relational maintenance communication and self expansion ... · enhancement (bell et al., 1987),...

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,

[email protected].

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 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.


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


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


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


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


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).


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


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.


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


(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


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


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


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


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


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


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


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


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


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


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


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


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 &


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.


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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)


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)


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


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.


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.


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


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.


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.


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


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.


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.


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


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

relational maintenance communication and self expansion ... · enhancement (bell et al., 1987),...

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