FEELING CLOSER BUT FURTHER APART? MEDIA MODALITY’S EFFECTS ON 
UNCERTAINTY PERCEPTION IN ROMANTIC RELATIONSHIPS 

By 

Andrew Bredland 

A THESIS 

Submitted to 
Michigan State University 
in partial fulfillment of the requirements   
for the degree of 

Communication – Master of Arts 

2024 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ABSTRACT 

For decades Communication scholars theorized how uncertainty will influence various 

communication phenomena. The present study seeks to differentiate personal, interpersonal, and 

transactional uncertainty under the Uncertainty Reduction framework. The study finds that there 

is a qualitative difference between a personal vs. interpersonal uncertainty in close relationship, 

and these uncertainties varies as function of communication channels. A new set of measures of 

uncertainties in close relationship is established, showing casing excellent convergent, 

discriminate, and predictive validity and internal consistency.      

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
This thesis is dedicated to my beloved advisor Brandon, and my wonderful committee members 
Sandi and Dave. Thank you all so much for always believing in me. It is my absolute honor to 

have all of you to be part of my academic journey.       

 iii 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
TABLE OF CONTENTS 

INTRODUCTION…………………………………………………………………………………1 

METHOD………………………………………………………….….………………………….14 

RESULTS………………………………………………………………………………………...16 

DISCUSSION……………………………………………………………………………………19 

LIMITATIONS  …………………………………………………………………………………24 

CONCLUSION………………………………………………………………………………….25 

BIBLIOGRAPHY………………………………………………………………………………...26 

APPENDIX  A:  SURVEY  ITEMS  …….…………….…………………………….………….30 

APPENDIX  B:  CFA……………………………………………………………….……….……32

 iv 

INTRODUCTION 

When making a decision, interacting with a loved one, or planning an event, humans 

experience uncertainty. Mathematically, uncertainty refers to the error terms in estimating a 

parameter or its variability from a true state. It is one of the fundamental reasons that make 

humans hard to predict. Over the years, multiple disciplines have attempted to address questions 

of uncertainty in human experience, such as explicating its forms and influence on human 

decision-making, enumerating contextual factors that influence its potency, and even developing 

mathematical models to quantify its existence in human psychology. Communication scholars 

have been fascinated by the concept of uncertainty since the 1970s and have taken a keen interest 

in its forms, exploring various communicative behaviors that reduce or amplify its experience 

(Brashers, 2007). Communication is a dynamic process that requires interaction between two or 

more individuals. Uncertainty fluctuates and changes during a conversation (Afifi & Burgoon, 

2000), suggesting that it is both interactive and time dependent. However, few communication 

studies have delved into interactive and dynamic dimensions of uncertainty, with much attention 

only paid to global assessments of uncertainty, such as relational and personal (Afifi & Weiner, 

2004; Berger & Calabrese, 1975; Knobloch & Solomon, 1999). Thus, the goal of the present 

study is to explicate the temporal and interactive nature of uncertainty during the communicative 

process. This paper will first explicate various forms of uncertainty and their definitions. Then, 

specific hypotheses are outlined based on theorical relationship. A survey experiment was 

conducted to establish a new set of uncertainty measures and test various hypothesized 

relationships. Lastly, the results and implications for the study is highlighted at the end.    

 1 

 
 
Uncertainty Reduction Theory (URT) 

In the communication literature, there are more than seven well-studied and developed 

theoretical perspectives for theorizing uncertainty under numerous communication contexts, such 

as Uncertainty Reduction Theory (URT) (Berger & Calabrese, 1975), Anxiety/Uncertainty 

Management (Gudykunst, 1995), theories of Uncertainty Management (Brashers, 2007), and 

Predicted Outcome Value Theory (Sunnafrank, 1986), to name just a few. Including literature 

outside of communication reveals even more sibling constructs, such as uncertainty orientation 

(Sorrentino et al., 1995), need for closure (Kruglanski et al., 2006), and equivocality (Weick, 

1979). Perhaps the most influential work in communication studies on uncertainty is URT by 

Berger and Calabrese (1975). 

In their original work on URT, Berger and Calabrese defined uncertainty along two 

theoretical dimensions: proactive and retroactive. Proactive focuses on an individual’s ability to 

predict their partner’s future behaviors, while retroactive focuses on people’s ability to make 

sense of and explain a communicating partner's past behaviors. Feelings of uncertainty arise 

from the lack of information people have about the interacting individuals (Berger & Calabrese, 

1975). In further developments of URT, Berger and colleagues divided uncertainty into cognitive 

uncertainty and behavioral uncertainty in an effort to specify the contents of various kinds of 

uncertainties (Berger, 1979; Berger & Bradac, 1982). Cognitive uncertainty arises when 

individuals lack clear perceptions of their own and others’ beliefs and attitudes in a relationship. 

Behavioral uncertainty refers to an individual’s lack of ability to predict others' behaviors and 

attitudes in particular circumstances. These modifications were added to account for the 

influences of norms and interaction rituals. For example, when an individual interviews for a job, 

certain actions are expected, such as being asked about job experience, thereby making the 

 2 

situation lower in behavioral uncertainty. However, an individual may still possess high levels of 

cognitive uncertainty if they lack information about their interacting partner’s beliefs and 

attitudes. At other times, both cognitive and behavioral uncertainty may be high, such as meeting 

someone in a place the individual has never been before. 

