Relational uncertainty and dyadic synchrony within the interaction of couples

Abstract

Relational uncertainty refers to the questions people have about their perceptions of involvement within close relationships. To complement a wealth of research examining the link between relational uncertainty and people’s self-reported communication strategies, we investigate relational uncertainty as a predictor of behavioral sequences within interaction. We draw on both seminal and contemporary theorizing to hypothesize that relational uncertainty impedes dyadic synchrony, or the coordination between partners within interaction. Couples (N = 97) participated in a 5-minute discussion designed to facilitate expressions of intimacy; we coded these interactions using the circumplex-based Structural Analysis of Social Behavior (SASB) model. We evaluated whether couples experiencing relational uncertainty exhibit forms of dyadic asynchrony in which self-disclosure and validation are met by a partner’s hostility. Sequential analyses revealed that, after covarying relationship quality, relationship uncertainty was associated with sequences of self-disclosure/hostility among men (H1), and self uncertainty and relationship uncertainty were associated with sequences of validation/hostility among both men and women (H2). Partner uncertainty did not predict dyadic asynchrony in either form. These findings advance scholarship on relational uncertainty by underscoring the importance of sequential exchanges within couple interaction.

Keywords

Dyadic synchrony relational uncertainty sequence analysis Structural Analysis of Social Behavior

Relational communication expresses information about the nature and status of a relationship (Burgoon & Hale, 1984; Foley & Duck, 2006). Within interactions, individuals seek to gather information so they can better anticipate interpersonal shifts and changes (Berger & Bradac, 1982). When these efforts are unsuccessful, the result is uncertainty, an inability to predict and explain interpersonal behavior (Berger & Calabrese, 1975).

Relational uncertainty refers to the degree of confidence people have in their perceptions of involvement within close relationships (Knobloch & Solomon, 1999). A wealth of research suggests that the interpersonal interactions of couples are more difficult when partners are unsure about the nature of their association (Goodboy et al., 2020; Theiss, 2018). For example, individuals experiencing relational uncertainty report disclosing less to their partner (Imai et al., 2023), being more reluctant to discuss sensitive topics (Leustek & Theiss, 2018), feeling more negative emotions such as anger, sadness, jealousy, fear, and guilt (Knobloch, Miller, & Carpenter, 2007; Lillie et al., 2021), and taking conflict more personally (Brisini & Solomon, 2020). Unsurprisingly, then, relational uncertainty is strongly correlated with relationship dissatisfaction (Brisini & Solomon, 2020; Imai et al., 2023).

Given the negative associations between relational uncertainty and dyadic well-being, scholars have called for theory and research designed to understand how relational uncertainty corresponds with specific episodes of interaction as they unfold (Solomon et al., 2016). Although many studies have documented links between relational uncertainty and self-reported communication behavior (e.g., Delaney & Sharabi, 2020; Lillie et al., 2021; Solomon & Brisini, 2019), far fewer have investigated couple interaction directly (King & Theiss, 2016). Thus, an important task is to identify behavioral markers of relational uncertainty embedded within the interaction of couples (Theiss, 2018).

To address this gap, we propose that dyadic synchrony, defined as the coordination between partners in interaction (see Harrist & Waugh, 2002), may be hampered when individuals are grappling with questions about the status of their relationship (Solomon et al., 2016). In this study, we investigate whether relational uncertainty predicts the dyadic synchrony of couples within conversations designed to facilitate expressions of intimacy. We begin by explicating our logic linking relational uncertainty with dyadic synchrony in couples. Then, we test our reasoning using turn-by-turn sequential analysis of data from an observational study of 97 couples.

Relational uncertainty within couple interaction

Relational uncertainty is a multifaceted construct closely associated with interpersonal communication (Berger & Bradac, 1982; Knobloch, 2006; Theiss, 2018). It originates from three sources. Self uncertainty encompasses the questions people have about their own involvement in the relationship, partner uncertainty denotes the questions people have about their partner’s involvement in the relationship, and relationship uncertainty indexes the questions people have about the relationship itself (Knobloch & Solomon, 1999). Factor analytic results demonstrate that these three sources of relational uncertainty are strongly correlated yet empirically distinct (Goodboy et al., 2021; Solomon et al., 2016).

A long tradition of theory and research suggests that communication is more challenging when partners are grappling with questions about who they are to each other (Knobloch & Satterlee, 2009; Solomon et al., 2016; Theiss, 2018). If people lack the information they need to make sense of interactional cues, they are likely to have difficulty planning what to say and interpreting their partner’s behavior accurately (Knobloch & Satterlee, 2009). For example, individuals who are unsure about their relationship tend to produce less fluent messages (Knobloch, 2006) and judge their partner’s behavior in conversation more negatively (Knobloch, Miller, Bond, & Mannone, 2007). Such results imply that relational uncertainty hampers a person’s ability to produce and process messages effectively.

We seek to extend this scholarship on the behavior of individuals to more fully consider exchanges between couples as they unfold within interaction. We suggest that couples experiencing relational uncertainty are prone to interpersonal sequences marked by less dyadic synchrony. Logic supporting this claim stems from both seminal and present-day theorizing. More than four decades ago, uncertainty reduction theory argued that a lack of information makes conversation more complex (Berger & Calabrese, 1975). Individuals who do not know what to expect from each other, according to the theory, may have trouble coordinating their verbal and nonverbal cues (Berger & Bradac, 1982). More recently, relational turbulence theory implied that a downstream outcome of relational uncertainty is less dyadic synchrony in interaction via people’s perceptions of relational turbulence (Solomon et al., 2016, 2019). In particular, the theory posited that relational uncertainty hinders people’s ability to comprehend interaction episodes, which leads to cognitive biases that crystalize over time into a perception of the relationship as turbulent, and ultimately disrupts dyadic synchrony (Solomon et al., 2016). Taken together, both uncertainty reduction theory and relational turbulence theory imply that relational uncertainty corresponds with less dyadic synchrony in interaction.

We are aware of only one study, conducted by Blalock and Bartle-Haring (2022) using self-report data, that has examined the link between relational uncertainty and dyadic synchrony. Employing a sample of 234 newlywed couples married for less than a year, Blalock and Bartle-Haring (2022) measured dyadic synchrony using a multi-item scale gauging people’s perceptions of coordination/authenticity and comfort/ease in conversation. Relational uncertainty shared a direct negative association with coordination/authenticity and an indirect negative association with comfort/ease. Although Blalock and Bartle-Haring’s (2022) findings provide evidence in favor of our general logic, the link between relational uncertainty and dyadic synchrony within couple interaction as it unfolds sequentially remains unexplored.

Dyadic synchrony within interaction

Dyadic synchrony involves coordinated and co-regulated exchanges between people; it is a property of interaction rather than a property of individuals or relationships (Harrist & Waugh, 2002). Dyadic synchrony is independent of positive affect (Harrist & Waugh, 2002), includes both verbal and nonverbal cues (Ivy & Gleason, 2022; Knobloch & Solomon, 2003), and is evaluated positively by interactants (Ebesu Hubbard, 2000). Although often studied in the context of parent/child relationships (e.g., McKillop & Connell, 2018), dyadic synchrony also has relevance for romantic relationships (Ivy & Gleason, 2022; Solomon et al., 2016).

Dyadic synchrony can be conceptualized in a variety of ways and with respect to a variety of foci (cf. Blalock & Bartle‐Haring, 2022; Reiss et al., 2019; Vicaria & Dickens, 2016). Here we concentrate on dyadic synchrony within interactions designed to facilitate expressions of intimacy, given the centrality of negotiating intimacy for people’s ability to maintain relationships (Reis & Patrick, 1996; Reis & Shaver, 1988).

The interpersonal process model of intimacy (Reis & Patrick, 1996; Reis & Shaver, 1988) suggests that intimacy develops through an unfolding sequence of dyadic synchrony during which an individual self-discloses personal information and interprets the partner’s response as understanding, caring, and validating. Self-disclosure involves revealing private information about oneself (Collins & Miller, 1994; Petronio et al., 2022), and has long been considered a key process in the formation and maintenance of intimacy in relationships (e.g., Jourard, 1964; Worthy et al., 1969). Self-disclosure is positively associated with relationship satisfaction (Hendrick, 1981; Meeks et al., 1998), love (Hendrick & Hendrick, 1987; Sprecher & Hendrick, 2004), and perceptions of closeness (Aron et al., 1997). Validation conveys empathic understanding and acceptance of another’s thoughts, feelings, behaviors, or experiences (Fruzetti & Payne, 2015). Validation, an aspect of partner responsiveness (Reis, 2007; Reis et al., 2004), plays a key role in intimacy development because it communicates that the other is accepted and appreciated.

