This article was downloaded by: [Umeå University Library]On: 24 November 2014, At: 01:13Publisher: RoutledgeInforma Ltd Registered in England and Wales Registered Number: 1072954 Registeredoffice: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK

Journal of Family CommunicationPublication details, including instructions for authors andsubscription information:http://www.tandfonline.com/loi/hjfc20

Family Communication Patternsand Communication Competence asPredictors of Online CommunicationAttitude: Evaluating a Dual PathwayModelAndrew M. Ledbetter aa School of Communication Studies , Ohio University ,Published online: 09 Apr 2010.

To cite this article: Andrew M. Ledbetter (2010) Family Communication Patterns and CommunicationCompetence as Predictors of Online Communication Attitude: Evaluating a Dual Pathway Model,Journal of Family Communication, 10:2, 99-115, DOI: 10.1080/15267431003595462

To link to this article: http://dx.doi.org/10.1080/15267431003595462

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of all the information (the“Content”) contained in the publications on our platform. However, Taylor & Francis,our agents, and our licensors make no representations or warranties whatsoever as tothe accuracy, completeness, or suitability for any purpose of the Content. Any opinionsand views expressed in this publication are the opinions and views of the authors,and are not the views of or endorsed by Taylor & Francis. The accuracy of the Contentshould not be relied upon and should be independently verified with primary sourcesof information. Taylor and Francis shall not be liable for any losses, actions, claims,proceedings, demands, costs, expenses, damages, and other liabilities whatsoever orhowsoever caused arising directly or indirectly in connection with, in relation to or arisingout of the use of the Content.

This article may be used for research, teaching, and private study purposes. Anysubstantial or systematic reproduction, redistribution, reselling, loan, sub-licensing,systematic supply, or distribution in any form to anyone is expressly forbidden. Terms &Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions

Journal of Family Communication, 10: 99–115, 2010Copyright © Taylor & Francis Group, LLCISSN: 1526-7431 print / 1532-7698 onlineDOI: 10.1080/15267431003595462

HJFC1526-74311532-7698Journal of Family Communication, Vol. 10, No. 2, Feb 2010: pp. 0–0Journal of Family Communication

Family Communication Patterns and Communication Competence as Predictors of Online Communication

Attitude: Evaluating a Dual Pathway Model

Family Communication PatternsLedbetter Andrew M. LedbetterSchool of Communication Studies, Ohio University

This manuscript reports an empirical study investigating the extent to which family communicationpatterns predict attitudes toward online communication within interpersonal relationships. Groundedin both Koerner and Fitzpatrick’s (2002a) generalized family communication theory and Bandura’s(1977) social cognitive theory, structural equation modeling tests a model whereby family commu-nication patterns act as both direct and indirect (i.e., via communication competence) predictors.Results find support for both theoretical perspectives, albeit more strongly for Koerner andFitzpatrick’s approach. Practical application of these results suggests that high conversation andmoderate conformity orientations most likely produce healthy attitudes toward online communica-tion in young adult children.

Since the widespread introduction of computers into American homes in the 1980s, researchershave devoted much attention to computer use in family contexts. Most such research conceptu-alizes computer use as an independent variable that influences family communication behavior.For example, research addresses whether computer use, and more specifically online com-munication, reduces time communicating with family (Kraut, Patterson, Lundmark, Kiesler,Mukhopadhyay, & Scherlis, 1998), changes work-life balance (Chelsey, 2005), and fosters fam-ily conflict (Mesch, 2006). What remains unanswered from this approach is the extent to whichfamily communication behavior generates, or constitutes, family members’ attitudes towardonline communication. As such attitudes vary across people (McKenna, Green, & Gleason,2002) and some cognitive/emotional orientations toward online communication predict negativepsychosocial outcomes (Caplan, 2007; Ledbetter, 2009b), it is worth investigating the mecha-nism via which family communication behavior might shape online communication attitude.Toward this end, the current research project adopts family communication patterns (FCP), aresearch tradition originally grounded in understanding the association between family commu-nication environments and media use (Chaffee, McLeod, & Atkin, 1971), as a theoretical start-ing point.

The overarching purpose of this investigation is to test two theoretical pathways via whichfamily communication patterns predict online communication attitude. The first pathway follows

Correspondence should be addressed to Andrew M. Ledbetter, School of Communication Studies, Ohio University,43 West Union St., Lasher Hall 206, Athens, OH 45701. E-mail: ledbette@ohio.edu

Dow

nloa

ded

by [

Um

eå U

nive

rsity

Lib

rary

] at

01:

13 2

4 N

ovem

ber

2014

100 LEDBETTER

Koerner and Fitzpatrick’s (2002a) generalized theory of family communication schemata bytesting the extent to which family communication patterns serve as direct predictors. The secondpathway follows recent theoretical argument, grounded in Bandura’s (1977) social cognitivetheory, that behavioral outcomes mediate the association between family communication pat-terns and psychosocial outcomes (Ledbetter, 2009a; Schrodt, Witt, & Messersmith, 2008); morespecifically, given evidence that communication competence predicts online communicationattitude (Caplan, 2007), this pathway tests whether communication competence serves as amediator. If practitioners wish to change a person’s online communication attitude, such inter-vention is aided by precisely understanding FCP’s mechanism of influence; illuminating suchmechanisms is a chief pragmatic goal of this investigation.

THEORETICAL WARRANT

Online Communication Attitude

Before considering the contribution of family communication to online communication attitude,it is necessary to properly conceptualize the construct in light of extant research. Recently,Ledbetter (2009b) synthesized several related lines of research into an overarching theoreticalstructure of online communication attitude, or “a cluster of cognitive and affective orientationsthat may foster or inhibit an individual’s tendency to communicate online.” Within this struc-ture, two constructs particularly motivate social use of online communication. First, Ledbetteridentifies online self disclosure (OSD) as tendency to prefer self disclosure in online contexts.Previous research indicates that some prefer self disclosing online versus face-to-face (Ho &McLeod, 2008) and that poor social skill may motivate this tendency (McKenna et al., 2002).Caplan’s (2007) program of research further demonstrates that attraction to the perceived inter-actional safety of online communication provokes avoidance of offline communication and thusgenerates “a growing neglect of offline professional, social, and personal responsibilities thatresult in negative consequences” (p. 557). Consistent with this expectation, OSD demonstratesan inverse association with communication competence (Ledbetter, 2009b).

In contrast, the second core motivation, online social connection (OSC), facilitates healthierrelational outcomes. Scholars have long recognized that online communication serves to maintainboth local and long distance relationships (Baym, Zhang, & Lin, 2004; Quan-Haase, Wellman,Witte, & Hampton, 2002), and Donath (2007) argues that online communication augmentssocial network size and access. Ledbetter’s (2009b) instrument measures tendency to use onlinecommunication to maintain social network ties, with OSC positively predicting relational close-ness indirectly via Facebook communication (Ledbetter, Mazer, DeGroot, Mao, Meyer, &Swafford, 2009). Thus, OSC may foster the creation and maintenance of online social networkswhich, in turn, builds social capital (Ellison, Steinfeld, & Lampe, 2007).

