Aviva Gordon1
Matthew L. Cole2*- 1College of Arts and Sciences, Lawrence Technological University, Southfield, MI, United States
- 2College of Business and Information Technology, Lawrence Technological University, Southfield, MI, United States
Nonverbal immediacy, the set of communicative behaviors that reduce psychological distance between individuals, has been a cornerstone of instructional communication research for over half a century. However, the proliferation of online, hybrid, and blended learning environments has challenged traditional immediacy measurement, creating a need for a modernized tool. This study reports on the development and psychometric validation of the Virtual and Interpersonal Nonverbal Immediacy (VINI) Scale, a 12-item self-report instrument designed to measure instructor immediacy across contemporary learning modalities. The VINI Scale comprises two distinct but correlated factors: Physiological Immediacy (gestures, eye contact, vocal expressiveness, animated movement, body orientation, smiles) and Virtual Immediacy (emoji use, colorful visuals, embedded video, varied typography, interactive technology, collaborative tools). Virtual Immediacy is operationalized as technologically-mediated, design-based behaviors enacted in digital environments—particularly asynchronous online contexts—that signal instructor presence and engagement. Data were collected through a dual-framework design, capturing both learner observations of instructor behaviors (VINI-O, N = 447) and learner preferences for those behaviors (VINI-P, N = 295). Confirmatory factor analysis supported a first-order, two-factor model as optimal across both samples. Multi-group analyses established configural invariance across participant gender, instructor gender, and instructional method comparisons, with VINI-P achieving scalar invariance across instructional method, enabling meaningful comparisons between face-to-face and online contexts. The VINI Scale offers researchers a reliable and valid instrument to investigate modernized nonverbal immediacy and provides practitioners across educational, corporate, and health contexts with an evidence-based framework to enhance social presence and engagement in any learning environment.
1 Introduction: reconceptualizing nonverbal immediacy in the contemporary educational landscape
1.1 The importance of nonverbal immediacy pedagogy
Effective communication is central to the pedagogical process, with the relationship between instructor and student serving as a critical conduit for learning (Richey et al., 2010; Witt et al., 2004). Within this dynamic, nonverbal immediacy has long been recognized as a powerful catalyst for positive educational outcomes. While the study of nonverbal behavior dates to the 19th century (Darwin, 1872), the specific construct of Nonverbal Immediacy (NVI) was defined by Mehrabian (1971) as the degree of perceived physical or psychological closeness and warmth between individuals, primarily facilitated by specific nonverbal behaviors. Rooted in Mehrabian's (1971) “principle of immediacy,” the construct posits that “people are drawn toward people and things they like, evaluate highly, and prefer; and they avoid or move away from things they dislike” (p. 1). Immediacy behaviors, therefore, function as “approach behaviors” that signal warmth, openness, and availability for communication, thereby increasing sensory stimulation and fostering a sense of interpersonal closeness (Mehrabian, 1971; Richmond et al., 1987; Richmond et al., 2003; Witt et al., 2004).
For over five decades, a substantial body of research has documented the specific nonverbal cues associated with this construct in face-to-face settings. These classic immediacy behaviors include kinesics (the study of body movement) such as gesturing and adopting a relaxed posture; proxemics, such as reducing physical distance to the audience; oculesics, particularly the use of direct eye contact; smiling; using varied vocal expressions (vocalics); and appropriate use of touch (Mehrabian, 1971). Collectively, these actions convey positive affect, enhance the likeability of the instructor, and are interpreted by receivers as signs of liking and affiliation (Richmond et al., 1987; Richmond et al., 2003).
The impact of these behaviors within the classroom is profound and well-documented. A robust and consistent line of inquiry has demonstrated that students’ perceptions of instructor immediacy are positively correlated with a wide range of desirable outcomes. These include enhanced affective learning (i.e., positive attitudes toward the course content and instructor), greater perceived and actual cognitive learning, and increased student motivation (Witt et al., 2004). Furthermore, instructor immediacy has been shown to increase student participation and compliance while reducing verbal aggression and resistance (Kearney et al., 1988; Rocca, 2008). Given that nonverbal cues are believed to communicate a majority of interpersonal attitude, their role in establishing a positive and effective learning environment cannot be overstated (Burroughs, 2007).
