relational maintenance communication and self expansion ... · enhancement (bell et al., 1987),...
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Running head: RELATIONAL MAINTENANCE AND SELF EXPANSION 1
Relational Maintenance Communication and Self Expansion Theory: Low-Inference Measure
Development and Dyadic Test of Inclusion of the Other in the Self as a Predictor
Andrew M. Ledbetter
Texas Christian University
This manuscript has been accepted for presentation as the top paper in the Interpersonal
and Small Group Communication Interest Group of the Central States Communication
Association, Cleveland, OH, March 2012. This manuscript has not been published in a
peer-reviewed journal. The author makes no warranty of this manuscript’s
merchantability or fitness for a particular purpose. The reader of this manuscript assumes
all liability for application, citation, or other use of the information herein. This manuscript
may not be reproduced or transmitted in any form, in whole or in part, without the express
written permission of the author (excepting fair use as determined by applicable law).
Author note: Andrew M. Ledbetter (Ph.D., University of Kansas, 2007) is an assistant professor
in the Department of Communication Studies at Texas Christian University and a member of
CSCA. The second study reported in this manuscript was supported by an internal grant from
Texas Christian University. Please direct all correspondence regarding this manuscript to the
author at: Department of Communication Studies, TCU Box 298045, Fort Worth, TX 76129,
RELATIONAL MAINTENANCE AND SELF EXPANSION 2
Abstract
This manuscript reports a series of two studies that develop and validate a low-inference measure
of relational maintenance behavior. The first study evaluated an initial item pool, identified 11
dimensions of maintenance behavior, and established concurrent/divergent validity with
theoretically related constructs. The second study further tested the instrument in a sample of 123
romantic dyads, demonstrating that inclusion of the other in the self (Aron, Mashek, & Aron,
2004) predicts frequency of maintenance behavior in a communally-oriented fashion. These
results commend self expansion theory as a promising direction for future maintenance research
and offer a maintenance measure for such future theoretical development and practical
application.
Keywords: Relational maintenance, romantic relationships, self expansion theory, actor-partner
interdependence model, dyadic data analysis
RELATIONAL MAINTENANCE AND SELF EXPANSION 3
Relational Maintenance Communication and Self Expansion Theory: Low-Inference
Measure Development and Dyadic Test of Inclusion of the Other in the Self as a Predictor
Dominant theoretical models divide the life cycle of close relationships into three phases:
(a) relational initiation (i.e., how relationships begin), (b) relational maintenance (i.e., how
relationships continue), and (c) relational termination (i.e., how relationships end) (Duck, 1988).
Although scholars initially devoted greater attention to initiation and termination (Dindia, 2003),
Dindia and Baxter (1987) noted that understanding maintenance is arguably more important,
given that ―across the history of a long-term relationship most of the time is spent in its
maintenance‖ (p. 143). Accordingly, communication scholars and those in affiliated fields now
have devoted nearly three decades to understanding such relational maintenance behavior.
Although multiple typologies and measures of maintenance behavior exist (e.g., Oswald
et al., 2004; Stafford & Canary, 1991), these have conceptualized maintenance behavior as
abstract categories encompassing several specific acts. Without diminishing the prodigious
contribution of such research, what we yet lack is a measure of specific, low-inference
communication behaviors that maintain relationships. Apart from such an instrument, the ability
to make specific claims about specific communication acts may remain limited (Murray, 1983).
Recently, Ledbetter, Stassen, Muhammad, and Kotey (2010) advanced a qualitatively-derived
typology of such low-inference maintenance behaviors; they also argued that a communal
approach to maintenance, grounded in self expansion theory (Aron, Mashek, & Aron, 2004),
may yield theoretical and practical insight complementary to the dominant equity theory
approach (Stafford & Canary, 2006). In the context of romantic relationships, the two studies
reported here deductively evaluated Ledbetter et al.‘s (2010) typology and tested the explanatory
ability of self expansion theory vis-à-vis frequency of maintenance behavior.
RELATIONAL MAINTENANCE AND SELF EXPANSION 4
Theoretical Background
Ayres (1983) published one of the first empirical studies measuring relational
maintenance behavior, noting that, at the time, ―only the briefest of excursions into interpersonal
communication literature is required to discover . . . that little is known about the process of
communication in stable relationships‖ (p. 62). Other scholars soon noted this paucity of
research, offering additional typologies (Bell, Daly, & Gonzalez, 1987; Baxter & Dindia, 1990)
and empirical studies (Shea & Pearson, 1986). These early efforts conceptualized relational
maintenance in diverse ways, including patterns of exchange (Ayres, 1983), affinity
enhancement (Bell et al., 1987), stability reinforcement (Shea & Pearson, 1986), and sustainment
of dynamic equilibrium (Baxter & Dindia, 1990).
Stafford and Canary (1991) brought a degree of theoretical coherence to the field by
conceptualizing their approach to relational maintenance under equity theory, predicting that
―people who perceive their relationships as equitable will engage in efforts to maintain those
relationships as they are‖ (Stafford & Canary, 2006, p. 229). Stafford and Canary‘s relational
maintenance strategy measure (RMSM) assessed five dimensions of maintenance behavior: (a)
positivity, (b) openness, (c) assurances, (d) shared social networks, and (e) shared tasks. The
RMSM has passed through two revisions (Stafford, Dainton, & Haas, 2000; Stafford, 2011), as
well as more minor refinements to assess maintenance germane to specific relationships (e.g.,
cross-sex friendships; Messman, Canary, & Hause, 2000). More recently, Oswald and her
colleagues (2004) advanced a similar four-dimensional measure of friendship maintenance.
Table 1 summarizes the (a) definitions, (b) dimensionalities, and (c) scale metrics
employed by previous typologies and measures of relational maintenance. Despite the diverse
methodologies and assumptions across these studies, three threads unite them. First, most studies
RELATIONAL MAINTENANCE AND SELF EXPANSION 5
have defined relational maintenance in regard to some relationship characteristic, such as
stability (Dindia & Baxter, 1987) or equity (Canary & Stafford, 1992); moreover, these
definitions often have employed social exchange theory as the theoretical link between
maintenance and outcomes (Ayres, 1983; Bell et al., 1987; Stafford & Canary, 1991). Second,
most measures and typologies have conceptualized maintenance behaviors as individually
engaged (e.g., using ―I‖ as the subject of survey items; Oswald et al., 2004; Stafford & Canary,
1991). Third, all of the foregoing studies have identified maintenance dimensions that are, as
Bell and his colleagues (1987) describe, ―abstract [categories] of largely symbolic behaviors.‖ (p.
446). The measure developed in the current series of studies departs from these trends, offering
an instrument that (a) assumes close relationships are communal in nature (Clark & Mills, 1979)
and therefore (b) focuses on mutually-enacted behaviors (Goldsmith & Baxter, 1996) at (c) a low
level of inference (Murray, 1983). The following addresses each of these qualities in turn.
First, extant relational maintenance research has devoted much attention to
conceptualizing maintenance (see Dindia, 2003) but has given almost no attention to
conceptualizing relationships. Examination of the literature reveals that most maintenance
scholarship (a) has studied close relational bonds, typically romantic ties (Stafford, 2003), and
(b) has conceptualized such bonds as exchange-oriented in nature (Stafford & Canary, 2006).