It is intuitively obvious that communication competence underlies theoretical considerationof these two core online communication motivations. Wilson and Sabee (2003) identify commu-nication competence as a generalized theoretical term whose meaning is dependent on the com-munication context of interest. This study uses a skills-based approach to communicationcompetence (Spitzberg & Cupach, 2002); this conceptualization has deep roots in previousresearch that identifies an inverse association between generalized perception of interpersonal

Dow

nloa

ded

by [

Um

eå U

nive

rsity

Lib

rary

] at

01:

13 2

4 N

ovem

ber

2014

FAMILY COMMUNICATION PATTERNS 101

skill and motivation to self disclose online (Ho & McLeod, 2008; McKenna et al., 2002).Caplan (2007) identifies such motivation toward online communication as associated withnegative psychosocial outcomes. Thus:

H1: Communication competence inversely predicts OSD.

Ledbetter (2009b) finds this hypothesized association between generalized perception ofcommunication competence and OSD, but reports a significantly positive association betweencommunication competence and OSC. This finding is consistent with Valkenburg and Peter’s(2008) positive association between communicating with a variety of people online and socialcompetence among Dutch teenagers. Following this previous research:

H2: Communication competence positively predicts OSC.

Family Communication Patterns

Building from previous work in schema theory (Fletcher & Thomas, 1996), Koerner andFitzpatrick (2002a) identify family communication patterns (FCP) as overarching schematatoward family functioning. Two dimensions comprise family communication schemata withinthe Koerner and Fitzpatrick (2002a) approach. First, conversation orientation addresses“the degree to which families create a climate in which all family members are encouraged toparticipate in unrestrained interaction about a wide array of topics” (Koerner & Fitzpatrick,2002a, p. 85).

As such, low conversation orientation families discourage talk and open expression of emo-tion, whereas high conversation orientation promotes mutual family discussion and decision-making. Second, conformity orientation addresses “the degree to which family communicationstresses a climate of homogeneity of attitudes, values, and beliefs” (Koerner & Fitzpatrick, 2002a,p. 85). High conformity orientation families emphasize parental authority, whereas low conformityorientation families may lack explicit rules and possess less hierarchical power distribution.These two dimensions intersect to form a four category typology of family types: (a) laissez-faire(low conversation and conformity), (b) protective (low conversation, high conformity), (c) pluralistic(high conversation, low conformity), and (d) consensual (high conversation and conformity)families. Thus, Koerner and Fitzpatrick (2002a) argue that the interaction between the twoorientations is of utmost theoretical importance.

The most comprehensive evaluation of FCP outcomes is Schrodt and his colleagues’ (Schrodt,Witt, & Messersmith, 2008) meta-analysis of nearly four decades of FCP research. Schrodt et al.identify three general classes of outcome variables associated with FCP: (a) psychosocialoutcomes (e.g., relational health, mental well-being), (b) behavioral outcomes (e.g., conflictmanagement skill), and (c) information processing outcomes (e.g., informational receptionapprehension). Though the meta-analysis revealed significant associations between both FCPdimensions and all three outcome classes, the strongest association emerged between con-versation orientation and psychosocial outcomes. Yet, Schrodt et al. further argue that behav-ioral outcomes might serve as the mechanism via which FCP influence psychosocial outcomes.Recent empirical evidence supports this theoretical claim by identifying potential behavioralmediators (such as conflict behavior, Schrodt & Ledbetter, 2007; parental confirmation, Schrodt,Ledbetter, & Ohrt, 2007). The current investigation focuses on conversation and conformity

Dow

nloa

ded

by [

Um

eå U

nive

rsity

Lib

rary

] at

01:

13 2

4 N

ovem

ber

2014

102 LEDBETTER

orientations as predictors of communication competence (i.e., a behavioral outcome) and onlinecommunication attitude (i.e., a psychosocial outcome).

Perhaps only Schrodt and his colleagues’ (2009) investigation directly examines the associa-tion between FCP and communication competence. Building from Bandura’s (1977) social cog-nitive theory, Schrodt and his colleagues posit that children learn communication skill, in part,by observing and actively modeling parental communication behavior. To the extent that highconversation orientation families engage in both everyday and serious conversation, children insuch families might learn to communicate competently; relatedly, to the extent that high confor-mity orientation suppresses discussion of high-risk topics, it might inhibit competence develop-ment. Schrodt et al.’s results confirm both of these expectations in the zero-order correlationmatrix. However, data analytic limitations prevented Schrodt and his colleagues from investigat-ing the interaction effect between conversation and conformity orientations. Following thesefindings:

H3: Conversation orientation positively predicts communication competence.H4: Conformity orientation inversely predicts communication competence.

RQ1: Do conversation and conformity orientations produce an interaction effect that predictscommunication competence?

If FCP predicts communication competence, and communication competence predicts onlinecommunication attitude, then it stands to reason that communication competence may serve asthe mechanism for the FCP/competence association. Such an expectation carries forwardSchrodt et al.’s (Schrodt, Ledbetter, Jernberg, Larson, Brown, & Glonek, 2009) and Ledbetter’s(2009a) adoption of social cognitive theory (Bandura, 1977) as an explanatory engine forFCP and behavioral outcomes. If this pathway is significant, this suggests that practitionerscan alter online communication attitude via training in communication skills. Stated moreformally:

RQ2: Does communication competence mediate the association between FCP and online communi-cation attitude?

However, previous theory suggests that this indirect path may not fully capture the relation-ship between FCP and online communication attitude; rather, schemata for family communicationmay directly influence schemata for online communication attitude. Building from relationalschema theory (Fletcher & Thomas, 1996), Koerner and Fitzpatrick (2002a) synthesize theirFCP approach into a generalized theory of family communication. This theoretical perspectivelocates family communication schemata within “a hierarchical organization of relational knowl-edge used by individuals to process information relevant to their relationships and interpersonalbehavior” (Koerner & Fitzpatrick, 2002a, p. 88). Though Koerner and Fitzpatrick focus chieflyon how families reproduce relationship-type–specific family communication schemata amongfamily members, Ledbetter (2009a) argues that family communication schemata also influencehigher-order generalized relational schemata that function across relationship types. As the familyrepresents the first context for understanding social relationships, one might expect that familycommunication schemata also influence schemata for interpersonal relationships more gener-ally; as cognitive/emotional orientations toward communication technology are one dimensionof such generalized knowledge (Ledbetter, 2009b), FCP may be directly associated with suchschemata in predictable ways.