Interpersonal Uncertainty 

More recently, Walther (2019) raised issues with the original conceptualization of 

uncertainty, making a more precise distinction between personal and interpersonal impressions. 

In his writings, personal impressions or information refer to mental models we hold about a 

unique individual that differ from those about other people. As proposed by the Uncertainty 

Reduction Theory (URT) framework, individuals can reduce uncertainty or gain information 

about another individual by using three different types of strategies: active strategies, where the 

individual directly observes the target; passive strategies, where the individual observes the 

target indirectly; and interactive strategies, where the individual interacts with the target directly. 

Although an individual may be capable of reducing various kinds of behavioral and 

cognitive uncertainty through observation, asking others, and reliance on norms and interactional 

rituals, one's information or impression of another individual does not become interpersonal until 

the individual interacts with the target directly (Walther, 2019). In other words, an interpersonal 

impression or knowledge is idiosyncratic to a particular dyad. Taking the previous example, 

while an individual may decrease their uncertainty of the interviewer by relying on interactional 

rituals and observation of how the target interacts with other interviewees, one still lacks the 

idiosyncratic knowledge of how the interviewer will interact with themselves specifically. 

Interpersonal uncertainty arises from uncertainties about how a target individual will respond to 

oneself. Interpersonal uncertainty is particularly important in established relationship where the 

 3 

communication dyads have unique knowledge of their relational partner. Any variability in the 

interpersonal uncertainty could directly mediated relational outcomes, such as trust and relational 

satisfaction where the relational dyads feel uncertain how relational partner will react towards 

themselves. Interpersonal uncertainty may be particularly important in established relationship 

where the communication dyads have unique knowledge of their relational partner. Any 

variability in the interpersonal uncertainty could directly mediated relational outcomes, such as 

trust and relational satisfaction where the relational dyads feel uncertain how relational partner 

will react towards themselves.  

Walther's (2019) distinction between personal and interpersonal impressions is not 

unprecedented. Knobloch & Solomon (1999, 2002) made a similar distinction between self, 

partner, and relational uncertainty. In contrast to the majority of work on URT, which focuses on 

initial interactions, Knobloch & Solomon (1999, 2002) were more concerned with the role of 

uncertainty beyond initial interactions and further conceptualized Berger & Bradac's (1982) work 

on uncertainty in interpersonal communication. It was theorized and validated that questions, 

doubts, and ambiguities about one's relationship involvement are present in all close 

relationships. In contrast with an individual's lack of ability to predict or explain a target's 

attitudes, emotions, and behaviors, relational uncertainty arises from the relationship itself and is 

unique to a particular dyad. Self-uncertainty pertains to the individual's doubts regarding their 

own role and involvement in the relationship, while partner uncertainty addresses concerns one 

may have about their partner's engagement and commitment. Relationship uncertainty, on the 

other hand, refers to the overall uncertainties regarding the nature and future of the relationship 

itself (Knobloch & Solomon, 1999, 2002). Self and partner uncertainties are considered lower-

order constructs of relationship uncertainty. Although arising differently in the literature, both 

 4 

Walther (2019) and Knobloch & Solomon (1999, 2002) emphasize the unique level of 

uncertainty that is specific to a particular dyad. This is not to equate the two constructs of 

uncertainty, as they are distinct. Relational uncertainty is focused on an individual’s view of the 

interpersonal relationship itself, while interpersonal uncertainty concerns one's unique 

perceptions and knowledge of the target. 

The question remains: how does one capture the additional variance, if any, that 

interpersonal uncertainty may explain in addition to personal uncertainty? One of the most 

widely used measures of uncertainty, CLUES-7 (Clatterbuck, 1979), already contains one item 

that assesses the unique dyadic knowledge of an individual to the target (e.g., “How certain are 

you that he/she likes you?”). In contrast with the other four items, which capture more personal 

rather than interpersonal uncertainty (e.g., “How confident are you of your general ability to 

predict how he/she will behave?”). To increase the reliability and validity of the current 

measures, the present study seeks to develop a new set of measures factor design to capture the 

various types of uncertainties. 

Uncertainty and Information Environment 

Uncertainty is a self-perception where individuals perceive that details about the 

interacting individual are unavailable or inconsistent. It is one’s own cognitive state in which the 

individual feels insecurity and is negatively aroused by their lack of knowledge of the interacting 

party (Brasher, 2001; Berger & Calabrese, 1975; Gudykunst, 1995). Uncertainty is a cognitive 

state that resides in individual perceptions, but uncertainty reduction is a process in which this 

cognitive state changes as individuals interact with the target. In this process, an individual is 

inevitably influenced not only by the target but also by the information environment. Here, the 

information environment is defined as the channel through which the interaction occurs. The 

 5 

information environment in which uncertainty reduction unfolds will predetermine the types of 

cues or information sources that individuals have to rely on when reducing their uncertainty with 

another party. The type of cues that are afforded in the information environment will not only 

determine the rate at which uncertainty reduction can unfold (Walther, 1992) but should also 

shape the perceived amount of uncertainty in a given interaction. To put it simply, if two men are 

tasked with digging a hole in the ground and one is given a spoon while the other a shovel, the 

relative size of the hole to the individual would differ. The same could be said for uncertainty: 

when the information environment limits the types of cues that can be utilized to fill in the hole 

of uncertainty, cues that carry more weight should reduce individuals' perceptions of the 

uncertainty level. Therefore, the present study seeks to advance the idea of transactional 

uncertainty. Transactional uncertainty is people's confidence in deciphering the information cues 

in a given communication environment. It is the degree of negative arousal that individuals feel 

towards the target and the messages at a given moment in time in a given communication 

environment. It captures the discrepancies in uncertainty perception with respect to different 

information environments. 