A key premise of the interpersonal process model of intimacy is that sequences of dyadic synchrony in which self-disclosure is met by a partner’s validation result in greater closeness. By extension, it stands to reason that an opposing response from the partner, invalidation, constitutes a form of dyadic asynchrony that may serve as a marker of relational uncertainty within interaction. Invalidation communicates that a person’s thoughts, feelings, behaviors, and experiences are incorrect, socially unacceptable, or undesirable (Shenk & Fruzzetti, 2011), and it often contains expressions of hostility, such as criticism or contempt (Fruzetti & Payne, 2015).

Two lines of evidence support our logic that couples experiencing relational uncertainty may be prone to forms of dyadic asynchrony in which self-disclosure and validation are met by a partner’s invalidation. First, uncertainty reduction theory suggests that uncertainty is associated with interactions in which the other person behaves in unexpected ways that violate expectations (Berger & Bradac, 1982). A partner’s invalidation is associated with confusion in the recipient, along with negative arousal, emotional reactivity, and a lack of closeness within the couple (Fruzzetti & Worrall, 2010; Shenk & Fruzzetti, 2011). Second, people who report relational uncertainty interpret their partner’s behavior as more hostile (Knobloch, Miller, Bond, & Mannone, 2007), judge their partner’s messages as more intentionally hurtful (McLaren et al., 2011), and perceive their partners as less responsive (Theiss & Knobloch, 2014; Theiss & Nagy, 2013).

A related question is whether the link between relational uncertainty and dyadic asynchrony persists beyond relationship quality. Given its associations with both relational uncertainty and the interactions of couples, relationship quality is a logical construct for conceptual and methodological differentiation. People tend to be less satisfied with their relationship under conditions of relational uncertainty (e.g., Goodboy et al., 2020), and in interaction, individuals experiencing relationship distress tend to exhibit more negative behaviors (e.g., hostility, criticism, blame, defensiveness, withdrawal) and less positive behaviors (e.g., approval, empathy, affection; for review, see Balderrama-Durbin et al., 2020). Guided by research showing divergence between relational uncertainty and relationship quality as predictors of communication behavior (e.g., Knobloch et al., 2021; Knobloch & Theiss, 2017), we expect that relational uncertainty is negatively associated with dyadic synchrony over and above people’s reports of relationship quality. Formally stated:

After covarying relationship quality, relational uncertainty corresponds with less dyadic synchrony via more frequent behavioral sequences of self-disclosure/hostility (H1) and validation/hostility (H2).

To summarize, the link between relational uncertainty and dyadic synchrony has received initial empirical support (Blalock & Bartle‐Haring, 2022), but we seek a more detailed understanding by evaluating whether relational uncertainty is associated with behavioral sequences of dyadic asynchrony within interactions designed to facilitate expressions of intimacy. Our study strives to advance the literature in three ways. Conceptually, we extend theorizing about relational uncertainty by considering exchanges between partners rather than the behavior of individuals. Methodologically, we use observational techniques and sequential analysis to examine moment-by-moment speaking turns within interaction (Bakeman & Gottman, 1997). Empirically, we evaluate the role of relationship quality as a key covariate.

Method

After securing Institutional Review Board approval, we recruited couples in the Chicago, Illinois, United States area via newspaper, radio, and Internet advertisements that targeted partners in committed romantic relationships experiencing relationship distress. We sought distressed couples to maximize variance in relational uncertainty, as well as to diverge from the high-functioning samples that comprise the bulk of prior work. The text of our advertisements read, in part, “Are you and your partner unhappy in your relationship? Are you distant from each other? Do problems in your relationship affect your health, work, or family? Call [clinic name and phone number].”

For participating in the study, couples were offered either 100 U.S. dollars or 16 sessions of free couple therapy conducted at an urban, university-affiliated mental health training clinic. To be eligible for the free therapy, couples were required to meet criteria for relationship distress based on a Dyadic Adjustment Scale (DAS; Spanier, 1976, 1988) cutoff score of ≤ 97 (Jacobson et al., 1987). A total of 38 couples who qualified for the free therapy chose this option, and after completing the current study, they went on to participate in a second, separate treatment study. The remaining 59 couples, who either did not qualify for the subsequent treatment study or chose not to participate in it, were paid $100 for their participation in this study. Couples were ineligible for the current study if either partner reported active suicidal ideation, substance abuse, psychosis, and/or domestic violence; these couples received referrals to alternative treatment services. Prior bivariate analyses from the larger study demonstrated that relational uncertainty is negatively associated with people’s relationship quality (Knobloch & Knobloch-Fedders, 2010) and positively associated with their tendency to seek negative feedback from their partner in conversation (Knobloch et al., 2011).

Participants

The sample consisted of 97 heterosexual, mixed gender couples (N = 194 individuals; 97 cisgender women and 97 cisgender men). A total of 74 couples were married, 16 were cohabiting, and 7 were dating. Their average relationship length was 10.48 years (SD = 11.02 years, range = 3 months to 65 years); 68.8% had children. Participants ranged in age from 21 to 90 years old (M = 42.73, Mdn = 41.00, SD = 12.11). Participants identified as White (69.0%), Black (14.7%), Latinx (8.7%), Asian (5.4%), Native American/Pacific Islander (1.6%) and Biracial (.6%). Couples reported their annual household income as under $10,000 (5.4%), $10,000–$40,000 (24.5%), $41,000–$70,000 (19.8%), $71,000–$100,000 (22.8%), and over $100,000 (27.5%).

Measures

Relationship quality

The 32-item Dyadic Adjustment Scale (DAS; Spanier, 1976, 1988) measured relationship quality. The DAS demonstrates excellent measurement properties (Carey et al., 1993; Kurdek, 1992; Sabourin et al., 1990). Participants’ average DAS score was 86.58 (SD = 22.07, range = 9–134; men: M = 90.12, SD = 20.03, range = 19–131, α = .94; ω = .94; women: M = 83.04, SD = 23.51, range = 9–134, α = .94; ω = .94). A total of 132 participants (62 men and 70 women) met criteria for relationship distress based on a DAS cutoff score of ≤ 97 (Jacobson et al., 1987).

Relational uncertainty

Participants completed brief measures of self, partner, and relationship uncertainty developed by Knobloch and Solomon (1999). Four items for each scale completed the stem ‘‘How certain are you about …?” (1 = completely uncertain, 6 = completely certain; all items were reverse scored and averaged). The items for self uncertainty were (a) how you feel about this relationship, (b) your view of this relationship, (c) your goals for the future of this relationship, and (d) how important this relationship is to you (men: M = 2.55, SD = 1.08, α = .88, ω = .89; women: M = 2.86, SD = 1.29, α = .90, ω = .90). Items assessing partner uncertainty included (a) how your partner feels about this relationship, (b) your partner’s view of this relationship, (c) your partner’s goals for the future of this relationship, and (d) how important this relationship is to your partner (men: M = 2.76, SD = 1.11, α = .87, ω = .87; women: M = 2.96, SD = 1.48, α = .94, ω = .94). Relationship uncertainty contained the following items: (a) how you can or cannot behave around your partner, (b) the current status of this relationship, (c) the definition of this relationship, and (d) the future of this relationship (men: M = 2.76, SD = 1.08, α = .87, ω = .87; women: M = 3.06, SD = 1.27, α = .87, ω = .88).

Observational assessment of interaction

Couples participated in a series of six standardized discussions that were videotaped and transcribed. The first discussion, which was designed to help couples get acclimated to the research laboratory and videotaping procedures, asked couples to plan their next vacation. The following two discussions invited couples to discuss recent conflicts in their relationship. Next, couples completed two conversations in which they were asked to describe what happens when each partner feels sad, down, or worried, and how they handle those situations together. We selected data for this study from the final discussion, in which couples were asked to talk about the three best things in their relationship. This conversation lasted for 5 minutes, and was designed to elicit expressions of self-disclosure, validation, and intimacy.