Dow

nloa

ded

by [

Um

eå U

nive

rsity

Lib

rary

] at

01:

13 2

4 N

ovem

ber

2014

FAMILY COMMUNICATION PATTERNS 103

Schrodt and his colleagues (2008) identify conversation orientation as a particularly strongpredictor of positive psychosocial outcomes. Most germane to this study, Koesten (2004)identifies comfort with relational self disclosure as an outcome associated with high conversa-tion orientation. Thus, if fear of relational self-disclosure fosters a positive attitude towarddisclosing online (Caplan, 2007; McKenna et al., 2002), one might expect that conversationorientation is inversely associated with OSD. Yet, to the extent that high conversation orienta-tion emphasizes extrafamilial social connections as important support sources (Koerner &Fitzpatrick, 1997), then children from high conversation orientation families may value onlinecommunication as a means for sustaining network ties (Ellison et al., 2007). Following theselines of argumentation:

H5: Conversation orientation is inversely associated with OSD.H6: Conversation orientation is positively associated with OSC.

Scholars have long recognized that the asynchronous nature of online communicationprovides additional time for message construction (Walther, 1996), with some preferringonline communication because it affords more time for crafting self presentation (McKennaet al., 2002). Relatedly, within the FCP research tradition, conformity orientation is associ-ated with apprehension and decreased ability to generate and interpret messages (e.g., whenprocessing intellectual arguments; Ledbetter & Schrodt, 2008). Thus, children of high con-formity families may prefer the additional time afforded by many online communicationforms. Thus:

H7: Conformity orientation is positively associated with OSD.H8: Conformity orientation is positively associated with OSC.

Following Koerner and Fitzpatrick (2002b), it is worth considering whether conversation andconformity orientations interact to influence online communication attitude. Some evidencesuggests that high conversation orientation might reduce negative outcomes associated withhigh conformity orientation (Schrodt, 2005), but Ledbetter and Schrodt (2008) did not find thispattern in their study of message processing. Given this conflicting evidence:

RQ3: Do conversation and conformity orientations produce an interaction effect that predicts OSD?RQ4: Do conversation and conformity orientations produce an interaction effect that predicts OSC?

Figure 1 synthesizes the foregoing hypotheses and research questions into a hypothesizeddual-pathway model. Overall, this model represents both direct and indirect (i.e., via communi-cation competence) associations between FCP and online communication attitude. In summary,the potential theoretical and practical implications of this investigation are as follows: If themodel primarily supports an indirect association between FCP and online communication atti-tude (i.e., mediated via communication competence), this (a) theoretically, supports a socialcognitive approach to FCP outcomes (Bandura, 1977; Schrodt et al., 2008) and (b) practically,suggests changing online communication attitude by focusing on development of communica-tion skills. On the other hand, if the model primarily supports a direct association between FCPand online communication attitude, this (a) theoretically, supports Koerner and Fitzpatrick’s(2002a) schematic approach to family communication and (b) practically, suggests changingonline communication attitude by addressing cognitive and emotional responses to interpersonalcommunication more broadly.

Dow

nloa

ded

by [

Um

eå U

nive

rsity

Lib

rary

] at

01:

13 2

4 N

ovem

ber

2014

104 LEDBETTER

METHOD

Participants

The sample consisted of 417 participants (55.6% female) recruited from communicationstudies courses at a large public university in the U.S. Midwest. Participant age ranged from18 to 45 years (M = 20.4, SD = 2.8) and most reported their racial/ethnic identity as Caucasian(87.5%). Results from these data were also reported in Ledbetter (2009b) and the second studyin Ledbetter (2009a).

Procedures

All participants completed either a paper-based (N = 102) or web-based (N = 315) questionnaire.Most participants received course credit (less than 2%) for participation.

Family communication patterns. Koerner and Fitzpatrick’s (2002b) revised family com-munication patterns (RFCP) measure assessed both conversation (15 items, e.g., “My parentsoften ask my opinion when the family is talking about something”) and conformity (11 items,e.g., “My parents feel that it is important to be the boss”) orientations. Responses were solicitedon a 7-point Likert-type scale (1 = Strongly Disagree, 7 = Strongly Agree). Decades of previous

FIGURE 1 Hypothesized dual pathway structural model predictingonline communication attitude.

Note. RQ2 (which addresses communication competence as a mediatorvariable) is not depicted.

(RQ3)

(RQ1)

ConversationOrientation

OSD

ConformityOrientation

OSC

+ (H3)

– (H4)

– (H1)

+ (H6)

– (H5)

Comm.Competence

ConversationX Conformity

Orientation

+ (H2)

+ (H7)

+ (H8)

(RQ4)

Dow

nloa

ded

by [

Um

eå U

nive

rsity

Lib

rary

] at

01:

13 2

4 N

ovem

ber

2014

FAMILY COMMUNICATION PATTERNS 105

research establish the measure’s validity and reliability (see Schrodt et al., 2008), and bothconversation (a = .91) and conformity (a = .84) orientations likewise yielded acceptable reliabilityestimates in this investigation.

Communication competence. Guerrero’s (1994) generalized communication compe-tence instrument assessed the construct via six items (e.g., “I am a good communicator,” “I havea wide variety of social skills”) measured on a 5-point Likert scale (1 = Strongly Disagree, 5 =Strongly Agree). This measure is both brief and behaviorally focused, thus rendering it appropri-ate for this study. Schrodt and his colleagues (2009) validate this measure as associated withFCP in expected ways and demonstrate the measure’s reliability (in the current study, a = .77).

Online communication attitude. Attitudes toward online self-disclosure (OSD) (six items;e.g., “I feel less nervous when sharing personal information online”) and online social connec-tion (OSC) (seven items; e.g., “If I couldn’t communicate online, I would feel ‘out of the loop’with my friends”) were assessed via Ledbetter’s (2009b) measure of online communication atti-tude. Responses were solicited on a 7-point Likert-type scale (1 = Strongly Disagree, 7 =Strongly Agree), and both OSD (a = .90) and OSC (a = .84) exhibited acceptable reliability esti-mates in this investigation.

Data Analysis

A small amount of missing data (less than 1%) was imputed using an expectation-maximization(EM) algorithm (Kline, 2005). All hypotheses and research questions were addressed via confirma-tory factor analysis (CFA) and structural equation modeling (SEM) using the LISREL 8.80 for Win-dows statistical package. Four common fit indices assessed model fit: (a) model chi-square, (b) rootmean square error of approximation (RMSEA), (c) comparative fix index (CFI), and (d) non-normed fit index (NNFI). Inclusion of sample size in the chi-square formula renders the statisticoverly conservative for most CFA/SEM applications. The other fit statistics correct for this, withRMSEA values below .08 and CFI/NNFI values above .90 indicating acceptable fit (Kline, 2005).

