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published online 10 January 2014Journal of Social and Personal RelationshipsKeith Sanford

behaviors bad, benign, or beneficial?A latent change score model of conflict resolution in couples: Are negative

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Article

A latent change scoremodel of conflictresolution in couples: Arenegative behaviors bad,benign, or beneficial?

Keith SanfordBaylor University, USA

AbstractThis study used latent change score models to examine how couples make progresstoward resolution when they experience conflicts. It examined why negative conflictengagement might sometimes predict increased resolution, and how this processmight be moderated by relationship satisfaction. A sample of 734 people in hetero-sexual marriages or cohabitation relationships were asked to identify an episode ofrelationship conflict and complete a questionnaire measuring types of negative beha-vior, attributions, anger, and soft emotion as well as measures of current discord,peak discord, positive behavior, and types of conflict disengagement. Negativeengagement predicted peak levels of conflict discord, but for people in satisfyingrelationships, this effect was benign because large conflicts predicted large resolutionsregardless of negative engagement levels.

KeywordsCommunication, conflict resolution, couples, latent change, marriage

When couples experience relationship conflicts, partners are likely to have perceptions

regarding levels of conflict discord. These perceptions may include appraisals regarding

the extent to which a conflict is a cause of personal distress, a source of relationship

Corresponding author:

Keith Sanford, Department of Psychology and Neuroscience, Baylor University, One Bear Place #97334,

Waco, TX 76798, USA.

Email: [email protected]

Journal of Social andPersonal Relationships

1–21ª The Author(s) 2014

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tension, and an issue that is unresolved. A couple, then, could be defined as making

progress toward conflict resolution when partners move from a point of experiencing

high discord on a particular issue of conflict to a new point of low discord (or high

accord) in regard to the same issue of conflict. It is important to note that this type of

progress toward resolution involves experiencing a change in conflict discord, and it is

different from merely experiencing a state of resolution at a single point in time. In and

of itself, a person’s current state of resolution may not be highly informative, because if a

person has a conflict that is small and inconsequential, he or she could easily experience

a state of resolution without any meaningful change taking place. Consequently, a state

of resolution might not tell us anything about the processes a person uses when he or

she experiences a significant conflict and needs to move from a point of discord to a new

point of accord with a partner. To understand how people do this, it may be important to

assess perceived resolution progress. Perceived resolution progress could be defined as

the difference between the level of discord a person recalls experiencing at a previous

point in time when a particular conflict was at its peak and that person’s level of discord

regarding the same issue of conflict at the current point in time.

This definition sets a foundation for addressing a key question. How is perceived

resolution progress influenced by the types of negative behaviors, thoughts, and emo-

tions that often occur during conflict interactions? Although these types of negative

conflict variables logically would be inversely correlated with experiencing a state of

resolution, it is not clear how they will predict progress toward resolution. Just because a

behavior, thought, or emotion occurs during conflict does not mean it hinders progress.

In fact, it is possible that couples might use negative behaviors, think negative thoughts,

and experience negative emotions as part of the natural process of addressing and

resolving conflicts (Fincham & Beach, 1999), and, if so, this raises a possibility that

these negative variables might merely be inconsequential or possibly even adaptive

(McNulty & Russell, 2010). This issue is important because, presumably, a couple’s

relationship health will depend on their ability to make progress toward resolution on

those occasions when they experience significant conflicts (Johnson & Roloff, 1998;

Markman, Stanley, & Blumberg, 2001).

Although there is scant research on the predictors of perceived resolution progress,

clues regarding this process may be drawn from a long line of longitudinal research

investigating change in relationship satisfaction. In this area, research has produced two

contrasting sets of findings. One set of studies has found that negative communication

behaviors and negative attributions during conflict are sometimes associated with

beneficial long-term outcomes (Gottman & Krokoff, 1989; Heavey, Layne, & Christen-

sen, 1993; Karney & Bradbury, 1997; McNulty, O’Mara, & Karney, 2008; McNulty &

Russell, 2010; Overall, Fletcher, Simpson, & Sibley, 2009). In line with this type of

inverse effect, studies have also identified situations where the use of positive commu-

nication is associated with an increased risk of future relationship distress (Baucom,

Hahlweg, Atkins, Engl, & Thurmaier, 2006; Schilling, Baucom, Burnett, Allen, &

Ragland, 2003). This body of research is counterbalanced by another equally persua-

sive body of research that has failed to find such inverse effects and instead has found

that negative conflict variables predict future relationship distress (e.g., Karney &

Bradbury,1995; Lavner & Bradbury, 2012; Markman, Rhoades, Stanley, Ragan, &

2 Journal of Social and Personal Relationships



Whitton, 2010). Thus, some studies suggest that negative conflict variables are

sometimes adaptive, whereas other studies suggest that these variables are mostly

detrimental. Notably, all these studies have focused on predictors of change in rela-

tionship satisfaction and not on predictors of perceived conflict resolution progress.

However, a consideration of these contrasting findings can suggest three crucial

issues for research on conflict resolution. First, there is a need to clarify the function

of different negative conflict variables; second, there is a need to consider all rele-

vant pathways when modeling change processes in relationships; and third, there is a

need to investigate possible moderating variables.

The first issue is that it is important to clarify the function of negative conflict

variables. These variables include types of negative behavior, cognition, and emotion

that are commonly observed when couples experience conflict. For example, couples

often use hostile forms of communication behavior that include types of criticism and

defensiveness (Gottman, 1994; Heyman, 2001), they make cognitive attributions in

which they blame each other (Bradbury & Fincham, 1990), and they experience

feelings of anger (Sanford, 2007a). In addition, a slightly different negative variable is

called ‘‘soft’’ emotion, and it includes feelings of sadness, hurt, and concern, which are

associated with expressions of vulnerability (Sanford, 2007a) and which sometimes

predict positive outcomes to relationship therapy (Cordova, Jacobson, & Christensen,

1998; Johnson & Greenberg, 1988). In seeking to understand how all these common

negative conflict variables might predict perceived resolution progress, each variable

could be conceptualized to function in two basic ways. As described below, a negative

variable could function primarily as a type of conflict engagement or as a type of

adversarial interaction.