Measurement of interpersonal behavior

We coded the interactions using Structural Analysis of Social Behavior (SASB; Benjamin, 1979, 1987, 2000), a theoretically derived, empirically validated, circumplex-based model for measuring interpersonal behavior. SASB is built around three primary constructs: behavioral focus, affiliation, and interdependence. The interpersonal focus of behavior is measured using two types: “I focus on you” (other focus) or “I react to your focus on me” (self focus; Benjamin, 2006, p. 20).¹ These behavioral foci are represented spatially on the SASB model using two separate circular (circumplex) surfaces (see Figure 1). Focus on Other behavior, depicted in the top circumplex of Figure 1, is transitive, describing behavior done to, for, or about another person (e.g., “he controls her” or “she protects him”). Focus on Self behavior (the bottom circumplex of Figure 1) is intransitive, describing behavior done to, for, or about the self in relation to the other person (e.g., “she submits to him” or “he relies on her”).

Figure 1.

Structural Analysis of Social Behavior (SASB). Note. The two-word, eight cluster version of SASB used for the coding in this study is from Benjamin (1987), copyright Guilford Press. The quadrant version is from Benjamin (1979), copyright Taylor & Francis. The combination of the quadrant and cluster version reprinted here is from Benjamin (2000), copyright Interpersonal Reconstructive Therapy Institute, with permission. Reprinted here with permission.

Each SASB circumplex is comprised of two orthogonal dimensions (see Figure 1). Along the horizontal dimension, affiliation assesses degrees of hostility to friendliness, and ranges from hate (direct attack of another; fearful recoil from another’s attack) to love (loving and approaching; joyfully connecting). Along the vertical dimension, interdependence ranges from extremes of differentiation (give autonomy; be separate) to enmeshment (control; submit). For Focus on Other behavior, autonomy ranges from granting the other person autonomy to taking control. For Focus on Self behavior, autonomy ranges from taking one’s autonomy to submission.

Through its combination of behavioral focus, affiliation, and interdependence, SASB measures the full array of interpersonal behavior, including mild, moderate, and extreme displays of affiliation, hostility, enmeshment, and differentiation. Specific behavioral combinations of the underlying interpersonal dimensions are represented on the SASB model as clusters. Descriptive labels for each cluster are shown in Figure 1.

Observational coding

Under the supervision of the first author, a team of 36 research assistants coded the videotaped discussions. The coding team included undergraduate and graduate students, postdoctoral fellows, and research staff. Coders completed at least 50 hours of formal training in the SASB model, including didactic instruction, practice assignments, and reliability checks using pilot data. Following the training criterion recommended by Benjamin and Cushing (2000), coders were required to achieve weighted κ ≥ .70 on pilot data before coding study data.

Coding procedure

Coding followed the steps outlined in the SASB coding manual (Benjamin & Cushing, 2000). Written transcripts of couples’ interactions were unitized into segments of behavior defined by independent clauses or sentences. Using transcripts and videotapes, pairs of coders rated the behavior of both partners using both verbal and nonverbal cues. First, coders identified the focus of each behavior (either self or other). Next, they categorized each behavior in terms of affiliation (friendly, neutral, or hostile) and interdependence (autonomous, neutral, or enmeshed). Finally, these ratings were used to locate each behavior on the SASB model (see Figure 1). For example, if a woman said to her partner, “You did a great job with the kids,” her behavior would be judged as other-focused, friendly, and allowing autonomy, and categorized within the Affirming and Understanding cluster (see top circumplex of Figure 1). Coders assigned behavior into more than one cluster if necessary to capture its full meaning. For example, if a man said to his partner, “If you don’t ask your parents to leave right now, I will,” his behavior would be coded as both Watching and Controlling and Asserting and Separating. Coding disagreements were resolved by discussion. To prevent coder drift, coders met weekly as a group under the supervision of the first author.

Coding reliability

Although our coding data represent a consensus between two coders, we chose to measure reliability based on two coders working independently to ensure conservative estimates (Benjamin & Cushing, 2000). To assess reliability, two coders working separately coded the first 50 segments of behavior for each couple. This independent coding was used to estimate reliability only.

We computed two complementary measures of reliability to provide a comprehensive picture of coder agreement. First, we calculated intraclass correlation coefficients reflecting the average of two raters (i.e., ICC [1, 2] in Shrout & Fleiss, 1979). ICCs for self-disclosure (men = .84, women = .89), validation (men = .83, women = .75), and hostility (men = .77, women = .82) demonstrated good reliability. Second, we computed weighted κ, which is recommended for sequential analyses using SASB data (Benjamin & Cushing, 2000). Weighted κ is a much more conservative index than ICC because it measures the extent of coder agreement at the utterance-by-utterance level. We calculated weighted κ according to the formula provided by Benjamin and Cushing (2000), which assigns weights (ranging from +1.0 to −1.0) to each pair of codes according to the similarity of their position around the circumplex. As expected, weighted κ showed lower but still adequate reliability (.67), and is comparable to other SASB studies of complex sequential interaction marked by a high degree of relational pathology (Benjamin & Cushing, 2000; Knobloch-Fedders et al., 2014).

Operationalization of interactional sequences

We computed the frequency of self-disclosure/hostility and validation/hostility sequences in several steps. First, each person’s talk turn was evaluated for the presence versus absence of self-disclosure [coded on the SASB model as Disclosing and Expressing], as well as for the presence versus absence of validation [coded on the SASB model as Affirming and Understanding]. Next, the partner’s immediately following talk turn was evaluated for the presence versus absence of hostility [coded on the SASB model as Ignoring and Neglecting; Attacking and Rejecting; Belittling and Blaming; Walling Off and Distancing; Protesting and Recoiling; and/or Sulking and Scurrying]. Finally, the total number of self-disclosure/hostility and validation/hostility sequences were tallied for the partner who initiated the sequence. For example, the frequency of women’s self-disclosure/hostility sequence was calculated as the number of times women self-disclosed and their partner followed with hostile behavior.

Examples of interactional sequences

To illustrate the self-disclosure/hostility and validation/hostility sequences, we present excerpts from the interactions of four couples. All names have been changed to protect confidentiality. Participants’ words are enclosed in quotation marks, italics are used to describe nonverbal cues, and SASB codes are provided in brackets. After illustrating the relevant sequence, we include one or more subsequent talk turns to provide additional context regarding the immediate outcome of the exchange.

Self-disclosure/hostility sequence

In a first example, a woman discloses that she has faith in her partner and knows she can rely on him. However, he responds with hostility, walling off and distancing from her disclosure by suggesting that she was not following the experimenter’s instructions to discuss the three best things about the relationship. After this self-disclosure/hostility exchange, he goes on to criticize her comment in a subsequent talk turn:

Woman: … “And I have faith that you are a good person [Disclosing and Expressing; Affirming and Understanding], and that you would be there for me if there was something I needed.” [Disclosing and Expressing; Trusting and Relying].

Man: (eyes downcast, mumbling in a sarcastic tone): “Um, the three best things about our relationship, OK.” [Deferring and Submitting; Walling Off and Distancing].

Woman: “Um, that’s not exactly about the relationship.” [Deferring and Submitting].

Man: “Yeah, it’s a little weak.” [Belittling and Blaming].

In a second example, a woman discloses that she enjoys when she and her partner get along, and goes on to ask him for his ideas. Seemingly offended that she did not mention their sex life as a good thing about their relationship, he responds by sulking and criticizing her. She proceeds to dismiss him, and they start to argue:

Woman: “I like when we’re not arguing. [Disclosing and Expressing]. I like when, um, when we laugh, we are on good terms, I like that. [Disclosing and Expressing]. When we can laugh [Disclosing and Expressing], and when we talk [Disclosing and Expressing]. That’s that – and I like – I don’t know what the three best things are. [Asserting and Separating]. What’s the three best things?” [Trusting and Relying].

Man: (whining tone): “What about sex? [Watching and Controlling; Sulking and Scurrying]. Our sex life isn’t good?” [Sulking and Scurrying; Belittling and Blaming].

Woman: (dismissive tone): “That’s not important.” [Watching and Controlling; Ignoring and Rejecting].

Man: “Yeah, that’s important in marriage. [Watching and Controlling]. I think so. [Asserting and Separating]. I don’t think people stay married if it ain’t.” [Asserting and Separating].