Although the latter assumption serves as a starting point for a rich body of scholarship (Dindia,
2003), an equally deep tradition has conceptualized close ties as communally-oriented (Clark &
Mills, 1979) such that that ―in a close relationship the individual acts as if some or all aspects of
the partner are partially the individual‘s own‖ (Aron et al., 1992, p. 598). Starting from this
perspective, Ledbetter et al. (2010) considered relational maintenance from the standpoint of self
expansion theory. The theory contains two central principles. First, it postulates that ―a central
RELATIONAL MAINTENANCE AND SELF EXPANSION 6
human motivation is self-expansion‖ achieved by incorporating the resources, perspectives, and
identities of others (Aron & Aron, 2001, p. 478); such self-expansion facilitates the individual‘s
goal attainment. The second principle moves from this general motivation to the specific,
arguing ―that the desire to enter and maintain a particular relationship can be seen as one,
especially satisfying, useful, and human, means to this self-expansion‖ (Aron & Aron, 2001, p.
484). Thus, as a close relationship progresses, cognitions alter such that each person includes the
other in the sense of the self, and consequentially ―being in a close relationship . . . [seems] to
subvert the seemingly fundamental distinction of self from other‖ (Aron et al., 2004, p. 31).
Building from self-expansion theory, Ledbetter and his colleagues (2010) suggested that
relational maintenance may not arise so much from perceived equity (Canary & Stafford, 1992)
as from this shared sense of collective identity. Because such a collective identity is subject to
entropy over time (Levinger, 1983), maintenance behaviors counteract such entropy (Sigman,
1991) and thus sustain close communal relationships.
Second, the ―I‖ orientation of maintenance measures (Oswald et al., 2004; Stafford &
Canary, 1991) is consistent with an exchange orientation but, arguably, not with a communal
orientation. Rather, if relational maintenance is ―located in the essence between two people‖
(Ledbetter et al., 2010, p. 27; Buber, 1923/1970), it stands to reason that maintenance behaviors
possess a ―we‖ rather than ―I‖ orientation. Such an approach has appeared in other measures of
relationally-oriented communication such as relationship rituals (Pearson, Child, & Carmon,
2010) and everyday talk (Ledbetter, Broeckelman-Post, & Krawsczyn, 2011). Following self
expansion theory (Aron et al., 2004), this measure does likewise.
Third, all extant maintenance typologies have posited categories that are fundamentally
high-inference in nature. In other words, they require a high degree of interpretation to determine
RELATIONAL MAINTENANCE AND SELF EXPANSION 7
whether a particular communication behavior indicates openness, positivity, supportiveness, and
so forth. In this respect, the state of the relational maintenance literature bears remarkable
similarity to what Murray (1983) observed in instructional literature three decades ago, noting
that measures of student‘s teaching evaluations assessed ―global factor-analytic dimensions‖ that
may not clearly translate to ―‗low inference‘ teaching behaviors, or in other words, specific
classroom behaviors of the instructor‖ (p. 138). Murray further noted the practical challenge of
providing advice without such low-inference measures. Likewise for the relational maintenance
literature, a low-inference measure could greatly enhance practical guidance to dyads seeking
relational help, and thus the time is ripe for such a measure.
This manuscript reports a series of two studies that developed and validated such an
instrument. Guided by Ledbetter et al.‘s (2010) qualitative analysis, the first study tested and
refined the factor structure of an initial item pool. The subsequent study employed a sample of
heterosexual romantic dyads to test the tenability of a communal, self expansion theory approach
to relational maintenance behavior.
Method: Study 1
Participants
To obtain a diverse set of romantic relationships, participants were recruited via three
mechanisms: (a) with the help of the technology department at a medium-sized U.S. Midwestern
university, e-mails were sent to a random sample of undergraduate and graduate students, (b)
messages were posted on Facebook by the research team, and (c) participants were solicited
through posting on area Craigslist pages. Participants completed an online survey and were
instructed to complete several measures with their romantic partner in mind (or a friend if the
participant had no romantic partner). After removing participants who reported on a friend and
RELATIONAL MAINTENANCE AND SELF EXPANSION 8
other unusable data, the final sample contained 474 participants (120 males, 354 females).
Participant age ranged from 18 to 63 years (M = 23.4, SD = 6.1) and most participants (87.8%)
reported a white/Caucasian ethnic identity.
Measures
Relational maintenance communication scale. The main purpose of the first study was
to establish the validity, reliability, and dimensionality of the relational maintenance
communication scale (RMCS). Responses were solicited on a 6-point Likert-type scale (0 =
Never, 5 = Very Frequently). The initial version of the measure contained 59 items derived from
the categories identified in Ledbetter and his colleagues‘ (2010) inductive analysis (such as
spending time together, informal talk, deep talk, and physical affection), with some items
informed by Goldsmith and Baxter‘s (1996) work on everyday talk. Results of confirmatory
factor analyses on this scale are described in the results section below.
Validity measures. Because establishment of criterion validity is an important step in
measure development (Keyton, 2006), participants completed 6 measures designed to assess 3
constructs examined in previous relational maintenance research: (a) Vangelisti and Caughlin‘s
(1997) 7-item relational closeness measure (α = .91), (b) Canary, Weger, & Stafford‘s (1991) 6-
item control mutuality measure (α = .84), and (c) Aron and his colleagues‘ (2004) single-item
pictorial measure of inclusion of the other in the self (IOS). Additionally, 2 dimensions of
attachment (i.e., anxiety and avoidance) were measured using two 6-item versions of Fraley,
Waller, and Brennan‘s (2000) attachment styles measure (αanxiety = .90, αavoid = .85). Finally,
previous research indicates relational communication is associated with well-being (Malis &
Roloff, 2006), and thus stress was measured using the 4-item version of Cohen, Kamarch, &
Mermelstein‘s (1983) stress measure (α = .74).
RELATIONAL MAINTENANCE AND SELF EXPANSION 9
Data Analysis
Exploratory factor analysis (EFA) is commonly used in scale development, including
previous relational maintenance measures (e.g., Ayres, 1983; Oswald et al., 2004; Stafford &
Canary, 1991). Despite its utility when little or no theoretical guidance exists regarding a
measure‘s factor structure, EFA is a data-driven versus theory-driven method, and thus EFA can
fail to accurately report underlying factor structure (Little, Lindenberger, & Nesselroade, 1999).
Given the wealth of maintenance research and the inductively-grounded typology (Ledbetter et
al., 2010), the measure was tested using confirmatory factor analysis (CFA), an approach that
evaluates the extent to which an a priori model fits the data‘s observed covariance matrix. CFA
analyses were conducted using the Lavaan package in the R statistical computing environment
(Rosseel, 2011). Three fit indices assessed model fit: (a) model chi-square, (b) root mean square
error of approximation (RMSEA), and (c) standardized root mean square residual (SRMR).
RMSEA and SRMR values below .08 indicate adequate model fit (Kline, 2005).
Results: Study 1
The initially hypothesized model contained 12 factors: (a) shared resources, (b) shared
information, (c) shared tasks, (d) shared media use, (e) verbal affection, (f) informal talk, (g)
deep talk, (h) relationship management, (i) time together, (j) shared humor, (k) physical
affection, and (l) shared social networks. This model exhibited acceptable fit, χ2(1109) =
3665.46, p < .01, RMSEA = .070[90% CI: .067:.072], SRMR = .078, but examination of the parameters
and modification indices suggested two modifications. First, the shared information and shared
tasks constructs were strongly correlated (r = .98), with a chi-square difference test indicating
that a single-factor solution was statistically equivalent, Δ χ2(11) = 15.21, p > .05, and second, a
small number of items exhibited tendency toward cross-loadings and correlated residuals. After
RELATIONAL MAINTENANCE AND SELF EXPANSION 10
collapsing the information and task factors (deleting four items to reduce survey length), and
deleting two cross-loading items and five items with correlated residuals, the simplified model
exhibited improved fit, χ2(647) = 1705.62, p < .01, RMSEA = .059[90% CI: .055:.062], SRMR = .059.