1.2 The digital shift: limitations of traditional nonverbal immediacy constructs
The theoretical and empirical foundation of nonverbal immediacy was built almost exclusively within the context of physically co-present, face-to-face interaction. The rapid and widespread integration of technology into education, creating a landscape of online, hybrid, and blended learning modalities, presents a significant challenge to this traditional paradigm. When courses are delivered online, instructor-student immediacy must expand to include not only the perceived nonverbal behaviors of the instructor but also the logistical aspects of instruction in a virtual environment, such as the use of emojis, colors, fonts, and feedback.
Early theories of computer-mediated communication (CMC), such as the Reduced Social Cues model, characterized online environments as inherently impersonal and socio-emotionally limited due to the absence of the very nonverbal cues that define immediacy (Waldeck et al., 2001). This perspective suggested that distance education environments suffered from an unavoidable “immediacy deficit,” making it difficult to foster the psychological closeness essential for effective learning (Carrell and Menzel, 2001).
This initial pessimism was later countered by the development of more nuanced theoretical frameworks, most notably Social Presence Theory. This theory posits that social presence, the degree to which a person is perceived as a “real person” in mediated communication, is not solely dependent on the medium’s richness but also on the communicator’s ability to adapt and utilize available cues to project themselves socially and emotionally (Gunawardena and Zittle, 1997). Research within this framework began to identify ways instructors could create intimacy and presence even in text-only media, using behaviors such as prompt feedback, frequent messaging, and the use of emoticons (Tu and McIsaac, 2002). Several recent studies have focused exclusively on these logistical online-virtual constructs, examining the impact of adapting to new technologies, use of capital letters, color, emails, emojis, feedback, third-party instruction (e.g., YouTube), and collaborative tools like wikis.
Despite these theoretical advancements, a significant measurement gap has persisted. Many existing instruments designed to measure nonverbal immediacy remain focused on the classic, physical behaviors of face-to-face interaction. For example, well-regarded observational scales like the Teacher nonverbal immediacy (TeNOI) scale operationalize the construct through factors such as physical proximity, body orientation, eye contact, and vocal variety, behaviors that are either impossible or fundamentally altered in asynchronous online settings (Toivanen et al., 2025). This leaves researchers and practitioners without a validated tool to assess instructor immediacy in a way that is relevant to the full spectrum of contemporary learning environments.
The theoretical basis for rethinking immediacy rests on two main premises. First, immediacy behaviors are defined by their psychological function rather than the specific actions used to achieve them (Mehrabian, 1969). This perspective, central to social presence theory (Biocca et al., 2003), suggests that different communication environments require different behaviors to create the same sense of connection and social presence. Second, the principle of medium affordances (Gibson, 1979/2014) suggests that every communication channel offers unique tools and limitations. While physical classrooms allow for immediate body language (gestures, eye contact, proximity), digital learning environments rely on persistent design elements (visuals, media, and structure). The VINI model combines these ideas, proposing that modern instructors need two complementary skill sets tailored to these different environments.
1.3 Bridging the modality gap: the theoretical case for a unified VINI construct
To address this measurement gap, a reconceptualization of immediacy is required (Gordon, 2020). The central premise of the present study is that the focus must shift from a literal translation of specific physical behaviors to an understanding of the underlying psychological function that those behaviors serve. The core function of immediacy is to reduce psychological distance and signal presence, approachability, and connection (Mehrabian, 1969). This function can be fulfilled through different sets of behaviors depending on the affordances of the communicative medium.
This study introduces the concept of “Virtual Immediacy” as a parallel construct to traditional, embodied immediacy (Gordon, 2020). Drawing on the literature of online course design and social presence, Virtual Immediacy is operationalized as the set of asynchronous, design-based, and logistical cues that convey instructor effort, care, and engagement within a digital learning environment. These behaviors are the “nonverbal” cues of the online classroom. They include the strategic use of visual elements like color and imagery to create an aesthetically pleasing and welcoming course site, the embedding of rich media like video to personalize the instructor, the use of varied typography and emoticons to convey tone and emotion in text, and the implementation of interactive and collaborative technologies to foster a sense of community (Tu and McIsaac, 2002). Chronemics, or the use of time, also plays a critical role, with prompt and frequent responses signaling attentiveness and presence (Dixson et al., 2017).