Woman: (condescending tone): “That’s a shame if that’s the one topic you can come up with.” [Belittling and Blaming; Sulking and Scurrying].

Validation/hostility sequence

In an exchange that illustrates the validation/hostility sequence, a man compliments his partner’s generosity, but she responds by criticizing him. After this validation/hostility exchange, he replies with disclosure, but she continues putting him down:

Man: “I like how you let me drive your car.” [Affirming and Understanding].

Woman: “Shut up. [Watching and Controlling]. You’re so material!” [Belittling and Blaming].

Man: “I’m not material. [Asserting and Separating]. I like that, uh, I can show you new things. [Disclosing and Expressing]. I like that in our relationship I can show you a new world that you have never seen before. [Disclosing and Expressing; Nurturing and Protecting]. You know I like that, I feel like I’ve helped you to become a better person.” [Disclosing and Expressing; Nurturing and Protecting].

Wife: (sarcastic laugh): “You’re so [slur]. You’re such a liar!” [Belittling and Blaming].

In a final example, a woman validates her partner by saying that he is a good father. However, he responds to her compliment by withdrawing in hostile silence. She then goes on to criticize him:

Woman: … “I’ll start with one thing. [Asserting and Separating]. You’re a good father. [Affirming and Understanding].

Man: (slumped down in his chair, eyes downcast, barely audible): “Mmhm.” (long silence). [Deferring and Submitting; Walling Off and Distancing].

Woman: (sarcastic tone): “Don’t think too hard, Peter.” [Belittling and Blaming].

Results

Preliminary analyses

Descriptive statistics are displayed in Table 1. The number of talk turns per participant within the 5-minute interaction ranged from 3–57 (men: M = 23.42, SD = 11.79, range = 3–57; women: M = 23.38, SD = 11.75, range = 3–57). The number of talk turns containing self-disclosure ranged from 0–32 (men: M = 6.30, SD = 5.23, range = 0–32; women: M = 5.65, SD = 4.15, range = 0–18), the number of talk turns containing validation ranged from 0–25 (men: M = 6.96, SD = 4.84, range = 0–23; women: M = 7.25, SD = 4.88, range = 0–25), and the number of talk turns containing hostility ranged from 0–22 (men: M = 3.24, SD = 4.66, range = 0–22; women: M = 4.02, SD = 4.36, range = 0–17).

Table 1.

Paired-sample t tests comparing men and women on the independent and dependent variables

	Women		Men		t (96)
	M	SD	M	SD	t (96)
Relationship quality	83.036	23.508	90.123	20.035	−3.618***
Self uncertainty	2.862	1.294	2.552	1.083	2.507*
Partner uncertainty	2.955	1.481	2.758	1.113	1.406
Relationship uncertainty	3.059	1.271	2.760	1.083	2.648**
Total number of talk turns	23.381	11.748	23.423	11.792	−.575
Self-disclosure talk turns	5.650	4.146	6.299	5.234	−1.408
Validation talk turns	7.247	4.880	6.959	4.843	.638
Hostility talk turns	4.021	4.356	3.237	4.661	2.097*
Self-disclosure/hostility sequence	.948	1.318	.691	1.176	1.571
Validation/hostility sequence	1.010	1.237	.536	.842	3.467***

N = 97 couples.

*p < .05. **p < .01. ***p < .001.

In a first preliminary analysis, we compared men and women on relationship quality; relational uncertainty; the number of talk turns containing self-disclosure, validation, or hostility; and the frequency of self-disclosure/hostility and validation/hostility sequences. Paired-sample t tests revealed that, compared to men, women exhibited lower levels of relationship quality, higher levels of self uncertainty and relationship uncertainty, more frequent talk turns containing hostility, and more frequent validation/hostility sequences. Second, we computed bivariate correlations among men, among women, and within couples (see Table 2). For both men and women, the three sources of relational uncertainty were positively associated with each other, the three sources of relational uncertainty were negatively associated with relationship quality, and the two types of interactional sequences were positively associated with each other. In addition, self uncertainty (for men) and relationship uncertainty (for both men and women) were positively correlated with talk turns containing hostility. Within couples, all variables shared strong positive correlations except the self-disclosure/hostility sequence. In a last preliminary analysis, we examined whether demographic covariates such as age, relationship length, and parental status were correlated with the three sources of relational uncertainty. No statistically significant correlations were apparent for men or for women.

Table 2.

Bivariate correlations among the independent and dependent variables for men, for women, and within couples

Variable	1	2	3	4	5	6	7	8	9
1. Relationship quality	.639***	−.689***	−.629***	−.733***	.142	.166	−.093	−.202*	−.242*
2. Self uncertainty	−.622***	.487***	.707***	.845***	−.099	−.156	.097	.240*	.298**
3. Partner uncertainty	−.643***	.752***	.461***	.743***	−.148	−.178	.127	.113	.146
4. Relationship uncertainty	−.666***	.826***	.798***	.565***	−.141	−.160	.206*	.266**	.324**
5. Self-disclosure talk turns	.083	−.158	−.179	−.135	.552***	.485***	.023	−.168	−.216*
6. Validation talk turns	.155	−.144	−.184	−.113	.449**	.580***	.059	−.218*	−.209*
7. Hostility talk turns	−.112	.216*	.194	.234*	−.140	−.136	.669**	.085	.064
8. Self-disclosure/hostility sequence	.060	−.059	−.115	−.103	.058	−.071	.173	.164	.527***
9. Validation/hostility sequence	.044	.127	.057	.102	.015	.000	.187	.537***	.205*

Note. N = 97 men, women, or dyads. Correlations for men appear above the diagonal; correlations for women appear below the diagonal. Within-couple correlations appear on the diagonal and are underlined.

*p < .05. **p < .01. ***p < .001.

Hypothesis tests

We tested our hypotheses using multilevel modeling (Snijders & Bosker, 2012) to accommodate the dependency inherent in our dyadic data. All multilevel models were constructed such that individuals were nested within couples, and partners were distinguished by an actor’s sex (coded men = 1, women = −1). Following recommendations by Kenny et al. (2006), all multilevel models were estimated using restricted maximum likelihood, heterogeneous compound symmetry was employed as the covariance structure, and the predictors were grand-mean centered to facilitate interpretation of the intercepts. Each model included two random effects: variance in the intercepts and error variance. Standardized regression coefficients (βs) are provided as effect size estimates. All analyses were conducted using SPSS version 28.0.

Relational uncertainty predicting self-disclosure/hostility sequences (H1)

H1 suggested that, after covarying relationship quality, relational uncertainty is positively associated with self-disclosure/hostility sequences. To test this hypothesis, we constructed three multilevel models using self uncertainty, partner uncertainty, and relationship uncertainty as independent variables in separate models; we also employed sex and relationship quality as covariates. Thus, each model contained four Level 1 predictors: (a) sex, (b) relationship quality, (c) a source of relational uncertainty, and (d) an interaction term constructed as relational uncertainty x sex. See Table 3 for unstandardized parameter estimates from these models.

Table 3.

Results of multilevel models predicting the frequency of self-disclosure/hostility sequences

Effect	B	SE	95% CI	95% CI	t	p
Effect	B	SE	LL	UL	t	p
Self uncertainty
Intercept	.843	.095	.654	1.032	8.849	<.001***
Relationship quality	−.000	.005	−.011	.010	−.62	.951
Self uncertainty	.094	.099	−.102	.290	.947	.345
Sex	−.113	.082	−.276	.050	−1.376	.172
Self uncertainty * sex	.152	.071	.012	.292	2.152	.033*
Partner uncertainty
Intercept	.831	.097	.639	1.023	8.597	<.001***
Relationship quality	−.007	.005	−.018	.004	−1.304	.194
Partner uncertainty	−.079	.089	−.255	.097	−.883	.379
Sex	−.112	.081	−.272	.048	−1.384	.169
Partner uncertainty * sex	.118	.065	−.011	.247	1.805	.073

Effect	B	SE	95% CI	95% CI	t	p
Effect	B	SE	LL	UL	t	p
Relationship uncertainty
Intercept	.849	.096	.659	1.038	8.880	<.001***
Relationship quality	−.000	.006	−.012	.011	−.083	.934
Relationship uncertainty	.086	.105	−.120	.293	.823	.411
Sex	−.114	.081	−.275	.046	−1.415	.160
Relationship uncertainty * sex	.194	.070	.055	.332	2.770	.006**

Note. N = 97 couples.