Table 2 presents items retained in the final model, with Table 3 reporting items‘ standardized and
unstandardized loadings and error residuals. Table 4 presents means, standard deviations, scale
alphas, and zero-order correlations for the identified factors.
Two additional analyses tested the criterion validity and theoretical coherence of the
factor structure. First, a confirmatory model evaluated whether maintenance factors are
associated with (a) relational closeness, (b) control mutuality, (c) stress, (d) attachment anxiety,
(e) attachment avoidance, and (f) IOS. Because three indicators are ideal for just identification
and mathematical representation of latent constructs (Kline, 2005), all constructs in this model
were identified by parcels (Little, Cunningham, Shahar, & Widaman, 2002); the only exception
was the single-item IOS indicator. Aron and his colleagues (1992) examined the IOS measure‘s
reliability, finding 72.25% shared variance in two-week test-retest reliability and 95% shared
variance across two different versions of the instrument (one with circles, one with diamonds).
The average of these two estimates (83.6%) was used to fix error variance using the method
described by Stephenson and Holbert (2003). This model demonstrated acceptable fit, χ2(992) =
2209.89, p < .01, RMSEA = .051[90% CI: .048:.054], SRMR = .052. Table 5 presents correlations
between the maintenance variables and these theoretically-related constructs. Overall, the
maintenance variables were positively associated with relational closeness, control mutuality,
and IOS, and inversely associated with attachment anxiety, attachment avoidance, and, to a
lesser degree, stress. Most correlations were of moderate magnitude, supporting the discriminant
and convergent validity of the factors.
RELATIONAL MAINTENANCE AND SELF EXPANSION 11
Study 1 Discussion
The primary goal of Study 1 was to develop and validate the RMCS instrument. CFA
yielded 39 items assessing 11 dimensions of maintenance communication. Although this
represents more dimensions than some previous maintenance typologies (e.g., the 5 in Stafford &
Canary, 1991 or 4 in Oswald et al., 2004, but fewer than the 28 in Bell et al., 1987), it is no more
than other communication typologies (e.g., the 16 compliance-gaining tactics of Marwell &
Schmitt, 1967), and is perhaps to be expected when assessing lower-inference behaviors.
The dimensions were associated with related constructs in expected ways, such that
maintenance behavior was positively associated with closeness, control mutuality, and IOS and
inversely associated with attachment anxiety, attachment avoidance, and, to a lesser degree,
stress. Specifically, shared resources, media, deep talk, and relationship management were
unassociated with stress. Previous research has reported that shared resources (such as money)
are a frequent locus of couple conflict (Erbert, 2000) and perhaps both deep talk and relationship
management indicate stressful conflict engagement, thus accounting for the nonsignificant
associations between stress and these maintenance behaviors.
Study 2 extends these results by testing whether self expansion theory (Aron & Aron,
1986) accounts for frequency of maintenance behavior. Specifically, the study tests whether, as
Ledbetter et al. (2010) postulated, relational maintenance arises from the extent to which each
relational partner includes the other in the sense of the self. Aron and Aron (2001) claim this
process is fundamentally dyadic, such that ―cognitively, the self is expanded through including
the other in the self, a process which in a close relationship becomes mutual, so that each person
is including the other in his or her self‖ (p. 484).
One flexible approach to dyadic questions is the actor-partner interdependence model
RELATIONAL MAINTENANCE AND SELF EXPANSION 12
(APIM; Kenny, Kashy, & Cook, 2006) conducted with an SEM framework. Figure 2 depicts the
type of APIM tested in this investigation. Not only do APIMs control for the degree of dyadic
interdependence within the data, but particularly relevant to self expansion theory, they also
permit direct test of the extent to which relational maintenance is communal. The APIM
facilitates such tests by deriving estimates for three different types of paths. First, an actor effect
is the extent to which one dyad member‘s independent variable score predicts that same dyad
member‘s dependent variable score (e.g., in this study, a male actor effect is male IOS predicting
male frequency of maintenance behavior). Second, the partner effect is the extent to which one
dyad member‘s independent variable score predicts the other dyad member‘s dependent variable
score (e.g., in this study, a male partner effect is female IOS predicting male frequency of
maintenance behavior). According to Kenny et al. (2006), a model is communally oriented when
the actor effect equals the partner effect: ―the person is affected as much by his or her own X as
by his or her partner‘s X. . . . such an orientation would be characteristic of communal
relationships‖ (p. 148). However, this fulfills Aron and his colleagues‘ (1991) definition of a
communal relationship only partially. Rather, they argue, ―to the extent a partner is perceived as
part of one's self, allocation of resources is communal (because benefiting other is benefiting
self), actor/observer perspective differences are lessened, and other's characteristics become
one's own‖ (p. 242, emphasis added). Phrased statistically, the theory predicts not only equality
of actor and partner effects within an individual, but also equality of actor and partner effects
across both individuals. Thus, we hypothesize that not only do men and women enact
relationships communally, but the strength of this effect is equal in magnitude across men and
women, such that IOS predicts maintenance behavior in a communal and symmetric fashion:
H1: Actor and partner IOS will predict relational maintenance behavior according to
RELATIONAL MAINTENANCE AND SELF EXPANSION 13
Aron et al.‘s (1991) definition of a communal relationship, such that actor and partner
effects are (a) equal within each dyad member, (b) equal across dyad members, and (c)
significantly positive.
Beyond actor and partner effects, Kenny et al. (2006) also call attention to the interaction
between the actor and partner independent variables. This interaction is the relationship effect,
and it tests whether the actor effect changes as a function of the partner effect (or vice versa).
Aron and Aron (2001) frame IOS as fundamentally relational, such that it is ―a process which in
a close relationship becomes mutual, so that each person is including the other in his or her self‖
(p. 484). Thus, it could stand to reason that high levels of partner IOS might magnify the
contribution of actor IOS to maintenance frequency:
H2: Actor and partner IOS will interact to predict relational maintenance such that
high partner IOS increases the predictive strength of actor IOS.
Method: Study 2
Participants
Dyads were recruited via three mechanisms: (a) messages on Craigslist and e-mail
listservs, (b) announcements on Facebook, and (c) offering extra credit (less than 3% of course
grade) to communication undergraduate students who referred romantic couples to the study. Per
IRB requirements, credit was awarded upon reference of a couple regardless of whether the
couple eventually completed the survey. The final sample contained complete heterosexual
dyads with 123 men (ages 18-75, M = 34.19, SD = 13.69) and 123 women (ages 18-74, M =
33.06, SD = 13.23), with most of the 246 participants reporting a white/Caucasian ethnic identity
(93.1%). Most dyads reported that they were married (72 dyads, 58.5%), with others dating
seriously (41 dyads, 33.3%), engaged to be married (6 dyads, 4.9%), and dating casually (4
RELATIONAL MAINTENANCE AND SELF EXPANSION 14
dyads, 3.3%). Couples reported an average relationship length of 9.87 years (SD = 11.6).