Both the measurement and structural models contained six constructs: (a) conversation orien-tation, (b) conformity orientation, (c) a construct representing the orthogonalized interactionterm between conversation and conformity orientations, (d) communication competence, (e)OSD, and (f) OSC. Parcels, or “aggregate-level [indicators] comprised of the sum (or average)of two or more items, responses, or behaviors” (Little, Cunningham, Shahar, & Widaman, 2002,p. 152), served as indicators for most constructs. Given no a priori rationale for parcel forma-tion, parcels were formed by taking the mean of items by thirds (e.g., for conformity orientation,the first parcel averaged items 1, 4, 7, and 10). The orthogonalized interaction term was createdusing the method described by Little and his colleagues (Little, Card, Bovaird, Preacher, &Crandall, 2007). More specifically, each mean-centered conversation orientation parcel wasmultiplied by each mean-centered conformity orientation parcel, yielding nine estimates of theinteraction effect. Each of these nine estimates was then regressed onto all six of the first-orderparcels, saving the unstandardized residuals from each regression equation. These nine residualswere then averaged into three parcels such that each interaction term parcel contains only oneinstance of each of the first-order parcels (see Marsh, Wen, Hau, Little, Bovaird, & Widaman, 2007).This approach removes all multicollinearity between the interaction term and its first-order

Dow

nloa

ded

by [

Um

eå U

nive

rsity

Lib

rary

] at

01:

13 2

4 N

ovem

ber

2014

106 LEDBETTER

components, and thus results in a purer estimate of both first-order and interaction term effectsin the structural model (Little, Card, Bovaird, Preacher, & Crandall, 2007).

RESULTS

According to Wright (2005), web-based data collection methods generally do not bias parameterestimates; as participant responses were collected via both paper- (n = 102) and web-based (n = 315)methods, Little’s (1997) procedures for establishing metric invariance tested this assumption.Results of these tests indicated that both weak (i.e., indicator loadings) and strong (i.e., indicatormeans) metric invariance are tenable assumptions; more importantly, the variance/covariancematrices exhibited homogeneity, Δc2(19) = 17.19, p > .05, thus indicating that the constructs areempirically indistinct across groups. Following the procedure recommended by Kline (2005), aconfirmatory factor analysis evaluated the overall fit between the manifest indicators and theirhypothesized latent constructs. This measurement model demonstrated acceptable model fit,c2(120) = 270.42, RMSEA = .055[90% CI = .047:.064], NNFI = .97, CFI = .97. Table 1 presentsmeans, standard deviations, and zero-order correlations.

The hypothesized structural model (Figure 1) likewise demonstrated acceptable model fit,c2(122) = 270.42, RMSEA = .055[90% CI = .046:.063], NNFI = .97, CFI = .97. Figure 2 presents sig-nificant paths obtained in the final model. The first hypothesis predicted an inverse associationbetween communication competence and OSD, and the final model supported this prediction(B = −0.29[95% CI = −0.56:−0.01], b = −.11[95% CI = −.22:−.01], p < .05). However, support did notemerge for H2’s prediction that communication competence would positively predict OSC(B = 0.05[95% CI = −0.23:0.33], b = .02[95% CI = −.10:.14], p > .05).

The next set of hypotheses and RQ1 addressed the relationship between FCP and commu-nication competence, with H3 predicting a positive association with conversation orientation,H4 predicting an inverse association with conformity orientation, and RQ1 addressing a possi-ble interaction effect between these independent variables. In the final model, conversationorientation positively and significantly predicted competence (B = 0.13[95% CI = 0.07:0.19],b = .25[95% CI = .14:.35], p < .01). However, neither conformity orientation (B = −0.04[95% CI = −0.10:0.02],

TABLE 1Descriptive Statistics and Bivariate Correlations Among Manifest Indicators (N = 325)

Variables M SD 1 2 3 4 5

1. Conversation O. 4.81 1.00 1.00 −.24** .26** −.18** −.092. Conformity O. 3.70 1.00 −.18** 1.00 −.13* .38** .24**3. Comm. Compet. 4.06 0.59 .23** −.19** 1.00 −.18** −.024. Self Disc. (OSD) 3.82 1.31 −.16** .32** −.23** 1.00 .54**5. Soc. Conn. (OSC) 3.94 1.32 −.08 .21** −.07 .48** 1.00

* p < .05; **p < .01.Note. Correlation coefficients below the diagonal represent the associations at the manifest level; those above the

diagonal represent latent constructs in the confirmatory model.

Dow

nloa

ded

by [

Um

eå U

nive

rsity

Lib

rary

] at

01:

13 2

4 N

ovem

ber

2014

FAMILY COMMUNICATION PATTERNS 107

b = −.07[95% CI = −.18:.05], p > .05) nor the interaction effect (B = −0.001[95% CI = −0.04:0.04],b = −.002[95% CI = −.10:.10], p > .05) significantly predicted competence (though conformity wasinversely associated with competence in the latent construct correlation matrix, r = −.13,p < .05). This pattern of results supports H3, rejects H4, and answers RQ1 negatively.

Following recent use of social cognitive theory (Bandura, 1977) in FCP research (Ledbetter,2009a; Schrodt et al., 2009), the second research question addressed whether FCP (i.e., conver-sation and conformity orientations) indirectly predict online communication attitude (i.e., OSDand OSC) via communication competence. Though previous research assessed indirect effectsby examining the change in the direct effect when including a mediator variable in a regressionmodel (Baron & Kenny, 1986), statisticians in the behavioral sciences now recommend explic-itly testing the significance of the indirect effect (i.e., the product of the effect’s component betaweights) (Little et al., 2007). Thus, nonparametric bootstrapping (Preacher & Hayes, 2004)evaluated the model’s six indirect paths (i.e., one path each for conversation, conformity, andtheir interaction term as predictors of both OSD and OSC). Only the indirect effect fromconversation orientation to OSD achieved statistical significance, B = −0.03[95% CI = −0.07:−0.002],b = −.02[95% CI = −.05:−.002], p < .05.

Building from Koerner and Fitzpatrick’s (2002a) generalized theory of family communica-tion schemata, the remaining hypotheses and research questions addressed direct associationsbetween FCP and online communication attitude. The fifth and sixth hypotheses respectivelyadvanced that conversation orientation would inversely predict OSD and positively predict OSC.Though conversation orientation was inversely associated with OSD in the latent construct cor-relation matrix (r = −.18, p < .05), it directly predicted neither OSD (B = −0.09[95% CI = −0.22:0.05],b = −.07[95% CI = −.17:.04], p > .05) nor OSC (B = −0.06[95% CI = −0.20:0.09], b = −.04[95% CI = −.16:.07],p > .05) in the final model, and thus neither hypothesis received support. H7 and H8 predicted

FIGURE 2 Final dual pathway structural model predicting onlinecommunication attitude.

Note. c2(122) = 270.42, RMSEA = .055[90% CI = .046:.063], NNFI = .97,CFI = .97. Nonsignificant paths not shown.

