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Running head: PARTNER EFFECTS ON ALCOHOL USE
1
Relationships on the Rocks:
A Meta-analysis of Romantic Partner Effects on Alcohol Use
Lydia Muyingo
Dalhousie University
Martin M. Smith
York St John University
Simon B. Sherry
Dalhousie University
Eleri McEachern
Dalhousie University
Kenneth E. Leonard
University at Buffalo
Sherry H. Stewart
Dalhousie University
Accepted March 5, 2020
© 2020, American Psychological Association. This paper is not the copy of record and may not
exactly replicate the final, authoritative version of the article. Please do not copy or cite without
authors' permission. The final article will be available, upon publication, via its DOI:
10.1037/adb0000578
Lydia Muyingo, Department of Psychology and Neuroscience, Dalhousie University, Halifax,
Nova Scotia, Canada; Martin M. Smith, School of Science, Technology and Health, York St
John University, York, United Kingdom; Simon B. Sherry, Department of Psychology and
Neuroscience, Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada;
Eleri McEachern, Department of Psychology and Neuroscience, Dalhousie University, Halifax,
Nova Scotia, Canada; Kenneth E. Leonard, Department of Psychiatry, University at Buffalo,
Buffalo, NY; Sherry H. Stewart, Department of Psychiatry, Department of Psychology and
Neuroscience, Dalhousie University, Halifax, Nova Scotia, Canada. Correspondence concerning
this article should be addressed to Lydia Muyingo, Department of Psychology and Neuroscience,
Dalhousie University, Life Sciences Centre, 1355 Oxford Street, Halifax, Nova Scotia, Canada,
B3H 4R2, Email: Lydia.Muyingo@dal.ca, Phone: (902) 494-3793, Fax: (902) 494-6585.
This study was funded by an operating grant from the Social Sciences and Humanities Research
Council of Canada (SSHRC) awarded to Sherry H. Stewart, Simon B. Sherry, and Kenneth E.
Leonard [grant # 435-2015-1798]. Sherry H. Stewart was supported through a CIHR Tier 1
Canada Research Chair in Addictions and Mental Health. Lydia Muyingo was supported through
PARTNER EFFECTS ON ALCOHOL USE
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a Scotia Support award from the Nova Scotia Health Research Foundation (NSHRF), a Nova
Scotia Graduate Scholarship from the Government of Nova Scotia, and a SSHRC Canada
Graduate Scholarship-Masters award. The funding sources had no involvement in data
collection, analysis and interpretation, in the writing of the report, and in the decision to submit
the article for publication.
Preliminary findings of the report were presented at the Canadian Research Initiative in
Substance Misuse Maritimes Symposium (CRISM) in Halifax, Canada in 2017, and at the
Canadian Psychological Association (CPA) Meeting in Montreal, Canada in 2018. A portion of
the final results were presented at the Research Society on Alcoholism (RSA) Meeting in
Minneapolis, USA in 2019, and the CPA Meeting in Halifax, Canada in 2019. Our meta-analysis
was pre-registered with PROSPERO’s International prospective register of systematic reviews
(CRD42018089699)
PARTNER EFFECTS ON ALCOHOL USE
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Abstract
The partner influence hypothesis postulates one partner's alcohol use influences the other
partner’s alcohol use over time. While several studies have examined the partner influence
hypothesis, the magnitude and gender-specific nature of partner influences on alcohol use are
unclear and have yet to be examined meta-analytically. We addressed this by conducting a
traditional bivariate meta-analysis and two-stage meta-analytic structural equation modelling
(TS-MASEM) across 17 studies (N = 10,553 couples). Studies that assessed both romantic
partners’ alcohol use at a minimum of two time-points were selected. Results suggest romantic
partners do influence one another’s drinking, to a small but meaningful degree, with women (β =
.19) exerting a statistically stronger (p < .05) influence than men (β = .12). Results also suggest
time lag between assessment, alcohol indicator, married, and year of publication may moderate
partner influence. Thus, social influences on individual alcohol use include important partner
influences. These influences can serve either risk or protective functions. Given the economic,
social, and health consequences associated with alcohol misuse, advancing knowledge of social
risk factors for alcohol misuse is essential. Therefore, assessment and treatment of alcohol
misuse should extend beyond the person to the social context. We encourage clinicians to
consider involving romantic partners when assessing and treating alcohol misuse.
Keywords: alcohol, romantic relationships, dating, married, meta-analysis.
PARTNER EFFECTS ON ALCOHOL USE
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Relationships on the Rocks: A Meta-analysis of Romantic Partner Effects on Alcohol Use
Alcohol use is widespread in North America. In 2016, 70.1% of American adults reported
drinking alcohol, and in 2013, 80% of Canadian adults reported alcohol use (Substance Abuse
and Mental Health Services Administration, 2017; Public Health Agency of Canada [PHAC],
2016). Despite the prevalence and general acceptance of alcohol use in North America, there are
numerous adverse outcomes associated with alcohol misuse. Indeed, alcohol use cost the United
States $249 billion in 2010 and represented the second-costliest substance; three-quarters of
these costs were associated with binge drinking (National Institute on Drug Abuse, 2017; Sacks,
Gonzales, Bouchery, Tomedi, & Brewer, 2015). In 2014, alcohol use cost Canada $14.1 billion
and represented the costliest substance (Canadian Centre for Substance Use and Addiction,
2018). The social costs of alcohol misuse include damaged relationships, family conflict,
violence, and impaired driving (PHAC, 2016). Moreover, there are over 200 health conditions
linked with excessive alcohol use, including gastrointestinal diseases, cancers, and
cardiovascular diseases (World Health Organization, 2014).
Alcohol is frequently consumed socially and often associated with positive social
experiences (PHAC, 2016); therefore, individuals’ alcohol use may be influenced by others in
their environment. Research shows drinking-supportive social networks have a strong influence
on individual alcohol misuse and alcohol problems over time (Homish, & Leonard, 2008). One
potentially important social influence on alcohol use occurs in the context of romantic
relationships (Homish, & Leonard, 2007). Research on alcohol use in romantic couples is
essential since alcohol use is implicated in several key aspects of romantic relationships,
including marital satisfaction, partners’ emotional well-being, and domestic violence. Spouses of
individuals with alcohol use disorders (AUDs), for example, report lower marital satisfaction and
PARTNER EFFECTS ON ALCOHOL USE
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elevated depression, anxiety, and psychological distress compared to spouses of individuals
without AUDs (Rodriguez, Neighbors, & Knee, 2014). Furthermore, heavy alcohol use within
romantic couples may be associated with other negative social consequences such as relationship
dissolution (Torvik, Røysamb, Gustavson, Idstad & Tambs, 2013).