Measures
Relational maintenance communication scale. The final 39-item version of the RMCS,
developed in Study 1, assessed relational maintenance communication. Responses were solicited
on a 6-point scale (0 = Never, 5 = Very Frequently). With the exception of shared networks (α =
.55), informal talk (α = .62), and shared media (α = .65), all 11 dimensions demonstrated internal
reliability greater than .70 (mean α = .74).
IOS. Aron and his colleagues‘ (2004) single-item pictorial measure assessed IOS. This
measure presented participants with a series of seven overlapping circles (1 = minimal overlap, 7
= almost total overlap). Previous research has established the measure‘s discriminant, construct,
and predictive validity, including test-retest reliability (Aron et al., 1992).
Data Analysis
The hypotheses were investigated via APIMs conducted via SEM using the Lavaan
package for the R statistical computing environment (Rosseel, 2011). Missing data were imputed
using the Amelia missing data package for R. All maintenance constructs were modeled using
three indicators, parceling items where appropriate (Little et al., 2002). The single-item IOS
construct was identified as in Study 1, with the interaction term between male IOS and female
IOS (i.e., representing the relationship effect) created using the orthogonalization procedure
described by Little, Card, Bovaird, Preacher, and Crandall (2007) and error variance fixed using
the square of the reliability for the first-order terms (.836 * .836 = .699; see Cohen, Cohen, West,
and Aiken, 2003). To control for relationship type effects, all manifest indicators were regressed
onto relationship type (dummy code for married vs. unmarried), relationship length, and their
interaction. Because the small number of parameters in APIM models renders many traditional
RELATIONAL MAINTENANCE AND SELF EXPANSION 15
model fit measures inappropriate (Kenny, 2010), the results report only the chi-square fit statistic
for the confirmatory models.
Results: Study 2
Prior to conducting APIM analyses, a series of 12 confirmatory models (i.e., one for each
maintenance type and for IOS) compared means, standard deviations, and degree of
interdependence (i.e., correlations) across men and women. A series of model constraints
sequentially evaluated whether the constructs differed by sex on (a) equality of indicator
loadings, (b) equality of indicator means, (c) equality of latent construct variances, and (d)
equality of latent construct means (Little, 1997). Table 6 presents the results of these analyses.
All baseline models obtained excellent model fit as demonstrated by non-significant chi-square
statistics; furthermore, all maintenance behaviors demonstrated full equality of indicator loadings
(except for verbal affection, which demonstrated partial equality when allowing one indicator
loading to vary between the sexes) and equality of indicator intercepts (except for shared humor
and time together, which demonstrated partial equality when allowing one indicator mean to vary
between the sexes). These tests demonstrate that the constructs are statistically comparable
across men and women. Tests for equality of means and standard deviations demonstrated a
small number of differences between men and women, with differences in the direction of higher
means for women (physical affection, humor, informal talk and deep talk) and larger variances
for men (tasks, informal talk, and deep talk). Overall, the maintenance types differed greatly in
degree of interdependence, with resources demonstrating the most interdependence (r = .86) and
time together the least (r = .22).
Building from Kenny et al.‘s (2006) and Aron et al.‘s (1991) definitions of a communal
relationship, a series of APIM analyses tested H1 by simultaneously (a) constraining actor and
RELATIONAL MAINTENANCE AND SELF EXPANSION 16
partner effects to equality within each dyad member, (b) constraining actor/partner effects to
equality across dyad members, and (c) constraining relationship effects to equality across dyad
members. Because differences in latent construct variances can bias direct tests of structural
parameter equality (Kenny et al., 2006), APIMs with significant variance differences (see Table
6) placed constructs on an equivalent variance metric using ‗phantom‘ constructs (Rindskopf,
1984). More specifically, this procedure (a) fixes each maintenance construct‘s variance to zero,
(b) creates a second-order (i.e., phantom) construct for each maintenance behavior with phantom
construct variance fixed to 1.0, (c) regresses each first-order maintenance construct onto the
relevant phantom construct, and (d) fixes this regression path to 1.0 for men and freely estimates
the path for women. This procedure shifts the variance difference to this regression path,
permitting direct equality tests on the actor, partner, and relationship effects of interest.
For nine of the eleven maintenance behaviors (i.e., all except physical affection and
networks), the constraints for H1 were tenable; in other words, constraining all four direct effects
in the model (i.e., male actor, male partner, female actor, female partner) to equality, as well as
the relationship effects to equality, produced a nonsignificant decline in model fit. Table 7
reports the regression parameters, variance explained, and chi-square difference tests for these
nine models. For six of these nine maintenance behaviors (i.e., verbal affection, humor, deep
talk, informal talk, relationship management, and tasks) the relationship effect also achieved or
approached (p < .08) statistical significance.
For physical affection, the communal constraint significantly worsened model fit, Δχ2(2)
= 13.75, p < .01. According to Kenny et al. (2006), it is possible for one dyad member to possess
a communal orientation while the other does not. Follow-up tests indicated that constraining
male actor and partner effects to equality was tenable, Δχ2(1) = 1.66, p > .05, but such a
RELATIONAL MAINTENANCE AND SELF EXPANSION 17
constraint for female actor and partner effects was not, Δχ2(1) = 8.12, p < .01. Examination of
this model revealed a strong actor effect for women with nonsignificant partner and relationship
effects; according to Kenny et al., such a pattern indicates ―an actor-oriented model‖ in which ―a
person‘s outcomes are a function of that person‘s characteristics only‖ (p. 148). The tenability of
this model was tested by constraining female partner and interaction effects to zero, and this
constraint (along with the communal constraint on the male actor and partner effects) did not
differ significantly from the unconstrained model, Δχ2(3) = 2.10, p > .05. This constrained model
revealed a significant actor/partner effect for men (B = 0.34, SE = 0.07, β = .29, p < .01) and a
relationship effect approaching statistical significance (B = -0.21, SE = 0.12, β = -.21, p < .07),
explaining 26.4% of men‘s physical affection. Among women, the actor effect was significant (B
= 0.62, SE = 0.12, β = .53, p < .01) and the partner and relationship effects were constrained to
zero, explaining 28.0% of the variance in women‘s physical affection.
Like physical affection, the omnibus communal constraint was not tenable for network
maintenance, Δχ2(2) = 13.80, p < .01; also likewise, follow-up tests revealed the communal
constraint was tenable for men, Δχ2(1) = 2.46, p < .05, but not women, Δχ
2(1) = 6.62, p < .05.
Examination of the results for women revealed significant actor and partner effects of nearly
equivalent magnitude but opposite sign. According to Kenny et al. (2006), actor and partner
effects of this pattern indicate a ―social comparison model‖ whereby ―the person implicitly or
explicitly compares him- or herself with the partner‖ (p. 149). This orientation stands in
contradistinction to the communal pattern: ―In contrast to the couple-oriented case, in which the
partner‘s success is valued as much as one‘s own outcome, the social comparison orientation
typically involves dissatisfaction with the partner‘s success. Both imply couple effects, but their
conceptual meanings are totally opposite‖ (Kenny et al., p. 149). This possibility was tested by
RELATIONAL MAINTENANCE AND SELF EXPANSION 18
constraining the female partner effect to equal the female actor effect multiplied by -1. Because
Lavaan currently offers only limited support for constraints of this type, this test was performed
using LISREL 8.80. Simultaneously placing this constraint and the communal constraint for men
produced a nonsignificant decline in model fit as compared to the unconstrained model, Δχ2(1) =
2.54, p > .05. In this model, men obtained significant male actor/partner effects (B = 0.23, SE =
0.07, β = .21, p < .01) but not a significant relationship effect (B = -0.12, SE = 0.14, β = -.11, p >
.05), explaining 13.7% of the variance in men‘s report of network maintenance. A significant
relationship effect also did not emerge for women (B = 0.03, SE = 0.14, β = .03, p > .05), with
the actor effect producing a significantly positive (B = 0.40, SE = 0.12, β = .37, p < .01) and the
partner effect an equivalently negative (B = -0.40, SE = 0.12, β = -.37, p < .01) association.