.10*

ConversationOrientation

OSD

Conformity Orientation

OSC

.25**

–.11*

Comm.Competence

ConversationX Conformity

Orientation

.23**

–.24**.45**

.35**

R2 = .07

R2 = .06

R2 = .17

Dow

nloa

ded

by [

Um

eå U

nive

rsity

Lib

rary

] at

01:

13 2

4 N

ovem

ber

2014

108 LEDBETTER

positive associations between conformity orientation and both OSD and OSC. Conformityorientation was a positive predictor of both OSD (B = 0.47[95% CI = 0.33:0.61], b = .35[95% CI = .45:.24],p < .01) and OSC (B = 0.31[95% CI = 0.16:0.46], b = .23[95% CI = .12:.34], p < .01) in the structural model,supporting both H7 and H8.

The third and fourth research questions addressed whether the interaction between conversa-tion and conformity orientations predicted OSD and OSC. Though the path to OSC was not sig-nificant, B = 0.04[95% CI = −0.07:0.15], b = .04[95% CI = −.06:.14], p > .05, the interaction termsignificantly predicted OSD, B = 0.10[95% CI = 0.001:0.21], b = .10[95% CI = .0001:0.19], p < .05. Taken asa whole, then, the model suggests that FCP predict OSD via a direct main effect path (i.e., fromconformity orientation), a direct moderation path (i.e., from the interaction term), and an indirectmediation path (i.e., from conversation orientation to OSD via communication competence; seeRQ2). To decompose the interaction effect while simultaneously accounting for the significantfirst-order main and indirect effects, the model was recomputed with a mean structure identifiedvia the contrast coding method recommended by Little, Slegers, and Card (2006). This proce-dure generates estimates of the latent construct means in the metric of the manifest variables.Then, using the method described by Cohen, Cohen, West, and Aiken (2003), the interactioneffect was probed at the minimum, midpoint, and maximum scores of each independentvariable.

Figure 3 presents the results of this decomposition. Inspection of the decomposition revealsthat the strength of the association between conformity orientation and OSD decreases withlower levels of conversation orientation. In other words, conformity most strongly predicts OSDat the highest possible level of conversation orientation, B = 0.70[95% CI = 0.52:0.87], b = .51[95%

CI = .38:.64], p < .01. The discrepancy between this regression line’s endpoints is particularly note-worthy, ranging from OSD = 2.69 (i.e., between 2 = Disagree and 3 = Somewhat Disagree)

FIGURE 3 Decomposition of interaction (with main and indirecteffects) between conversation and conformity orientations as predictorsof OSD.

Note. Dashed vertical line represents latent construct mean for conformityorientation.

Dow

nloa

ded

by [

Um

eå U

nive

rsity

Lib

rary

] at

01:

13 2

4 N

ovem

ber

2014

FAMILY COMMUNICATION PATTERNS 109

when conformity is at a minimum to OSD = 6.86 (i.e., nearly 7 = Strongly Agree) at the maxi-mum level of conformity. The association between conformity and OSD remains significant at amean level of conversation orientation, B = 0.47[95% CI = 0.29:0.66], b = .34[95% CI = .13:.34], p < .01,but is nonsignificant at minimal values of conversation orientation, B = 0.08[95% CI = −0.10:0.25],b = .06[95% CI = −.07:.19], p > .05.

When considering this interaction effect from the perspective of conversation orientation, itappears that the directionality of conversation orientation’s effect reverses at high levels of con-formity orientation. Solving for this point of reversal (i.e., where Figure 3’s plotted lines con-verge) reveals that this occurs when conformity = 4.89 (i.e., slightly below 5 = Somewhat Agree).Converting this value to a standardized score (using latent construct mean = 3.73 and standarddeviation = 0.92) yields z = 1.26. In other words, conversation orientation tends to reduce OSDwhen a family’s conformity orientation is below this threshold, but conversation orientation pos-itively contributes to OSD when level of conformity is quite higher than average. Probing theinteraction effect from this standpoint verifies that conversation orientation is a significant inversepredictor when conformity is low (B = −0.40[95% CI = −0.57:−0.23], b = −.31[95% CI = −.44:−.18], p < .01),a nonsignificant predictor at a mean level of conformity orientation (B = −0.12[95% CI = −0.29:0.05],b = −.09[95% CI = −.22:.04], p > .05), and a significant positive predictor when conformity is high(B = 0.22[95% CI = 0.05:0.39], b = .17[95% CI = 0.03:0.30], p < .05).

DISCUSSION

The chief purpose of this investigation was to evaluate a dual-pathway model of the relationshipbetween FCP and online communication attitude. Overall, results supported both the direct asso-ciation predicted by Koerner and Fitzpatrick’s (2002a) generalized theory of family communica-tion schemata and, to a lesser extent, the indirect association predicted by social cognitive theory(Bandura, 1977) (Ledbetter, 2009a; Schrodt et al., 2009) and related online communicationresearch (Caplan, 2007; McKenna et al., 2002). This discussion will consider both the theoreticaland practical implications of these findings.

Family Communication Patterns and OSC

In the final structural model, only conformity orientation predicted OSC after controlling for allother model effects. That this association is not mediated via communication competence pre-cludes explanations based on the extent to which conformity orientation facilitates or inhibitssocial skill (McKenna et al., 2002) and, rather, focuses attention on Koerner and Fitzpatrick’s(2002a) theorized connection between family communication schemata and more generalizedsocial cognitive processes. Interpersonal communication scholars have long recognized thatinterpersonal relationships function according to structured rules, such as commemorating spe-cial occasions (e.g., birthdays) and sharing important news (Argyle & Henderson, 1984).

To the extent that high conformity orientation families are inherently rule-based and thusemphasize rule-keeping, children may be more likely to perceive online communication asuseful for keeping such social rules. This may be especially so in long distance relationships, assuch rules cannot be kept face-to-face; in the absence of face-to-face contact, children from lowconformity orientation families may discard such rules, whereas children from high conformity

Dow

nloa

ded

by [

Um

eå U

nive

rsity

Lib

rary

] at

01:

13 2

4 N

ovem

ber

2014

110 LEDBETTER

orientation families may both perceive such rules as in force and online communication as usefulfor keeping them. As most research finds negatively-valenced outcomes associated with confor-mity orientation (Schrodt et al., 2008), it is worth noting that such a directly positive relationaloutcome of conformity orientation is rare (but not unique; e.g., conformity orientation isinversely associated with depression, Koerner & Fitzpatrick, 1997).

More practically, then, we might expect children from high conformity orientation families tomore successfully maintain long-distance relationships primarily conducted online. To theextent that such contacts may function as supportive weak ties (Haythornthwaite, 2005), this is adesirable outcome. Families, then, should emphasize the importance of keeping social rules,even when not spatially copresent; and practitioners seeking to encourage such behavior shouldfocus on broader perceptions of relationship rule-keeping rather than behavioral skills. In otherwords, some people may possess communicative skills necessary to maintain long distance rela-tionships, but fail to understand the force of social rules that guide such contact. For example,awareness that even long-distance relationships function according to social rules may partic-ularly help men, who often possess dormant friendships with only minimal contact (Rawlins,1992); such an understanding may also equip men to perform family kinkeeping, a task thatoverwhelmingly falls to women (Leach & Braithwaite, 1996).