One breakthrough in exploring the role of time in interpersonal communication comes 

from studies in Computer-Mediated Communication (CMC). It was theorized and validated that, 

given enough time, people can establish comparable levels of interpersonal impressions and 

reduce uncertainty using text-based messages alone (Walther, 1992; Tidwell & Walther, 2002). 

Later research further supported these arguments by introducing cues interchangeability, where 

individuals can sense relational-level information, often derived from non-verbal cues, from text 

messages as well, albeit it takes longer and more effort (Siegel et al., 1986; Walther et al., 2005). 

The discrepancy in how long it takes for relational information to transfer between CMC and FtF 

 6 

is a direct result of limited access to non-verbal cues. Non-verbal cues carry information. 

Decades of research on non-verbal cues in psychological research have pointed to the role of 

non-verbal cues in carrying relational information in social interaction, and in various instances, 

non-verbal cues carry more weight or trump verbal cues (see Burgoon & Le Poire, 1999; Hall et 

al., 1978; Argyle et al., 1970; 1972). 

If non-verbal cues carry more important interpersonal information, such as intimacy 

(Burgoon & Le Poire, 1999) and interpersonal attitudes (Argyle et al., 1970), it is also the case 

that non-verbal cues satisfy individual uncertainty arousal. These discrepancies in available 

sources of information may be particularly salient at the micro-transitional level. As 

demonstrated in the series of studies on Social Information Processing theory, by modifying the 

number of sources of available information (e.g., non-verbal cues), the time it takes for people to 

satisfy their uncertainty towards another individual also varies (see Walther, 1992; Tidwell & 

Walther, 2002). By extension, people's perception of uncertainty should vary by medium as a 

function of the amount of verbal and non-verbal cues the medium carries at the transactional 

level. 

The more sources of information people have access to, the quicker uncertainty can be 

decreased. The discrepancy in the number of available sources of information should translate to 

different perceptions of uncertainty when communicating FtF versus via mediated channels. 

More specifically, it is hypothesized that people should have lower uncertainty perception when 

communicating FtF than CMC. For example, a message sent through text carries less weight than 

an identical message paired with non-verbals. To compensate for the discrepancy, it takes longer 

and demands more effort for people to satisfy uncertainty through messages alone. To be clear, 

the difference between transactional uncertainty (personal/interpersonal) and personal and 

 7 

interpersonal uncertainties is not particularly situational but perceptual. First, the perceptual 

differences lie in the availability and type of information source (e.g., What cues are available 

when communicating in different environments?), which in turn guides individuals' uncertainty 

judgments. Some evidence can be found in modality switch (see Ramirez & Zhang, 2007) and 

the Hyperpersonal model (Walther, 1996) literature, a special case of SIP. 

In modality switch and the hyperpersonal model literature, impressions people form 

online do not often coincide with reality. Perceptions people form online tend to be hyperbolized, 

either positively (Ramirez & Zhang, 2007; Walther, 1996) or negatively. It is theorized that due 

to limited access to non-verbals information, people over-attribute based on a limited number of 

information sources (in this case, it was text only), thus forming impressions that are inconsistent 

with reality. Underlying these results, these studies may actually be pointing out different 

uncertainty perceptions with respect to different sources of information and uncertainty 

assessment. Verbal and non-verbal cues formulate distinct perceptions of uncertainty with 

varying weights in the uncertainty reduction process. This perceptual difference can also be 

inferred from studies in psychology comparing the influence of verbal and non-verbal cues 

(Argyle et al., 1970), where non-verbal information can either triumph, compensate for, or 

replace verbal information.  

Second, the communicating environment should bias the initial assessment of the 

uncertainty level. For example, the lack of available information sources with lower weight 

should heighten the perception of uncertainty. As Walther (2019) pointed out, the inclusion of 

non-verbal information, such as images, does not necessarily give individuals more information 

about the target. However, when we limit the types of information available in a communication 

setting, individuals will formulate a distinct assessment of the uncertainty level. This could have 

 8 

a profound impact on how individuals assess uncertainty. The absent of important non-verbal 

information restricts sources in which uncertainty can reduced. This in turn should increase the 

sense of anxiety or negative arousal people experience with interacting with a target.  

Lastly, transactional uncertainty is to be distinguished from both personal and 

interpersonal uncertainty. Both interpersonal and personal are more global judgments of the 

target individual, while transactional uncertainty is not. In contrast to URT’s cognitive 

uncertainty and behavioral uncertainty, where evaluation falls primarily on the target, the content 

of transactional uncertainty should fall on the message perception. Transactional uncertainty 

seeks to specifically explain the variance captured by the type of information source at an 

interaction level. This message perception uncertainty is directly influenced by the type of 

information cues utilized in the information environment. The discrepancy in the types of 

information sources shapes individuals’ efficacy beliefs (Bandura, 1997) in their ability to satisfy 

uncertainty arousal.       

