is depicted in Figure 2. Data were
in wide format for dyads (i.e., separate columns for partner A
and B), with dyadic non-independence handled using corre-
lated residuals. Because dyads are indistinguishable, partners
were randomly assigned the role of Partner A or B, and paths
were constrained to equality as depicted in Figure 2. Data were
in long format for the longitudinal component (i.e., one row
per timepoint), and non-independence was handled using
multilevel modeling. The multilevel model partitioned vari-
ance into between- and within-subjects levels using latent
mean centering (see Hamaker & Muth´
en, 2020). We used
random intercepts and fixed slopes; this essentially assumes
compound symmetry for the longitudinal residual variances
(Streja et al., 2017, p. ii80). Missing data was handled using a
600 Emerging Adulthood 10(3)
full information maximum likelihood approach. Significance
of indirect effects was calculated using the delta method, as
bootstrapping is incompatible with TYPE = TWOLEVEL in
Mplus software. Nonnormality of residuals was accounted for
by using a robust estimator of fit indices and standard errors
(MLR estimator in Mplus). Moreover, because skewness was
severe enough that the MLR estimation might still be biased,
CDM, CAM, and the RAPI were log
10
transformed
3
prior to
analysis to deal with positive skew (this also matches analytic
procedures used in Lambe et al., 2015).
Our choice to use MLR estimation and log transforms
instead of other common alternatives for dealing with violations
of the normality assumption was based on pragmatic concerns.
Bootstrapping is incompatible with multilevel models in Mplus.
Count models would require switching to the WLS estimator
which requires listwise deletion for missing data. Moreover,
count models make interpretation of mediation models difficult,
because indirect effects become conditional on values of X
(Geldof et al., 2018). Thus, we did not analyze the data using
count models. Finally, using log transforms matches the ap-
proach used in the replication target (Lambe et al., 2015).
Results
Out of four waves of surveys, individuals within dyads
completed an average of 3.49 waves (SD = 0.93), with 70.7%
of individuals completing all four waves. Across waves, 100%
of individuals completed Wave 1, 89.9% of individuals
completed Wave 2, 85.3% completed Wave 3, and 73.6%
completed Wave 4. During the study, three dyads reported
ending their friendship at Wave 3, and two dyads at Wave 4.
For these dyads, data collected before their friendships ended
were included in the analyses, while data collected after the
friendships ended were coded as missing (0.01% of wave
entries). Data from individual participants who reported ab-
staining from alcohol for any of the waves were excluded from
analyses for only the wave that they abstained from drinking
alcohol (5.7% of wave entries). In cases where one partner
abstained and the other drank alcohol, we still retained data
from the individuals who drank alcohol in analyses. No other
data were excluded from analyses. A total of 80.2% of the
surveys were completed on time, and 19.8% were completed
late (ranging from 1–8 days delayed). Across the four waves,
20.3% of data were missing, with covariance coverage ranging
from .76–.91.
Table 1 presents the means and SDs of values across all four
waves. All values were comparable to the replication target
(i.e., within 1 SD of the mean), with the exception of the RAPI
mean for wave 2, which was still within 2 SD of the mean from
previous samples (Lambe et al., 2015).
4
Table 2 presents the
within- and between-subject level correlations, intraclass
correlations, and reliabilities. At both the between-and within-
subject levels, all variables were significantly and positively
correlated with one another, with the magnitudes of correla-
tions being greater at the between-subjects level. Intraclass
correlations show the percentage of the variance available to
be explained at the between-subjects level; the majority of the
variance was calculated at the within-subjects level for all
variables (i.e., a state-trait with substantial state and trait
variance). All measures showed good reliability at both the
between-and within-subject levels, with the exception of
CAM at the within-subject level. When cluster sizes are small,
within-subjects level reliabilities may be underestimated, and
reliability cutoff scores are not as well-established in multi-
level models (Geldhof et al., 2014). Therefore, despite one low
reliability value, we proceeded with the planned analysis.
Nonetheless, analyses utilizing CAM have a high degree of
measurement error at the within-subjects level, and thus an
elevated potential for Type II error.
Mediation
Excellent model fit was defined as follows: root-mean-square
error of approximation (RMSEA) < .06, standardized root-
mean-square residual (SRMR) < .08, and comparative fit
index (CFI) and Tucker–Lewis index (TLI) > .95 (Hu &
Bentler, 1999;Kline, 2015). Finally, internal consistency
was examined at the between- and within-subject levels using
a multilevel adaptation of Cronbach’s alpha (Geldhof et al.,
2014). The hypothesized model fit well, χ
2
(20) = 32.51,
p <.05; RMSEA = .03; SRMR (within) = .07; SRMR (be-
tween) = .06, CFI = .98, TLI = .95. The unstandardized path
coefficients and the associated R
2
values are presented in
Figure 3, with indirect effects presented in Table 3.
Confirmatory analyses. At both the between- and within-subject
levels, conflict significantly predicted CDM and CAM. At the
within-subjects level, significant actor effects were found where
CDM significantly predicted alcohol-related problems in the
same individual; partner effects were not significant. Tests of
indirect effects revealed that CDM significantly mediated the
link between conflict and alcohol-related problems.