*p < .05. **p < .01. ***p < .001.

Contrary to H1, after covarying relationship quality, self uncertainty did not predict self-disclosure/hostility sequences, β = .09, p = .35. However, an interaction emerged between self uncertainty and sex in predicting self-disclosure/hostility sequences, β = .15, p = .033. To probe this interaction, we constructed a two-intercept model to estimate separate coefficients for men and women (following recommendations by Kenny et al., 2006). Men’s self uncertainty predicted self-disclosure/hostility sequences at a level approaching significance, β = .24, p = .051, but this trend did not hold for women, β = −.06, p = .63.

No main effects emerged for partner uncertainty, β = −.08, p = .38, or relationship uncertainty, β = .08, p = .41, in the prediction of self-disclosure/hostility sequences. However, an interaction emerged between relationship uncertainty and sex in predicting self-disclosure/hostility sequences, β = .18, p = .006. To probe this interaction, we again constructed a two-intercept model to estimate separate coefficients for men and women. Although men’s relationship uncertainty predicted self-disclosure/hostility sequences, β = .27, p = .03, this association did not hold for women, β = −.10, p = .39.

Relational uncertainty predicting validation/hostility sequences (H2)

H2 predicted that, after covarying relationship quality, relational uncertainty is positively associated with validation/hostility sequences. To evaluate H2, we repeated the procedures we used to test H1 but substituted validation/hostility sequences as the dependent variable (see Table 4 for unstandardized parameter estimates from these models).

Table 4.

Results of multilevel models predicting the frequency of validation/hostility sequences

Effect	B	SE	95% CI	95% CI	t	p
Effect	B	SE	LL	UL	t	p
Self uncertainty
Intercept	.782	.080	.623	.941	9.748	<.001***
Relationship quality	.003	.004	−.006	.011	.617	.538
Self uncertainty	.200	.081	.040	.360	2.471	.014*
Sex	−.216	.069	−.352	−.079	−3.134	.002**
Self uncertainty * sex	.055	.058	−.059	.170	.958	.340
Partner uncertainty
Intercept	.777	.082	.614	.939	9.494	<.001***
Relationship quality	−.005	.004	−.013	.004	−1.092	.276
Partner uncertainty	.001	.073	−.143	.145	.013	.990
Sex	−.220	.070	−.359	−.082	−3.155	.002**
Partner uncertainty * sex	.034	.054	−.073	.140	.625	.533

Effect	B	SE	95% CI	95% CI	t	p
Effect	B	SE	LL	UL	t	p
Relationship uncertainty
Intercept	.785	.080	.625	.944	9.756	<.001***
Relationship quality	.004	.005	−.005	.013	.908	.365
Relationship uncertainty	.223	.085	.055	.392	2.616	.010**
Sex	−.219	.068	−.354	−.083	−3.203	.002**
Relationship uncertainty * sex	.077	.058	−.037	.191	1.335	.184

Note. N = 97 couples.

*p < .05. **p < .01. ***p < .001.

In support of H2, main effects for two of the three sources of relational uncertainty emerged: both self uncertainty, β = .23, p = .014, and relationship uncertainty, β = .25, p = .010, predicted validation/hostility sequences. In contrast, partner uncertainty was not associated with validation/hostility sequences, β = .01, p = .99, and no interactions between relational uncertainty and sex emerged.

Discussion

More than 40 years of communication theory (Berger & Bradac, 1982; Berger & Calabrese, 1975; Solomon et al., 2016) and research (Goodboy et al., 2020; Theiss, 2018) ground relational uncertainty within the interaction of couples. A notable limitation of this scholarship, however, is that most empirical tests have evaluated self-reported behavior rather than observing conversations directly (Theiss, 2018). We sought to address that gap by examining how relational uncertainty corresponds with dyadic synchrony within interactions designed to facilitate people’s expressions of intimacy. Using observational assessment, we evaluated whether couples experiencing relational uncertainty exhibit forms of dyadic asynchrony in which self-disclosure and validation are met by a partner’s hostility. Our analyses revealed that, after covarying relationship quality, relationship uncertainty was associated with sequences of self-disclosure/hostility among men (H1), and self uncertainty and relationship uncertainty were associated with sequences of validation/hostility among both men and women (H2). Next, we contextualize our findings, evaluate our study’s strengths and limitations, and suggest avenues for future research.

Implications of the results

Ample scholarship suggests that intimacy develops through sequences of dyadic synchrony in which an individual self-discloses and a partner offers a validating response (e.g., Reis & Patrick, 1996; Reis & Shaver, 1988). Based on logic that people who are unsure about their relationship may have difficulty coordinating intimacy within interaction, we hypothesized that relational uncertainty corresponds with asynchronous sequences of self-disclosure met with hostility (H1). Our findings showed mixed support for this hypothesis: Men, but not women, who were experiencing relationship uncertainty (and to a lesser extent, self uncertainty) were more likely to engage in self-disclosure that was followed by hostility from their partner. Whereas prior work has documented a negative bivariate association between relational uncertainty and self-disclosure (Imai et al., 2023; Schrodt & Phillips, 2016; Theiss & Knobloch, 2013), our investigation is unique in evaluating relational uncertainty as a predictor of sequences of self-disclosure paired with an ensuing hostile response. On one hand, our data echo those of Imai and colleagues (2023), who identified a complex pattern of sex differences among relational uncertainty, self-disclosure, and relationship satisfaction. On the other hand, our findings will require replication before any conclusions can be drawn about differences in relational uncertainty between men and women in the context of interpersonal exchanges in which self-disclosure is met with hostility.

We also hypothesized that relational uncertainty corresponds with less dyadic synchrony via sequences of validation met with hostility (H2), and our results largely supported that prediction. Individuals experiencing both self uncertainty and relationship uncertainty were more likely to engage in asynchronous exchanges marked by validation followed by hostility. These findings both corroborate and extend prior work. People experiencing relational uncertainty report that they communicate with less warmth (Theiss & Knobloch, 2014) and more aggression (Delaney & Sharabi, 2020; Theiss & Knobloch, 2013), and they judge their partner to communicate less constructively (Knobloch, Miller, Bond, & Mannone, 2007) and more destructively (Knobloch, Miller, Bond, & Mannone, 2007; Theiss & Knobloch, 2013), and, to our knowledge, our study is the first to identify an association between relational uncertainty and sequences of validation/hostility. Most broadly, our findings for H1 and H2 suggest initial support for the logic that relational uncertainty may constrain the ability of couples to engage in synchronous, fluid, and coordinated interaction (Berger & Calabrese, 1975; Solomon et al., 2016).

Our results also provide an opportunity to compare the three sources of relational uncertainty. Unlike self uncertainty and relationship uncertainty, partner uncertainty was not associated with either type of asynchronous exchange. Both theory and research suggest that the three sources of relational uncertainty, despite substantial overlap, are unique constructs (Goodboy et al., 2021; Knobloch & Solomon, 1999; Solomon et al., 2016). Stated differently, the questions people have about their own involvement in the relationship (self uncertainty) and the status of the relationship (relationship uncertainty) are conceptually and empirically distinguishable from their questions about their partner’s participation in the relationship. A potential explanation may be that people who experience partner uncertainty may be more reluctant to self-disclose or express validation in the first place, thereby constraining the frequency of the sequence. However, no evidence emerged in our data to support that explanation, given the lack of statistically significant bivariate correlations between these variables (see Table 2). Another possibility is that individuals experiencing partner uncertainty may be motivated to match their spouse’s tone while they gather information about what their spouse is thinking and feeling (e.g., Knobloch & Satterlee, 2009). We await future research designed to investigate the divergence between self, partner, and relationship uncertainty in their associations with dyadic synchrony.