Though the impact of heavy partner drinking has garnered much attention, the impact of
a partner’s alcohol use on an individual’s subsequent alcohol use is unclear. Though data exist
that would allow for a large-scale empirical evaluation of partner influences on alcohol use, these
data have not been empirically synthesized. Research on the role of partner alcohol use on
subsequent use in romantic couples is important, given the numerous negative consequences of
alcohol misuse noted above. We addressed this gap in the literature by synthesizing findings of
longitudinal studies that examined alcohol use in couples.
Partner Influence Hypothesis
The partner influence hypothesis (Mushquash et al., 2013) postulates one partner’s
alcohol use influences the other partner’s alcohol use over time. This hypothesis stems from
earlier research on spousal concordance in alcohol use (e.g., Leonard & Eiden, 1999; Leonard &
Senchak, 1993; Yamaguchi & Kandel, 1993). Several theories help explain why partner
influences might be operative. One pertains to social conformity pressure, which research on
interpersonal influences has identified as a predictor of alcohol use and misuse (Fairlie, Wood, &
Laird, 2012). Similarly, social impact theory (Latané, 1981) postulates that as the importance of
individuals within one’s social context increases, and as time spent with the social network
increases, the more likely an individual will conform to the social network’s normative
pressures. A romantic partnership is an example of an important relationship where individuals
can be subjected to pressures to conform. Likewise, interdependence theory posits that as
PARTNER EFFECTS ON ALCOHOL USE
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individuals in romantic relationships build their partnership through rewarding interactions, they
become increasingly dependent on one another (Wickham & Knee, 2012) and, therefore, more
susceptible to being influenced by one other’s behaviors.
Furthermore, given the human need for social approval and acceptance (Baumeister &
Leary, 1995), partners in a romantic relationship may change their drinking behaviors to match
those of their romantic partner to receive partner approval and thereby maintain the relationship
(Mushquash et al., 2013). According to family systems theory, couples respond to each other’s
behaviors within a system established by roles and expectations (Bowen, 1974). Partners may
shift their drinking behaviors to maintain balance in the family system.
Following the theory of exposure effects in person perception (Moreland & Zajonc,
1982), since partners are highly exposed to one another, they are likely to develop positive
attitudes toward one another’s drinking behavior and therefore adopt similar drinking behaviors.
Yet another theory that may explain partner influences involves the notion of a “drinking
partnership” (Roberts & Leonard, 1998) – an accord between the partners’ drinking levels,
patterns, or contexts of use that is suggested to develop over time in some couples. Such couples
may develop enduring drinking rituals, especially when alcohol becomes an integral part of the
relationship. Next, following Bandura’s (1977) social learning theory, one individual may imitate
their partner’s (“model’s”) drinking after a period of directly observing the rewards their partner
obtains from drinking.
In line with the robust literature of homophily in social networks, partners are likely to
select individuals who engage in similar drinking behaviors (Leonard & Mudar, 2003;
McPherson, Smith-Lovin, & Cook, 2001). However, research on substance use over the
transition to marriage has demonstrated that selection effects do not account for all the
PARTNER EFFECTS ON ALCOHOL USE
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differences observed in alcohol use between married and single individuals (Labouvie, 1996;
Merline, 2004). Similarly, Aikins, Simon, and Prinstein (2010) found both selection and partner
influence effects on alcohol use in adolescent romantic partnerships. In sum, various theories
help explain the mechanisms through which partner influence effects might operate, and they
converge in suggesting partners may adopt one another’s drinking behaviors over time.
Alternatively, a negative association between partners’ drinking may exist (e.g., in social
learning theory, when an actor observes punishing consequences following the drinking
behaviour of their partner, they may decrease their own drinking). Moreover, the direction or
strength of partner influences may be impacted by moderating factors including couple age or
relationship length. The partner influence hypothesis and its related theories imply that couples
who fail to influence each other’s drinking may be at risk of lower relationship satisfaction or
relationship dissolution.
Advancing research on the partner influence hypothesis using meta-analysis
Despite sustained research, the magnitude and gender-specific nature of partner
influences on alcohol use are unclear. Correlations between a partner’s baseline alcohol use and
an individual’s own subsequent alcohol use range from small (r = .25; Otten, van der Zwaluw,
van der Vorst, & Engels, 2008) to large (r = .55; Bartel, Sherry, Molnar, Mushquash, Leonard,
Flett, & Stewart, 2017). Moreover, Leonard and Mudar (2004) found the direction of gender-
specific spousal influence changed over time: husbands influenced wives from the pre-marriage
period to the first year of marriage, but wives influenced husbands from the first year of marriage
to the second. Other studies found partner influences on alcohol use are equal for women and
men (e.g., Bartel et al., 2017).
A thorough understanding of partner influences on alcohol misuse is beneficial for
PARTNER EFFECTS ON ALCOHOL USE
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validating existing efforts to incorporate social network drinking in biopsychosocial assessment
settings (e.g., American Society of Addiction Medicine, 2015) and for improving intervention
efforts. For instance, if robust partner influences exist, then partner drinking should continue to
be assessed when establishing a prognosis or treatment plan for an alcohol misusing client. If the
partner’s drinking level is high, it may hinder the efficacy of an individual’s treatment, or impede
the individual’s change in drinking behavior. However, if a partner’s drinking is low, it may
bode well for recovery, reinforce the efficacy of an individual’s treatment, and accelerate change
in the individual’s alcohol use. Therefore, a clinician could harness the therapeutic potential of a
client having a low-drinking partner or could treat the couple together in the case of a heavy-
drinking partner.
Given the useful clinical implications of the partner influence hypothesis, a synthesis of
available data on this hypothesis is valuable. This would allow the implementation of statistical
controls (e.g., controlling for actor effects – i.e., relative stability in the individual’s own
drinking behavior over time) and robust testing of gender differences (e.g., to test whether the
magnitude of partner influence is statistically stronger in one vs. the other gender) that are
missing from many studies (e.g., Gudonis-Miller, Lewis, Tong, Tu, & Aalsma, Carpentier,
Azzouz, & Fortenberry, 2012). We used two-stage meta-analytic structural equation modeling
(TS-MASEM) in addition to traditional meta-analyses. The tendency to rely solely on traditional
meta-analyses in psychology is limiting; studies often examine multiple and correlated outcomes
even though effects are often multivariate rather than univariate (Eysenck, 1994; Jackson, Riley
& White, 2011). Instead of performing multiple traditional analyses, multivariate meta-analyses
such as TS-MASEM provide all parameter estimates within a single model (e.g., testing both
actor and partner effects simultaneously instead of performing separate analyses). Furthermore,
PARTNER EFFECTS ON ALCOHOL USE
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TS-MASEM can assess models’ fit and estimate effects while controlling for other variables and
is the preferable approach to permit integration of meta-analysis and structural equation
modeling (Cheung & Hong, 2017; Landis, 2013).