The theoretical rationale for explaining why negative behaviors, thoughts, and

feelings might be beneficial for conflict resolution is that these variables might reflect

types of conflict engagement (Fincham & Beach, 1999), and such engagement may be

necessary for making progress toward resolution. Along this line, studies have found

that withdrawal from conflict is associated with relationship distress (Eldridge, Sevier,

Jones, Atkins, & Christensen, 2007), and researchers have often suggested that neg-

ative conflict processes could be adaptive if they reduce avoidance (Gottman &

Krokoff, 1989; Karney & Bradbury, 1997; McNulty, 2010). If negative conflict

variables reflect types of active conflict engagement, then these variables should have

the opposite function from types of conflict disengagement. For example, their func-

tion would be opposite from disengagement due to withdrawal (including what Wang,

Fink, & Cai, 2012, call ‘‘negative withdrawal’’ and what Gottman, 1994, calls

‘‘stonewalling’’) as well as from disengagement that occurs when one partner passively

hopes the other will take the initiative to address an issue (similar to what Eidelson &

Epstein, 1982, call an ‘‘expectation of mindreading’’).

A contrasting possibility is that negative conflict variables primarily function as types

of adversarial interaction and as such they may produce no direct benefits for resolution

progress. Accordingly, some negative conflict variables have been described as being

‘‘corrosive’’ (Gottman, 1994) or ‘‘hostile’’ (Heyman, 2001). Moreover, behaviors that

involve displays of anger and distress, as well as tactics that involve arguing, comba-

tiveness, yelling, and aggression, may violate people’s standards for ‘‘rationality’’ in

Sanford 3



conflict interaction (Honeycutt & Bryan, 2011). If negative variables are adversarial in

nature, then they are likely to produce conflict escalation (Markman et al., 2001) and

moreover, negative conflict variables would be expected to have the opposite function

from types of collaborative communication. For example, their function would be

opposite from the types of listening and constructive self-expression that are promoted in

educational programs for couples (Markman et al., 2001). In sum, one way to clarify

the function of negative behaviors, thoughts, and emotions is to contrast the function of

these negative variables both with conflict disengagement and with collaborative

communication.

A second issue involves a need to consider all relevant pathways when modeling

change processes. It is especially important to take into account the fact that the ‘‘pre-

change’’ level of a variable will often predict the extent to which that variable changes

(McArdle, 2009). Thus, in considering perceived resolution progress, it is possible that a

person’s level of discord when a conflict is at its peak (the pre-change level of discord)

might predict the total extent of perceived resolution progress (defined as the difference

between peak discord and current discord). In other words, the size of the conflict could

determine the amount of movement toward resolution. This opens a possibility for a

mediated effect pathway in which negative conflict variables predict the level of peak

discord during a conflict, and peak discord in turn predicts the extent of perceived res-

olution progress. This would occur, for example, if negative conflict variables were pri-

marily adversarial in nature, if they consequently produced high levels of peak discord

during conflicts, and if high peak discord was followed by making substantial progress

toward resolution. This would mean that if negative conflict variables predict larger con-

flicts, and if larger conflicts produce larger resolutions, then negative conflict variables

would also predict larger resolutions. An alternate possibility, however, is that there is a

direct effect pathway in which negative conflict variables directly predict the extent of

perceived resolution progress. That is, negative conflict variables may have a direct

effect that remains evident even after controlling for the effects of peak discord. This

type of direct effect would occur, for example, if negative conflict variables were a nat-

ural part of active conflict engagement and if such active engagement was necessary or

beneficial for conflict resolution. In this case, greater engagement would directly predict

greater resolution progress even after accounting for effects pertaining to conflict size.

A crucial feature to notice in these examples is that both a mediated effect (in which

negative conflict variables have an adversarial function) and a direct effect (in which

negative variables function as types of engagement) could potentially produce identical

patterns of results showing that negative conflict variables are positively correlated with

resolution progress. Thus, it is important to use methods that can tease apart distinctions

between direct effects and mediated effects. Notably, this has not always been done in

studies investigating change in relationship satisfaction. Although this issue is arguably

salient to all studies of change processes in couples, studies that have investigated

change across just two time points (e.g., Gottman & Krokoff, 1989) have sometimes

been singled out for criticism in this regard (Cramer, 2003; Woody & Costanzo, 1990).

However, it is possible to address this issue, and to do so even if change is measured

across only two points, by using an approach called a ‘‘latent change score model’’

(McArdle, 2001, 2009). This is a type of structural equation model in which two scores




pertaining to a variable are measured in regard to two different points in time. A latent

change score is created by first accounting for measurement error in the indicators and

then placing constraints on the pair of scores, specifying that the second score (in this

case, current discord) is exactly equal to the first score (in this case, recalled peak dis-

cord) plus change. This approach produces a latent change score (in this case, repre-

senting the extent of perceived resolution progress), which can be used as an outcome

variable in path models. Importantly, these models can include a path from peak discord

to the latent change score, accounting for the fact that that the size of the conflict might

predict the size of the progress toward resolution.

A final issue is that the effects of negative conflict variables may be moderated. Even

if negative conflict variables are associated with resolution progress for some couples,

they may not predict such progress for all couples. For some couples, engagement might

exacerbate conflicts, and large conflicts might fail to produce large resolutions. One

likely possibility is that the direction of effect depends on levels of relationship satis-

faction. According to Karney and Bradbury’s (1995) vulnerability-stress-adaptation

model, a couple’s ability to utilize adaptive processes in their relationship is hindered

when satisfaction is low. Similarly, Gottman (1994) suggests that when satisfaction is

low, conflict interactions often become ‘‘absorbing states,’’ whereby couples are unable

to escape from cycles of negative behavior once those cycles have begun. In this way,

dissatisfied couples might easily become stuck at their peak levels of conflict intensity

and fail to make progress toward resolution. In contrast, when couples are satisfied with

their relationship, the mechanisms of conflict resolution might run more efficiently

(Johnson & Roloff, 1998). This suggests that if negative conflict variables are associated

with greater levels of conflict resolution, either through direct effects or through

mediated effects, these effects may occur primarily when relationship satisfaction is high

and not when satisfaction is low.