Exploratory findings. At the within-subjects level, conflict sig-
nificantly predicted alcohol-related problems after controlling
for all other variables; no effects were found at the between-
subjects level. At the within-subjects level, significant actor
effects were found where CAM significantly predicted alcohol-
related problems in the same individual; partner effects were not
significant. Tests of indirect effects revealed that CAM sig-
nificantly mediated the link between conflict and alcohol-
related problems. No other significant indirect effects were
found at the within-subjects level. At the between-subjects
level, significant actor effects were found where CDM (and
not CAM) significantly predicted alcohol-related problems in
the same individual; partner effects were not significant. At the
between-subjects level, tests of indirect effects revealed that
CDM (but not CAM) significantly mediated the link between
conflict and alcohol-related problems. Indirect effects tests also
revealed that the actor’s alcohol-related problems significantly
Mackinnon et al. 601
mediated the link between the actor’s CDM and the partner’s
alcohol-related problems (an indirect partner effect). No other
significant indirect effects were found at the between-subjects
level.
At the within-subjects level, the correlated error terms
between the actor’s and partner’s alcohol-related problems
were not significant (B= 0.003, p= .47). Although the cor-
related error terms between the actor’s and partner’s CDM (B
< 0.001, p= .88) and CAM (B= 0.001, p= .30) were also not
significant, CDM and CAM were strongly related within the
same individual (B= 0.005, p< .001). The correlated error
terms between alcohol-related problems were significant at the
between-subjects level (B= 0.018, p= .001). Although the
correlated error terms between partner’s CDM (B< 0.001, p=
.55) and CAM (B< .0001, p= .96) remained unrelated, CDM
and CAM maintained their relationship within the same in-
dividual (B= 0.011, p< .001). In general, effect sizes at the
within-subjects level were small with about 1–3% of the
variance explained in both coping motives, and 9% of the
variance explained in alcohol-related problems. At the
between-subjects level, for both coping motives, effect sizes
were small with 3–8% of variance explained in both coping
motives. For alcohol-related problems, however, a large
amount of the variance was explained by conflict and coping
motives combined (35–41%).
Supplementary Analyses
Upon reviewer request, we included a few additional un-
planned analyses. We tested the same model depicted in
Figure 2, replacing alcohol problems with total volume of
consumption using the STLFB measure. As in prior analyses,
alcohol consumption was log
10
transformed prior to analysis.
The hypothesized model fit well, χ
2
(20) = 37.24, p = .01;
RMSEA = .04; SRMR (within) = .06; SRMR (between) = .06,
CFI = .97, TLI = .94. Broadly speaking, variables were weaker
predictors of alcohol consumption (R
2
= .12–.15) than of
alcohol problems (R
2
= .35–.41). In terms of hypothesis tests,
the following differences from the analyses with alcohol
problems as the outcome were noted: (a) Actor effects from
CDM to alcohol consumption were non-significant at both the
between- and within-subjects levels; (b) Actor effects from
CAM to alcohol consumption became statistically significant
and positive at the between-subjects level; and (c) The cor-
related error term for alcohol consumption in each partner
became statistically significant and positive. All other paths
showed the same pattern of statistical significance and di-
rection of effects as the model with the alcohol problems
measure as the outcome. All unstandardized path coefficients
and the associated R
2
values are presented in Supplementary
Figure 1, with indirect effects presented in Supplementary
Table 1.
5
We also ran a supplementary analysis where we entered sex
as a between-subjects covariate (i.e., a predictor of all vari-
ables in Figure 3). Note that sex cannot predict within-subjects
variables, due to how the variance was partitioned for the
multilevel model (i.e., it is a between-subjects variable that
Table 1. Means and Standard Deviations.
Variable
Wave 1 Wave 2 Wave 3 Wave 4
Actual Range Possible RangeMSDMSDMSDMSD
RAPI 4.14 3.60 4.04 4.14 3.58 4.08 2.79 3.77 0–23 0–23
CDM 1.42 0.69 1.37 0.59 1.33 0.60 1.38 0.77 1–51–5
CAM 1.85 0.83 1.79 0.70 1.73 0.72 1.75 0.84 1–51–5
Social conflict 1.63 0.65 1.59 0.65 1.60 0.71 1.50 0.64 1–4.80 1–9
Rejecting behaviors 1.64 0.97 1.66 0.96 1.83 1.16 1.65 1.18 1–91–9
Interpersonal qualities 3.08 1.61 2.98 1.60 3.06 1.74 3.07 1.85 1–91–9
STLFB 42.89 40.26 31.43 32.34 28.19 29.25 23.98 27.91 0–376 Open-ended numerical
Note. Ns vary by variable and wave and range from 197 to 348. Ms and SDs are shown for the averaged total of the summed dichotomized RAPI items, as well as
for the averaged subscale totals for the CDM and CAM motives, and the three conflict variables prior to log
10
transformation. RAPI = Rutgers Alcohol Problem
Index; CDM = coping with depression motives; CAM = coping with anxiety motives. STLFB = Volume of alcohol consumption on the self-reported timeline
follow-back measure. Actual range is the minimum and maximum values across all 4 waves.