Uncertainty and Communication Quality 

A new construct lacking predictive power is akin to a knife without an edge. By 

accounting for uncertainty arising from the information environment, transactional uncertainty 

should explain variance above and beyond both personal and interpersonal uncertainty. One of 

the most obvious ways to test this assumption is to examine whether transactional uncertainty 

can predict communication outcome variables, such as the quality of a communication 

interaction. Lower transactional uncertainty should predict higher quality of communication 

perceptions, as individuals experience lower levels of arousal towards communicative 

interactions (Gudykunst, 1995). Conversely, higher transactional uncertainty appraisals should 

diminish individuals' perceptions of communication quality. In other words, the availability of 

 9 

different information cues in a communication environment is theorized to shape individuals' 

perceptions of uncertainty levels, as they rely on these cues to satisfy their uncertainty needs. In a 

semantic analysis of intergroup communication over time, Guerrero & Gudykunst (1996) found 

that "The majority of respondents alluded to non-verbal communication as the prime indicator of 

successful communication" (p. 65). Therefore, by comparing transactional uncertainty in 

different information environments, it is expected that people will perceive different 

transactional uncertainty levels with different information environments. This transactional 

uncertainty should, in turn, influence perceptions of the quality of communication within that 

information environment. Thus, the following hypotheses are proposed: 

H1: There is a positive relationship between (a) interpersonal transactional uncertainty 

and (b) personal transactional uncertainty on perceived communication quality in both text and 

face-to-face (FtF) conversations. 

H2: Individuals will perceive higher (a) interpersonal transactional uncertainty and (b) 

personal transactional uncertainty in text communication than in FtF communication. 

H3: Perceived Communication quality will be higher in FtF communication than Texting 

communication.  

Relationship and Uncertainty 

One of the most fundamental functions that URT explains is how individuals develop 

relationships and formulate impressions. However, the majority of research inspired by URT has 

focused on zero-history dyads. Later research on relational uncertainty (for a review, see 

Knobloch & Solomon, 2002) extended URT into established relationships. It has been found that 

the presence of relational uncertainty is correlated with decreased liking (Gudykunst, 1985), 

feelings of jealousy (Knobloch et al., 2001), and a variety of other negative relational outcomes 

 10 

(Sorrentino et al. 1995). In contrast to URT, research on uncertainty management and relational 

uncertainty argues that individuals do not always seek to reduce uncertainty. In fact, relational 

uncertainty has been found to be associated with both relational excitement and love (Baxter & 

Montgomery, 1996).  

More recently, there has been growing scholarship using both Social Information 

Processing Theory and the Hyperpersonal Model to explain the communicative process in 

established romantic relationships (Hampton et al., 2017; Holtzman, 2021; Jiang & Hancock, 

2013; Toma & Choi, 2016; Brody, 2013). Some of these researchers argue that since romantic 

couples rely on mediated communication channels to engage in relationship maintenance 

behaviors, communication between established romantic relationships will also be subject to the 

intensification process as described in the hyperpersonal model. Other research claims that since 

relational information takes longer and more effort to transmit through mediated forms of 

communication, there will be an increasing amount of uncertainty buildup (Jiang & Hancock, 

2013). Thus, the following hypotheses are raised: 

Much of the literature on interpersonal computer-mediated communication comes from 

studies on Long-Distance Romantic Relationships (LDRRs) (Brody, 2013; Hampton et al., 2017; 

Holtzman, 2021; Stafford & Merolla, 2007; Jiang & Hancock, 2013), as LDR couples usually 

have an established relationship status before being separated and heavily rely on various media 

channels for communication when separated. This creates an ideal situation for researchers to 

observe the effects of mediated communication on interpersonal relationships. It is widely 

theorized that once romantic partners become geographically separated, uncertainty is created 

(Stafford & Merolla, 2007; Jiang & Hancock, 2013). When romantic couples become 

geographically separated, they have lower access to their partner's interpersonal repertoire 

 11 

(Schulman, 1974; Miller et al., 2003). The lack of exposure to mundane experiences weakens 

individuals’ confidence and ability to predict what their relational partner thinks, feels, and 

behaves. By extension, individuals are likely to grow less confident in their ability to predict how 

their partner will react towards themselves, increasing interpersonal uncertainty. Transactional 

uncertainty should be particularly pronounced in LDRRs when communicating through media 

that afford a lower amount of non-verbal cues. In these instances, individuals are not only faced 

with an interaction where they know less about the target but also have to rely on a medium that 

is less efficient than others. Thus, the following hypotheses are raised: 

H4: LDRRs will perceive higher (a) personal uncertainty, (b) interpersonal uncertainty, 

and (c) transactional personal/interpersonal uncertainty in both FtF and texting than 

geographically close romantic relationship (GCRR) couples. 

It is generally believed that as a relationship develops, uncertainty decreases (Berger & 

Calabrese, 1975). The more individuals observe, interact with, and connect to a target's social 

circles, the fewer questions they will have and the more confident they will be in how the target 

will behave and feel in various situations. This is also true for both transactional and 

interpersonal uncertainty. The more interactions individuals have with the target, the more they 

can learn how their partner reacts to their feelings and behaviors (Bandura, 1986), decreasing 

their interpersonal uncertainty. Transactional uncertainty functions similarly to other types of 

uncertainty. For example, research on channel expansion theory (Carlson & Zmud, 1999) 

emphasizes the importance of individual experience with a particular medium and the 

communicating partner in directly shaping media richness perceptions. The more familiar an 

individual is with utilizing a particular medium and the communicating partner, the higher their 

ability to encode and decode the meaning of the target messages, leading to more efficient 

 12 

communication and a higher perceived social presence (Carlson & Zmud, 1999; D’Urso & 

Rains, 2008). By extension, as the relationship progresses and interaction through a variety of 

mediated and unmediated communication channels increases, the relative weight discrepancy 

between verbal and non-verbal cues in satisfying uncertainty should diminish. People should 

grow more comfortable and efficient in communicating with their partner through media 

channels that afford a lower amount of non-verbal cues, decreasing the level of uncertainty due 

to the communication medium. Thus, as the relationship progresses, transactional uncertainty 

should decrease. Therefore, the following hypotheses are raised: 

H5: As the relationship progresses, (a) personal uncertainty, (b) interpersonal uncertainty, 

and (c) transactional personal/interpersonal uncertainty in both FtF and texting decrease. 