To investigate these issues, it is first necessary to develop a valid method for assessing

peak discord and current discord to provide a measure of perceived resolution progress.

Because perceived conflict discord involves personal perceptions of an episode of

conflict, assessments may best be obtained via self-report. In addition, ratings specifi-

cally pertaining to peak discord may need to be obtained retrospectively. This is because

it would be difficult to arrange for participants in a study to provide ratings of peak

discord during actual conflict interactions at the precise moments, on the particular

days, when those conflicts were reaching their peaks. The convergent validity of both

peak discord and current discord could be tested, in part, by examining cross-spouse cor-

relations. Because personal perceptions of conflict discord arise out of a common con-

flict experience that is shared by two partners, it is likely that ratings from two

partners will be at least moderately correlated.

It is important to note that because perceptions of conflict discord are at the core a

type of relationship sentiment, ratings of discord are likely to be highly correlated with

ratings of relationship satisfaction. However, a distinct feature of conflict discord is that

it should pertain to a single issue of conflict, and, thus, ratings of discord should be more

specific than general ratings of global relationship sentiment. This means that if two

partners make ratings of conflict discord for the same issue of conflict, their ratings

should share a context-specific similarity that cannot be explained by partners’ ratings of

Sanford 5



global relationship satisfaction. In other words, the cross-partner correlation for conflict

discord should remain significant after controlling for relationship satisfaction. Another

unique feature of conflict discord is that it should often show substantial change over the

course of a single conflict, and this would contrast with global measures of relationship

satisfaction that often remain relatively stable even over the course of several years.

Overview

This study began with an examination of validity data for a measure of conflict discord,

and then it focused on testing a latent change model of perceived conflict resolution. The

study included four key independent variables: negative communication behavior,

blaming attributions, anger, and soft emotion. Results from these negative conflict

variables were contrasted with collaborative communication behavior and two types of

disengagement: withdrawal and passive immobility (the latter being a type of conflict

disengagement in which a person desires to address an issue but passively waits for a

partner to initiate engagement). To the extent that negative conflict variables might

produce direct effects, the effects were expected to be positive, to be strongest when

satisfaction was high, and to be the opposite of the effects for withdrawal and passive

immobility. To the extent that mediated effects might be present, it was expected that

negative conflict variables would predict high peak discord, that collaborative engage-

ment would have the opposite effect, that peak discord would positively predict reso-

lution progress, and that this effect would be the strongest when satisfaction was high.

Method

Participants

Participants included 734 people in heterosexual marriages or cohabitation relationships.

A total of 687 were married and the remaining 47 were cohabitating (because a majority

were married, the terms ‘‘wife’’ and ‘‘husband’’ will be used throughout). Age of

participants ranged from 18 to 82 years (M ¼ 40.20, SD ¼ 13.15); and of the

married participants, length of marriages ranged from less than 1 to 51 years (M¼ 13.36,

SD ¼ 12.38). The sample comprised 64% female, 11% Asian, 7% Black or African

American, 10% Hispanic, 69% White (non-Hispanic), and 3% other races. Annual

family income ranged from less than US$10,000 to more than US$500,000 (median

¼ US$78,000, M ¼ US$102,000, SD ¼ US$85,000).

The total sample was divided into two overlapping subsamples. First, a paired-data

subsample included 117 couples with two participating partners, with both partners com-

pleting an assessment pertaining to the same issue of relationship conflict. Second, an

independent-cases subsample had 617 participants. These included (a) 1 randomly

selected member from each of the 117 couples in the paired-data subsample, (b) 1 ran-

domly selected member from each of 28 couples where partners completed assessments

pertaining to different issues of conflict, and (c) 472 married or cohabitating individuals

participating without their partners.




Procedure

An interactive Web site was created, which allowed participants to create an anonymous

account, complete an assessment, submit responses, receive personalized feedback, and

view a resource bank of information for couples. On the first page of the assessment,

participants were instructed to ‘‘Think about a single, specific episode of conflict in your

relationship,’’ and they were given a text box in which they were asked to write a brief

conflict description that would be acceptable to be viewed by their partners (if partici-

pating). When both members of a couple participated, the first partners’ incident

description was automatically displayed on the first page of the questionnaire for the

second partner, and the second partner was asked to indicate whether they had both

identified the same incident. When the incident was reported to be the same for both

partners, the couple was included in the first subsample of paired partners. No other parts

of the assessment were shared between partners, and partners were instructed to com-

plete their assessments independently.

After identifying an episode of conflict, participants then completed several ques-

tionnaire scales regarding that conflict. Participants were included in the data set only if

they completed the entire questionnaire and responded affirmatively to a question about

providing valid answers. A portion of the sample was recruited using procedures outlined

by Feeney (1999) in which students from upper level undergraduate psychology courses

invited their married parents, relatives, and acquaintances to complete the Web ques-

tionnaire. Drawing from student recruitment records, it is estimated that approximately

60% of the couples were recruited by students and that the remaining 40% simply dis-

covered the questionnaire while searching the Internet or via links from other Web sites.

Compared with other nonclinical samples of couples (e.g., Funk & Rogge, 2007), the

participants in the present study had a lower mean and a wider spread of relationship

satisfaction scores (Couples Satisfaction Inventory M ¼ 49.44, SD ¼ 21.54), and there

was a significant gender difference, d ¼ .32, t(615) ¼ 3.70, p < .001, with women

reporting lower satisfaction than men. There is a possibility that the recruitment methods

inadvertently oversampled distressed women compared with distressed men, and, thus,

any gender differences in this study should be interpreted with caution.