Table 2. Correlation Matrix, Intraclass Correlations, and
Reliabilities for Analyses at Between- and Within-Subject Levels.
Variable 12345
1. Conflict –.18*** .15*** .21*** .03
2. CDM .43*** –.64*** .38*** .17***
3. CAM .26* .79*** –.35*** .24***
4. RAPI .33*** .54*** .53*** –.32***
5. STLFB .06 .26** .34 .72 –
ICC .42 .33 .29 .36 .51
Alpha reliability (within) .85 .92 .66 .80 –
Alpha reliability (between) .95 .98 .86 .95 –
Note. Between-subject correlations are below the diagonal; within-subject
correlations are above the diagonal. CDM, CAM and RAPI were log
10
transformed. CDM = coping with depression motives; CAM = coping with
anxiety motives; RAPI = Rutgers Alcohol Problem Index; STLFB = Volume of
alcohol consumption on the self-reported timeline follow-back measure; ICC
= intraclass correlation.
*p< .05. **p< .01. ***p< .005.
602 Emerging Adulthood 10(3)
does not vary over time). In this model, sex was not signif-
icantly related to any other variable (all ps > .05). Moreover,
all the coefficients in Figure 3 retained the same pattern of
statistical significance and direction of effects when sex was
included as a covariate. The raw output of this supplementary
analysis can be found on our OSF page: https://osf.io/krs3v/
Discussion
In both Lambe et al. (2015) and the present paper, relationship
conflict positively predicted both CAM and CDM. That is,
conflict in close relationships predicts drinking to cope at the
between-person and within-person levels. Thus, the findings
Figure 3. Results of the multilevel structural equation model predicting alcohol-related problems. Note. Solid lines indicate significant paths,
dashed lines indicate nonsignificant paths. Rectangles indicate manifest variables. Single-headed arrows indicate paths. Double-headed
arrows indicate covariances. Coefficients are unstandardized and paths were constrained to equality across both partners. R
2
values are
indicated in the upper right-hand corner of endogenous variables. See https://osf.io/krs3v/for full output.
Mackinnon et al. 603
for the link of conflict to both types of coping drinking motives
were successfully replicated. Where the results diverge is at
the coping motives to alcohol problems stage. In Lambe et al.
(2015), there was an actor effect only for CDM predicting
alcohol problems, whereas the present paper finds actor effects
for both CDM and CAM, at the within-subjects level. At the
between-subjects level, there was substantial collinearity—
that is, CAM and CDM were more strongly correlated with
each other. This is not surprising, as depression and anxiety
have been well-known to be comorbid for decades (Stavrakaki
& Vargo, 1986). Thus, in the original Lambe et al. (2015)
paper, CDM and CAM both predicted alcohol problems, but
when entered together, the results became inconclusive. The
present paper had a larger sample size, and was better able to
handle the collinearity issue, and thus we observed CDM
emerging as statistically significant predictor at the between-
subjects level, even when controlling for CAM. Thus, the
actor effects from CDM to alcohol problems were successfully
replicated from Lambe et al. (2015). Additionally, supple-
mentary analyses suggest that these findings persist even when
controlling sex, and that coping motives and conflict are
stronger predictors of alcohol-related problems than of vol-
ume of alcohol consumption. However, in the present paper
relative to Lambe et al., we saw a stronger relationship be-
tween CAM and alcohol problems at the within-subjects level,
which bears further consideration.
When conflict is present, friends may be turning to mal-
adaptive coping mechanisms (i.e., coping drinking) for dealing
with feelings of anxiety over time. There is some evidence to
suggest that anxiety and depression are each associated with
distinct patterns of alcohol use (Grant, Stewart, O’Connor,
et al., 2007). Given that the mediation occurred in the cur-
rent study with friendship pairs, and not when romantic couples
were assessed, the type of relationship may explain the dif-
ference in CAM as a mediator between conflict and alcohol-
related problems. Sex differences are reported in response to
conflict, where men respond to conflict with both depression
and anxiety, whereas women respond with only anxiety (El-
Sheikh et al., 2013). Considering that the romantic couples
study involved similar numbers of men and women, and this
study was comprised of more women (66.1%), perhaps these
differences are attributable to the greater proportion of women
in the present study. Given that depression and anxiety may be
associated with different alcohol-use patterns (Grant, Stewart,
O’Connor, et al., 2007), the mechanisms and contextual factors
underlying depression-related drinking may be distinct from
those underlying anxiety-related drinking (Grant & Stewart,
2007;Grant, Stewart, & Birch, 2007;Grant et al., 2009). Of
interest, supplementary analyses suggested that CAM (but not
CDM) were positively associated with volume of drinking when
both motives were entered as simultaneous mediators. This
makes some sense, given the well-known anxiolytic properties
of alcohol (Stewart et al., 2016).
Further, the two studies used different measurement time-
frames; whereas Lambe et al. (2015) followed romantic couples
over four 1-week study waves, the current study followed
friendship pairs over four 1-month study waves. The longer
intervals between waves inthe current study may have provided
greater opportunity for CAM to exert effects on alcohol-related
problems over time (i.e., at the within-subjects level). Con-
sistent with our finding of CAM as a predictor of alcohol-related
problems at the within-subjects level (over time), Grant,
Stewart, O’Connor, et al. (2007) identified that only CAM
were predictive of alcohol-related problems prospectively over
a mean follow-up interval of 94.8 days, when usual alcohol use
was controlled. Thus, CAM may be more relevant for alcohol
problems over longer time frames.