Family Communication Patterns and OSD

In contrast to OSC’s predictive simplicity, the final model predicted OSD via a direct maineffect from conformity orientation, an indirect effect from conversation orientation viacommunication competence, and a direct interaction effect between conversation and con-formity orientations. This discussion will consider each of these effects individually andtogether.

As hypothesized, conformity orientation positively predicted OSD. To the extent that confor-mity orientation suppresses open discussion and sharing of emotions, children may learn that itis appropriate to suppress their own thoughts, opinions, and emotions for the sake of the family.Thus, when engaging in self disclosure, they may adopt online communication to engage in self-disclosure at some distance from the target (i.e., removed from immediate social sanction). Thisexplanation differs from a social skill account (i.e., that conformity orientation inhibits skillsnecessary for proper self-disclosure; McKenna et al., 2002), as no such significant indirect pathappeared in the final model. Indeed, that Schrodt et al. (2009) also found no significant associa-tion between conformity orientation and communication competence in their final model sug-gests that apparently significant inverse zero-order correlations between conformity orientationand communication competence are artifacts of conformity’s shared variance with conversationorientation.

Thus, though conformity orientation may influence relational schemata and interpersonalscripts (such as those related to online communication), it does not seem associated with com-munication competence after accounting for conversation orientation. Therefore, children fromhigh conformity orientation families may prefer online self disclosure due to greater comfortversus lacking ability to self disclose when face-to-face. This suggests that interventions aimedat correcting overreliance on online self disclosure should not focus on behavior (e.g., by practicingreal or hypothetical disclosure scenarios), but rather on education about the benefits of self dis-closure (e.g., that it can enhance relational intimacy).

Dow

nloa

ded

by [

Um

eå U

nive

rsity

Lib

rary

] at

01:

13 2

4 N

ovem

ber

2014

FAMILY COMMUNICATION PATTERNS 111

In support of the social skill account (Bandura, 1977), conversation orientation positivelypredicted communication competence (consistent with Schrodt et al., 2009) and communicationcompetence, in turn, inversely predicted OSD (consistent with Ledbetter, 2009b). This supportsCaplan’s (2007) argument that “socially anxious individuals may develop a preference foronline social interaction because they perceive greater control over self-presentation online thanthey do in [face-to-face] encounters” (p. 240), as well as Schrodt and his colleagues’ (2008)claim that “conversation orientation equips children with the information processing skills andcommunication behaviors necessary for coping with stress and for developing healthy rela-tionships” (p. 263).

Nevertheless, this indirect association, though significant, must be interpreted in light of thesmall regression weight and the comparatively larger sizes of the other main effects from FCP toonline communication attitude generally (and OSD specifically). Thus, practicing face-to-facecommunication skills may reduce OSD only minimally. Of course, that the indirect path throughcommunication competence was a relatively weak predictor does not preclude the possibilitythat other behavioral outcomes of conversation orientation (such as conflict management; Koesten,2004) might mediate this association; however, these results caution against overstating the roleof communication competence as a mediator between FCP and online communication attitude.

Finally, the interaction term between conversation and conformity orientations predictedOSD. Thus, OSD is a psychosocial outcome that cannot be fully understood without consideringthe joint effect of both FCP dimensions (as recommended by Koerner & Fitzpatrick, 2002a).Again, this interaction effect is not mediated by communication competence, and thus Koernerand Fitzpatrick’s (2002a) generalized theory of family communication schemata is a preferredinterpretive framework. When conformity orientation is low but conversation orientation is high(i.e., a highly pluralistic family), OSD tends to be relatively low. In such families, children expe-rience “open, unconstrained discussions that are open to . . . all family members,” and thus con-sider such personal communication as appropriate when face-to-face (Koerner & Fitzpatrick,2002b, p. 44). Consistent with the gestalt of media richness theory (Daft & Lengel, 1986), suchchildren may even see face-to-face as the most meaningful context for such communication, asface-to-face communication affords temporal synchronicity and a full nonverbal cue range.Toward mean levels of conformity, the association between conversation orientation and OSDremains inverse (albeit nonsignificant).

The positive association between conversation orientation and OSD is significant only forstrongly consensual families (i.e., possessing high conversation and conformity orientations)When conformity orientation is especially strong, conversation orientation may no longerreduce the association between conformity orientation and negative psychosocial outcomes(Schrodt, 2005). Rather, additional interaction time may reinforce the exceptionally (and per-haps unhealthily) strong family authority structure. This may especially be the case for theyoung adult sample here, as they are in a time of life when some autonomy from parental controlis developmentally appropriate (Arnett, 1994).

Valuing control, young adults from high conformity families may appreciate that online com-munication affords additional time for self presentation that, for them, facilitates conformity tonormative expectations (Walther, 1996). Taken together with findings for pluralistic families,this pattern of results deviates from some previous research, as pluralistic and consensual familiesare considered preferred family types (i.e., due to the positive influence of high conversationorientation; Schrodt et al., 2008) yet exhibit markedly different psychosocial outcomes here. In

Dow

nloa

ded

by [

Um

eå U

nive

rsity

Lib

rary

] at

01:

13 2

4 N

ovem

ber

2014

112 LEDBETTER

contrast, the remaining family types (laissez-faire and protective) exhibit a similar pattern ofresults, as OSD is nearly constant (i.e., between 5 = Somewhat Agree and 6 = Agree) at low lev-els of conversation orientation. As neither family type emphasizes interpersonal communicationwithin the family, children from such families may not see face-to-face relating as particularlyadvantageous, and thus may have a slight preference for online self disclosure.

Synthesizing this pattern of results, conversation orientation emerges as a potential theoreticalmechanism via which conformity orientation influences subsequent schemata for the role oftechnology in interpersonal relationships. In other words, conformity orientation may not influ-ence children’s online communication attitude unless the family emphasizes frequent interper-sonal communication (i.e., conversation orientation) among family members; yet when familiesdo exhibit high conversation orientation, attitude toward online self disclosure varies greatlydepending on the family’s level of conformity orientation. This explanation is consistent withLedbetter’s (2009a) argument that family communication schemata directly influence both gen-eralized and other relationship-specific schemata. Application of this explanation must accountfor the young adult nature of this sample; for younger children, when stronger parental control ismore developmentally appropriate, high conformity and conversation outcomes may not yieldan unhealthy OSD outcome. Of course, this speculation awaits empirical investigation.

Overall, then, these results generally support Koerner and Fitzpatrick’s (2002a) schematicapproach to family communication yet provide some weaker support for the claim that specificbehaviors serve as mediators for relevant psychosocial outcomes (Schrodt et al., 2008). Ofcourse, this interpretation must be considered in light of previous research indicating full media-tion for psychosocial outcomes such as relational closeness (Ledbetter, 2009a) and mentalhealth (Schrodt & Ledbetter, 2007). Only future research can determine whether the currentresults indicate a direct inter-schematic effect or whether other behavioral variables serve asmediators of the relationship between FCP and online communication attitude.