Together, these effects explained 16.7% of the variance in women‘s report of network
maintenance. Thus, it appears network maintenance is distinct not only because an omnibus
communal constraint is not tenable, but also because men adopt a communal approach to
networks in contrast to women‘s competitive approach. Overall, then, analyses supported H1‘s
expectation of a communal orientation toward maintenance for all male maintenance
communication, as well as all female maintenance except physical affection and networks.
H2 predicted relationship effects, such that high partner IOS magnifies the positive actor
effect. Relationship effects were obtained for several behaviors (verbal affection, humor, tasks,
informal talk, deep talk, relationship management, and, for men, physical affection).
Decomposition using the method described by Cohen et al. (2003) revealed a nearly identical
pattern for each maintenance behavior, albeit not as hypothesized. Rather than the expected
magnification effect, results indicated that only one member of the dyad needed to possess high
IOS for high levels of the maintenance behavior to occur. Speaking in terms of simple slopes, the
RELATIONAL MAINTENANCE AND SELF EXPANSION 19
actor effect was significantly positive except in the presence of high partner IOS, in which case
the actor effect was nonsignificant. We may refer to this as a compensatory pattern, such that
lack of IOS in one pattern is compensated by the presence of IOS in the other vis-à-vis
maintenance behavior. In contrast, other communal behaviors (resources, media, time, and, for
men, networks) did not obtain relationship effects. Thus, frequency of the maintenance behavior
is an additive combination of the two member‘s IOS, such that members enact low levels of
maintenance when both possess low IOS, moderate levels when only one member has high IOS,
and high levels when both have high IOS. Taken together with (a) women‘s actor-oriented
approach to physical affection and (b) women‘s social comparison approach to networks
(discussed previously), the communal pattern (c) with relationship effect and (d) without
relationship effect yields four distinct dyadic patterns across the behaviors (see Figure 2).
General Discussion
After establishing the dimensionality and validity of the RMCS in Study 1, the central
goal of Study 2 was to test Ledbetter et al.‘s (2010) claim, building from self expansion theory
(Aron et al., 2004), that IOS predicts maintenance communication in heterosexual romantic
dyads. With only a few exceptions, results supported this expectation. This discussion will first
consider the majority of maintenance behaviors for which a communal approach was tenable,
including relationship effects, before considering exceptions to the communal pattern.
Relational Maintenance as Communal
Central to self expansion theory rests the claim that, in close relationships,
―actor/observer perspective differences are lessened, and other's characteristics become one's
own‖ (Aron et al., 1991, p. 242). Thus, building from Ledbetter et al. (2010) and Kenny et al.
(2006), this study reasoned that actor and partner IOS should predict maintenance with equal
RELATIONAL MAINTENANCE AND SELF EXPANSION 20
magnitude, both within and across dyad members. Results supported this expectation (H1) for
nine of the eleven maintenance behaviors (i.e., all except physical affection and social networks).
These deviations will be discussed later, but the overall trend of the results clearly and strongly
supports self expansion theory as an explanatory mechanism for relational maintenance behavior.
This communal approach differs from the dominant equity theory approach to relational
maintenance (Stafford & Canary, 1991). Some have argued the exchange- and communally-
oriented approaches to relationships are fundamentally incompatible; if maintenance arises from
an individual‘s cost-benefit analysis and demands equitable reciprocation (Canary & Stafford,
1992), it would seem maintenance does not arise from a sense of interconnectedness (Ledbetter
et al., 2010) with reciprocation unnecessary (Clark & Mills, 1993). Yet, at least some previous
empirical evidence suggests that equity fosters relational maintenance behavior (Stafford &
Canary, 2006). Reconciling these findings is a heuristic theoretical task, and thus three tentative
explanations are advanced here. First, it remains possible that methodological differences
between the RMCS and RMSM account, at least partially, for evidence supporting both
approaches. Whereas the RMCS was designed as a low-inference measure of behavior
frequency, the RMSM is a high-inference measure of agreement that behaviors take place.
Perhaps equity motivates members‘ overarching agreement about the existence of maintenance
behavior, but interconnectedness motivates day-to-day behavior enactment. Second, perhaps
equity mediates the association between IOS and maintenance behavior. To the extent that high
IOS removes cognitive distinction between self and other (Aron & Aron, 2001), members of
such relationships may tend to report that their relationships are equitable and this, in turn, may
predict maintenance behavior. Finally, and perhaps most heuristically, some third factor may
moderate whether equity or interconnectedness fosters maintenance behavior. For example, some
RELATIONAL MAINTENANCE AND SELF EXPANSION 21
work has suggested that orientation toward communality may be trait-like (Clark, Ouellette,
Powell, & Milberg, 1987), and this may serve as a moderator; attachment styles may function
similarly (Fraley et al., 2000). In any case, understanding when exchange (and communal)
orientations do (and do not) operate is a clear direction for future theoretical refinement (Canary
& Stafford, 2007).
Relationship Effect
Despite Aron and Aron‘s (2001) claim that close relational partners possess mutually
high IOS, the moderate correlation between male and female IOS in these data suggests it is
quite possible for romantic partners to possess divergent IOS. Such a moderate correlation has
been found in prior research as well (Simpson, Oriña, & Ickes, 2003). The relationship effect
(i.e., interaction between actor and partner IOS) assessed the extent to which the degree of
similarity between members‘ IOS predicted maintenance behavior, and such an effect emerged
for several maintenance behaviors (verbal affection, humor, tasks, informal talk, deep talk, and
relationship management).
The relationship effects did not occur as hypothesized (Aron & Aron, 2001); versus a
magnification pattern, a compensatory pattern emerged such that only one member needed to
possess high IOS for the maintenance behavior to occur. With the exception of tasks, all of these
behaviors are verbal in nature; indeed, every fundamentally verbal behavior in the typology
obtained a significant relationship effect. In contrast, the behaviors that exhibited communality
but not a relationship effect—resources, media, and time—are not only nonverbal but scarce.
Giving informal talk to one person does not mean one has ‗less‘ to give to another; but giving a
resource (such as money) to another person necessarily means the giver cannot allocate it
elsewhere. This principle of scarcity may explain why tasks also demonstrated this pattern, for
RELATIONAL MAINTENANCE AND SELF EXPANSION 22
the supply of tasks to accomplish approaches inexhaustibility (Demo & Acock, 1993).
Maintenance Behaviors Departing From the Communal Trend
Results indicated that relationship-level communality was not tenable for physical
affection and shared networks, although further tests indicated communality was tenable for men
but not women. Rather, women‘s physical affection unexpectedly emerged as actor-oriented,
such that only her level of IOS served as a predictor, and women‘s network maintenance was
competitively oriented, such that her level of IOS positively predicted networks but her partner‘s
IOS was an inverse predictor. The following considers each of these behaviors in turn.