Similar to the findings of Lambe et al. (2015), no direct
partner effects were found, suggesting that coping motives in
response to interpersonal conflict may influence alcohol
problems at only the individual level. An indirect partner effect
observed by Lambe et al. (2015) was replicated, with the re-
lationship between an individual’s CDM and their partner’s
alcohol-related problems being mediated by the individual’sown
alcohol-related problems at the between-subjects level. There-
fore, although no direct influence of a friend’sCDMonthe
individual’s alcohol-related problems was found, the presence of
these motives in the friend indirectly led to alcohol-related
Table 3. Tests of Indirect Effects for the Multiple Mediation Model with CDM and CAM.
Predictor (X) Mediator (M) Outcome (Y) CI (within-subjects) CI (between-subjects)
Conflict Actor’s CDM Actor’s RAPI
a
[.000, .004]*
a
[.002, .021]*
Conflict Actor’s CDM Partner’s RAPI [.001, .002] [.009, .003]
Actor’s CDM Actor’s RAPI Partner’s RAPI [.002, .004] [.004, .031]*
Actor’s CDM Partner’s RAPI Actor’s RAPI [.001, .001] [.013, .005]
Conflict Actor’s CAM Actor’s RAPI [.000, .005]* [.001, .007]
Conflict Actor’s CAM Partner’s RAPI [.002, .001] [.002, .007]
Actor’s CAM Actor’s RAPI Partner’s RAPI [.002, .005] [.002, .015]
Actor’s CAM Partner’s RAPI Actor’s RAPI [.002, .001] [.003, .014]
Note. Indirect effects were derived using unstandardized coefficients. CI = Confidence Interval (95% level of confidence); RAPI = Rutgers Alcohol Problem Index;
CDM = coping with depression motives; CAM = coping with anxiety motives.
a
Confirmatory analyses; Bold CIs with * identify significant indirect effects whose 95% CIs do not cross zero.
604 Emerging Adulthood 10(3)
problems for the individual by way of first influencing the
friend’s alcohol-related problems which in turn influenced the
individual’s alcohol-related problems (potentially via modeling
of maladaptive drinking; see Muyingo et al., 2020).
At both the within- and between-subject levels, conflict
was a significant predictor of CDM and CAM, and an indirect
predictor of alcohol-related problems. At the within-subjects
level, both CDM and CAM subsequently predicted alcohol-
related problems. These findings are broadly consistent with
previous research with romantic couples (Lambe et al., 2015),
and support predictions of drinking motives theory (Cooper
et al., 2016). These findings suggest that emerging adults may
use alcohol to cope with feelings of depression and anxiety
following conflict with their friends, a maladaptive behavior
that leads to alcohol-related problems. Further, over time,
alcohol-related problems may occur following conflict with
friends. Coping with both feelings of depression and anxiety
appear to be important mechanisms through which friendship
conflict leads to alcohol-related problems over time, whereas
only CDM appears to be an important mechanism through
which conflict leads to alcohol-related problems at the
between-subjects level.
In adolescents, it has been found that peer drinking motives
influence individual drinking motives (Kuntsche & Stewart,
2009). It was therefore unexpected in the present study that
coping motives were unrelated between friends over time.
However, this finding replicated the work of Lambe et al.
(2015) that also found no partner effects for coping motives
within romantic couples. The lack of partner effects found in
both studies may be related to contextual factors for coping
drinking, in that individuals drinking to cope with negative
affect are more likely to do so alone at home than with a friend
or partner (Cooper, 1994). Therefore, drinking to cope with
feelings of anxiety or depression resulting from interpersonal
conflict may be a relatively solitary process that does not
influence a friend’s coping motives. This is supported by our
supplementary analyses, which demonstrate a positive cor-
related error term for volume of consumption, but not for
alcohol problems—that is, partners influence each other’s
drinking habits, but may not be similar in their levels of
alcohol-related problems.
Clinical Implications
These results extend the Lambe et al. (2015) findings with
emerging adults’romantic partners to the role of coping
drinking motives mediating the relationship between friend-
ship conflict and both alcohol-related problems and alcohol
volume in emerging adults. Interventions for emerging adults’
heavy and problem drinking should consider the influence of
conflict within close friendships and its impact on tendencies
to drink to cope with both depression and anxiety. Further,
interventions may include strategies for reducing CDM and
CAM to reduce associated heavy drinking and alcohol-related
problems. Considering that our results showed that alcohol-
related problems and alcohol volume changed systematically
in the same direction between friends, dyadic-level inter-
ventions may be useful to address both friends’alcohol-related
problems and associated influences, like conflict.
Limitations and Future Directions
This study is not without its limitations. The sample was pri-
marily White university students who were less than 21 years
old, which limits generalizability. Additionally, there may have
been some self-selection of dyads in the participation pool (i.e.,
people who agree to participate in research studies may be
different from those who do not). Despite rejecting the null
hypothesis, the magnitudes of relationships were often small.