Objectives and Hypotheses
We tested whether one partner’s baseline alcohol use predicted changes in the other
partner’s alcohol use by follow-up, by conducting TS-MASEM (Cheung, 2005). Despite some
inconsistencies in the literature, overall, research does suggest the presence of partner effects
over time (e.g., Aalsma et al., 2012; Van der Wulp, Hoving, & De Vries, 2015). Therefore, we
expected to observe robust partner effects. We hypothesized that after accounting for individual
baseline alcohol use, that an individual’s future alcohol use would be significantly and positively
predicted by their partner’s baseline alcohol use. Our test of the magnitude of partner influence
was exploratory. Additionally, we investigated whether the magnitude of partner influences
differ by alcohol indicator by comparing partner effects derived from measures of alcohol use vs.
measures of alcohol-related problems. Next, we examined whether partner influences on alcohol
use differ in magnitude by gender; however, given inconsistencies in the literature, these
analyses were exploratory
1
. Finally, to evaluate publication bias and to catalyze a search for
moderators that may resolve heterogeneity, we conducted a traditional meta-analysis to test the
moderating effect of year of publication, mean age of couple, alcohol indicator (i.e., measure of
alcohol use vs. alcohol-related problems), time lag, married (i.e., predominantly married couples
vs. community/dating/other couples), attrition, and relationship length on observed relations.
1
Our meta-analysis was pre-registered with PROSPERO’s International prospective register of systematic reviews
(CRD42018089699).
PARTNER EFFECTS ON ALCOHOL USE
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Method
Study Identification
Six databases (i.e., Academic Search Premier, the Cumulative Index of Nursing and
Allied Health Literature, PsycINFO, PubMed, and Social Work Abstracts, and Proquest
Dissertations and Theses) were searched to locate longitudinal studies of alcohol use in romantic
couples. Literature searches were conducted using keywords and Boolean search terms (couple*
OR marriage OR married OR marital OR partner* OR dyad* OR spous* OR husband* OR wife
OR wives OR boyfriend OR girlfriend OR fiancé OR “common law” OR companion OR dating
OR “same-sex relationship*” OR “heterosexual relationship” OR “homosexual relationship” OR
“intimate relationship*” OR “committed relationship*” OR “closed relationship*” OR
“exclusive relationship*” OR “monogamous relationship*” OR “covenant relationship*” OR
“significant other” OR “life partner”) AND (alcoho* OR drinking) AND (longitudinal OR
“repeated measure” OR “serial measure” OR prospective OR “multi-wave” OR “follow up” OR
“over time”). The search was not restricted by year of publication, language, or publication
status. Studies were included if they met the following six criteria: the study used a longitudinal
design; the study collected data on romantically-involved couples; alcohol use was assessed at
baseline; the same measure of alcohol use was assessed at follow-up; both members of the
couple’s alcohol use was assessed at each wave; and couples remained in the same romantic
partnership at each wave. Intervention studies including these six components were eligible if
data from an untreated control group were available; in such cases, only the data from the
untreated control group were used. We placed no restrictions on study samples with respect to
sex, gender, sexual orientation, age, or ethnicity.
The search returned 4,902 studies. After removing duplicates, 3,655 studies remained.
PARTNER EFFECTS ON ALCOHOL USE
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The first and fourth authors screened the abstracts for inclusion (agreement rate: 95.1%). Next,
two raters reviewed the full text of remaining articles for inclusion (agreement rate: 100.0%). At
each stage, rating discrepancies were resolved through discussion and consensus with co-authors.
Following full-text screening, the references and publications citing each article that met
inclusion criteria were screened. Studies known to the authors that were not detected through the
literature search were also screened for inclusion (n = 3). Following the addition of these three
articles, a total of 26 studies met inclusion criteria, and 17 studies were included in the final
analyses (see Supplemental Material A for a sample of excluded studies, and Figure A1 for the
PRISMA flowchart of the literature search and study selection; Moher et al., 2009). Information
was requested from the primary author (n = 18) when a study nearly met criteria but did not
report effect sizes or reported insufficient information to compute effect sizes. Nine of the
contacted authors provided the requested information (and were thus included in the final 17
articles), whereas another nine of the authors contacted were unable to provide the necessary
statistical information (i.e., no longer had access to the data, had already destroyed data). In
December 2017, we concluded the literature search and began data extraction.
Coding of Studies
The first and fourth authors coded the 17 included studies using ten characteristics:
sample size, type of sample, type of romantic relationship, sexual orientation of the couple,
relationship length, mean age of participants, percentage of Caucasian participants, percentage of
female participants, publication type, and measure(s) used to assess alcohol outcomes. The
characteristics of included studies appear in Table 1.
Measures
Four primary alcohol outcomes were included: frequency, frequency of binge drinking,
PARTNER EFFECTS ON ALCOHOL USE
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quantity, and alcohol problems (assessed using one or more of three measures). We refer to these
outcomes collectively as “alcohol indicators.” For our subgroup analysis, we refer to measures of
frequency, frequency of binge drinking, and quantity collectively as “alcohol use,” to
differentiate from “alcohol-related problems” (see Supplemental Material B).
Procedure
To combat overrepresentation of studies including multiple effects, studies using multiple
alcohol indicators had their correlations averaged, so the analysis only included one effect from
each included study (Card, 2012). Prior to averaging, correlations were transformed into Fisher’s
Z (Card, 2012). Correlations within each individual study across every wave available appear in
Supplemental Material C. We used all available alcohol indicators data by averaging effects
across all waves and interpret effects following Cohen’s (1992) guidelines for small, medium,
and large effect sizes (r = .10, .30, .50).
Traditional meta-analysis
We used Comprehensive Meta-Analysis (Version 2; Borenstein, Hedges, Higgins, &
Rothstein, 2005) to evaluate overall bivariate effects using random-effect models. Weighted
mean effects were calculated following procedures recommended by Hunter and Schmidt
(1990). To assess heterogeneity, we calculated the total heterogeneity of weighted mean effect
sizes (QT) and the total variation across studies attributable to heterogeneity (I2). When QT was
significant, we used random-effect meta-regressions with maximum likelihood estimations to
test the potential moderating effects of five continuous and two categorical covariates: year of
publication, mean age of couple, time lag, attrition, relationship length, alcohol indicator, and
married. Only continuous moderators evaluated in 10 or more samples and categorical
moderators evaluated in three or more samples per subgroup could be considered for meta-
PARTNER EFFECTS ON ALCOHOL USE
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regression. For each observed relationship, we tested eight models with the following predictors:
year of publication; mean age of couple; alcohol indicator (alcohol use vs. alcohol problems);
time lag between baseline and follow-up assessments; married (predominantly married couples
vs. community/other couples); attrition (%); and all seven of the above simultaneously (see
Supplemental Material D). When moderators were significant, corresponding scatter plots were
provided in Supplemental Material E. Publication bias was tested by inspecting funnel plots with
observed and imputed studies (Supplemental Material F), and through calculation of Egger’s test
of regression to the intercept (Egger, Smith, Schneider, & Minder, 1997; see Table 2).