We considered the role of relationship quality as well. A major rival explanation for the link between relational uncertainty and dyadic synchrony is that couples experiencing relationship distress are both more unsure about their relationship (e.g., Goodboy et al., 2020) and more likely to communicate with hostility (see Balderrama-Durbin et al., 2020, for review). In fact, prior analyses from this same sample showed that poorer relationship quality corresponded with relational uncertainty (Knobloch & Knobloch-Fedders, 2010). We opted, then, for a particularly stringent test by covarying relationship quality when predicting sequences of self-disclosure/hostility and validation/hostility. By demonstrating an association between relational uncertainty and dyadic asynchrony above and beyond relationship quality (H1, H2), our findings not only rule out a potential confound, but also contribute to a growing body of evidence differentiating relational uncertainty and relationship quality (e.g., Knobloch et al., 2021; Knobloch & Theiss, 2017).

Strengths, limitations, and future directions

Our study makes several contributions to scholarship on relational uncertainty and couple interaction. By theorizing about sequences of self-disclosure/hostility and validation/hostility within interaction, we direct attention to the dynamic interplay between partners to supplement prior research on the behavior of individuals (e.g., Solomon et al., 2016). Moreover, by harnessing observational methods, our investigation moves beyond self-report to directly examine sequences of behavior (e.g., Theiss, 2018), and appears to be the first to corroborate a link between people’s relational uncertainty and their sequential exchanges within conversations designed to facilitate expressions of intimacy. In addition, our clinical sample of couples experiencing considerable relationship distress complements the high-functioning samples typical of research investigating the link between relational uncertainty and communication (e.g., King & Theiss, 2016; Knobloch, Miller, Bond, & Mannone, 2007).

Limitations of our study exist as well. First, because we recorded couples’ interactions in a laboratory setting, their exchanges may not fully represent their naturally-occurring behavior. Web-based observational methods designed to assess the interactions of couples in their own homes show promise for bolstering ecological validity (Perry et al., 2021). Second, participants reported relatively low levels of relational uncertainty (see Table 1) despite the relationship distress they were experiencing. An alternative measure of relational uncertainty, developed after we began data collection for this study, appears to be superior to the original scale in maximizing variance (Solomon & Brisini, 2017); we encourage researchers to employ the new measure in future research to combat possible floor effects. Third, our approach to coding involved evaluating each speaking turn for the presence or absence of self-disclosure, validation, and hostility, regardless of the frequency or intensity of those behaviors. Future studies evaluating the degree of self-disclosure, validation, and hostility are necessary given data linking relational uncertainty to both the frequency and intensity of language (King & Theiss, 2016; Knobloch, 2006). Fourth, our cross-sectional design prevented us from testing reverse temporal pathways. We positioned relational uncertainty as a predictor of dyadic synchrony based on extant theorizing, but people’s communication behavior also has reciprocal effects on their relational uncertainty (Solomon et al., 2016). For example, when individuals engage in self-disclosure or express validation, they are likely to expect positive support or affirmation in return (Collins & Feeney, 2000; Holmes & Rempel, 1989), so receiving a hostile response may violate their expectations in ways that spark relational uncertainty (e.g., Ramirez & Wang, 2008). Finally, although participants were diverse with respect to age, race/ethnicity, relationship length, and household income, we did not collect information on disability, and our sample was limited to mixed-gender couples. Investigating dyadic synchrony among participants with disabilities, as well as among same-gender couples, is important given the wealth of sociopolitical factors that give rise to relational uncertainty within marginalized groups (Monk & Ogolsky, 2019).

Our study illuminates two avenues for future research. First, observational studies of dyadic interaction are essential for advancing scholarship across a variety of domains, including relational uncertainty (King & Theiss, 2016; Theiss, 2018), interpersonal communication (Caughlin & Basinger, 2014), the assessment of couple functioning (Balderrama-Durbin et al., 2020), and couple therapy (Friedlander et al., 2019). We encourage scholars to build on this project by leveraging the SASB circumplex model for coding interaction (Benjamin & Cushing, 2000). Grounded in a rich tradition of conceptual and empirical support (Benjamin, 2018), SASB is a sophisticated measurement tool designed to assess moment-by-moment interactional behavior. It provides an accessible way of probing turn-by-turn exchanges that are essential for sequential analysis (Knobloch-Fedders et al., 2014), and measures behavior with the specificity necessary for clinical assessment and treatment planning (Benjamin, 2006, 2018).

Second, we call for additional research triangulating the perceptions of insiders versus outsiders. Couple members do not always agree with third-party coders in their appraisals of interaction, and partners experiencing relational uncertainty may be particularly prone to biased appraisals of each other’s behavior (Knobloch, Miller, Bond, & Mannone, 2007). More work is needed to examine how couples judge their exchanges in real time compared to the cues visible to independent observers such as family members, friends, and practitioners (Friedlander et al., 2019). As an added benefit, such studies would inform guidelines for clinicians to help romantic partners manage relational uncertainty more effectively.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by the American Psychological Foundation’s Randy Gerson Memorial Research Award presented to the first author.

Authors’ note

Portions of this paper were presented at the 2018 International Association for Relationship Research Conference, Ft. Collins, Colorado, United States.

Open research statement

As part of IARR’s encouragement of open research practices, the author(s) have provided the following information: This research was not pre-registered. The data used in the research cannot be publicly shared but are available upon request. The data can be obtained by emailing lynne.knobloch-fedders@marquette.edu. The materials used in the research cannot be publicly shared but are available upon request. The materials can be obtained by emailing lynne.knobloch-fedders@marquette.edu.

ORCID iDs

Lynne M. Knobloch-Fedders

Leanne K. Knobloch

Note

References

Aron

Melinat

Aron

E. N.

Vallone

R. D.

Bator

R. J.

(1997). The experimental generation of interpersonal closeness: A procedure and some preliminary findings. Personality and Social Psychology Bulletin, 23(4), 363–377. https://doi.org/10.1177/0146167297234003

Bakeman

Gottman

J. M.

(1997). Observing interaction: An introduction to sequential analysis (2nd ed.). Cambridge University. https://doi.org/10.1017/CBO9780511527685

Balderrama-Durbin

C. M.

Snyder

D. K.

Heyman

R. E.

Haynes

S. N.

(2020). Systematic and culturally sensitive assessment of couple distress. In Wampler

K. S.

Blow

A. J.

(Eds.), The handbook of systemic family therapy (Vol. 3, pp. 27–48). John Wiley and Sons. https://doi.org/10.1002/9781119790945.ch2

Benjamin

L. S.

(1979). Structural analysis of differentiation failure. Psychiatry, 42(1), 1–23. https://doi.org/10.1080/00332747.1979.11024003

Benjamin

L. S.

(1987). Use of the SASB dimensional model to develop treatment plans for personality disorders, I: Narcissism. Journal of Personality Disorders, 1(1), 43–70. https://doi.org/10.1521/pedi.1987.1.1.43

Benjamin

L. S.

(2000). SASBWorks coding software. [Computer software]. University of Utah.

Benjamin

L. S.

(2006). Interpersonal reconstructive therapy: An integrative, personality-based treatment for complex cases. Guilford.

Benjamin

L. S.

(2018). Interpersonal reconstructive therapy for anger, anxiety and depression: It’s about broken hearts, not broken brains. American Psychological Association. https://doi.org/10.1037/0000090-000

Benjamin

L. S.

Cushing

(2000). Manual for Coding Social Interactions in Terms of Structural Analysis of Social Behavior. University of Utah.

10.

Berger

C. R.

Bradac

J. J.

(1982). Language and social knowledge: Uncertainty in interpersonal relationships. Edward Arnold.

11.

Berger

C. R.

Calabrese

R. J.

(1975). Some explorations in initial interaction and beyond: Toward a developmental theory of interpersonal communication. Human Communication Research, 1(2), 99–112. https://doi.org/10.1111/j.1468-2958.1975.tb00258.x

12.

Blalock

Bartle‐Haring

(2022). Transition into marriage: A test of relational uncertainty, turbulence, dyadic synchrony, and cohabitation in newlyweds. Family Relations, 71(4), 1637–1657. https://doi.org/10.1111/fare.12686

13.

Brisini

K. S. C.

Solomon

D. H.

(2020). Relational uncertainty and taking conflict personally: Comparing parents of children with and without autism spectrum disorder. Journal of Autism and Developmental Disorders, 50(12), 4401–4411. https://doi.org/10.1007/s10803-020-04492-6

14.

Burgoon

J. K.

Hale

J. L.

(1984). The fundamental topoi of relational communication. Communication Monographs, 51(3), 193–214. https://doi.org/10.1080/03637758409390195

15.