Through supplementary analyses, Lambe et al. (2015) identi-
fied sex differences where the mediation of CDM between
conflict and alcohol-related problems was significant only for
women. Due to the relatively small sample size of men in the
current study, sex differences were not explored, although our
results did hold when sex was controlled in supplementary
analyses. Future research with a larger sample size and more
male participants might examine sex differences in friendship
dyads to determine if thesesex differences extend to other forms
of interpersonal conflict. Statistical power for the within-
subjects path from dyadic conflict to CAM was low; thus,
non-significant results here may reflect a Type II error. Data
were collected retrospectively at the end of every 30-day period.
Future studies may examine these variables daily via ecological
momentary assessment for more accurate in-time measurement,
as drinking to cope may occur relatively quickly following
conflict. This study focused on the relationship between conflict
and alcohol-related problems. It may be interesting to explore
the role of conflict resolution, and whether this has any in-
fluence on associated drinking motives (Rodriguez et al., 2019).
The study sample consisted of emerging adult undergraduate
friendship pairs, with 66.1% of the sample being women. The
findings may not be generalizable to other samples (e.g., ad-
olescents, older friends, and opposite-sex friendships). At least
one member of the dyad was always a first-year undergraduate
student; these students may be particularly vulnerable to
friendship conflict due to the social difficulties of transitioning
to university so these results may not generalize to all types of
friendships. Nonetheless, it is interesting that these findings
occur in comparatively new friendships, which may be a more
socially tumultuous time as friends test new boundaries and
limits in their friendship. Finally, although existing theory
suggests that other drinking motives should be unrelated to
conflict, future research may explore these other motives to
establish the specificity of the present findings to the coping
motives.
Our analytic strategy has two notable limitations. First, our
use of a fixed slopes, random intercepts model assumes
compound symmetry for the longitudinal correlated residuals,
which may be overly simplistic. Though more complex, other
analysts are developing approaches to incorporate AR(1)
Mackinnon et al. 605
correlated residual structures, which might fit the data better
(Gistelinck & Loeys, 2019). Second, our use of log trans-
formations trades interpretability for robustness. That is, by
transforming the raw data, our residuals look closer to a
normal distribution and thus the estimates for p-values will
likely be more trustworthy. However, because coefficients are
difficult to interpret in the log scale, it is more difficult to
assess the magnitude of effects.
Conclusions
This study replicated and extended the work of Lambe et al.
(2015), confirming that in undergraduate friendships, the
relationship between friendship conflict and alcohol-related
problems is mediated by CDM at the within-subjects level.
Further, the replication extended to CDM mediating friend-
ship conflict and alcohol-related problems at the between-
subject level. Exploratory analyses also revealed indirect
partner effects where CDM in the friend lead to alcohol-related
problems in the individual by way of first influencing the
friend’s alcohol-related problems. Unique to our results was
the finding that in friendships, the relationship between
conflict and alcohol-related problems is also mediated by
CAM at the within-subjects level. The findings from this work
are important in contributing understanding of conflict and
coping drinking motives to intervention efforts for preventing
and intervening with alcohol-related problems in emerging
adults.
Acknowledgments
Trevor Shannon, Brett Hopkins, Lauren Shenkar, Kyra Farrelly, Nacera
Hanzal, Jocelyn Brown, Kaitlin Coker, Sarah Wells, Pam Collins, and
Jennifer Swansburg are thanked for their research assistance.
Author Contributions
Mackinnon, S., contributed to conception and design, contributed to
analysis and interpretation, drafted manuscript, critically revised man-
uscript, gave final approval, agrees to be accountable for all aspects of
work ensuring integrity and accuracy. Tougas, M., contributed to
conception, contributed to analysis and interpretation, drafted manu-
script, gave final approval, agrees to be accountable for all aspects of
work ensuring integrity and accuracy. Kehayes, I-L., contributed to
conception and design, contributed to acquisition and interpretation,
critically revised manuscript, gave final approval, agrees to be ac-
countable for all aspects of work ensuring integrity and accuracy.
Stewart, S., contributed to conception and design, contributed to ac-
quisition and interpretation, critically revised manuscript, gave final
approval, agrees to be accountable for all aspects of work ensuring
integrity and accuracy.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to
the research, authorship, and/or publication of this article.
Funding
The author(s) disclosed receipt of the following financial support for
the research, authorship, and/or publication of this article: This re-
search was supported by a Social Sciences and Humanities Research
Council Award (435–2015-1798).
ORCID iD
Michelle Tougas https://orcid.org/0000-0002-0015-6705
Supplemental Material
Supplemental material for this article is available online.
Open Practices.
The analysis code and materials used in this manuscript are openly
available (https://osf.io/krs3v/) The raw data contained in this
manuscript are not openly available due to privacy restrictions set
forth by the institutional research ethics board but can be obtained
from the corresponding author following the completion of a privacy
and fair use agreement. No aspects of the study were pre-registered.
Notes.
1. Data collection in this study continued until the timeline for our
funding ran out. However, we conducted a power simulation prior
to our analyses. Broadly, this analysis suggested that the sample size
was adequate to detect similarly-sized effect sizes to Lambe et al.