Two-Stage Meta-Analytic Structural Equation Modeling
To test whether partners’ baseline alcohol indicators predicted individuals’ follow-up
alcohol indicators after controlling for individuals’ baseline alcohol indicators, we conducted TS-
MASEM (Cheung, 2014; Cheung & Chan, 2005) via the metaSEM package for R (Cheung,
2015; Version 3.2: R Core Team, 2013). The first stage in TS-MASEM uses multigroup
confirmatory factor analyses to test the homogeneity of correlation matrices across studies and to
compute a pooled correlation matrix and an asymptotic covariance matrix. The degree of
heterogeneity in each pooled correlation matrix was evaluated by computing QT and I2. A
significant QT suggests the pooled correlation matrix is heterogeneous and that the variance in
weighted mean effect sizes is larger than would be expected due to sampling error (Cheung,
2014). We used random effects, as opposed to fixed effects, so that findings could be generalized
beyond the studies included. The second stage in TS-MASEM used the weighted least squares
(WLS) estimation to fit path models, estimate parameters, and estimate model fit. Chi-square
difference tests (i.e., ∆χ2) were used to test if an unconstrained model differed significantly from
the more parsimonious constrained model (see Supplemental Material H-J for syntax). The
PARTNER EFFECTS ON ALCOHOL USE
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overall group refers to the entire sample of studies (n = 17), a subset of data refers to measures of
alcohol use (n = 14), and another refers to studies that measured alcohol-related problems
2
(n =
5). Following Hu and Bentler (1999), model fit was interpreted using the Comparative Fit Index
(CFI; cut off > .95), the Tucker-Lewis Index (TLI; cut off > .95), the Standardized Root Mean
Squared Error (SRMR; cut off < .08), and the Root Mean Squared Error of Approximation
(RMSEA; cut off < .06).
Results
Sample Characteristics
The final sample consisted of 10,553 couples (21,106 individuals). Mean sample
size/study was 621 couples (SD = 1,297); women were on average 32.8 years (SD = 12.0); men
were on average 34.6 years (SD = 12.9); the mean time lag between the first and last assessment
was 37.1 months (SD = 44.3; range: 1 to 144); the attrition rate by the final wave was on average
36.8% (SD = 21.5); the mean percentage of Caucasian couples was 71.7% (SD = 27.3); mean
relationship length was 9.8 years (SD = 10.0); and average year of publication was 2009 (SD =
8.17 years). The full characteristics of the final sample appear in Table 1.
Traditional Meta-Analysis
Overall weighted mean effects for the relationships between female and male baseline
and follow-up alcohol indicators/alcohol use/alcohol problems appear in Table 2. In brief,
baseline female alcohol indicators (referred to as FAI-T1) had small relationships (r = .29, p <
.001) with male follow-up alcohol indicators (referred to as MAI-T2), medium relationships (r =
.35, p < .001) with baseline male alcohol indicators (referred to as MAI-T1), and large
relationships (r = .58, p < .001) with female follow-up alcohol indicators (referred to as FAI-T2).
2
Groupings are not mutually exclusive: two studies examined both alcohol use and alcohol-related problems.
PARTNER EFFECTS ON ALCOHOL USE
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MAI-T1 had small relationships (r = .29; p < .001) with FAI-T2 and large (r = .62; p < .001)
relationships with MAI-T2. Finally, MAI-T2 had medium relationships (r = .37; p < .001) with
FAI-T2. The percentage of total heterogeneity across studies ranged from 0.0% to 93.4%,
suggesting the possible influence of moderators on certain relationships.
Meta-Regression
Results from random effect meta-regressions appear in Supplemental Material D. After
controlling for mean age of couple, alcohol indicator, time lag, married, attrition, and
relationship length, year of publication moderated the following relationships: FAI-T1 and MAI-
T1 (β = .024, p = .001), MAI-T1 and FAI-T2 (β = .017, p = .002), and FAI-T2 and MAI-T2 (β =
.026, p = .007). This suggests FAI-T1’s positive relationship with MAI-T1 increased as year of
publication increased, as did FAI-T2’s positive relationships with MAI-T1 and MAI-T2.
Nonetheless, upon inspection of the scatterplot, the moderating effect of year of publication on
the relationship between FAI-T1 and MAI-T1 may be driven by outliers and should be
interpreted with caution (see Supplemental Material E).
After controlling for other potential moderators, mean age of couple moderated the
following relationships: FAI-T1 and MAI-T1 (β = .065, p = .002), and FAI-T2 and MAI-T2 (β =
.073, p = .009). This suggests FAI-T1’s positive relationship with MAI-T1 increased as mean
age of couple increased, as did FAI-T2’s positive relationship with MAI-T2. Nonetheless, upon
inspection of the scatterplot, it appears the moderating effect of mean age of couple on the
relationship between FAI-T1 and MAI-T1 may have been driven by outliers and should be
interpreted with caution (see Supplemental Material E).
After controlling for other potential moderators, the alcohol indicator moderated the
following relationships: FAI-T1 and MAI-T1 (β = .25, p = .002), FAI-T1 and MAI-T2 (β = .185,
PARTNER EFFECTS ON ALCOHOL USE
16
p = .025), and FAI-T2 and MAI-T2 (β = .27, p = .012). This implies FAI-T1’s positive
relationship with MAI-T1 increased when measures of alcohol use as opposed to alcohol
problems were employed, as did FAI-T2’s positive relationship with MAI-T2, and FAI-T1’s
positive relationship with MAI-T2.
After controlling for other potential moderators, time lag between assessments moderated
the following relationships: FAI-T1 and MAI-T1 (β = -.009, p < .001), FAI-T1 and MAI-T2 (β =
-.005, p <.001), MAI-T1 and FAI-T2 (β = -.006, p <.001), and FAI-T2 and MAI-T2 (β = -.009, p
<.001). This implies FAI-T1’s positive relationship with MAI-T1 and MAI-T2 decreased as time
lag increased. FAI-T2’s positive relationship with MAI-T1 and MAI-T2 also decreased as time
lag increased.
After controlling for other potential moderators, the married variable moderated the
following relationships: FAI-T1 and MAI-T1 (β = -.274, p = .002), and FAI-T1 and MAI-T2 (β
= -.327, p <.001). This suggests FAI-T1’s positive relationships with MAI-T1 and MAI-T2
decreased for samples which were primarily married versus other types of samples (e.g.,
community samples).