 13 

 
 
 
 
 
 
 
 
 
 
 
 
 
Procedure 

METHOD 

An online study was conducted using Qualtrics, where eligible participants were recruited 

from a university website. Participants' identities were secured with two-factor authentication 

and an associated ID number. The study received approval from the Institutional Review Board 

of the principal investigator’s university. All identifying information was removed immediately 

after data collection. Initially, participants answered a set of questionnaires measuring personal 

and interpersonal uncertainties about their romantic partners. Subsequently, they completed 

questionnaires on their perceived transactional uncertainties (personal and interpersonal) through 

texting and face-to-face conversation. The order of the three sets of uncertainty measures was 

randomly assigned to each individual. Participants were then randomly assigned to recall their 

communication quality on a 10 points semantic differential scale (Duck et al., 1991) with their 

romantic partner via face-to-face (FtF) and texting. The order of these two measures was 

counterbalanced. Finally, participants answered a set of general demographic questions, 

including their relationship length, relationship type (GCRR or LDRR), age, and sexual 

orientation. The final dataset contained 179 participants, aged 17 to 34, with 143 female 

participants, an average relationship length of 18.4 months, and 52 self-identified as in long-

distance romantic relationships (LDRR).  

Results: 

Measures Development 

To expand and validate current measures on uncertainty, Clatterbuck's (1979) measures 

on attributional confidence were first segmented into personal and interpersonal parts. The item 

"How certain are you that he/she likes you?" was categorized as interpersonal, while the 

 14 

remaining four items (e.g., "How confident are you of your general ability to predict how he/she 

will behave? How accurate are you at predicting the values he/she holds") were categorized as 

personal uncertainty measures. Using this separation as a guide, a new set of personal and 

interpersonal uncertainty measures was developed using a 100-point scale (see Table 1 & 2). To 

develop transactional uncertainty measures, the interpersonal and personal uncertainty measures 

were used as benchmarks. The measures were also segmented into personal transactional 

uncertainty and interpersonal transactional uncertainty (see Table 3 & 4). As attributional 

confidence is theorized as reliable and valid measures of certainty, to all values are reversed 

coded to reflect the nature of this scale to measure uncertainty present (Clatterbuck’s, 1979).   

 15 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Reliability and Validity Assessments 

RESULTS 

To assess the reliability and validity of the measures, a first-order confirmatory factor 

analysis (CFA) was conducted using JASP version 0.18.3. To assess the sampling adequacy of 

the CFA model, a Kaiser-Meyer-Olkin (KMO) test (0.94) and Bartlett’s test of sphericity 

(χ²=2919.9, df=153, p<.001) were conducted, indicating excellent sampling adequacy and that 

the correlation matrix was not an identity matrix (Kaiser, 1974). To assess the correlation 

between individual indicators and the latent variables, loadings were computed, with all factor 

loadings above 0.7, indicating high validity of an indicator to the latent variable (Hair et al., 

2010). To assess convergent validity of the latent constructs, Average Variance Extracted (AVE) 

tests were performed, with Interpersonal at 0.61, Personal at 0.70, transactional interpersonal at 

0.80, and transactional personal at 0.80 (greater than 0.7 is excellent, whereas greater than 0.5 is 

acceptable; Fornell & Larcker, 1981). To assess discriminant validity, the Heterotrait-Monotrait 

ratio of the correlations was performed, and the resulting matrix values (see Table 5) were all 

below the 0.85 threshold (Henseler et al., 2015). To assess the reliability of the measures, 

Cronbach's Alpha was tested, with all individual factors and overall reliability above 0.85 (see 

Table 4). Lastly, to assess model fit, the following goodness-of-fit indices were used: the 

Comparative Fit Index (CFI) at 0.96, and Root Mean Square Error of Approximation (RMSEA) 

at 0.07 (RMSEA: less than .08 is acceptable, less than .05 is excellent), and Standardized Root 

Mean Square Residual (SRMR) at 0.046 (Hu & Bentler, 1999. In summary, the developed 

measures achieved good model fit and passed both reliability and validity assessments. 

For Hypothesis 1, two multiple regression analyses were used to test if (a) interpersonal 

transactional uncertainty and (b) personal transactional uncertainty predict perceived 

 16 

communication quality in both text and FtF conversations. The first multiple regression analysis, 

focusing on text communication, indicated that the two predictors explained 34.7% of the 

variance (Adjusted R² = .347, F(2,177) = 47.12, p < .01). It was found that transactional 

interpersonal uncertainty significantly predicted perceived text communication quality (β = 

0.012, p < .01), as did transactional personal uncertainty (β = .010, p < .01), supporting 

Hypothesis 1(a). A second multiple regression analysis tested if interpersonal and personal 

transactional uncertainty predicts perceived communication quality in FtF conversations. The 

results indicated that the two predictors explained 22.1% of the variance (Adjusted R² = .221, 

F(2,177) = 26.4, p < .01). Interpersonal transactional uncertainty significantly predicted 

perceived FtF communication quality (β = .016, p < .01), but not personal transactional 

uncertainty (β = 0, p > .05), partially supporting Hypothesis 1(b).  