(2015). See the online supplementary materials for more details.
2. The full questionnaires for the materials listed here can be found at
https://osf.io/krs3v/. Other variables measured in the study are
available upon request.
3. When the log transformations are not used, the pattern of statistical
significance and direction of effects noted in Figure 3 do not
change. However, the overall model fit indices are considerably
poorer when the log transformations are not used: χ
2
(20) = 52.85,
p < .001; RMSEA = .05; SRMR (within) = .11; SRMR (between)
= .09, CFI = .94, TLI = .88. Simulation work by Gao et al. (2020)
suggests that, in the presence of non-normality, robust fit indices
tend to erroneously reject models as fitting poorly. Thus, we
believe the log transform is useful in the present case. The raw
output of this analysis available at https://osf.io/krs3v/.
4. In the current study, the RAPI assessed alcohol-related problems
over the past 30 days. In past studies (e.g., Lambe et al., 2015), the
RAPI has been used to assess a 1-week time frame. Due to these
differences in time frame, the means are not directly comparable,
and it was expected that the 30-day RAPI scores would be higher.
5. Readers should note that this analysis overlaps somewhat with
another paper that analyzes the same dataset (Kehayes et al.,
2021). Specifically, they tested the bivariate relationships between
drinking motives and alcohol quantity and frequency separately.
Though these analyses differ slightly (i.e., using volume instead of
separate quantity/frequency indices; adding conflict and both
motives as simultaneous mediators), readers interested in this topic
are encouraged to also refer to Kehayes et al. (2021) for further
analyses on alcohol consumption using this dataset.
606 Emerging Adulthood 10(3)
References
Abbey, A., & Andrews, F. M. (1985). Modeling the psychological
determinants of life quality. Social Indicators Research,16(1),
1–34. https://doi.org/10.1007/BF00317657
American College Health Association (2019). American College Health
association-National College Health assessment II: Reference
group executive summary spring 2019.https://www.acha.org/
documents/ncha/NCHAII_SPRING_2019_US_REFERENCE_
GROUP_EXECUTIVE_SUMMARY.pdf.
Arnett, J. J,
ˇ
Zukauskien˙
e, R., & Sugimura, K. (2014). The new life
stage of emerging adulthood at ages 18–29 years: Implications
for mental health. The Lancet Psychiatry,1(7), 569–576. https://
doi.org/10.1016/S2215-0366(14)00080-7
Boman, J. H., Stogner, J., & Lee Miller, B (2013). Binge drinking,
marijuana use, and friendships: The relationship between similar
and dissimilar usage and friendship quality. Journal of Psy-
choactive Drugs,45(3), 218–226. https://doi.org/10.1080/
02791072.2013.803646
Buote, V., Pancer, S., Pratt, M., Adams, G., Birnie-Lefcovitch, S.,
Polivy, J., & Wintre, M. (2007). The importance of friends:
Friendship and adjustment among 1st-year university students.
Journal of Adolescent Research,22(6), 665–689. https://doi.
org/10.1177/0743558407306344
Camirand, E., & Poulin, F. (2019). Changes in best friend quality
between adolescence ad emerging adulthood: Considering the
role of romantic involvement. International Journal of Be-
havioral Development,43(3), 231–237. https://doi.org/10.1177/
0165025418824995
Canadian Centre on Substance Use and Addiction (2017). Ca-
nadian drug summary: Alcohol.https://www.ccsa.ca/sites/
default/files/2019-04/CCSA-Canadian-Drug-Summary-Alcohol-
2017-en.pdf.
Chow, C. M., Tan, C. C., & Ruhl, H. (2015). Misery loves company: A
dyadic approach to examining the effects of depressive symptoms
on friendship discord. Journal of Social and Clinical Psychology,
34(9), 774–787. https://doi.org/10.1521/jscp.2015.34.9.774
Collins, R. L., Kashdan, T. B., Koutsky, J. R., Morsheimer, E. T., &
Vetter, C. J. (2008). A self-administered Timeline Followback to
measure variations in underage drinkers’alcohol intake and
binge drinking. Addictive Behaviors,33(1), 196–200. https://
doi.org/10.1016/j.addbeh.2007.07.001
Cooper, M. L. (1994). Motivations for alcohol use among adoles-
cents: Development and validation of a four-factor model.
Psychological Assessment,6(2), 117–128. https://doi.org/10.
1037/1040-3590.6.2.117
Cooper, M. L., Frone, M. R., Russell, M., & Mudar, P (1995).
Drinking to regulate positive and negative emotions: A moti-
vational model of alcohol use. Journal of Personality and Social
Psychology,69(5), 990–1005. https://doi.org/10.1037//0022-
3514.69.5.990
Cooper, M. L., Kuntsche, E., Levitt, A., Barber, L., & Wolf, S (2016).
Motivational models of substance use: A review of theory and
research on motives for using alcohol, marijuana, and tobacco.
In K. Sher (Ed.), Oxford handbook of substance use disorders
(pp. 375–421). Oxford Handbooks Online.
El-Sheikh, M., Keiley, M., Erath, S., & Dyer, W. J. (2013).
Marital conflict and growth in children’s internalizing
symptoms: The role of autonomic nervous system activity.