Publication Bias
Funnel plots (Supplemental Material F) and Egger’s regression to the intercept (Table 2)
provided mixed evidence for publication bias. Egger’s regression to the intercept was not
significant (p < .05) for all observed relationships with the exception of the following
relationships: MAI-T1 and MAI-T2, -3.06 [95% CI: -6.71; 0.59], and MAU-T1 and MAU-T2, -
4.34 [95% CI: -8.21; -0.47]. However, the "trim and fill" method only increased the estimated
relationship between MAI-T1 and MAI-T2 by .02 and the estimated relationship between MAU-
T1 and MAU-T2 by .01, suggesting small publication bias but no substantive difference in
PARTNER EFFECTS ON ALCOHOL USE
17
interpretation (see Table 2).
TS-MASEM Overall Effect Sizes
Estimates of mean correlations between female and male alcohol indicators/alcohol
use/alcohol problems at T1 and T2 appear in Supplemental Material C. Longitudinal alcohol
indicators’ effect estimates were small-to-large (r = .00 to .90) and all but two longitudinal
alcohol indicators’ effect estimates were positive. FAI-T1 strongly predicted FAI-T2 (β = 0.54,
[95% CI: .47; .60]) and MAI-T1 strongly predicted MAI-T2 (β = 0.54, [95% CI: .47; .62]). FAI-
T1 predicted MAI-T2, after controlling for MAI-T1 (β = .19, [95% CI.12; .25]) to a small
degree. Similarly, MAI-T1 predicted FAI-T2 while controlling for FAI-T1 (β = .12, [95% CI:
.06; .18]) to a small degree. The same pattern of results was found for measures of alcohol use
(Supplemental Material C). Comparable results were found for measures of alcohol problems
except baseline male alcohol problems did not predict female follow-up alcohol problems (see
Supplemental Material C). Lastly, the path corresponding to baseline female alcohol problems
predicting male follow-up alcohol problems was significantly (p < .05) weaker than baseline
female alcohol use predicting male follow-up alcohol use.
QT was significant for the overall effect of baseline alcohol indicators predicting change
in alcohol indicators at follow-up (QT = 381.4, p < .001) and for the overall effect of baseline
alcohol use predicting change in alcohol use at follow-up (QT = 365.6, p < .001). In contrast, QT
was nonsignificant for the overall effect of baseline alcohol problems predicting change in
alcohol problems at follow-up (QT = 25.3, p > .05). Lastly, I2 ranged from medium-to-large for
alcohol indicators (I2 = 71.8 to 90.0) and alcohol use (I2 = 69.8 to 91.7). In line with the overall
nonsignificant test of heterogeneity, little heterogeneity was found for the alcohol problems
weighed effects. Indices of heterogeneity and variance owing to heterogeneity for each group of
PARTNER EFFECTS ON ALCOHOL USE
18
data are reported in Supplemental Material C.
Model Comparisons
Four models were compared within each group of data (see Table 3 for fit indices) to test
the presence and magnitude of partner effects, and any gender differences in effects. Model A
was just-identified (df = 0) and was used to compare other models. For Model B, the correlation
between MAI-T2 and FAI-T2 was constrained to zero. For Model C, the same correlation was
constrained to zero, and the path from FAI-T1 to MAI-T2 was constrained to equal the path from
MAI-T1 to FAI-T2. Building from Model B, Model C tested whether equating partner effects
across genders would result in better fit. For Model D, the correlation between FAI-T2 and MAI-
T2 was constrained to zero and the path from FAI-T1 to FAI-T2 was constrained to equal the
path from MAI-T1 to MAI-T2. Building from Model B, Model D tested whether equating actor
effects (i.e., individual relative stability) across genders would result in better fit. Model B was
the best-fitting and selected Model as determined by stand-alone fit indices (CFI, TLI, RMSEA,
SRMR) and the chi-square difference test for all data (see Supplemental Figures G1-3). Model
B’s selection over Model C suggests the magnitude of partner effects vary across genders, with
women exerting stronger partner effects on their male partner’s alcohol indicators than vice versa
(see Figure G1). Next, the male partner effect for alcohol problems was nonsignificant (p > .05),
suggesting men may not influence their female partner’s alcohol problems (see Figure G3).
Model B’s selection over Model D suggests alcohol use has greater relative stability in women
than in men as the female actor effect was significantly stronger than the male actor effect,
though the magnitude of this difference was small (see Figure G2). We found the opposite with
regards to alcohol-related problems: the male actor effect was significantly stronger (p < .05)
than the female actor effect, suggesting that alcohol problems are relatively more stable in men
PARTNER EFFECTS ON ALCOHOL USE
19
(see Figure G3). Model B yielded no significant differences in actor effects on the overall
alcohol indicators across genders.
Discussion
The magnitude and gender-specific nature of partner influences on alcohol use required
clarification due to some inconsistencies in findings. Furthermore, understanding partner
influences on alcohol use could have implications for prevention and treatment efforts.
Therefore, we conducted a comprehensive meta-analysis of 17 longitudinal studies examining
partner influences in romantic couples. Our best-fitting model allowed both partner and actor
effects to vary freely across genders and fit the data well, as evidenced by strong stand-alone and
relative goodness-of-fit indices. As hypothesized, results suggested the partner’s baseline alcohol
use positively predicts the individual’s alcohol use (i.e., partner effects), while accounting for the
relative stability of alcohol use within the individual (i.e., actor effects). Overall, we found
significant partner effects on alcohol use that were small in magnitude; however, given the
strong relative stability of alcohol use within an individual, the detection of partner effects that
control for actor effects is meaningful as small effects that are positive and consistent may have a
cumulative effect over time (Abelson, 1985; Otten et al., 2008).
Gender Differences in Partner Influence
We conducted a subgroup analysis to compare partner influences across measures of
alcohol use, and alcohol problems as the former represent a behavior and the latter, consequences
of a behavior. We found the female partner effect was stronger than the male partner effect for
alcohol use. Additionally, we found a significant female partner effect yet a nonsignificant male
partner effect for alcohol-related problems.
Our results contribute to the literature on gender differences in the social context of
PARTNER EFFECTS ON ALCOHOL USE
20
alcohol use by showing that women exert stronger partner influences than men. Though
discordance in heavy drinking among couples is associated with decreased marital satisfaction
(Homish & Leonard, 2007), some research suggests couples where only the woman reports
heavy drinking are at increased risk of divorce compared to man-only heavy drinking couples
(Keenan, Kenward, Grundy, & Leon, 2013; Torvik et al., 2015). Another possible explanation
for the gender difference in partner influence pertains to the fact that women engage in lower
alcohol consumption than men and that men may shift their drinking to match the lower levels of
their female partners (Wilsnack, Wilsnack, Kristjanson, Vogeltanz‐Holm, & Gmel, 2009).
Engels and Knibbe (2000) found male adolescents shifted their drinking patterns to that of their
female romantic partner by drinking less and being intoxicated less often whereas female
adolescents exhibited significantly less changes in their drinking patterns after entering a
romantic relationship. Taken together, women may influence their male partner’s drinking more
strongly than the reverse, in either a risky and/or a protective manner.