To test for Hypothesis 2, two paired samples T-tests were conducted. For interpersonal 

transactional uncertainty, perceived interpersonal transactional uncertainty was significantly 

higher in texting communication (M = 69.88, SD = 18.71) than FtF communication (M = 82.28, 

SD = 12.52);  t(179) = 10.54, p < .01. For personal transactional uncertainty, perceived 

interpersonal transactional uncertainty was significantly higher in texting communication (M = 

71.95, SD = 16.84)than FtF communication (M = 84.84, SD = 10.93); t(179) = 10.85, p < .01, 

supporting Hypothesis 2. 

To test for hypothesis 3, a paired samples T-tests were conducted. As expected, perceived 

communication quality for FtF (M=4.01 SD = 0.034) were significantly higher than texting 

communication (M=3.5 SD = 0.046); t(179)=11.42, p<0.01, supporting hypothesis 3.   

To test for Hypothesis 4, a MANOVA were conducted using relationship types as 

independent variable, and six types of uncertainty as dependent variables, which includes, 

 17 

interpersonal, personal, transactional personal (FtF/Text), transactional interpersonal (FtF/Text). 

Findings revelated a nonsignificant associations between the relationship types and the siz types 

of uncertainties; F(1, 178)=0.102, p>0.05.  

To test for Hypothesis 5, a multivariate regression were conducted. As expect, The 

regression model for Interpersonal uncertainty revealed a significant effect of relationship length, 

with a coefficient of 0.214, t(177) = 3.785, p < .001, explaining 7.49% of the variance (Adjusted 

R² = .06967). As did personal uncertainty with a coefficient of 0.216, t(177) = 4.123, p < .001, 

with an explained variance of 8.25% (Adjusted R² = .08248), transactional personal (Texting) 

with a coefficient of 0.313, t(177) = 3.822, p < .001, with the model explaining 7.13% of the 

variance (Adjusted R² = .07103), transactional interpersonal (Texting) with a coefficient of 

0.280, t(177) = 3.146, p = .002, accounting for 4.76% of the variance (Adjusted R² = .04761), 

transactional personal (FtF) with a coefficient of 0.200, t(177) = 3.654, p < .001, explaining 

6.49% of the variance (Adjusted R² = .06490), and transactional interpersonal (FtF) a coefficient 

of 0.171, t(177) = 2.780, p = .006, with an explained variance of 3.64% (Adjusted R² = .03643), 

supporting all of H5.  

 18 

 
 
 
 
 
 
 
 
DISCUSSION 

The information environment shapes our perception of uncertainty in interactions with 

targets. Uncertainty reduction is a process where increased information leads to reduced 

uncertainty. However, it is the type of information source that not only determine the rate in 

which uncertainty reduction occurs, but also the amount of uncertainty that individuals perceive. 

This study established a new set of measures for uncertainty reduction, specifically interpersonal 

and transactional uncertainty, and demonstrated their validity, reliability, and predictive power in 

both mediated and FtF communication. 

Initially, existing uncertainty measures guided the creation of updated measures for 

personal and interpersonal uncertainty, laying the groundwork for transactional uncertainty 

measurements. A first order CFA was conducted, which demonstrated excellent model fits, 

factor loading, validity, and reliability. In evaluation of its predictive power, a series of 

regression were conducted with communication quality and relationship length measures, 

demonstrating good predictive power. 

As expected, there is a positive relationship between interpersonal transactional 

uncertainty and personal transactional uncertainty on perceived communication quality in both 

text and face-to-face conversations. Due to cue discrepancies, individuals perceive higher 

interpersonal transactional uncertainty and personal transactional uncertainty in text 

communication than in FtF communication. Higher uncertainty perceptions lead to biased 

perceptions of information of the information environment, undermining the perceived quality of 

texting over FtF communication.     

When controlling for the effects of interpersonal transactional uncertainty, personal 

transactional uncertainty became an insignificant predictor in FtF communication, while both 

 19 

remained significant predictors of communication quality in texting communication. To further 

understand this relationship, two multiple regressions were conducted for FtF and texting 

communication with personal, interpersonal, and transactional personal/interpersonal variables. 

The results suggest that across both conditions, interpersonal transactional uncertainty was the 

best and most significant predictor of communication quality (see Table 6 & 7). This makes 

sense as transactional interpersonal uncertainty takes into account both the uniqueness of the 

relational dyads at interpersonal level and modality effects when measuring uncertainty. Personal 

and general or transactional uncertainties are likely encompassed by the measure of transactional 

interpersonal uncertainty. 