Carey

M. P.

Spector

I. P.

Lantinga

L. J.

Krauss

D. J.

(1993). Reliability of the Dyadic Adjustment Scale. Psychological Assessment, 5(2), 238–240. https://doi.org/10.1037/1040-3590.5.2.238

16.

Caughlin

Basinger

(2014). Measuring social interaction. In Berger

(Ed.), Interpersonal communication (pp. 103–126). De Gruyter Mouton. https://doi.org/10.1515/9783110276794.103

17.

Collins

N. L.

Feeney

B. C.

(2000). A safe haven: An attachment theory perspective on support seeking and caregiving in intimate relationships. Journal of Personality and Social Psychology, 78(6), 1053–1073. https://doi.org/10.1037/0022-3514.78.6.1053

18.

Collins

N. L.

Miller

L. C.

(1994). Self-disclosure and liking: A meta-analytic review. Psychological Bulletin, 116(3), 457–475. https://doi.org/10.1037/0033-2909.116.3.457

19.

Delaney

A. L.

Sharabi

L. L.

(2020). Relational uncertainty and interference from a partner as predictors of demand/withdraw in couples with depressive symptoms. Western Journal of Communication, 84(1), 58–78. https://doi.org/10.1080/10570314.2019.1635266

20.

Foley

M. K.

Duck

(2006). Relational communication. In Hargie

(Ed.), The handbook of communication skills (pp. 427–450). Routledge.

21.

Friedlander

M. L.

Lee

Escudero

(2019). What we do and do not know about the nature and analysis of couple interaction. Couple and Family Psychology: Research and Practice, 8(1), 24–44. https://doi.org/10.1037/cfp0000114

22.

Fruzetti

A. E.

Payne

(2015). Couple therapy and borderline personality disorder. In Gurman

A. S.

Lebow

J. L.

Snyder

D. K.

(Eds.), Clinical handbook of couple therapy (Vol. 5, pp. 606–634). Guilford.

23.

Fruzzetti

A. E.

Worrall

J. M.

(2010). Accurate expression and validating responses: A transactional model for understanding individual and relationship distress. In Sullivan

K. T.

Davila

(Eds.), Support processes in intimate relationships (pp. 121–150). Oxford University. https://doi.org/10.1093/acprof:oso/9780195380170.003.0005

24.

Goodboy

A. K.

Bolkan

Brisini

Solomon

D. H.

(2021). Relational uncertainty within relational turbulence theory: The bifactor exploratory structural equation model. Journal of Communication, 71(3), 403–430. https://doi.org/10.1093/joc/jqab009

25.

Goodboy

A. K.

Bolkan

Sharabi

L. L.

Myers

S. A.

Baker

J. P.

(2020). The relational turbulence model: A meta-analytic review. Human Communication Research, 46(2–3), 222–249. https://doi.org/10.1093/hcr/hqaa002

26.

Harrist

A. W.

Waugh

R. M.

(2002). Dyadic synchrony: Its structure and function in children’s development. Developmental Review, 22(4), 555–592. https://doi.org/10.1016/S0273-2297(02)00500-2

27.

Hendrick

S. S.

(1981). Self-disclosure and marital satisfaction. Journal of Personality and Social Psychology, 40(6), 1150–1159. https://doi.org/10.1037/0022-3514.40.6.1150

28.

Hendrick

S. S.

Hendrick

(1987). Love and sexual attitudes, self-disclosure, and sensation seeking. Journal of Social and Personal Relationships, 4(3), 281–297. https://doi.org/10.1177/026540758700400303

29.

Holmes

J. G.

Rempel

J. K.

(1989). Trust in close relationships. In Hendrick

(Ed.), Close relationships: Review of personality and social psychology (Vol. 10, pp. 187–220). Sage.

30.

Hubbard

A. S. E.

(2000). Interpersonal coordination in interactions: Evaluations and social skills. Communication Research Reports, 17(1), 95–104. https://doi.org/10.1080/08824090009388755

31.

Imai

Taniguchi

Umemura

(2023). Relational uncertainty and relationship satisfaction in a romantic relationship: Self-disclosure as a moderator and a mediator. Current Psychology, 42(2), 1585–1597. https://doi.org/10.1007/s12144-021-01478-0

32.

Ivy

D. K.

Gleason

S. A.

(2022). What words don’t tell us: Non-verbal communication and turmoil in romantic relationships. In Sternberg

R. J.

Kostić

(Eds.), Nonverbal communication in close relationships: What words don’t tell us (pp. 187–213). Palgrave Macmillan. https://doi.org/10.1007/978-3-030-94492-6_8

33.

Jacobson

N. S.

Schmaling

K. B.

Holtzworth-Munroe

(1987). Component analysis of behavioral marital therapy: Two year follow-up and prediction of relapse. Journal of Marital and Family Therapy, 13(2), 187–195. https://doi.org/10.1111/j.1752-0606.1987.tb00696.x

34.

Jourard

S. M.

(1964). The transparent self: Self-disclosure and well-being. Van Nostrand.

35.

Kenny

D. A.

Kashy

D. A.

Cook

W. L.

(2006). Dyadic data analysis. Guilford.

36.

King

M. E.

Theiss

J. A.

(2016). The communicative and physiological manifestations of relational turbulence during the empty-nest phase of marital relationships. Communication Quarterly, 64(5), 495–517. https://doi.org/10.1080/01463373.2015.1129353

37.

Knobloch

L. K.

(2006). Relational uncertainty and message production within courtship: Features of date request messages. Human Communication Research, 32(3), 244–273. https://doi.org/10.1111/j.1468-2958.2006.00275.x

38.

Knobloch

L. K.

Knobloch-Fedders

L. M.

(2010). The role of relational uncertainty in depressive symptoms and relationship quality: An actor-partner interdependence model. Journal of Social and Personal Relationships, 27(1), 137–159. https://doi.org/10.1177/0265407509348809

39.

Knobloch

L. K.

Knobloch-Fedders

L. M.

Durbin

C. E.

(2011). Depressive symptoms and relational uncertainty as predictors of reassurance-seeking and negative feedback-seeking in conversation. Communication Monographs, 78(4), 437–462. https://doi.org/10.1080/03637751.2011.618137

40.

Knobloch

L. K.

Knobloch-Fedders

L. M.

Yorgason

J. B.

Basinger

E. D.

Abendschein

McAninch

K. G.

(2021). Suspicion about a partner’s deception and trust as roots of relational uncertainty during the post-deployment transition. Journal of Social and Personal Relationships, 38(3), 912–934. https://doi.org/10.1177/0265407520970645

41.

Knobloch

L. K.

Miller

L. E.

Bond

B. J.

Mannone

S. E.

(2007). Relational uncertainty and message processing in marriage. Communication Monographs, 74(2), 154–180. https://doi.org/10.1080/03637750701390069

42.

Knobloch

L. K.

Miller

L. E.

Carpenter

K. E.

(2007). Using the relational turbulence model to understand negative emotion within courtship. Personal Relationships, 14(1), 91–112. https://doi.org/10.1111/j.1475-6811.2006.00143.x

43.

Knobloch

L. K.

Satterlee

K. L.

(2009). Relational uncertainty: Theory and application. In Afifi

T. D.

Afifi

W. A.

(Eds.), Uncertainty, information management, and disclosure decisions: Theories and applications. (pp. 106–127). Routledge.

44.

Knobloch

L. K.

Solomon

D. H.

(1999). Measuring the sources and content of relational uncertainty. Communication Studies, 50(4), 261–278. https://doi.org/10.1080/10510979909388499

45.

Knobloch

L. K.

Solomon

D. H.

(2003). Manifestations of relationship conceptualizations in conversation. Human Communication Research, 29(4), 482–515. https://doi.org/10.1111/j.1468-2958.2003.tb00853.x

46.

Knobloch

L. K.

Theiss

J. A.

(2017). Topic avoidance about deployment upon reunion: Applying the relational turbulence model. Military Behavioral Health, 5(2), 117–128. https://doi.org/10.1080/21635781.2016.1272013

47.

Knobloch-Fedders

L. M.

Critchfield

K. L.

Boisson

Woods

Bitman

Durbin

C. E.

(2014). Depression, relationship quality, and couples’ demand/withdraw and demand/submit sequential interactions. Journal of Counseling Psychology, 61(2), 264–279. https://doi.org/10.1037/a0035241

48.