Next, our moderation analyses suggest women exert less influence within married
samples compared to community/other samples. A possible explanation lies within Bowen’s
family systems theory (1974). Perhaps the alcohol-related roles and expectations for each
member of the married couple are more established and therefore more resistant to the women’s
influence compared to other types of samples. Furthermore, our moderation analyses suggest
male partner effects increased as year of publication increased. Women have historically held
less power in society; but given shifts in traditional gender-roles observed in North America in
recent decades perhaps women, with fewer traditional social constraints on their drinking
(Keyes, Grant & Hasin, 2008), are becoming more responsive to male partner influences on their
drinking. In fact, there has been a gender convergence in rates of AUDs in recent decades (Keyes
PARTNER EFFECTS ON ALCOHOL USE
21
et al., 2008). These socio-cultural trends may help explain the publication year effect observed in
our meta-analysis. Lastly, we found that both female and male partner effects decreased as the
time lag between assessments increased. Though partner influences continue to be significant, it
is possible that the predictive power of baseline partner drinking decreases with time as the
couple is more likely to experience other sources of influences that may impact their drinking
levels (e.g., change in social circles, pregnancy, stressful events).
Our finding that women influence their male partners more strongly than the reverse is
consistent with research on the gender differences in alcohol-related problems. Again, a possible
explanation pertains to the fact that women experience lower frequencies of alcohol problems
than men on average (Bischoff, 2007; Nolen-Hoeksema, 2004); men may shift their drinking to a
less problematic style to match that of their female partners. Moreover, it is essential to consider
gender differences in the way individuals view their own, and their partner’s, drinking behaviors
as these differences influence the expected, perceived and actual experiences of alcohol
problems (Bischoff, 2007). Research suggests that women are more likely than men to be
concerned for their partner’s drinking and to attempt to control it. In contrast, their male partners
display few concerns about their own drinking (Raitasalo & Holmila, 2005).
We found men influence their female partners’ drinking levels, but not their alcohol-
related problems. A possible explanation for this lies within the difference between a behavior
and a negative consequence. Following social learning theory (Bandura, 1977), a woman may
emulate her male partner’s heavy drinking after a period of directly observing rewards he obtains
from his drinking behaviors. However, given the negative valance of alcohol problems, a woman
may be less likely to imitate her husband’s problematic drinking. Interestingly, our results
revealed men’s alcohol problems are still influenced by their female partners’ alcohol-related
PARTNER EFFECTS ON ALCOHOL USE
22
problems. Still, in line with social learning theory, this influence on alcohol problems was
weaker than women’s influence on men’s alcohol use and further moderation analyses were
consistent with this conclusion. Differences in the experience of alcohol problems across genders
may contribute to the gender difference in partner influence. For instance, Bongers and
colleagues (1988) found men reported a greater accumulation of types of alcohol-related
problems; men were more likely than women to experience problems with their partner/family,
and problems with law enforcement. Moreover, in a review of consequences in college students,
Perkins (2002) found male college students’ alcohol problems gravitated towards consequences
for self and others that involved public deviance, whereas female college students tended to have
more personal and private alcohol-related problems. Perhaps the alcohol-related consequences
experienced by men are more observably deterring women from emulating those behaviors.
Gender Differences in Actor Effects
In our selected model which allowed actor and partner effects to vary freely, we found
the female actor effect to be significantly stronger than the male actor effect for alcohol use.
These autocorrelations suggest women may possess greater relative stability in their alcohol use
than men. National surveys in the U.S. have similarly reported women’s alcohol consumption
levels to be more stable than men’s over ten years (Kerr, Fillmore, & Bostrom, 2002). Other
longitudinal studies suggest heavy drinkers are less stable in their consumption than moderate
drinkers and abstainers (Kerr et al., 2002; Knott, Bell, & Britton, 2018). Thus, women’s greater
stability in alcohol consumption over time may be related to the fact women on average consume
less alcohol than men (Wilsnack et al., 2009). However, it is important to interpret our observed
gender difference in the magnitude of the actor effect for alcohol use cautiously as the absolute
magnitude of this gender difference was very small. Moreover, significant autocorrelations do
PARTNER EFFECTS ON ALCOHOL USE
23
not signify the absence of change but rather stability in the rank ordering of individuals in that
those who reported greater-than-average alcohol use at baseline continue to report greater-than-
average alcohol use at follow-up (Caspi, Roberts, & Shiner, 2005). Additionally, we found the
male actor effect was stronger than the female actor effect for alcohol-related problems, despite
the fact women report lower levels of such problems. This result contrasts previous studies that
reported greater relative stability for women (Brennan, Schutte, Moos, & Moos, 2011) or equal
stability across the genders (Caetano, 1997). Nonetheless, men may exhibit greater relative
stability in alcohol problems that are rooted in dependence as they arise from patterns of heavy
alcohol use, which are more likely in men (Caetano, 1997). It is important to interpret our
observed gender difference in the magnitude of the actor effect for alcohol problems cautiously,
as this group of data was limited to five studies.
Limitations and Future Directions
Alcohol use among individuals was highly stable. As such, the variance available to be
accounted for by partner use was relatively small. Moreover, our included studies involved
variable time lags (one month to 12 years; see Table 1) and focused primarily on young couples
(approximately 63% of couples were under 35 years old, on average). The influence of partners’
alcohol use should be studied across different kinds of romantic relationships (e.g., open
relationships, long-distance relationships) and across different developmental periods in which
commitment and desire to maintain the relationship may differ (e.g., young casually-dating
couples, older dating couples). Partner influences should also be studied using longer time lags
between measurement points, so there is more variability to predict once baseline levels are
controlled. However, the time lag between baseline and follow-up assessment in the included
studies in the present meta-analysis ranged from one month to 12 years; therefore, the detection
PARTNER EFFECTS ON ALCOHOL USE
24
of partner influences over-and-above actor effects over a varied period is noteworthy. Indeed,
our analyses revealed time lag as a significant moderator to partner influences.
Furthermore, our included studies lacked consistency regarding the way alcohol
indicators were measured. This may have oversimplified the relationship between the partner’s
baseline alcohol use and the individual’s subsequent alcohol use. For example, we were unable
to detect subtleties in relation to partner influences and type of alcohol-related problem (e.g.,
physical vs. interpersonal alcohol-related problems). Next, our test of partner effects controlled
for baseline alcohol use but did not account for selection effects prior to the baseline data. We
found multiple moderators for the baseline relationships between female and male alcohol
indicators (e.g., mean age of couple, measure of alcohol indicator time-lag). It is unclear how
these findings would differ if individuals were also assessed prior to their partnerships.