Developmental Psychology,49(1), 92–108. https://doi.org/
10.1037/a0027703
Gao, C., Shi, D., & Maydeu-Olivares, A. (2020). Estimating the
maximum likelihood root mean square error of approximation
(RMSEA) with non-normal data: A Monte-Carlo study. Struc-
tural Equation Modeling: A Multidisciplinary Journal,27(2),
192–201. https://doi.org/10.1080/10705511.2019.1637741
Geldhof, G. J., Anthony, K. P., Selig, J. P., & Mendez-Luck, C. A.
(2018). Accommodating binary and count variables in media-
tion: A case for conditional indirect effects. International
Journal of Behavioral Development,42(2), 300–308. https://
doi.org/10.1177/0165025417727876
Geldhof, G. J., Preacher, K. J., & Zyphur, M. J. (2014). Reliability
estimation in a multilevel confirmatory factor analysis frame-
work. Psychological Methods,19(1), 72–91. https://doi.org/10.
1037/a0032138
Gistelinck, F., & Loeys, T. (2019). The actor–partner interdepen-
dence model for longitudinal dyadic data: An implementation in
the SEM framework. Structural Equation Modeling,26(3),
329–347. https://doi.org/10.1080/10705511.2018.1527223
Grant, V. V., & Stewart, S. H (2007). Impact of experimentally in-
duced positive and anxious mood on alcohol expectancy strength
in internally motivated drinkers. Cognitive Behavior Therapy,
36(2), 102–111. https://doi.org/10.1080/16506070701223289
Grant,V.V.,Stewart,S.H.,&Birch,C.D(2007).Impactofpositive
and anxious mood on implicit alcohol-related cognitions in
internally motivated undergraduate drinkers. Addictive Be-
haviors,32(10), 2226–2237. https://doi.org/10.1016/j.addbeh.
2007.02.012
Grant, V. V., Stewart, S. H., & Mohr, C. D. (2009). Coping-anxiety
and coping-depression motives predict different daily mood-
drinking relationships. Psychology of Addictive Behaviors,
23(2), 226–237. https://doi.org/10.1037/a0015006
Grant, V. V., Stewart, S. H., O’Connor, R. M., Blackwell, E., &
Conrod, P. J. (2007). Psychometric evaluation of the five-factor
Modified Drinking Motives Questionnaire–Revised in under-
graduates. Addictive Behaviors,32(11), 2611–2632. https://doi.
org/10.1016/j.addbeh.2007.07.004
Hamaker, E. L., & Muth´
en, B. (2020). The fixed versus random
effects debate and how it relates to centering in multilevel
modeling. Psychological Methods,25(3), 365–379. https://doi.
org/10.1037/met0000239
Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in
covariance structure analysis: Conventional criteria versus new
alternatives. Structural Equation Modeling: A Multidisciplinary
Journal,6(1), 1–55. https://doi.org/10.1080/10705519909540118
Ibarra-Rovillard, M. S., & Kuiper, N. A. (2011). Social support and
social negativity findings in depression: Perceived responsive-
ness to basic psychological needs. Clinical Psychology Review,
31(3), 342–352. https://doi.org/10.1016/j.cpr.2011.01.005
Kehayes, I. L., Mackinnon, S. P., Sherry, S. B., Leonard, K. E., &
Stewart, S. H. (2021). The influence of drinking buddies: A
Mackinnon et al. 607
longitudinal investigation of drinking motivations and drinking
behaviors in emerging adults. Substance Use and Misuse,56(2),
286–296. https://doi.org/10.1080/10826084.2020.1861631
Kenny, D. A., & Ledermann, T. (2010). Detecting, measuring, and
testing dyadic patterns in the actor-partner interdependence
model. Journal of Family Psychology,24(3), 359–366. https://
doi.org/10.1037/a0019651
Kim, A. J., Sherry, S. B., Shannon, R., Kehayes, I-L., & Stewart, S. H
(in press). A matter of perspective: The convergent and in-
cremental validity of informant-reported drinking motives.
Drug and Alcohol Review. Online ahead of print.
Kline, R. B (2015). Principles and practice of structural equation
modeling (4th ed.). The Guilford Press.
Kuntsche, E., & Stewart, S. H (2009). Why my classmates drink:
Drinking motives of classroom peers as predictors of individual
drinking motives and alcohol use in adolescence –a mediational
model. Journal of Health Psychology,14(4), 536–546. https://
doi.org/10.1177/1359105309103573
Lambe, L., Mackinnon, S. P., & Stewart, S. H. (2015). Dyadic
conflict, drinking to cope, and alcohol-related problems: A
psychometric study and longitudinal actor-partner interdepen-
dence model. Journal of Family Psychology,29(5), 697–707.
https://doi.org/10.1037/fam0000098
Laursen, B., & Bukowski, W. M. (1997). A developmental guide to
the organisation of close relationships. International Journal of
Behavioral Development,21(4), 747–770. https://doi.org/10.
1080/016502597384659
Lewis, M. A., Sheng, E., Geisner, I. M., Rhew, I. C., Patrick, M. E., &
Lee, C. M. (2015). Friend or foe: Personal use and friends’use
of protective behavioral strategies and spring break drinking.