Kurdek

L. A.

(1992). Dimensionality of the Dyadic Adjustment Scale: Evidence from heterosexual and homosexual couples. Journal of Family Psychology, 6(1), 22–35. https://doi.org/10.1037/0893-3200.6.1.22

49.

Leustek

Theiss

J. A.

(2018). Features of illness versus features of romantic relationships as predictors of cognitive and behavioral coping among individuals with type 2 diabetes. Health Communication, 33(12), 1549–1559. https://doi.org/10.1080/10410236.2017.1384346

50.

Lillie

H. M.

Chernichky-Karcher

Venetis

M. K.

(2021). Dyadic coping and discrete emotions during COVID-19: Connecting the communication theory of resilience with relational uncertainty. Journal of Social and Personal Relationships, 38(6), 1844–1868. https://doi.org/10.1177/02654075211009302

51.

McKillop

H. N.

Connell

A. M.

(2018). Physiological linkage and affective dynamics in dyadic interactions between adolescents and their mothers. Developmental Psychobiology, 60(5), 582–594. https://doi.org/10.1002/dev.21630

52.

McLaren

R. M.

Haunani Solomon

Priem

J. S.

(2011). Explaining variation in contemporaneous responses to hurt in premarital romantic relationships: A relational turbulence model perspective. Communication Research, 38(4), 543–564. https://doi.org/10.1177/0093650210377896

53.

Meeks

B. S.

Hendrick

S. S.

Hendrick

(1998). Communication, love and relationship satisfaction. Journal of Social and Personal Relationships, 15(6), 755–773. https://doi.org/10.1177/0265407598156003

54.

Monk

J. K.

Ogolsky

B. G.

(2019). Contextual relational uncertainty model: Understanding ambiguity in a changing sociopolitical context of marriage. Journal of Family Theory & Review, 11(2), 243–261. https://doi.org/10.1111/jftr.12325

55.

Perry

N. S.

Sullivan

T. J.

Leo

Huebner

D. M.

O’Leary

K. D.

Baucom

B. R. W.

(2021). Using web-based technologies to increase reach, inclusion, and generalizability in behavioral observation research. Journal of Family Psychology, 35(7), 983–993. https://doi.org/10.1037/fam0000856

56.

Petronio

Child

J. T.

Hall

R. D.

(2022). Communication privacy management theory: Significance for interpersonal communication. In Braithwaite

D. O.

Schrodt

(Eds.), Engaging theories in interpersonal communication: Multiple perspectives (3rd ed., pp. 314–327). Routledge. https://doi.org/10.4324/9781003195511-28

57.

Ramirez

Jr. Wang

(2008). When online meets offline: An expectancy violations theory perspective on modality switching. Journal of Communication, 58(1), 20–39. https://doi.org/10.1111/j.1460-2466.2007.00372.x

58.

Reis

H. T.

(2007). Steps toward the ripening of relationship science. Personal Relationships, 14(1), 1–23. https://doi.org/10.1111/j.1475-6811.2006.00139.x

59.

Reis

H. T.

Clark

M. S.

Holmes

J. G.

(2004). Perceived partner responsiveness as an organizing construct in the study of intimacy and closeness. In Mashek

D. J.

Arons

(Eds.), Handbook of closeness and intimacy (pp. 201–225). Erlbaum.

60.

Reis

H. T.

Patrick

B. C.

(1996). Attachment and intimacy: Component processes. In Higgins

E. T.

Kruglanski

A. W.

(Eds.), Social psychology: Handbook of basic principles. (pp. 523–563). Guilford.

61.

Reis

H. T.

Shaver

(1988). Intimacy as an interpersonal process. In Duck

Hay

D. F.

Hobfoll

S. E.

Ickes

Montgomery

B. M.

(Eds.), Handbook of personal relationships: Theory, research and interventions (pp. 367–389). Wiley.

62.

Reiss

P. T.

Gvirts

H. Z.

Bennet

Kanterman

Fahoum

Shamay-Tsoory

(2019). A time-varying measure of dyadic synchrony for three-dimensional motion. Multivariate Behavioral Research, 54(4), 530–541. https://doi.org/10.1080/00273171.2018.1547874

63.

Sabourin

Lussier

Laplante

Wright

(1990). Unidimensional and multidimensional models of dyadic adjustment: A hierarchical reconciliation. Psychological Assessment: Journal of Consulting and Clinical Psychology, 2(3), 333–337. https://doi.org/10.1037/1040-3590.2.3.333

64.

Schrodt

Phillips

K. E.

(2016). Self-disclosure and relational uncertainty as mediators of family communication patterns and relational outcomes in sibling relationships. Communication Monographs, 83(4), 486–504. https://doi.org/10.1080/03637751.2016.1146406

65.

Shenk

C. E.

Fruzzetti

A. E.

(2011). The impact of validating and invalidating responses on emotional reactivity. Journal of Social and Clinical Psychology, 30(2), 163–183. https://doi.org/10.1521/jscp.2011.30.2.163

66.

Shrout

P. E.

Fleiss

J. L.

(1979). Intraclass correlations: Uses in assessing rater reliability. Psychological Bulletin, 86(2), 420–428. https://doi.org/10.1037/0033-2909.86.2.420

67.

Snijders

A. B.

Bosker

R. J.

(2012). Multilevel analysis: An introduction to basic and advanced multilevel modeling (2nd ed.). Sage.

68.

Solomon

D. H.

Brisini

K. S. C.

(2019). Relational uncertainty and interdependence processes in marriage: A test of relational turbulence theory. Journal of Social and Personal Relationships, 36(8), 2416–2436. https://doi.org/10.1177/0265407518788700

69.

Solomon

D. H.

Knobloch

L. K.

Theiss

J. A.

McLaren

R. M.

(2016). Relational turbulence theory: Explaining variation in subjective experiences and communication within romantic relationships. Human Communication Research, 42(4), 507–532. https://doi.org/10.1111/hcre.12091

70.

Solomon

D. H.

Theiss

J. A.

Knobloch

L. K.

McLaren

R. M.

(2019). Reflections on the development of relational turbulence theory. In Wilson

S. R.

Smith

S. W.

(Eds.), Reflections on interpersonal communication research (pp. 315–335). Cognella.

71.

Spanier

G. B.

(1976). Measuring dyadic adjustment: New scales for assessing the quality of marriage and similar dyads. Journal of Marriage and Family, 38(1), 15–27. https://doi.org/10.2307/350547

72.

Spanier

G. B.

(1988). Assessing the strengths of the Dyadic Adjustment Scale. Journal of Family Psychology, 2(1), 92–94. https://doi.org/10.1037/h0080477

73.

Sprecher

Hendrick

S. S.

(2004). Self-disclosure in intimate relationships: Associations with individual and relationship characteristics over time. Journal of Social and Clinical Psychology, 23(6), 857–877. https://doi.org/10.1521/jscp.23.6.857.54803

74.

Theiss

J. A.

(2018). The experience and expression of uncertainty in close relationships. Cambridge University. https://doi.org/10.1017/9781316423264

75.

Theiss

J. A.

Knobloch

L. K.

(2013). A relational turbulence model of military service members’ relational communication during reintegration. Journal of Communication, 63(6), 1109–1129. https://doi.org/10.1111/jcom.12059

76.

Theiss

J. A.

Knobloch

L. K.

(2014). Relational turbulence and the post-deployment transition: Self, partner, and relationship focused turbulence. Communication Research, 41(1), 27–51. https://doi.org/10.1177/0093650211429285

77.

Theiss

J. A.

Nagy

M. E.

(2013). A relational turbulence model of partner responsiveness and relationship talk across cultures. Western Journal of Communication, 77(2), 186–209. https://doi.org/10.1080/10570314.2012.720746

78.

Vicaria

I. M.

Dickens

(2016). Meta-analyses of the intra- and interpersonal outcomes of interpersonal coordination. Journal of Nonverbal Behavior, 40(4), 335–361. https://doi.org/10.1007/s10919-016-0238-8

79.

Worthy

Gary

A. L.

Kahn

G. M.

(1969). Self-disclosure as an exchange process. Journal of Personality and Social Psychology, 13(1), 59–63. https://doi.org/10.1037/h0027990