For families interested in teaching their children to use social technology appropriately, theseresults qualify Ledbetter’s (2009b) claim that online communication attitude is associated withcommunication competence. Moreover, these results identify the family communication envi-ronment as a stronger direct predictor of online communication attitude. For families, then,teaching children to use social technology appropriately may not rest on modeling communicationskills, but rather maintaining a healthy family communication climate that values open conver-sation and sharing among family members. For scholars, these results suggest that theorizationof relational multimodality (i.e., use of multiple media within relationships; Baym et al., 2004)perhaps should not rest too strongly on communication competence as a key theoretical mechanism(Spitzberg, 2006).

Finally, these results highlight the need for family communication scholars to theorize thenature of the interaction between conversation and conformity orientations. Despite Koerner andFitzpatrick’s (2002a) suggestion, researchers only seldom examine this interaction, and thus itremains undertheorized. In this case, the interaction effect suggests that high conversation orien-tation is a necessary precondition for conformity orientation’s influence on online communicationattitude. As conversation orientation necessarily fosters the communicative exchanges via whichfamily socialization occurs (Koerner & Fitzpatrick, 2002b), one might expect a similar patternof findings for other relationally-oriented outcomes.

Practically, these results suggest that heightened conversation orientation with moderatelevels of conformity orientation may best foster healthy attitude toward online communication.

Dow

nloa

ded

by [

Um

eå U

nive

rsity

Lib

rary

] at

01:

13 2

4 N

ovem

ber

2014

FAMILY COMMUNICATION PATTERNS 113

When parents and children communicate frequently and openly with each other, children aremore likely to learn how to communicate competently and, thus, lack a debilitating reliance ononline communication for self disclosure (Caplan, 2007). Yet if such high conversation orienta-tion is accompanied by extremely low conformity orientation, then the benefits to social capital(Ellison et al., 2007) arising from OSC may not develop. Thus, high conversation with a moderatelevel of conformity may best equip children to self disclose online when appropriate (e.g., forsocial support on sensitive topics, Walther & Boyd, 2002) and use online communication formaintenance of local and long distance social networks (Ellison et al., 2007).

CONCLUSION

Of course, any study must be interpreted within the limitations of the research design. The rela-tively homogeneous nature of the sample (e.g., predominantly Caucasian young adults) suggestscaution when applying these findings to other populations. These young adults had already com-pleted their childhood years, arguably the most intensive phase of parent-child communicationand behavioral modeling. In one sense, this is theoretically advantageous, as young adult childrenpossess more freedom to make independent choices about communication behavior; thus, anyeffects detected are likely to be persistent (and, perhaps, permanent).

The limitation of this approach is that it captures the final state of a modeling process that can onlybe captured via longitudinal research, and one might expect higher effect sizes earlier in the develop-mental course (before peer influence exerts stronger influence on attitudes and behaviors). Apartfrom longitudinal designs, future cross-sectional research could compare the relative influence offamily and peer influence on young adults’ online communication attitude. Finally, though Ledbetter(2009b) focuses on OSD and OSC as two fundamental motives toward online communication withininterpersonal relationships, he identifies three additional dimensions (e.g., apprehension) as part of anindividual’s underlying attitudinal orientation. Future research could advance theory by further con-ceptualizing the role of these constructs and modeling their association with FCP.

For most in the Western world, social networks are too large and dispersed to maintain com-petently via face-to-face communication alone (Donath, 2007). As online communication nowassumes a fundamental role in both interpersonal and organizational relationships, it is worththeorizing attitudinal contributors to specific patterns of online communication. These resultssuggest that family communication environments meaningfully predict such attitudes and thatcommunication competence alone does not account for this association. Future theoreticaladvance may stem from conceptualizing and empirically testing both the antecedents and schematicstructure of online communication attitude.

REFERENCES

Argyle, M., & Henderson, M. (1984). The rules of friendship. Journal of Social and Personal Relationships, 1, 211–237.Arnett, J. J. (1994). Are college students adults? Their conceptions of the transition to adulthood. Journal of Adult

Development, 1, 213–224.Bandura, A. (1977). Social learning theory. Englewood Cliffs, NJ: Prentice Hall.Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research:

Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173–1182.

Dow

nloa

ded

by [

Um

eå U

nive

rsity

Lib

rary

] at

01:

13 2

4 N

ovem

ber

2014

114 LEDBETTER

Baym, N. K., Zhang, Y. B., & Lin, M.-C. (2004). Social interactions across media: Interpersonal communication on theinternet, telephone, and face-to-face. New Media & Society, 6, 299–318.

Caplan, S. E. (2007). Relations among loneliness, social anxiety, and problematic Internet use. CyberPsychology &Behavior, 10, 234–242.

Chaffee, S. H., McLeod, J. M., & Atkin, D. K. (1971). Parental influences on adolescent media use. American Behav-ioral Scientist, 14, 323–340.

Chelsey, N. (2005). Blurring boundaries? Linking technology use, spillover, individual distress, and family satisfaction.Journal of Marriage and Family, 67, 1237–1248.

Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behav-ioral sciences (3rd ed.). Mahwah, NJ: Lawrence Erlbaum Associates.

Daft, R. L., & Lengel, R. H. (1986). Organizational information requirements, media richness and structural design.Management Science, 32, 554–571.

Donath, J. (2007). Signals in social supernets. Journal of Computer-Mediated Communication, 13. Retrieved on July 23,2009, from http://jcmc.indiana.edu/vol13/issue1/donath.html.

Ellison, N. B., Steinfield, C., & Lampe, C. (2007). The benefits of Facebook “friends:” Social capital and collegestudents’ use of online social network sites. Journal of Computer-Mediated Communication, 12. Retrieved on July 23,2009, from http://jcmc.indiana.edu/vol12/issue4/ellison.html.

Fletcher, G. J. O., & Thomas, G. (1996). Close relationship lay theories: Their structure and function. In G. J. O. Fletcher &J. Fitness (Eds.), Knowledge structures in close relationships: A social psychological approach (pp. 3–24). Mahwah,NJ: Lawrence Erlbaum.

Guerrero, L. K. (1994). “I’m so mad I could scream:” The effects of anger expression on relational satisfaction and com-munication competence. Southern Communication Journal, 59, 125–141.

Haythornthwaite, C. (2005). Social networks and Internet connectivity effects. Information, Communication & Society,8, 125–147.

Ho, S. S., & McLeod, D. M. (2008). Social-psychological influences on opinion expression in face-to-face andcomputer-mediated communication. Communication Research, 35, 190–207.