Measures

Conflict resolution. A new instrument called the conflict resolution questionnaire was

created for this study. This questionnaire asked participants to rate 10 pairs of items

pertaining to the specific episode of conflict they identified at the beginning of the

assessment protocol. The first item of each pair was used to assess peak discord, and each

of these items asked respondents to make a rating based on the conflict when it was at its

peak. The second item of each pair was used to assess current discord, and each of these

items asked participants to rate the same characteristic as the first item but to base the

rating on the conflict at the current moment in time. A list of items is provided in

Appendix 1. A confirmatory factor analysis of this questionnaire was conducted, and,

specifically, a model was tested with (a) six-item parcels, (b) two latent state factors, and

also (c) two indicator-specific factors accounting for variance shared by pairs of

Sanford 7



questions that were repeated in regard to two different time points (Geiser, 2013). This

model produced a good fit, w2(df ¼ 6) ¼ 41.64, comparative fit index (CFI) ¼ .99, stan-

dardized root mean square residual (SRMR)¼ .04; further details regarding this analysis

are available from the author. Cronbach’s as were .87 and .94 for peak discord and cur-

rent discord, respectively.

Communication behavior. The Conflict Communication Inventory (Sanford, 2010a) was

used to assess both adversarial (negative) communication and collaborative (positive)

communication during the specific episode of conflict that participants identified at the

beginning of the assessment protocol. The adversarial communication scale includes

seven items that ask participants to rate their use of negative communication (e.g.,

‘‘I said something mean’’ and ‘‘I defended my position’’). The collaborative commu-

nication scale includes seven items that ask participants to rate their use of positive

communication (e.g., ‘‘I politely talked about my feelings,’’ and ‘‘I carefully listened so

I could understand my partner’’). Previous research has found that scores on these scales

are highly correlated with observer ratings and, moreover, that the scores predict future

observed behavior nearly as well as do ratings that are obtained from trained observers

(Sanford, 2010a). In the present study, a was .85 for adversarial communication and

.86 for collaborative communication.

Negative attributions. Participants used a negative attribution scale (Sanford, 2010b) to

rate their appraisals during the episode of conflict that they identified at the beginning of

the assessment protocol. On this scale, participants rated the extent to which they agreed

with eight different attribution statements, such as ‘‘My partner deserves to be blamed’’

and ‘‘My partner did something on purpose that caused this problem.’’ Previous research

has found that scores on this scale correlate with observer ratings of verbalized attri-

butions (Sanford, 2010b) and, in the present sample, a was .89.

Emotion. Two scales from the Couples Emotion Rating Form (CERF; Sanford, 2007a)

were used to obtain ratings of negative emotion during the episode of conflict that

participants identified at the beginning of the assessment protocol. Anger was assessed

with a four-item ‘‘hard emotion’’ scale measuring feelings of anger, annoyance, irrita-

tion, and aggravation. Participants also completed a 4-item ‘‘soft emotion’’ scale measur-

ing feelings of sadness, hurt, concern, and disappointment. This instrument was

developed and validated in a series of studies (Sanford, 2007a, 2007b, 2012) demonstrat-

ing that (a) the CERF fits an expected factor structure, (b) scores on the CERF corre-

spond to observer ratings of expressed emotion, and (c) changes in emotion predict

corresponding changes in communication behavior and cognition. In the present study,

as were .85 for anger and .83 for soft emotion.

Conflict disengagement. A new instrument called the Conflict Disengagement Inventory

was created. This instrument contains two 7-item scales specifically pertaining to the

episode of conflict participants identified at the beginning of the assessment protocol.

One scale measures withdrawal and the other measures passive immobility. A list of

items is provided in Appendix 2. The instrument was developed through a series of four




studies (total n ¼ 3,715) conducted by this author (Sanford) in which pools of potential

items were factor analyzed, and items were selected and revised to produce an instru-

ment with two distinct factors. A subsequent validation study (n ¼ 297) comparing the

two scales found that the withdrawal scale had significantly stronger correlations with

other existing measures of withdrawal (measuring patterns of withdrawal rather than

withdrawal in a specific conflict interaction), avoidant attachment, and low relationship

commitment. In contrast, passive immobility had significantly stronger correlations with

having standards that a partner should be able to ‘‘mind read’’ one’s own desires and with

anxious attachment. In the present study, as were .85 for withdrawal and .88 for passive

immobility.

Relationship satisfaction. Participants completed the 16-item version of the Couples Satis-

faction Index (Funk & Rogge, 2007). This measure was developed using item response

theory analysis to select highly discriminating items from a pool of items drawn from

several existing measures. In the present study, a was .97.

Results

The first step in data analysis was to conduct a test of the convergent validity for the

ratings of peak discord and current discord. Using only the paired data set of 117 couples,

correlations were computed between wives and husbands. These cross-spouse correla-

tions were .60 (p < .001) for peak discord and .76 (p < .001) for current discord. Next,

partial correlations were computed after controlling for both wife relationship satisfac-

tion and husband relationship satisfaction. The partial correlations were .59 (p < .001)

for peak discord and .61 (p < .001) for current discord. Thus, the strong correlation

between partners in their ratings of conflict discord was more specific than merely shar-

ing perspectives that matched their overall relationship satisfaction. This provides initial

support for the convergent validity of the resolution scales.

The remaining analyses all used the nonpaired data set of 617 independent cases.

First, means, SDs, and correlations were computed for all the variables, and these are

listed in Table 1. Correlations between the various independent variables ranged from

being small and nonsignificant to large. Consistent with the fact that both conflict dis-

cord and relationship satisfaction tap aspects of relationship sentiment, the correlation

between relationship satisfaction and current discord was large. However, as reported

earlier, the correlation between partners in their ratings of current discord could not be

explained by their ratings of satisfaction, suggesting that discord scores are also distinct

from satisfaction. Notably, correlations between adversarial communication, anger, and

negative attributions were all .50 or greater. Along this line, a confirmatory factor

analysis demonstrated a good fit for a model in which these three variables were all

indicators of a single factor, and each of the other four variables were sole indicators of

separate factors, w2(df ¼ 8) ¼ 45.06, CFI ¼ .98, SRMR ¼ .03. Thus, to reduce the

number of equations tested in subsequent analyses, adversarial communication, anger,

and negative attributions were each standardized and then summed together to create a

composite variable called ‘‘negative process’’.