Based on this functionalist perspective, this study proposes the Virtual and Interpersonal Nonverbal Immediacy (VINI) model. This model posits that contemporary instructor immediacy is a latent construct best represented by two distinct yet correlated factors:
1) Physiological immediacy: operationally, physiological immediacy is defined as the set of observable nonverbal behaviors enacted by instructors in synchronous interaction—whether face-to-face or via live video—that convey warmth, openness, and psychological closeness. These behaviors include: gestures with hands, arms, and body that emphasize content and express emotion; body orientation and positioning that signals engagement and approachability; vocal expressiveness through varied pitch, rate, tone, and volume; animated movement that conveys energy and enthusiasm; direct eye contact that communicates attention and care; and smiling that signals positive affect and interpersonal warmth. These behaviors occur in real-time, co-present or video-mediated contexts where instructor and students can perceive and respond to each other’s nonverbal cues simultaneously.
2) Virtual immediacy: operationally, virtual immediacy is defined as the set of technologically-mediated, design-based behaviors enacted by instructors in digital learning environments—particularly asynchronous online contexts—that signal presence, effort, and engagement. These behaviors include: strategic use of visual design elements (color, imagery, typography) to create welcoming course spaces; embedding rich media (video, third-party platforms) to personalize instruction; employing emoticons and varied typography to convey tone in text-based communication; implementing interactive and collaborative technologies to foster community; and demonstrating responsiveness through prompt feedback and frequent communication. Unlike Physiological Immediacy behaviors that occur in real-time synchronous interaction, Virtual Immediacy behaviors are primarily asynchronous, persistent design choices that students experience across the temporal span of a course. This approach aligns with Biocca et al.’s (2003) theoretical framework, which distinguishes between co-presence (the simple awareness of others) and psychological involvement (the deeper sense of connection). Virtual immediacy behaviors serve to enhance both dimensions. For example, visual design elements and embedded media signal the instructor’s co-presence in the digital space, while interactive technologies and prompt communication foster psychological involvement.
This two-factor conceptualization acknowledges the reality that modern educators must be fluent in the communicative practices of multiple modalities. It provides a unified framework for understanding and measuring how instructors build connection with students, regardless of the physical or virtual space they occupy.
1.4 Overview of the present study
Demonstrating the reliability and validity of the VINI Scale is necessary if researchers are to have confidence in reporting the results obtained from it. Additionally, researchers should know if the VINI Scale can be used to generate a substantively meaningful total score, or if the two subscale scores should be used for interpretations. Accordingly, the purpose of this study was to assess the measurement properties of the VINI Scale in a sample of U.S. undergraduate and graduate students in face-to-face or online classes. We assessed the scale’s reliability, construct validity, dimensionality, and measurement invariance across gender and learning modality for two scale variations: VINI-O (observations) and VINI-P (preferences).
2 Literature review
2.1 Traditional nonverbal immediacy (physiological cues)
The original conception of Nonverbal Immediacy is rooted in physical-physiological constructs, specific nonverbal behaviors that promote likeability and reduce psychological distance. Research has well-established that learners are more receptive when in closer proximity to instructors they like (Furlich and Dwyer, 2007; Kalat et al., 2018; Liu, 2021). These physical behaviors create positive emotional responses that enhance the instructor-learner interaction. Key physiological cues include gestures, body orientation, vocal expressiveness, animated movement, eye contact, and smiling.
2.1.1 Gestures
Gestures made with the hands, arms, and legs are a natural part of communication and can indicate a range of emotions. Research shows that speakers’ gestures have a positive effect on listeners’ language comprehension, problem-solving, and learning (Özer and Göksun, 2020). In an instructional context, gestures can make an on-screen instructor seem more human-like and can direct the learner’s attention to important material, leading to better learning outcomes (Li et al., 2019).
2.1.2 Body orientation
Body orientation, or positioning, communicates one’s degree of self-esteem, status, and openness (Hall et al., 2019; Zloteanu et al., 2021). Mehrabian (1972) noted that body positioning varies based on emotional context, such as submission versus dominance or liking versus disliking. In online environments, instructor immediacy behaviors, including those related to presence and posture in videos, lead to significantly higher perceptions of instructor presence (Schutt et al., 2009).