Addictive Behaviors,50,96–101. https://doi.org/10.1016/j.
addbeh.2015.06.029.
Mackinnon, S. P., Sherry, S. B., Antony, M. M., Stewart, S. H.,
Sherry, D. L., & Hartling, N. (2012). Caught in a bad romance:
Perfectionism, conflict, and depression in romantic relationships.
Journal of Family Psychology,26(2), 215–225. https://doi.org/10.
1037/a0027402
Martens, M. P., Neighbors, C., Dams-O’Connor, K., Lee, C. M., &
Larimer, M. E. (2007). The factor structure of a dichotomously
scored Rutgers Alcohol Problem Index. Journal of Studies on
Alcohol and Drugs,68(4), 597–606. https://doi.org/10.15288/
jsad.2007.68.597
McNamara Barry, C., Madsen, S. D., & DeGrace, A (2015). Growing
up with a little help from their friends in emerging adulthood. In
J. J. Arnett (Ed.), The Oxford Handbook of Emerging Adulthood.
Oxford University Press. https://doi.org/10.1093/oxfordhb/
9780199795574.013.008
McNamaraBarry,C.,Madsen,S.D.,Nelson,L.J.,Carroll,J.S.,&
Badger, S. (2009). Friendship and romantic relationship qualities in
emerging adulthood: Differential associations with identity de-
velopment and achieved adulthood. Journal of Adult Development,
16(4), 209–222. https://doi.org/10.1007/s10804-009-9067-x
Murray, S. L., Griffin, D. W., Rose, P., & Bellavia, G. M. (2003).
Calibrating the sociometer: The relational contingencies of
self-esteem. Journal of Personality and Social Psychology,
85(1), 63–84. https://doi.org/10.1037/0022-3514.85.1.63
Muyingo, L., Smith, M. M., Sherry, S. B., McEachern, E., Leonard,
K. E., & Stewart, S. H. (2020). Relationships on the rocks: A
meta-analysis of romantic partner effects on alcohol use. Psy-
chology of Addictive Behaviors,34(6), 629–640. https://doi.org/
10.1037/adb0000578
Neal, D. J., Corbin, W. R., & Fromme, K (2006). Measurement of
alcohol-related consequences among high school and college
students: Application of item response models to the Rutgers
Alcohol Problem Index. Psychological Assessment,18(4),
402–414. https://doi.org/10.1037/1040-3590.18.4.402
Nogueira-Arjona, R., Shannon, T., Kehayes, I. L., Sherry, S. B.,
Keough, M. T., & Stewart, S. H (2019). Drinking to keep pace:
A study of the moderating influence of extraversion on alcohol
consumption similarity in drinking buddy dyads. Addictive Behav-
iors,92,69–75. https://doi.org/10.1016/j.addbeh.2018.12.023
Oishi, S., & Sullivan, H. (2006). The predictive value of daily vs.
retrospective well-being judgments in relationship stability.
Journal of Experimental Social Psychology,42(4), 460–470.
https://doi.org/10.1016/j.jesp.2005.07.001
Preacher, K. J., Zyphur, M. J., & Zhang, Z (2010). A general
multilevel SEM framework for assessing multilevel mediation.
Psychological Methods,15(3), 209–233. https://doi.org/10.
1037/a0020141
Rodriguez, L. M., Dell, J. B., Lee, K. D. M., & Onufrak, J. (2019).
Effects of a brief cognitive reappraisal intervention on reduc-
tions in alcohol consumption and related problems. Psychology
of Addictive Behaviors,33(7), 637–643. https://doi.org/10.
1037/adb0000509
Schwartz-Mette,R.A.,Lawrence,H.R.,&Harrington,R.V.
(2021). Transactional associations among adolescents’
depressive symptoms and self-and friend-reported friend-
ship experiences. Journal of Applied Developmental Psy-
chology,74, 101266. https://doi.org/10.1016/j.appdev.
2021.101266.
Stavrakaki, C., & Vargo, B. (1986). The relationship of anxiety
and depression: A review of the literature. The British
JournalofPsychiatry,149(1), 7–16. https://doi.org/10.1192/
bjp.149.1.7
Stewart, S. H., Grant, V. V., Mackie, C. J., & Conrod, P. J (2016).
Comorbidity of anxiety and depression with substance use
disorders. In K J. Sher (Ed.), The Oxford Handbook of Sub-
stance use and substance use disorders, Volume 2 (pp.
149–186). Oxford University Press. https://doi.org/0.1093/
oxfordhb/9780199381708.013.19
Streja, E., Goldstein, L., Soohoo, M., Obi, Y., Kalantar-Zadeh, K., &
Rhee, C. M (2017). Modeling longitudinal data and its impact on
survival in observational nephrology studies: Tools and consid-
erations. Nephrology Dialysis Transplantation,32(suppl_2),
ii77–ii83. https://doi.org/10.1093/ndt/gfx015
White, H. R., & Labouvie, E. W (1989). Towards the assessment of
adolescent problem drinking. Journal of Studies on Alcoholism,
50(1), 30–37. https://doi.org/10.15288/jsa.1989.50.30
608 Emerging Adulthood 10(3)