Sanford 9



Tab

le1.

Mea

ns,

SDs,

and

corr

elat

ions

bet

wee

nva

riab

les

usi

ng

617

indep

enden

tca

ses.

Satisf

action

Adve

rsar

ial

com

.N

egat

ive

attr

ibutions

Ange

rSo

ftem

otion

Colla

bora

tive

com

.W

ithdra

wal

Pas

sive

imm

obili

tyPea

kdis

cord

Curr

ent

dis

cord

Mea

n49.4

43.2

72.9

33.6

63.6

02.5

72.4

02.8

23.6

02.5

2SD

21.5

4.8

8.9

5.9

91.0

7.7

3.8

7.9

3.7

61.0

3A

dve

rsar

ialco

m.

�.2

6**

Att

ributions

�.5

0**

.53**

Ange

r�

.26**

.58**

.50**

Soft

emotion

�.4

0**

.44**

.50**

.42**

Colla

bora

tive

com

..1

7**

�.3

0**

�.3

0**

�.3

1**

.00

Withdra

wal

�.1

8**

�.0

9*

.09*

.02

.04

�.0

6Pas

sive

imm

obili

ty�

.18**

.28**

.34**

.23**

.34**

�.0

1.3

0**

Pea

kdis

cord

�.5

3**

.55**

.58**

.48**

.59**

�.3

7**

.10*

.21**

Curr

ent

dis

cord

�.8

1**

.34**

.51**

.28**

.45**

�.1

7**

.17**

�.2

3**

.58**

Not

e.Sa

tisf

action

score

sra

nge

from

0to

81.Sc

ore

son

alloth

ersc

ales

range

from

1to

5.co

m.¼

com

munic

atio

n.

*p<

.05;**

p<

.01.

10



Latent change score models

A series of five latent change score models were estimated, one for each of five conflict

process variables: negative process (which was a composite of adversarial communication,

anger, and negative attributions), soft emotion, collaborative engagement, withdrawal, and

passive immobility. These models were analyzed following procedures described by

McArdle (2001, 2009) using LISREL 8.80 software (Scientific Software International,

Lincolnwood, Illinois, USA; Joreskog & Sorbom, 2007). Figure 1 provides a depiction of

the basic model. This figure includes all components of the model except for variables

pertaining to gender, which were entered as covariates in all analysis but omitted from the

1 1

Satisfaction×

Conflict Process

Satisfaction×

PeakDiscord

SatisfactionConflictProcessVariable

Constant

CurrentDiscord

Change(PerceivedResolutionProgress)

Peak Discord

e e e e

e

C

A

B

eIndi-cator

Indi-cator

Indi-cator

Indi-cator

Indi-cator

Indi-cator

1

1

1 1 1 1

Figure 1. Latent change score model of conflict resolution. Each ‘‘e’’ indicates measurement errorvariance, which is fixed to equal an indicator’s variance times 1 minus its reliability. Each ‘‘1’’indicates a pathway fixed to equal one. Current discord is fixed to equal peak discord plus changewith no residual variance. Pathways A and B are the mediated effect pathways. Pathway C is thedirect effect pathway. To simplify presentation, covariates and interactions involving gender arenot depicted in the figure.

Sanford 11



figure to simplify the presentation. As seen in the figure, the model includes variables

representing peak discord and current discord and also an unobserved variable called

‘‘change.’’ Both peak discord and change have arrows pointing to current discord, and both

of these pathways are constrained to equal one. This means that current discord is equal to

the sum of peak discord plus change, and hence, change is a latent difference score equal to

current discord minus peak discord (if current¼ peakþ change, then change¼ current�peak). In other words, change is a measure of perceived resolution progress. The model

also includes a constant, depicted as a triangle and set equal to one for all participants,

which makes it possible to estimate intercepts. For example, the arrow from the

constant to change estimates the intercept for change, which is the average amount of

change after controlling for other predictors. In each model, peak discord was pre-

dicted by (a) one of the conflict process variables, (b) relationship satisfaction, and

(c) the interaction between the conflict process variable and relationship satisfaction.

Although not depicted in the figure, peak discord was also predicted by (d) gender,

(e) the interaction between gender and the conflict process variable, and (f) the

interaction between gender and relationship satisfaction. Change was predicted by

(a) peak discord (b) one of the conflict process variables, (c) relationship satisfaction,

(d) the interaction between the conflict process variable and relationship satisfaction,

and (e) the interaction between peak discord and relationship satisfaction. Although

not depicted in the figure, change was also predicted by (f) gender, (g) the interaction

between gender and peak discord, (h) the interaction between gender and the conflict

process variable, and (i) the interaction between gender and relationship satisfaction.

In Figure 1, the direct effect of the negative process variable on conflict resolution is

labeled as Path C, and the mediated effect is the product of the Paths labeled A and B.

As depicted in Figure 1, each latent variable has a single indicator (or observed

variable), and each indicator is constrained to have a unit loading on its target latent

variable. In line with McArdle (2001), the error variance for each indicator was set to

equal the observed variables’ variance times 1 minus its reliability. Prior to analysis,

and prior to calculating products for the interaction terms, scores for peak discord,

satisfaction, and the conflict process variable were all converted to z scores. Gender

was coded so that 0 ¼ wives and 1 ¼ husbands. Scores for current discord were

standardized using the mean and SD for peak discord. This means, for example, that a

change score of �1 indicates a decrease in conflict discord that is equal to a 1 SD drop

on the peak discord scale.