As our included studies were composed exclusively of participants from North America
and Western Europe, the extent to which these results generalize to other regions of the world is
unclear. Moreover, as the average ages of the samples in our included studies ranged from 15-54
years old, and only one of our 17 included studies involved a secondary/high school student
sample, our results may not extend across the lifespan. Next, our results are limited to
heterosexual partnerships despite our attempts to search for studies reporting on same-sex
couples. Thus, it remains to be determined through future research whether such partner
influences are operative in same-sex couples, and whether the observed gender differences are
operative in female-female vs. male-male couples or whether they are limited to male-female
relationships. Lastly, our results suggested publication bias for two relationships: MAI-T1 and
MAI-T2, and MAU-T1 and MAU-T2. Our attempts to address publication bias included
incorporating unpublished dissertations into our search strategy, calculating Egger’s regression
PARTNER EFFECTS ON ALCOHOL USE
25
to the intercept, and calculating “trim and effect” adjust estimates (a funnel-plot based method
that corrects plot asymmetry among smaller studies) which adjusted the relationships by
marginal amounts.
Conclusion
Our meta-analysis represents the most comprehensive test of partner influences on
alcohol use to date. Analyses indicated romantic partner alcohol use predicts subsequent alcohol
use in an actor for both men and women. Our results demonstrated that romantic partners affect
subsequent risky alcohol use behavior in an individual and support the need for partner
involvement in alcohol interventions. Indeed, a meta-analysis concluded that behavioral couples
therapy yields better outcomes than traditional individual-focused treatments for married or
cohabiting individuals seeking help for an AUD (Powers, Vedel, & Emmelkamp, 2008). We
found women influenced their male partner’s drinking more strongly than men influenced their
female partner’s drinking (although we also found men’s influence increased as year of
publication increased). Therefore, lighter partner drinking, particularly lighter drinking in the
female partner, may serve as a protective factor against alcohol misuse. Addressing the powerful
effects of partner drinking may assist in the modification of individual drinking behavior in the
therapeutic context. In contrast, heavier partner drinking may interfere with an individual’s
treatment for alcohol-use disturbances suggesting the need for treating the couple as a unit.
Lastly, couples at most risk of escalating one another’s drinking could be identified and targeted
for support tailored to the couple’s characteristics and needs. Our results further support the need
for couples-based interventions and support the involvement of a client’s partner when treating
AUDs.
PARTNER EFFECTS ON ALCOHOL USE
26
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Table 1
Characteristics of longitudinal studies included in the meta-analysis
N
Sample
type
Couple
age
Time
lag (months)
Attrition
%
Caucasian
%
Relationship
length (years)
Status
Alcohol
indicator
Aalsma et al. (2012)
80
Adolescents1
15.6
12.0
78.6
89.0
NR
A
Frequency
Bartel et al. (2017)
179
Community1
31.0
36.0
39.7
NR
7.45
A
Binge
Buu et al. (2011)
84
Married high-
risk parents2
32.0
144.0
69.2
100.0
NR
A
DDHQ
Cronkite et al. (1984)
245
Married2
45.1
12.0
8.2
82.0
18.7
A
Quantity
Desrosiers et al. (2016)
197
Postpartum
couples1
20.0
6.0
15.5
11.1
NR
A
Frequency
Hellmuth et al. (2013)
122
Postpartum
couples1
>18.0a
7.0
32.2
79.0
2.9
A
AUDIT
Kehayes et al. (2017)
108
Community
University
students1
22.6
1.0
46.8
83.5
2.3
A
Quantity
Frequency
RAPI–7Dc
Keller et al. (2009)
98
Married2
39.0
24.0
38
67.0
13.0b
A
MAST
ADS
Leonard & Eiden (1999)
491
Married2
23.4
12.0
23
75.0
1.0
A
Quantity
Binge
INTX
PARTNER EFFECTS ON ALCOHOL USE
56
Leonard & Mudar (2004)
468
Married2
27.9
12.0
26
62.0
1.0
A
Quantity
Binge
INTX
Mahedy et al. (2017)
5,535
Postpartum
couples1
NR
96.0
60.3
NR
NR
A
Quantity
Binge
Mushquash et al. (2013)
208
University
/post-
secondary
students1
21
1.0
2.9
88.9
1.8
A
Binge
Otten et al. (2008)
404
Married2
45
24.0
6.0
100.0
NR
A
Quantity
Rodriguez (2014)
61
Married2
29.8
6.0
50.4
69.6
6.00
A
Quantity
Frequency
RAPI
AUDIT
Rogers (2002)
1,182
Married
elderly
sample2
55.9
72.0
30.8
88.4
30.0
D
Quantity
Temple et al. (2008)
468
High-risk
couples1
33.3d
45.0
35.1
32.7
7.7
A
Frequency
Windle et al. (2014)
489
Married2
51.4
120.0
42.0
99.0
26.07
A
QFI
Binge
Note. Couple age, % Caucasian, and relationship length for the sample at baseline; Attrition by the last wave; Time lag is expressed in
months; Relationship length is expressed in years; 1Community/Other sample type and 2Married sample type; NR = not reported; N =
PARTNER EFFECTS ON ALCOHOL USE
57
total number of couples used for the analyses; Status = publication status of the study: A = article; D = dissertation; QFI = quantity-
frequency index (Armor & Polich, 1982); Binge = binge drinking frequency; INTX = frequency of intoxication; DDHQ = alcohol
problems items of the Drinking and Drug History Questionnaire (Zucker, Fitzgerald, & Noll, 1990); AUDIT = Alcohol Use Disorders
Identification Test (Saunders, Aasland, Babor, De La Fuente, & Grant, 1993); RAPI = Rutgers Alcohol Problem Index (White &
Labouvie, 1989; refers to a one year time period); RAPI-7D = Rutgers Alcohol Problem Index over the past 7 days; ADS = Alcohol
Dependence Scale (Skinner & Horn, 1984); MAST = Michigan Alcohol Screening Test (Selzer, M. 1971).