2.1.3 Vocal expressiveness
Vocal immediacy includes characteristics such as pitch, rate, tone, and volume (Andersen, 1979). Mehrabian (1981) stated that vocal expressiveness can depict interpersonal liking and that 38% of nonverbal communication occurs through voice tone. Vocal variety contributes to higher learning and retention, while a monotone voice is often related to negative instructor evaluations (Woolbert, 1920). Research on virtual models has shown that stronger vocal immediacy leads to greater affective learning and increased perceptions of learning (Fountoukidou et al., 2022).
2.1.4 Animated movement
Instructors with an animated presentation style are often perceived as lively, expressive, and approachable (Burroughs, 2007). In the context of distance education, instructors who express warmth and genuine investment in their instruction through conversational skills are more likely to be judged as highly competent (Guerrero and Miller, 1998). Recent studies also report that students are happier and more motivated when they learn with happy and expressive pedagogical agents (Wang et al., 2022).
2.1.5 Eye contact
Eye contact builds rapport, creates a pleasant atmosphere, and can open lines of communication (Mandal, 2014). Through eye contact, instructors communicate attention and care to students, which encourages participation and interaction. It reinforces students’ perceptions of their instructor’s immediacy and enjoyment of teaching. Furthermore, research suggests that direct gaze often elicits a smiling response, signaling a positive and mutual reception of attention (Liang et al., 2021).
2.1.6 Smiles
Smiling is a key non-verbal immediacy behavior associated with perceptual stimulation, indicating both likeability and arousal (Marici et al., 2025). It is often considered the single best predictor of perceived interpersonal warmth in instructional settings. However, context is important, as some research has found that the perceived meaning behind a pedagogical agent’s smile can differ among participants in virtual learning systems.
2.2 Virtual nonverbal immediacy (logistical cues)
With the shift to online learning, nonverbal immediacy research has expanded from physical attributes to logistical components that are mediated by a computer or mobile device. These virtual cues are design-based choices that signal instructor presence and care.
2.2.1 Use of emojis/emoticons
Emoticons (emotion icons) are pictorial representations of facial expressions used to convey tone, express emotion, and avoid miscommunication in text-based interactions (Dixson et al., 2017; Wolf, 2000). Research demonstrates that emoticons can enrich communication and build social presence (Lo, 2008). However, student preferences may vary, with some studies indicating that students with more online course experience may prefer fewer emojis as a means of engagement (Bello et al., 2020).
2.2.2 Use of colors and colorful visuals
Color is a key element of logistical instructional design that impacts student engagement and comfort, often referred to as the “language of screen design” (Garcia and Lee, 2020, p. 5). Different colors can elicit specific emotions; for example, red can signify passion, blue can represent tranquility, and orange can promote warmth (Pett and Wilson, 1996). The strategic use of color can create appeal, unity, and harmony in the virtual learning environment.
2.2.3 Use of YouTube/embedded video
Third-party platforms like YouTube are now popular methods for instructional design, used to introduce concepts and display information across all learning modalities. Research has shown that using such videos can have a significant positive impact on academic achievement and that student receptiveness is higher when the platform is easily accessible (Pearlman et al., 2024; Shoufan and Mohamed, 2022). A learner’s positive attitude toward third-party video platforms also influences their behavioral intention to learn from them.
2.2.4 Use of varied fonts and typography
The use of varied typography, including capital letters, has social properties in the text-based virtual learning environment (French et al., 2013; Lorch et al., 1995). Capitalization can be used to stress a point, demand a prompt response, or provide emphasis, analogous to loud communication in face-to-face settings. It can also be used for other functions, such as indicating deep laughter or shouting, or for showing corrections in spelling.
2.2.5 Use of interactive tech tools
Many instructors struggle to keep up with digitally fluent students, which can impact instructor credibility, a core component of nonverbal immediacy (Godsk and Møller, 2025). It is critical for instructors in online environments to develop confidence with their presentation methods, as this fosters a positive attitude toward online learning. Providing instructors with ongoing support and opportunities for collaboration can help them overcome concerns and effectively integrate new technologies (Wang et al., 2021).
2.2.6 Use of wiki or collaborative pages
A wiki is a website that allows users to collaboratively edit content, making it an effective tool for stimulating active learning and supporting student engagement (Zhang et al., 2013; Zheng et al., 2015). Wikis provide opportunities for social interaction and cooperative learning, allowing students from diverse backgrounds to learn from each other’s strengths and experiences.