The substantive parameters of interest in the latent change score analysis pertain to

the predictors of peak discord and the predictors of change (i.e., perceived resolution

progress). The results regarding predictors of peak discord are listed in Table 2. Both

negative process and soft emotion strongly predicted higher peak discord, whereas

collaborative communication predicted lower peak discord. The two disengagement

variables had small effects, with withdrawal being nonsignificant. As might be expected,

the level of peak discord was inversely related to relationship satisfaction, and it was also

inversely related to being a husband (which is consistent with the gender difference in

this sample). Importantly, the effects for negative process, soft emotion, and colla-

borative communication were all moderated by relationship satisfaction. The direction of

these interactions indicated that the effects were stronger when satisfaction was high.




The results regarding predictors of change are listed in Table 3. Note that change is

scored so that negative values indicate conflict resolution progress (i.e., a decrease in

discord), whereas positive values indicate conflict escalation. Because units of change

were scaled to the SD of peak discord, the change variable itself was not a z score, and,

therefore, path loadings near one do not indicate a near perfect correspondence. The

intercepts in Table 3 indicate that participants reported average reductions in conflict

discord that were approximately equal to 1.55 SDs on the peak discord scale. This is

consistent with the expectations that scores regarding conflict discord should have the

potential for changing substantially over the course of a single conflict. Importantly,

both peak discord and relationship satisfaction were strong predictors of greater per-

ceived resolution progress (predicting greater reductions in discord). In addition, peak

discord was moderated by satisfaction. The direction of the interaction indicated that

peak discord predicted the most resolution when satisfaction was high. In contrast to

the robust effects for peak discord, the direct effects for the conflict process variables

were generally small, and the only significant direct effects pertained to negative

process, soft emotion, and passive immobility. Each of these variables was associated

with reduced progress toward resolution. Thus, the results for passive immobility were

consistent with the idea that disengagement might hinder resolution, whereas the

results for negative process and soft emotion were opposite from expectations that

these variables might be adaptive and might facilitate resolution. Notably, there was an

interaction between soft emotion and satisfaction, suggesting that this direct effect for

soft emotion was weaker when satisfaction was high. Incidentally, there were also

some gender effects in that men reported slightly less progress toward resolution, and

in the equation testing effects of soft emotion, the interaction between gender and peak

discord fell in the significant range.

Table 2. Coefficients for pathways predicting peak discord.

Equation parameters

Name of conflict process variable used in the model

Negativeprocess

Softemotion

Collaborativecommunication Withdrawal

Passiveimmobility

Intercept .11 .11 .12 .15 .13Conflict process variable (Path A) .49* .50* �.34* .02 .11*Relationship satisfaction

Main effect �.41* �.40* �.51* �.54* �.53*Interaction with conflict

process.13* .09* �.14* .07 .05

GenderMain effect �.22* �.27* �.33* �.43* �.38*Interaction with conflict

process.10 �.04 .08 .09 .00

Satisfaction by gender interaction .32* .12 .13 .07 .09

Note. Each column contains parameter estimates obtained from a single model and all parameters pertain topathways predicting peak discord.*p < .05.

Sanford 13



To assist in interpreting the significant interactions in Tables 2 and 3, conditional

effects were computed for all variables. Specifically, mediated effects, direct effects, and

total effects were calculated for both: (a) people 1 SD above the mean on relationship

satisfaction and (b) people 1 SD below the mean. Because there was a significant gender

interaction in one of the equations pertaining to soft emotion (Path B), results for this

particular variable were calculated separately for wives and husbands. All conditional

effects are reported in Table 4. The results indicate that the mediated effects are both

robust and strongly moderated by satisfaction. Compared to people with low satisfaction,

the results for highly satisfied people show that negative process and soft emotion both

predicted comparatively greater increases in peak discord, but this was offset by the fact

that peak discord predicted comparatively greater levels of resolution progress. Taken

together, these mediated effects were substantial for people with high satisfaction (e.g.,

negative process-mediated effect was �.56), but small for people with low satisfaction

(e.g., negative process-mediated effect was �.22). The total effects listed at the bottom

of Table 4 show that these mediated effects were reduced, albeit only slightly, by small

direct effects in the opposite direction. Notably, the results for collaborative communi-

cation were mostly a mirror image of the results for negative process, suggesting that

these variables have opposite functions. In contrast, the two disengagement variables had

Table 3. Coefficients for pathways predicting change (perceived resolution progress).

Name of conflict process variable used in the model

Equation parametersNegativeprocess

Softemotion


Passiveimmobility

Intercept �1.55* �1.56* �1.55* �1.54* �1.55*Peak dissonance (Path B) �.74* �.76* �.66* �.68* �.69*Direct effects of conflict

process (Path C).09* .13* .03 .02 .08*

Relationship satisfactionMain effect �.98* �.97* �.97* �.97* �.97*Interaction with peak

dissonance�.14* �.09* �.12* �.13* �.12*

Interaction with conflictprocess

.01 �.07* .02 .02 �.05

GenderMain effect .14* .13* .13* .11 .14*Interaction with peak

dissonance.09 .13* .08 .06 .07

Interaction with conflictprocess

�.03 �.09 .01 .12 .02

Satisfaction by genderinteraction

.13 .10 .11 .13 .13

Note. Each column contains parameter estimates obtained from a single model and all parameters pertain topathways predicting perceived resolution progress. Negative values indicate greater perceived resolutionprogress (i.e., greater reductions in discord).*p < .05.




small effects that were, if anything, in the same direction as negative process rather than

opposite.

Discussion

Perceived conflict resolution progress in couples is a process that involves moving from

a point of high discord on a conflict to a new point of low discord. The present study

demonstrated how this process can be examined using latent change score models, and it

investigated the extent to which perceived resolution progress was associated with a set

of three negative process variables (including adversarial communication, blaming

attributions, and anger) and also a measure of soft emotion. The results were consistent

with a mediated pathway in which negative process variables predicted greater peak

discord during conflicts and where greater peak discord in turn predicted greater reso-

lution progress. In other words, large conflicts were offset by large resolutions.

Importantly, this mediated pathway was strong for people with high relationship satis-

faction, but weak for people with low satisfaction. These results help answer a key

question. Are negative process variables sometimes beneficial for resolution progress?