No uncertainty discrepancy seems to emerge by comparing LDRR and GCRR. This 

finding was not particularly surprising as previous studies found mixed support this hypothesis 

(See Stafford et al., 2006; Stafford & Merolla, 2007; Jiang & Hancock, 2013). One of the 

significant hallmarks of LDRR is idealization. It was theorized that when relational couples 

become geographically separated, uncertainties are induced (Stafford & Merolla, 2007; Jiang & 

Hancock, 2013). However, as relational partners generally want to preserve their romantic 

relationships, couples engage in idealization to ward off uncertainties. The push and pull results 

in a tug of war between uncertainty and idealization. Future studies should test these assumptions 

by using dyadic data analysis techniques to demonstrate these theorizations. 

Different modalities play a significant role in shaping our uncertainty perceptions. 

Individuals are less confident interpreting feelings, emotions, and attitudes of the interacting 

target in information environments where cues are restricted. This is an important extension to 

both URT and SIP theory. First, different media environments that afford varying amounts of 

non-verbal cues play a significant role in shaping uncertainty perceptions. When communicating 

 20 

through mediated channels, uncertainty is perceived to be higher than in FtF conversations, 

significantly impacting the perceived communication quality in relational dyads. Higher 

transactional uncertainty is associated with lower perceived communication quality. There has 

been much debate on the validity of SIP, particularly in survey studies. People consistently report 

lower perceived communication quality in survey studies, while pre/post-test design experiments 

fail to support these findings. The discrepancy in transactional uncertainty between FtF vs. 

texting communication highlights that people may be influenced by their perceptions of media 

environments, not their actual experiences with their communicating partner. This is particularly 

evident in the post-hoc analysis, where controlling for relationship length, interpersonal 

transactional uncertainty remained a significant predictor of perceived communication quality. 

A important distinction must be made in accessing uncertainty perception in 

communication. The present study concurs with Brashers’s definition of uncertainty with a 

particular emphasis on perceptions aspects of uncertainty. However, there is much a discrepancy 

between people perceptions of uncertainty and the actual uncertainty people experience. This 

apparent discrepancy have struggled CMC scholars for decades. When forcing individuals to 

communicate via lean cues modality, participants always express a desire to “see the other in 

person” as if seeing the other individual reveal more information of the interacting parties when 

indeed all information of the interacting party have been achieved via mediated communication. 

Perhaps, the people are just hard wired to prefers unmediated over mediated communication. 

From touch to smile, from social distance to vocalics, psychologist have long demonstrated the 

influence of non-verbal communication in human development (Bates, 1976; Buck, 1975; 

Harlow, 1958). The perceived discrepancy between mediated vs. FtF communication is fact so 

what a byproduct of machine heuristics (Sundar, 2008) in combination with an illusional reliance 

 21 

of non-verbal cues in gaining interpersonal knowledge. This is not to undermine the role of 

individual perceptions in communicative exchanges, as these fundamental discrepancy inevitable 

influence how we evaluate the communicative exchanges. Perhaps, one can even argued that it is 

ultimately that individual perceptions that guide our behaviors, and that the actual uncertainty 

between communicating individual is inferiors to the other. But it will do us no good when 

ignoring these fundamental misconceptions and simply conceptualized mediated communication 

as inferior as cues filtered out perspectives.  

The clear distinction between interpersonal and personal uncertainty marks an important 

diversion from the previous literatures on URT. For decades, URT was talked about as part of 

the larger impression formation literature where individual formulate some impression of the 

target. As Nalini Ambady and Robert Rosenthal (1993) have famously demonstrated in a matter 

of seconds, individuals could formulate a unique impression of the target on a wide range of 

characteristics. If research were to run the study again and asking them “How accurate are you at 

predicting the values your teachers hold about you?”, it is unlikely these participants would be 

particularly confident in their answer. Communication is a fundamentally a mutual and 

contingent process. Interpersonal uncertainty is one manifestation of communication unique 

characteristics.      

In a world where various modalities of mediated communication become more 

ubiquitous, the URT reduction process should reflect these changes and consider how 

uncertainty reduction may occur differently across various communication channels. The newly 

established measure offers researchers an additional tool with which they can tap into the 

dynamic process in interpersonal computer-mediated communication (CMC). For many years, 

CMC scholars have relied on measures that do not take into account elements of mediated 

 22 

communication and heavily rely on adaptive scales. The present study is a step towards 

developing a measurement scale that is more tailored to specific communication domains, which 

should enhance both the validity and reliability of our research.   

 23 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
LIMITATIONS 

There are several limitations to this study that are important to highlight. The study 

compared only FtF and texting communication, which are the most popular forms of 

communication in relational dyads. Future studies should compare other modalities of mediated 

communication. Different modalities will inevitably shape both the amount and types of cues 

available in the communication environment, which will, in turn, shape the uncertainty 

perceptions of the interacting partners. Further research should validate the measures in other 

communication environments. Additionally, the study relied on a single dependent variable to 

test the measures' predictive power. Although multiple measures of uncertainty were utilized in 

evaluating both transactional and interpersonal uncertainty measures, further studies are still 

needed to demonstrate a robust convergent and discriminant validity. 

In re reviewing the uncertainty literatures, it is not hard to see the abundance of paper 

published based on URT and its long-standing rival: Predicted Outcome Value Theory 

(Sunnafrank, 1986). Communication scholars have long talk to with itself in theorizing 

uncertainty is sibling concepts in communication. Little to no attention have been paid to 

theoretical frameworks across disciplines. Questions must be asked how POV differs from Needs 

to belong (Baumeister & Leary, 1995)? Is there qualitative difference uncertainty individual face 

in the face of making a financial decision (Alchian, 1950) vs. uncertainty people face when 

getting to know someone? Does people who have higher needs for closure (Kruglanski, 2006) 

tends to perceive higher amounts of uncertainty in social interaction? Or is the case that Needs 

for closure is more of a sibling construct to uncertainty? Depending on how we address these 

questions, communication scholars may be readily to incorporates these other frameworks in 

theorizing the functionalities of uncertainty in interpersonal relationships.        