aparticipants were all over the age of 18
byears living together
cRAPI-7 day is reported in Lambe et al. (2015), a subsample of Kehayes et al. (2017)
donly one partner’s age is reported
PARTNER EFFECTS ON ALCOHOL USE
58
Table 2
Summary of overall bivariate effect sizes for the relationships between female and male baseline and follow-up alcohol indicators, alcohol use, and
alcohol problems
Variable
k
N
r+
95% CI
QT
I2 (%)
Egger’s
intercept
95% CI
kTF
“Trim and fill”
estimates
r+ [95% CI]
Alcohol indicators–women, T1
Alcohol indicators–women, T2
17
10,382
.58***
[.52; .63]
196.45***
91.86
0.30
[-2.69; 3.29]
0
.58 [.52; .63]
Alcohol indicators–men, T1
17
6,367
.35***
[.29; .42]
128.96***
87.59
-2.10
[-5.70; 1.49]
0
.35 [.29; .42]
Alcohol indicators–men, T2
18
5,053
.29***
[.22; .35]
92.70***
92.70
-2.46
[-5.04; 0.13]
0
.29 [.22; .35]
Alcohol indicators–women, T2
Alcohol indicators–men, T1
18
5,053
.29***
[.22; .35]
88.29***
80.75
-0.77
[-3.56; 2.03]]
0
.29 [.22; .35]
Alcohol indicators–women, T1
16
4,734
.37***
[.30; .44]
95.73***
95.73
-0.50
[-3.79; 2.80]
0
.37 [.30; .44]
Alcohol indicators–men, T1
Alcohol indicators–men, T2
16
4,847
.62***
[.56; .67]
133.26***
88.74
-3.06
[-6.71; 0.59]
1
.60 [.54; .66]
Alcohol use–women, T1
Alcohol use–women, T2
15
10,213
.58***
[.52; .64]
192.30***
92.72
0.41
[-3.10; 3.92]
0
.58 [.52; .64]
Alcohol use–men, T1
14
5,976
.38***
[.31; .44]
110.68***
88.26
-1.34
[-5.91; 3.23]
0
.38 [.31; .44]
Alcohol use–men, T2
14
4,678
.32***
[.28; .38]
76.83***
83.08
-1.86
[-5.50; 1.78]
0
.32 [.28; .38]
Alcohol use–women, T2
Alcohol use–men, T1
14
4,678
.31***
[.24; .38]
82.77***
84.29
-0.06
[-4.03; 3.91]
0
.31 [.24; .38]
Alcohol use–men, T2
13
4,481
.40***
[.32; .47]
87.74***
86.32
0.56
[-3.83; 5.00]
0
.40 [.32; .47]
Alcohol use–men, T1
Alcohol use–men, T2
14
4,678
.61***
[.55; .66]
118.01***
88.98
-4.34
[-8.21; -0.47]
1
.60 [.53; .65]
Alcohol-related problems–women, T1
Alcohol-related problems–women, T2
2
169
.57***
[.32; .74]
3.67
72.76
—
—
—
—
Alcohol-related problems–men, T1
3
391
.21**
[.07; .33]
3.57
43.90
2.56
[-111.38; 116.50]
0
.21 [.07; .33]
Alcohol-related problems–men, T2
4
375
.16**
[.05; .26]
3.52
14.70
-4.83
[-20.66; 10.99]
0
.16 [.05; .26]
Alcohol-related problems–women, T2
Alcohol-related problems–men, T1
4
375
.19***
[.09; .29]
0.66
0.00
-1.33
[-9.74; 7.09]
0
.19 [.09; .29]
Alcohol-related problems–men, T2
3
253
.24***
[.13; .35]
1.61
0.00
-5.50
[-40.44; 29.44]
0
.24 [.13; .35]
Alcohol-related problems–men, T1
Alcohol-related problems–men, T2
2
169
.70**
[.23; .90]
15.25***
93.44
—
—
—
—
Note. Overall bivariate effects estimates for all available alcohol outcomes, including alcohol use and alcohol-related problems; Female Alcohol
Indicators = averaged female partner alcohol indicators; Male Alcohol Indicators = averaged male partner alcohol indicators; Female Alcohol
PARTNER EFFECTS ON ALCOHOL USE
59
Use = averaged female partner alcohol use; Male Alcohol Use = averaged male partner alcohol use; Female Alcohol problems = averaged female
partner alcohol-related problems; Male Alcohol problems = averaged male partner alcohol-related problems; TI = averaged baseline; T2 =
averaged follow-up; k = number of studies; N = total number of participants in the k samples; r+ = observed weighted mean correlation; CI =
confident interval for r+; QT = measure of heterogeneity for r+; I2 = percentage of heterogeneity for r+; kTF = number of imputed studies as part of
“trim and fill” method for r+.
*p < .05; **p < .01; ***p < .001.
PARTNER EFFECTS ON ALCOHOL USE
60
Table 3
Model comparison fit indicies for overall, alcohol use, and alcohol problems data
Stage/Model
k
N
χ2
df
p
CFI
TLI
SRMR
RMSEA [95% CI]
∆χ2
Overall
Model 1A
17
10,419
0.00
0
—
—
—
—
—
—
Model 1B
17
10,419
1.24
1
.265
.999
.999
.011
.005 [.000, .027]
1.24(1)
Model 1C
17
10,419
32.52
2
<.001
.973
.920
.069
.038 [.027, .050]
32.52***(2)
Model 1D
17
10,419
53.57
2
<.001
.954
.864
.082
.050 [.039, .062]
53.57***(1)
Measures of alcohol use
Model 2A
14
10,115
0.00
0
—
—
—
—
—
—
Model 2B
14
10,115
3.02
1
.082
.998
.989
.018
.014 [.000, .034]
3.02(1)
Model 2C
14
10,115
26.46
2
<.001
.977
.932
.065
.035 [.024, .047]
24.46***(2)
Model 2D
14
10,115
43.90
2
<.001
.961
.884
.081
.046 [.034, .058]
43.90***(1)
Measures of alcohol-related problems
Model 3A
5
473
0.00
0
—
—
—
—
—
—
Model 3B
5
473
0.73
1
.705
.999
.999
.013
.000 [.000, .000]
0.73(1)
Model 3C
5
473
11.71
2
.003
.933
.800
.108
.101 [.051, .161]
11.71**(2)
Model 3D
5
473
16.81
2
<.001
.898
.695
.092
.125 [.075, .184]
16.81***(1)
Note. p = p value of x2. Overall refers to all alcohol indicators (AI); Model 1A = no degrees of freedom; Model 1B = correlation between FAI-T2 and MAI-
T2 constrained to 0; Model 1C = correlation between FAI-T2 and MAI-T2 constrained to 0 and path from FAI-T1 to MAI-T2 constrained to equal path from
MAI-T1 to FAI-T2; Model 1D = correlation between FAI-T2 and MAI-T2 constrained to 0 and path from FAI-T1 to FAI-T2 constrained to equal path from
MAI-T1 to MAI-T2. The model selected is in bold. Model 2A = no degrees of freedom; Model 2B = correlation between FAU-T2 and MAU-T2 constrained
to 0; Model 2C = correlation between FAU-T2 and MAU-T2 constrained to 0 and path from FAU-T1 to MAU-T2 constrained to equal path from MAU-T1
to FAU-T2; Model 2D = correlation between FAU-T2 and MAU-T2 constrained to 0 and path from FAU-T1 to FAU-T2 constrained to equal path from
MAU-T1 to MAU-T2. The model selected is in bold. Model 3A = no degrees of freedom; Model 3B = correlation between FARP-T2 and MARP-T2
constrained to 0; Model 3C = correlation between FARP-T2 and MARP-T2 constrained to 0 and path from FARP-T1 to MARP-T2 constrained to equal path
from MARP-T1 to FARP-T2; Model 3D = correlation between FARP-T2 and MARP-T2 constrained to 0 and path from FARP-T1 to FARP-T2 constrained
to equal path from MARP-T1 to MARP-T2. The model selected is in bold.
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