Results of the present study suggest that negative process variables do, indeed, predict

positive change. However, the reason is not because these variables directly serve a

beneficial function, but rather, because when people are in satisfying relationships,

Table 4. Conditional effects of conflict process variables predicting change (perceived resolutionprogress) at levels of relationship satisfaction.

Name of conflict process variable

Negativeprocess

Soft emotion


Passiveimmobility

Name of conditionaleffect pathway Wives Husbands

Path A: Conflict process predicting peak discordSatisfaction Z ¼ þ1 .65 .59 .55 �.45 .12 .16Satisfaction Z ¼ �1 .39 .41 .37 �.17 �.02 .06

Path B: Peak discord predicting changeSatisfaction Z ¼ þ1 �.85 �.85 �.72 �.75 �.79 �.79Satisfaction Z ¼ �1 �.57 �.67 �.54 .51 �.53 �.55

Path C: Direct effects of conflict processSatisfaction Z ¼ þ1 .09 .06 �.03 .05 .08 .04Satisfaction Z ¼ �1 .07 .20 .11 .01 .04 .14

Path AB: Mediated effects of conflict processSatisfaction Z ¼ þ1 �.56 �.50 �.40 .34 �.09 �.13Satisfaction Z ¼ �1 �.22 �.27 �.20 .09 .01 �.03

Total effects of conflict process (AB þ C)Satisfaction Z ¼ þ1 �.47 �.44 �.43 .40 �.01 �.09Satisfaction Z ¼ �1 �.15 �.07 �.09 .10 .05 .10

Note. Negative values indicate greater perceived resolution progress (i.e., greater reductions in discord).

Sanford 15



perturbations away from a norm are followed by a return to the status quo. After con-

trolling for this robust effect, the remaining direct effect of negative process variables on

resolution progress was quite small in magnitude and in the direction of predicting

decreased, not increased, resolution.

The results help clarify the extent to which negative process variables function pri-

marily as types of adversarial interaction versus types of conflict engagement. If these

variables are adversarial in nature, they should function as the opposite of collaborative

engagement. They should violate what Honeycutt and Bryan (2011) call ‘‘rules for

positive understanding,’’ and they should be associated with conflict escalation

(Markman et al., 2001). In line with this assumption, the present study not only found

that negative process variables were correlated with higher peak discord but also that

these variables had the opposite effect from collaborative engagement, which predicted

reduced levels of peak discord. A contrasting (albeit not incompatible) theoretical per-

spective is that negative process variables are best conceptualized as types of conflict

engagement and that resolution will be facilitated by engagement and hindered by dis-

engagement (Gottman & Krokoff, 1989; Karney & Bradbury, 1997; McNulty, 2010).

The results of the present study were mixed regarding the overall salience of variables

pertaining to disengagement. One type of disengagement called ‘‘passive immobility’’

had a small but significant direct effect predicting lower perceived resolution progress,

but the direct effects for another type of disengagement, called ‘‘withdrawal,’’ were not

significant. Importantly, results regarding the two disengagement variables were, if

anything, similar in direction (rather than opposite in direction) from the effects for the

negative process variables. Thus, the pattern of results provided the strongest support for

conceptualizing negative process variables as being primarily adversarial in function.

In the present study, peak discord predicted the extent of perceived resolution

progress. These results followed a general pattern in which a pre-change condition of

having problems (high peak discord) predicted making subsequent improvements (per-

ceived resolution progress), and, thus, the variables that predicted having problems were

also variables that predicted making improvements. A similar pattern of results is evident

in longitudinal research on relationship satisfaction. For example, some previous studies

have found that forms of negative communication are associated with having initially

low levels of satisfaction and also with experiencing future improvements (Gottman

& Krokoff, 1989; Heavey et al., 1993). This type of pattern is also found in growth curve

modeling studies where the rate of improvement is assessed across multiple time points.

These studies have found that, when newly married couples report experiencing marital

problems, they tend to have low immediate relationship satisfaction but also a trajectory

of improvement over time; and moreover, when these couples with problems use nega-

tive communication and make negative attributions, it again predicts low immediate

satisfaction, but a trajectory of improvement (McNulty et al., 2008; McNulty & Russell,

2010). Similarly, Karney and Bradbury (1997) found that wives’ negative behavior was

inversely related to pre-change satisfaction levels (albeit with most effects falling short

of significance in this study), but it had a beneficial effect on that rate of change over

time. A mirror image of this effect was also observed in a study of couples attending

a premarriage education program where high satisfaction scores at the beginning of the

program predicted subsequent declines in satisfaction (Baucom et al., 2006). In sum,




several longitudinal studies of satisfaction in couples have produced a pattern of findings

that is consistent with a possibility that pre-change levels of satisfaction predict the sub-

sequent direction and rate of change. The findings from these studies are strikingly sim-

ilar to the total effects in the present study, in which the negative conflict variables were

associated with greater perceived improvement. Importantly, the present study also con-

trolled for the extent to which pre-change problems (peak discord) predicted subsequent

improvement (perceived resolution progress), and the results indicated that the negative

conflict variables were not directly beneficial, but instead they predicted improvement

merely because they predicted the extent of pre-change problems. This raises questions

about whether this same type of effect might also explain results in longitudinal studies

of relationship satisfaction.

Perhaps the most striking results in the present study were the extent to which the

mediated effects were moderated by relationship satisfaction. A purview of the condi-

tional effects listed in Table 4 reveals that the total mediated effects were, on average,

more than twice as large for satisfied people than for unsatisfied people. These results are

consistent with theories suggesting that conflict can be an absorbing state for distressed

couples (Gottman, 1994). Not only were the mediation effects moderated by satisfaction,

but satisfaction also strongly predicted both peak discord and conflict resolution. This

pattern suggests that, for distressed couples, conflicts might often escalate to high levels

of peak discord regardless of the presence or absence of negative process variables and

that large conflicts often fail to produce large resolutions. In contrast, for satisfied

couples, the level of escalation may be commensurate with the level of negative process

and the size of the conflict may determine the size of the resolution. If this is true, then it

would seem that conflict resolution is more dependent on levels of relationship satis-

faction than on other aspects of conflict process.