Physical affection. Physical affection is foundational to human bonding in romantic
relationships (Floyd & Morr, 2003), yet these data suggest that IOS predicts it differentially
across men and women. Specifically, only an actor effect predicted women‘s physical affection,
whereas actor, partner, and relationship effects all predicted men‘s physical affection. Although
only future research can identify and test theoretical perspectives that might explain these
differences, Andersen, Guerrero, and Jones‘ (2006) interaction-centered model of intimacy
processes suggests considering the mismatch between the experience and expression of intimacy
as a possible explanatory mechanism.
Arguing that ―nonverbal communication is the sine qua non of intimacy‖ (Andersen et
al., 2006, p. 260), the interaction-centered intimacy model has posited that intimacy consists of
overlapping relational schemas, an idea clearly akin to IOS (Aron et al., 2004). These schemas,
in turn, trigger the experience of intimacy (e.g., warm emotions) which then leads to expression
of intimacy via nonverbal communication. Thus, we might expect that men and women would
agree on their frequency of physical affection (i.e., intimacy expression) when their IOS levels
are equivalent, and decomposition revealed this was the case in these data. When IOS levels are
RELATIONAL MAINTENANCE AND SELF EXPANSION 23
not equivalent, the experience and expression of intimacy may be mismatched in the
relationship. For example, partners agreed when female IOS was high but male IOS was low,
with both reporting a high amount of physical affection; but given that physical affection is
primarily driven by the woman‘s level of IOS, she may expect that such drives the man‘s level of
physical affection as well, and thus a mismatch exists between their mutual expression of
intimacy and their divergent experiences of it. Contrariwise, when male IOS is high and female
IOS is low, results indicated that a man will seek a high level of physical affection that may not
be reciprocated by his partner; in this case, the man‘s desired intimacy expression may not match
his partner‘s experience of it. Clearly, either case could lead to relational conflict (Erbert, 2006).
Networks. As Knobloch and Donovan-Kicken (2006) note, most studies examine the
extent to which network members support romantic relationships (e.g., Sprecher & Felmlee,
1992) but few have examined the extent to which network members hinder them. One exception
is Bryan, Fitzpatrick, Crawford, and Fischer‘s (2001) report that support from a woman‘s
network contributes to romantic relationship satisfaction and network hindrance does not detract
from it. A clear lacuna in this study is the absence of assessing the extent to which the male
partner‘s network aids or hinders the romantic relationship.
Knobloch and Donovan-Kicken (2006) considered perceived helpfulness and perceived
hindrance of network members more broadly, examining a sex heterogeneous sample of dating
partners. Results indicated that network hindrance was most likely to occur at moderate levels of
intimacy and, running counter to their hypothesis, when level of partner uncertainty (i.e., ―the
ambiguity individuals experience about their partner‘s involvement in the relationship‖;
Knobloch & Donovan-Kicken, p. 283) was high. Although the association between relational
uncertainty and IOS awaits empirical investigation, it could stand to reason that low partner
RELATIONAL MAINTENANCE AND SELF EXPANSION 24
uncertainty indicates high partner IOS; and indeed, this study found that high partner IOS
reduced women‘s network maintenance. Taking these results together with the few previous
studies, it could be that women tend to view their own networks as helpful and their partner‘s
networks as a hindrance. In contrast, a communal orientation was tenable for men, with no
significant relationship effect; this may indicate that men view time with social networks as a
scarce resource most devoted to the relationship when both partners‘ IOS is high. This suggests
that a man with high IOS may see inclusion of his partner in his social networks as indicative of
interconnectedness whereas his partner sees such as a hindrance. Only future research can test
this tentative explanation.
Conclusion
Of course, all investigations possess both strengths and weaknesses, and the studies
reported here are no exception. Although both studies move beyond a college student sample,
and the second study employs dyadic data analysis for investigating decidedly dyadic questions,
both studies are only cross-sectional in nature. Longitudinal designs are complex, and dyadic
longitudinal designs especially so (Kenny et al., 2006), but such future research could prove
invaluable for theoretical refinement and practical application. Additionally, communal
explanations rely, to some extent, on the actor‘s perception of the partner‘s IOS; in other words,
an actor may think the partner has high IOS, but this may not be the case. Future research could
elaborate the model‘s mechanism by including perception of partner IOS, perhaps as a mediator.
Finally, although the samples were heterogeneous by age and relationship type, most participants
were Caucasian; only future research can assess generalizability to other populations.
The two studies reported here offer a low-inference measure as a complementary
alternative to other popular relational maintenance instruments (Oswald et al., 2004; Stafford &
RELATIONAL MAINTENANCE AND SELF EXPANSION 25
Canary, 1991). Additionally, these results commend further evidence supporting a communal
approach to relational maintenance, such that maintenance arises from partner‘s perceived
mutual interdependence. To the extent that romantic relationship quality predicts psychosocial
well-being (Malis & Roloff, 2006), future research may employ these findings to further refine
maintenance theory and identify specific maintenance behaviors that foster individual and
relational health.
RELATIONAL MAINTENANCE AND SELF EXPANSION 26
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RELATIONAL MAINTENANCE AND SELF EXPANSION 33
Table 1
Definitions, Dimensions, and Metrics of Previously Published Maintenance Typologies
Article Definition of maintenance Maintenance dimensions Scale metric
Ayres (1983) An ―exchange pattern‖ whereby
―basic patterns of exchange in the
relationship are established and
accepted‖ (p. 62).
Avoidance, balance,
directness
Likelihood
Bell, Daly, &
Gonzalez
(1987)
― . . . lines of behavior, which we
shall refer to as affinity-maintenance
strategies, to maintain and even
enhance the affinity in . . . marriage‖
(p. 446).
28 strategies, for example,
altruism, dynamism,
equality, faithfulness,
honesty, and openness
Importance;
frequency
Dindia &
Baxter (1987)
―Strategies that are employed to
stabilize the continuation of a
relationship‖ (p. 145).
6 major categories:
Communication strategies,
metacommunication,
prosocial strategies,
ceremonies, togetherness,
seeking outside help
n/a (typology
derived via
inductive
coding)
Baxter &
Dindia (1990)
―efforts to sustain a dynamic
equilibrium in their relationship
definition and satisfaction levels
as they cope with the ebb and flow of
everyday relating‖ (p. 188)
6 categories: Last resort,
satiation, inward
withdrawal, problem
avoidance, destructive,
constructive
n/a (typology
derived via
cluster analysis)
Stafford &
Canary (1991)
― . . . efforts expended to maintain the
nature of the relationship to the
actor‘s satisfaction‖ (p. 220)
5 categories: Positivity,
openness, assurances, social
networks, shared tasks
Agreement
Stafford,
Dainton, &
Haas (2000)
―behaviors . . . which individuals
enact with the conscious intent of
preserving or improving the
relationship‖ (p. 307)
7 categories: Positivity,
openness, assurances, social
networks, shared tasks,
conflict management,
advice
Agreement
Oswald, Clark,
& Kelly
(2004)
―behaviors . . . engaged in with the
goal of maintaining the [relationship]
at a satisfying and committed level‖
(p. 414)
4 categories: Positivity,
supportiveness, openness,
interaction
Frequency
Ledbetter,
Stassen,
Muhammad,
& Kotey
(2010)
― . . . communicative acts that foster
perception of shared resources,
identities, and perspectives‖ (p. 22)
3 meta-categories: Shared
resources, shared identities,
shared perspectives
n/a (typology
derived via
qualitative
thematic
analysis)
Stafford
(2011)