 24 

CONCLUSION 

It has been nearly six decades since the initial publication of Uncertainty Reduction 

Theory. Numerous adaptations and spin offs of the theory have emerged over this time. To this 

day, the theory remains one of the most cited in the field of communication. What has changed is 

the information environment that people encounter when communicating with their relational 

partners. Like any theory, the resilience of URT's core ideas has proven to be exceptional. The 

current studies reflect the evolving nature of how uncertainty can be understood in interpersonal 

communication, particularly highlighting the role of the information environment and unique 

interpersonal knowledge that formulates our perception of uncertainty. With this newly 

established measure, more experiments can be conducted in comparing interactive and non-

interactive social interactions. For example, online dating researcher can access the differences in 

uncertainty reduction between images with text information vs. synchronous interactions in 

gaining interpersonal impression and its impacts on relational outcomes. Interpersonal 

communication research could unitarize the scale how various relational phenomena increase or 

decrease interpersonal uncertainties and its impacts in mediating relational outcomes, such as 

trust, attraction, and relationship satisfaction.       

 25 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 29 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
APPENDIX A: SURVEY ITEMS 

Table 1 
Interpersonal Uncertainty 
How confident are you in your ability to anticipate the reactions of partner’s towards your own 
behavior? 
How accurate are you at predicting the values your partner holds about you? 
How accurate are you at predicting your partner’s attitudes towards you? 
How well can you predict your partner’s feelings and emotions towards you? 

Table 2 
Personal Uncertainty 
How confident are you in your ability to interpret the non-verbals of your partner? 
How accurate do you think your predictions of your partner’s behavior in social situations are?

How accurate are you at predicting the values he/she holds? 
How confident are you in your ability to predict the preferences of your partner? 
How accurate are you at predicting his/her attitudes? 

Table 3 
Transnational Personal Uncertainty 
How well can you predict the reactions of your partner through text messages/FtF 
Conversation? 
How certain are you that you are able to accurately predict the motives of your partner through 
texting/FtF conversation? 
How confident are you in your ability to predict the opinions of your partner about specific 
topics through texting/ FtF conversation? 
How confident are you in your ability to predict the outcomes of conversations with your 
partner through texting/ FtF conversation? 
When communicating via text/FtF, how accurate are you at understanding your partner's 
attitudes? 

Table 4 
Transnational interpersonal  
When texting with your partner, how much do you think your partner can emphasize with 
you? 
When texting with your partner, how confident are you in your partner’s ability to understand 
the tone of your text messages? 

How certain are you that your partner is able to accurately predict your motives through text 
messages? 
How confident are you in your partner's ability to relate to you through texting? 

 30 

 
 
 
 
 
 
 
 
 
 
Table 5 
Heterotrait-Monotrait Ratio 
Interpersonal 

Personal 

1.00 
0.81 
0.70 
0.60 

1.00 
0.63 
0.59 

Transactional 
Personal 

Transactional 
Interpersonal 

1.00 
0.82 

1.00 

Table 6 
Interpersonal, Personal, Transactional, Relationship Length on Texting Communication Quality 
Variable 

SE 

B 

p 

t 

-0.001 

0.006 

0.003 

0.005 

Standardized 
Coefficient 
-0.025 

5.866 

0.695 

0.112 

-0.393 

0.211 

-0.001 

0.005 

-0.026 

0.008 

0.011 

0.004 

0.003 

0.231 

0.346 

1.256 

2.245 

3.514 

0.769 

0.026 

<0.001 

Relationship 
Length 
Interpersonal 
Uncertainty 
Personal 
Uncertainty 
Transactional 
Personal 
Transactional 
Interpersonal 

Table 7 
Interpersonal, Personal, Transactional, Relationship Length on FtF Communication Quality 
Variable 

SE 

B 

p 

t 

Relationship 
Length 
Interpersonal 
Uncertainty 
Personal 
Uncertainty 
Transactional 
Personal 
Transactional 
Interpersonal 

0 

0.006 

0.008 

-0.006 

0.024 

0.002 

0.003 

0.005 

0.005 

0.004 

0.249 

1.865 

1.633 

0.803 

0.064 

0.104 

-1.349 

0.179 

3.301 

0.001 

Standardized 
Coefficient 
0.017 

0.171 

0.187 

-0.156 

0.36 

 31 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 1 
CFA Model for Texting Communication 

APPENDIX B: CFA 

Note:Factor 1: Interpersonal, Factor 2: Personal, Factor 3: Transcational Personal, Factor 4: 
Transcational Interpersonal 

Figure 2 
CFA model for FtF Communication*

Note:Factor 1: Interpersonal, Factor 2: Personal, Factor 3: Transcational Personal, Factor 4: 
Transcational Interpersonal  
*CFA model for FtF was not reported as part of the measurement development analysis, as the 
measure was exactly the same as texting communication with only change with respect to media 
modality. CFI 0.93, SRMR 0.04, RMSEA 0.08, Cronbach Alpha 0.95, and AVE all above 0.6, 
HTMT all below 0.9. 

 32