This does not mean, however, that negative process variables are entirely inert. After

controlling for the large effects produced by peak discord and relationship satisfaction,

there was still a small direct effect indicating that negative process during conflict

predicted reduced progress toward resolution. In addition, the soft emotion variable had a

similar direct effect predicting reduced progress toward resolution; however, this effect

applied primarily to people with low relationship satisfaction and not people with high

satisfaction. Importantly, the direction of these direct effects was opposite from the

direction of the mediated effects. When the direct effects were isolated, the negative

process variables (including soft emotion for people in unsatisfying relationships)

appeared to be more harmful than beneficial for perceived resolution progress. In this

respect, the findings of this study are consistent with those longitudinal studies that have

shown that negative process variables predict future relationship distress (e.g., Karney &

Bradbury,1995; Lavner & Bradbury, 2012; Markman et al., 2010).

There were several gender differences in the results, with women reporting less

satisfaction, greater peak dissonance, and greater perceived resolution progress. How-

ever, it is not clear if this indicates a genuine difference between women and men or if

the recruitment techniques in this study inadvertently oversampled distressed women

relative to distressed men. It is possible that men in distressed relationships are disin-

clined to complete relationship questionnaires on the Internet. Other limitations of the

present study include the fact that all variables were assessed via self-report, and data

Sanford 17



were collected over the Internet without control over the assessment environment. In

addition, ratings for peak discord were collected retrospectively (in part, because it did

not seem feasible to collect assessments of peak discord at the actual moments when con-

flicts were reaching their peaks), and there is some risk that participants may have failed

to remember their previous conflict experiences accurately. Also, because this study was

correlational, it could not address issues regarding direction of effects. For example, are

couples able to resolve big conflicts because they are satisfied? Or conversely, does the

process of having big conflicts and then resolving those big conflicts produce relation-

ship satisfaction?

Notwithstanding the limitations discussed above, the results of this study are valuable

for two key reasons. First, they provide preliminary evidence for a model of conflict

resolution in couples. According to this model, the primary function of negative process

variables is that they predict peak levels of conflict discord, but at least for people in

satisfying relationships, this effect is benign because large conflicts predict large reso-

lutions. Negative process variables may also have a direct effect predicting reduced

resolution progress, although this appears to be small in magnitude. Second, the results

demonstrate how latent change score models might be especially useful for research with

couples, and it highlights the importance of distinguishing between direct and mediated

effects. In the present study, scores from a pre-change point in time (peak discord)

strongly predicted the extent of subsequent change (perceived conflict resolution). These

results suggest that it is crucial for researchers to model this pathway when investigating

aspects of change in relationships.

Appendix 1

List of items for the conflict resolution questionnaire

(1) Think about the point during this particular conflict when you felt the most upset.

How upset did you feel at that point? (2) How upset do you feel right now? (3) Think

about the point during this particular conflict when it was the hardest to understand your

partner. How well did you understand your partner at that point? (4) How well do you

understand your partner right now? (5) Think about the point during this particular con-

flict when your partner showed the worst understanding of you. How well did your part-

ner understand you at that point? (6) How well does your partner understand you right

now? (7) Think about the point during this particular conflict when there was the most

tension between you and your partner. How much tension was there at that point? (8)

How much tension is there between you and your partner right now? (9) Think about the

point during this particular conflict when there was the most disagreement between you

and your partner. How much did you and your partner disagree with each other at that

point? (10) How much do you and your partner disagree with each other right now?

(11) Think about the point during this particular conflict when it was the furthest away

from being resolved. How much was there to be resolved at that point? (12) How much is

there that still needs to be resolved right now? (13) Think about the point during this par-

ticular conflict when you felt as if there was the most distance between you and your

partner. How distant did you feel from your partner at that point? (14) How distant do




you feel from your partner right now? (15) Think about the point during this particular

conflict when it was most difficult to be accepting of your partner. How accepting did

you feel at that point? (16) How accepting do you feel right now? (17) Think about the

point during this particular conflict when you were the most worried about your relation-

ship. How worried did you feel at that point? (18) How worried are you right now?

(19) Think about the point during this particular conflict when it was most difficult to

feel friendly toward your partner. How friendly did you feel at that point? (20) How

friendly do you feel toward your partner right now?

Note. Each item is rated on a 5-point scale: 1¼ Not at all (Zero), 2¼ A small amount,

3 ¼ A medium amount, 4 ¼ A large amount, and 5 ¼ An extreme amount (Completely).

Appendix 2

List of items for the Conflict Disengagement Inventory

(1) I avoided discussing the conflict. (2) I wanted my partner to be faster at recognizing

when I am upset. (3) I silently refused to say anything. (4) I decided to remain silent until

my partner stopped nagging. (5) I quietly waited, hoping my partner would notice I was

upset. (6) I wanted my partner to drop the issue. (7) I decided it was useless to reply to my

partner. (8) I wanted my partner to notice my feelings without me having to say anything.

(9) I withdrew from my partner. (10) I wanted my partner to take responsibility for find-

ing out how I felt. (11) I wanted my partner to figure something out without me having to

say it. (12) I chose not to respond to my partner. (13) I silently wished my partner would

ask about my feelings. (14) I wanted my partner to notice something was wrong without

me having to point it out.

Note. The Withdrawal Scale includes items 1, 3, 4, 6, 7, 9, and 12. The Passive Immo-

bility Scale includes items 2, 5, 8, 10, 11, 13, and 14. Each item is rated on a 5-point

scale: 1 ¼ Disagree strongly, 2 ¼ Disagree, 3 ¼ Agree somewhat, 4 ¼ Agree, and

5 ¼ Agree Strongly.

Funding

This study was supported in part by a grant from the Baylor University Research Committee.

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