Kline, R. B. (2005). Principles and practice of structural equation modeling (2nd ed.). New York: Guilford.Koerner, A. F., & Fitzpatrick, M. A. (1997). Family type and conflict: The impact of conversation orientation and con-

formity orientation on conflict in the family. Communication Studies, 48, 59–78.Koerner, A. F., & Fitzpatrick, M. A. (2002a). Toward a theory of family communication. Communication Theory, 12,

70–91.Koerner, A. F., & Fitzpatrick, M. A. (2002b). Understanding family communication patterns and family functioning:

The roles of conversation orientation and conformity orientation. Communication Yearbook, 26, 37–69.Koesten, J. (2004). Family communication patterns, sex of subject, and communication competence. Communication

Monographs, 71, 226–244.Kraut, R., Patterson, M., Lundmark, V., Kiesler, S., Mukhopadhyay, T., & Scherlis, W. (1998). Internet paradox: A

social technology that reduces social involvement and psychological well-being? American Psychologist, 53,1017–1031.

Leach, M. S., & Braithwaite, D. O. (1996). A binding tie: Supportive communication of family kinkeepers. Journal ofApplied Communication Research, 24, 200–216.

Ledbetter, A. M. (2009a). Family communication patterns and relational maintenance behavior: Direct and mediatedassociations with friendship closeness. Human Communication Research, 35, 130–147.

Ledbetter, A. M. (2009b). Measuring online communication attitude: Instrument development and validation. Communi-cation Monographs, 76, 463–486.

Ledbetter, A. M., Mazer, J. P., DeGroot, J. M., Mao, Y., Meyer, K. R., & Swafford, B. (in press). Attitudes towardonline social connection and self disclosure as predictors of Facebook communication and relational closeness.Communication Research.

Ledbetter, A. M., & Schrodt, P. (2008). Family communication patterns and cognitive processing: Conversation and con-formity orientations as predictors of informational reception apprehension. Communication Studies, 59, 388–401.

Little, T. D. (1997). Mean and covariance structures (MACS) analyses of cross-cultural data: Practical and theoreticalissues. Multivariate Behavioral Research, 32, 53–76.

Little, T. D., Card, N. A., Bovaird, J. A., Preacher, K., & Crandall, C. S. (2007). Structural equation modeling of media-tion and moderation with contextual factors. In T. D. Little, J. A. Bovaird, & N. A. Card (Eds.), Modeling contextualeffects in longitudinal studies (pp. 207–230). Mahwah, NJ: LEA.

Dow

nloa

ded

by [

Um

eå U

nive

rsity

Lib

rary

] at

01:

13 2

4 N

ovem

ber

2014

FAMILY COMMUNICATION PATTERNS 115

Little, T. D., Cunningham, W. A., Shahar, G., & Widaman, K. F. (2002). To parcel or not to parcel: Exploring the ques-tion, weighing the merits. Structural Equation Modeling, 9, 151–173.

Little, T. D., Slegers, D. W., & Card, N. A. (2006). A non-arbitrary method of identifying and scaling latent variables inSEM and MACS models. Structural Equation Modeling, 13, 59–72.

Marsh, H. W., Wen, Z., Hau, K.-T., Little, T. D., Bovaird, J. A., & Widaman, K. F. (2007). Unconstrained structuralequation models of latent interactions: Contrasting residual- and mean-centered approaches. Structural EquationModeling, 14, 570–580.

McKenna, K. Y. A., Green, A. S., & Gleason, M. E. J. (2002). Relationship formation on the Internet: What‘s the bigattraction? Journal of Social Issues, 58, 659–671.

Mesch, G. S. (2006). Family relations and the Internet: Exploring a family boundaries approach. Journal of FamilyCommunication, 6, 119–138.

Preacher, K. J., & Hayes, A. F. (2004). SPSS and SAS procedures for estimating indirect effects in simple mediationmodels. Behavior Research Methods, Instruments, & Computers, 36, 717–731.

Quan-Haase, A., Wellman, B., Witte, J. C., & Hampton, K. N. (2002). Capitalizing on the net: Social contact, civicengagement, and sense of community. In B. Wellman & C. Haythornthwaite (Eds.), The Internet in everyday life(pp. 291–324). Malden, MA: Blackwell.

Rawlins, W. K. (1992). Friendship matters: Communication, dialectics, and the life course. New York: Aldine deGruyter.

Schrodt, P. (2005). Family communication schemata and the circumplex model of family functioning. Western Journalof Communication, 69, 361–378.

Schrodt, P., & Ledbetter, A. M. (2007). Communication processes that mediate family communication patterns andmental well-being: A mean and covariance structures analysis of young adults from divorced and non-divorcedfamilies. Human Communication Research, 33, 330–356.

Schrodt, P., Ledbetter, A. M., Jernberg, K. A., Larson, L., Brown, N., & Glonek, K. (2009). Family communicationpatterns as mediators of communication competence in the parent/child relationship. Journal of Social and PersonalRelationships, 26, 853–874.

Schrodt, P., Ledbetter, A. M., & Ohrt, J. K. (2007). Parental confirmation and affection as mediators of family commu-nication patterns and children’s mental well-being. Journal of Family Communication, 7, 23–46.

Schrodt, P., Witt, P. L., & Messersmith, A. (2008). A meta-analytical review of family communication patterns and theirassociations with information processing, behavioral, and psychosocial outcomes. Communication Monographs, 75,248–269.

Spitzberg, B. H. (2006). Preliminary development of a model and measure of computer-mediated communication(CMC) competence. Journal of Computer-Mediated Communication, 11. Retrieved on July 23, 2009 from http://jcmc.indiana.edu/vol11/issue2/spitzberg.html.

Spitzberg, B. H., & Cupach, W. R. (2002). Interpersonal skills. In M. L. Knapp & J. A. Daly (Eds.), Handbook of Inter-personal Communication (pp. 564–611). Thousand Oaks, CA: Sage.

Valkenburg, P. M., & Peter, J. (2008). Adolescents’ identity experiments on the Internet: Consequences for social com-petence and self-concept unity. Communication Research, 35, 208–231.

Walther, J. B. (1996). Computer-mediated communication: Impersonal, interpersonal, and hyperpersonal interaction.Communication Research, 23, 3–43.

Walther, J. B., & Boyd, S. (2002). Attraction to computer-mediated social support. In C. A. Lin & D. Atkin (Eds.), Com-munication technology and society: Audience adoption and uses (pp. 153–188). Cresskill, NJ: Hampton Press.

Wilson, S. R., & Sabee, C. M. (2003). Explicating communicative competence as a theoretical term. In J. O. Greene &B. R. Burleson (Eds.), Handbook of communication and social interaction skills (pp. 3–50). Mahwah, NJ: LawrenceErlbaum Associates.

Wright, K. B. (2005). Researching Internet-based populations: Advantages and disadvantages of online survey research,online questionnaire authoring software packages, and web survey services. Journal of Computer-Mediated Commu-nication, 10. Retrieved on July 23, 2009 from http://jcmc.indiana.edu/vol10/issue3/wright.html.

Dow

nloa

ded

by [

Um

eå U

nive

rsity

Lib

rary

] at

01:

13 2

4 N

ovem

ber

2014