Same as Stafford & Canary (1991) 7 categories: Positivity,
understanding, self-
disclosure, relationship
talks, assurances, shared
tasks, social networks
Agreement
RELATIONAL MAINTENANCE AND SELF EXPANSION 34
Table 2
Dimensions and Items of the Relational Maintenance Communication Scale
Items
Factor 1: Shared Resources
1. We share financial resources (such as money, bank accounts, or investments) with each other.
2. We share low-cost items (such as office supplies or food) with each other.
3. We share high-cost items (such as cars or electronics) with each other.
Factor 2: Shared Tasks
4. We rely on each other to remember important information.
5. We have conversations where we are making a decision about some task.
6. We help each other with chores and tasks that we have to accomplish.
Factor 3: Shared Media
7. We watch movies together.
8. We watch TV shows together.
9. We play video games together.
10. We browse the Internet together.
Factor 4: Verbal Affection
11. We say ―I love you‖ to each other.
12. We say ―I miss you‖ to each other.
13. We talk in ways that express love and give attention and affection.
14. We use special nicknames for each other.
15. We use words and phrases that have meanings only we understand.
Factor 5: Informal Talk
16. We engage in playful talk to have fun or release tension.
17. We exchange opinions or information about someone else when that person isn‘t present.
18. We talk about what‘s up and about what happened during the day.
Factor 6: Deep Talk
19. We have serious conversations where we are both involved in an in-depth conversation about a
personal or important topic.
20. We have conversations in which one of us shares about a problem and the other person tries to
help.
21. We complain to each other, expressing negative feelings or frustrations directed toward a topic but
not toward each other.
RELATIONAL MAINTENANCE AND SELF EXPANSION 35
22. We disclose deeply personal, private information about ourselves to each other.
Factor 7: Relationship Management
23. We handle disagreements with each other.
24. When needed, we ―make up,‖ where one or both of us apologize for violating some expectations.
25. We talk about the state of our relationship.
Factor 8: Time Together
26. We eat meals together.
27. We participate in shared hobbies and interests together.
28. We go out on dates.
29. We spend time together just ―hanging out.‖
Factor 9: Humor
30. We try to make each other laugh.
31. We tell jokes and humorous stories to each other.
32. We laugh at the same things.
Factor 10: Physical Affection
33. We hug each other.
34. We kiss each other.
35. We hold hands.
36. We cuddle.
Factor 11: Shared Networks
37. We spend time together with friends.
38. We spend time together with family members.
39. We tell other people about the nature of our relationship.
RELATIONAL MAINTENANCE AND SELF EXPANSION 36
Table 3
Loading and Intercept Values, Residuals, and R2 Values for each Indicator in the Confirmatory
Factor Analysis Model for Relational Maintenance Communication Scale
LISREL Estimates
Standardized
Indicatora
Loading (SE) Intercept (SE) Loadingb Theta
b R
2
Shared Resources:
1. 1.44 (0.07) 2.42 (0.08) 0.81 0.34 0.66
2. 0.89 (0.05) 3.91 (0.06) 0.70 0.52 0.48
3. 1.53 (0.07) 2.80 (0.08) 0.89 0.21 0.79
Shared Tasks:
4. 0.76 (0.04) 3.95 (0.05) 0.74 0.48 0.55
5. 0.63 (0.04) 3.93 (0.04) 0.68 0.46 0.47
6. 0.77 (0.04) 3.86 (0.05) 0.76 0.45 0.57
Shared Media:
7. 0.61 (0.04) 4.31 (0.04) 0.69 0.39 0.48
8. 0.81 (0.05) 4.13 (0.05) 0.73 0.57 0.53
9. 0.84 (0.08) 2.63 (0.07) 0.52 1.88 0.27
10. 0.72 (0.06) 3.36 (0.06) 0.58 1.02 0.34
Verbal Affection:
11. 1.04 (0.06) 4.19 (0.07) 0.73 0.47 0.53
12. 0.76 (0.05) 4.01 (0.06) 0.63 0.61 0.39
13. 0.76 (0.04) 4.23 (0.04) 0.80 0.37 0.63
14. 0.85 (0.06) 3.44 (0.07) 0.61 0.63 0.37
15. 0.70 (0.06) 3.37 (0.06) 0.53 0.72 0.28
Informal Talk:
16. 0.58 (0.03) 4.43 (0.03) 0.80 0.36 0.64
17. 0.41 (0.05) 3.64 (0.05) 0.41 0.83 0.17
18. 0.45 (0.03) 4.66 (0.03) 0.73 0.47 0.53
Deep Talk:
19. 0.64 (0.04) 4.13 (0.04) 0.73 0.46 0.54
20. 0.68 (0.03) 4.11 (0.04) 0.85 0.28 0.72
21. 0.60 (0.04) 3.94 (0.04) 0.66 0.56 0.44
22. 0.69 (0.04) 4.10 (0.05) 0.70 0.51 0.49
Relationship Management:
23. 0.71 (0.04) 3.85 (0.04) 0.77 0.41 0.59
24. 0.80 (0.04) 3.95 (0.05) 0.81 0.35 0.65
25. 0.85 (0.05) 3.51 (0.05) 0.72 0.48 0.52
Time Together:
26. 0.64 (0.04) 4.13 (0.04) 0.68 0.54 0.46
27. 0.68 (0.03) 3.82 (0.04) 0.75 0.44 0.56
28. 0.60 (0.04) 3.55 (0.05) 0.60 0.64 0.36
29. 0.69 (0.04) 4.49 (0.03) 0.70 0.52 0.49
RELATIONAL MAINTENANCE AND SELF EXPANSION 37
Humor:
30. 0.57 (0.02) 4.60 (0.03) 0.89 0.22 0.78
31. 0.64 (0.03) 4.46 (0.03) 0.88 0.23 0.77
32. 0.52 (0.03) 4.42 (0.03) 0.74 0.45 0.55
Physical Affection:
33. 0.73 (0.03) 4.55 (0.04) 0.91 0.17 0.83
34. 0.74 (0.03) 4.57 (0.04) 0.92 0.15 0.85
35. 0.79 (0.04) 4.25 (0.05) 0.75 0.43 0.57
36. 0.74 (0.04) 4.38 (0.04) 0.80 0.37 0.63
Shared Networks:
37. 0.55 (0.05) 3.76 (0.05) 0.54 0.71 0.29
38. 0.88 (0.05) 3.45 (0.05) 0.76 0.42 0.58
39. 0.72 (0.05) 3.46 (0.05) 0.72 0.61 0.39 aIndicator numbers refer to Table 1 (consult for item text).
bEstimates are from completely standardized solution.
RELATIONAL MAINTENANCE AND SELF EXPANSION 38
Table 4
Descriptive Statistics and Bivariate Correlations Among Relational Maintenance Variables (N = 494)
Variables M
SD
α 1 2 3 4 5 6 7 8 9 10
1. Resources 3.04 1.39 .83 1.00
2. Task 3.91 0.82 .77 .68** 1.00
3. Media 3.61 0.90 .71 .14* .42** 1.00
4. Verbal Aff. 3.85 0.94 .79 .46** .72** .49** 1.00
5. Informal T. 4.24 0.60 .63 .14* .41** .44** .62** 1.00
6. Deep T. 4.07 0.72 .80 .24** .72** .44** .71** .69** 1.00
7. Rel. Man. 3.77 0.87 .81 .29** .62** .44** .82** .54** .76** 1.00
8. Time Tog. 4.00 0.68 .77 .37** .61** .62** .61** .57** .61** .55** 1.00
9. Humor 4.50 0.61 .87 .01 .29** .51** .49** .73** .56** .41** .63** 1.00
10. Physical 4.44 0.79 .90 .18** .44** .48** .64** .58** .54** .55** .65** .52** 1.00
11. Networks 3.55 0.86 .68 .24** .54** .55** .74** .45** .56** .70** .62** .44** .49**
* p < .05 ** p < .01
Note. Means, standard deviations, and Cronbach‘s alpha obtained from manifest-level composite scores. Correlation coefficients
obtained from the confirmatory model.
RELATIONAL MAINTENANCE AND SELF EXPANSION 39
Table 5
Latent Bivariate Correlations Among Relational Maintenance Variables and Validation
Constructs (N = 494)
Variables Closeness
Control
Mutuality
Stress
Attach-
Anxiety
Attach-
Avoidance IOS
1. Resources .38** .23** -.08 -.30** -.34** .31**
2. Task .62** .48** -.13* -.27** -.43** .37**
3. Media .37** .40** -.07 -.03 -.23** .27**
4. Verbal Aff. .78** .61** -.19** -.30** -.55** .56**
5. Informal T. .55** .53** -.21** -.16** -.38** .25**
6. Deep T. .60** .55** -.08 -.18** -.43** .33**
7. Rel. Man. .62** .55** -.09 -.26** -.48** .41**
8. Time Tog. .57** .52** -.14* -.16** -.36** .40**
9. Humor .44** .51** -.22** -.13** -.27** .25**
10. Physical .54** .51** -.18** -.21** -.43** .32**
11. Networks .54** .50** -.21** -.29** -.41** .44**
* p < .05 ** p < .01
RELATIONAL MAINTENANCE AND SELF EXPANSION 40
Table 6
Latent Means, Standard Deviations, and Interdependence Correlations for Maintenance
Communication and IOS
Construct Mmale (SD) Mfemale (SD) χ2(5) Mean Δχ
2(1) SD Δχ
2(1)
r
b
1. Resources 3.95 (1.32) 3.96 (1.25) 8.68 0.03 1.05 .86**
2. Verbal Aff. 3.94 (0.76) 4.01 (0.71) 10.87 0.34 0.08 .7 7**
3. Physical 4.12 (0.73) 4.26 (0.73) 9.83 3.97* < 0.01 .53**
4. Media 2.98 (0.62) 3.00 (0.65) 5.41 0.11 0.16 .49**
5. Humor
4.23 (0.66) 4.43 (0.60) 2.62 4.74* 1.09 .49**
6. Tasks 4.25 (0.67) 4.37 (0.50) 7.93 2.34 7.21** .43**
7. Informal T. 4.29 (0.55) 4.50 (0.35) 6.86 10.02** 13.58** .40**
8. Rel. Man. 3.74 (0.64) 3.75 (0.77) 8.54 0.03 2.83 .38**
9. Deep T. 4.17 (0.66) 4.42 (0.47) 3.34 11.38** 7.57** .35**
10. Networks 3.71 (0.55) 3.76 (0.63) 8.11 3.60 1.28 .29*
11. Time Tog. 4.02 (0.59) 4.18 (0.62) 1.02 0.57 0.03 .22*
12. IOS 5.42 (1.26) 5.35 (1.13) n/aa
-0.25 1.17 .34**
* p < .05 ** p < .01.
aBecause IOS uses one indicator, the baseline confirmatory model is saturated (i.e., zero degrees
of freedom).
bCorrelation coefficient between the male construct and female construct.
Note. Change in chi-square values indicate loss in model fit when constraining the relevant
parameter (means or standard deviations) to equality across partners.
RELATIONAL MAINTENANCE AND SELF EXPANSION 41
Table 7
Unstandardized Regression Coefficients for APIMs with IOS Predicting Maintenance Behaviors,
Communal and Male-Female Equality Constraints Tenable
Ba,p (βa,p)
SEa,p
Br (βr)
SEr
R2
Communal
Δχ2(2)
a
Equal
Δχ2(4)
b
1. Resources 0.16(.15)** 0.06 0.01(.01) 0.10 .06 < 0.01 1.54
2. Verbal Aff. 0.36(.31)** 0.07 -0.20(-.17)† 0.12 .29 4.18 4.47
3. Media 0.25(.23)** 0.06 -0.06(-.05) 0.10 .15 3.06 4.80
4. Humor
0.28(.25)** 0.06 -0.31(-.27)** 0.10 .24 4.70 6.22
5. Tasks 0.28(.25)** 0.06 -0.26(-.23)** 0.11 .22 5.06 6.27
6. Informal T. 0.27(.23)** 0.06 -0.41(-.35)** 0.12 .27 5.10 6.33
7. Rel. Man. 0.27(.24)** 0.06 -0.17(-.16)† 0.10 .19 3.70 5.16
8. Deep T. 0.28(.25)** 0.06 -0.19(-.17)† 0.10 .20 3.28 3.41
9. Time Tog. 0.26(.24)** 0.06 -0.07(-.06) 0.10 .16 3.35 9.23
† p < .085 * p < .05 ** p < .01.
aTest for communal orientation, i.e., that each person‘s actor effect is equivalent to his or her
partner effect (Kenny, Kashy, & Cook, 2006), as compared to the unconstrained model.
bTest for communal orientation and for equality of actor and partner effects across men and
women, as compared to the unconstrained model.
Note. Ba,p = Regression parameter for the actor effect and the partner effect, constrained to
equality. Br = Regression parameter for the relationship (i.e., interaction) effect.
RELATIONAL MAINTENANCE AND SELF EXPANSION 42
Figure 1
Actor-Partner Interdependence Model, IOS Predicting Relational Maintenance Communication
Male actor effect
Female actor effect
Male IOS X
Female IOS
Male IOS
Female IOS
Male
Maintenance
Female
Maintenance
Male rel. effect
Female rel. effect
Male partner effect
Female partner effect
RELATIONAL MAINTENANCE AND SELF EXPANSION 43
Figure 2. Four Patterns of Decomposition of the Interaction Effect Between Actor and Partner
IOS on Maintenance Behaviors
Communal Maintenance Behaviors
No Interaction (e.g., Time)
Interaction (e.g., Deep Talk)
Non-Communal Maintenance Behaviors
Physical (Female Only, Actor Effect)
Networks (Female Only)
-1.000
-0.800
-0.600
-0.400
-0.200
0.000
0.200
0.400
0.600
0.800
1.000
-2 -1.5 -1 -0.5 0 0.5 1 1.5 2
Actor IOS
-1.400
-1.200
-1.000
-0.800
-0.600
-0.400
-0.200
0.000
0.200
0.400
0.600
-2 -1.5 -1 -0.5 0 0.5 1 1.5 2
Actor IOS
-1.500
-1.000
-0.500
0.000
0.500
1.000
1.500
-2 -1.5 -1 -0.5 0 0.5 1 1.5 2
Female IOS
-1.500
-1.000
-0.500
0.000
0.500
1.000
1.500
-2 -1.5 -1 -0.5 0 0.5 1 1.5 2
Female IOS
-1.200
-1.000
-0.800
-0.600
-0.400
-0.200
0.000
0.200
0.400
0.600
0.800
-2 -1.5 -1 -0.5 0 0.5 1 1.5 2
Actor IOS
Partner IOS: 1.5 SD Partner IOS: Mean Partner IOS: -1.5 SD