2.3 Situating VINI within social presence theory
The VINI Scale sits within the broader concept of social presence, defined as the degree to which participants perceive themselves and others as “real people” in digital environments (Biocca et al., 2003; Kreijns et al., 2022). Modern theory has evolved beyond early frameworks to view social presence as a multidimensional construct. As outlined in the Community of Inquiry framework, this encompasses cognitive presence (shared understanding), teaching presence (design and facilitation), and social presence (interpersonal connection) (Cleveland-Innes et al., 2024; Garrison et al., 1999).
Within this framework, the VINI Scale focuses on a specific component of teaching presence. It targets the communicative behaviors instructors use to reduce psychological distance and signal availability. Kreijns et al. (2024) distinguish these dimensions along temporal (synchronous vs. asynchronous) and modal (text-based vs. multimedia) lines. The VINI Scale’s structure aligns with this distinction. Physiological Immediacy captures synchronous, embodied behaviors, while Virtual Immediacy captures asynchronous, design-based behaviors.
However, the VINI Scale does not attempt to measure the full spectrum of social presence. It intentionally excludes cognitive presence indicators (such as discussion quality) and community outcomes (such as peer-to-peer interaction). By maintaining this focused scope, the scale provides a detailed assessment of instructor-to-student behaviors while acknowledging they operate within a larger learning ecosystem (Kreijns et al., 2022). In this way, the VINI Scale complements holistic measures like the Community of Inquiry survey by offering practitioners concrete, actionable targets for professional development.
3 Methods
3.1 Study sample
Participants were recruited through in-class invitations for two parallel studies. The VINI-O (Observations) sample consisted of N = 447 students (all undergraduate students), while the VINI-P (Preferences) sample consisted of N = 295 students (n = 110 undergraduate students: female = 34%, male = 66%; n = 185 graduate students: female = 66%, male = 34%). Participants were enrolled in either a face-to-face (FTF) class or an online class (OL): VINI-O (FTF = 31%, OL = 69%); VINI-P undergraduate students (FTF = 80%, OL = 20%); VINI-P graduate students (FTF = 54%, OL = 46%). The project was approved by the Lawrence Technological University and the Wayne State University Institutional Review Boards. Data were collected via Qualtrics and Alchemer online surveys.
3.1.1 Item development and selection
The VINI Scale items were developed through literature review and theoretical analysis. First, we conducted a systematic review of nonverbal immediacy literature spanning five decades (1971–2020). This process identified six well-established physiological immediacy behaviors that consistently emerged across validated instruments (Mehrabian, 1971; Richmond et al., 2003). These behaviors include gestures, body orientation, vocal expressiveness, animated movement, eye contact, and smiles.
For Virtual Immediacy items, we reviewed emerging literature on online course design, social presence, and computer-mediated communication (Dixson et al., 2017; Tu and McIsaac, 2002; Waldeck et al., 2001). We prioritized asynchronous, design-based behaviors that signal instructor presence and effort in digital environments, remain observable to students across the temporal span of a course, and be actionable by instructors with varying levels of technical expertise. The six Virtual Immediacy items were selected based on their frequency of mention in the literature and their pedagogical accessibility. We excluded behaviors requiring high synchronous coordination, such as breakout rooms, or individualized delivery, such as personalized feedback. This exclusion was necessary to avoid conflating immediacy with other instructional constructs like classroom management or assessment practices. Consequently, the selected items represent discrete, observable design choices that instructors can implement independently of specific platform features or synchronous session structures.
The initial 50 completed surveys served as a pilot test to assess item clarity and comprehension. Analysis of response patterns and examination of any incomplete or ambiguous responses confirmed that items were clearly understood across diverse student populations. No substantive revisions were needed based on this pilot phase.
3.1.2 Instrument
The 12-item VINI Scale is a self-report survey designed to assess students’ perceptions of their instructor’s nonverbal immediacy behaviors. The scale is composed of two distinct first-order factors: Physiological Immediacy (6 items: Gestures, Body orientation, Vocal expressiveness, Animated movement, Eye contact, Smiles) and Virtual Immediacy (6 items: Emoji use, Colorful visuals, YouTube or embedded video, Varied fonts and typography, Interactive tech tools, Wiki or collaborative pages). Both the VINI-O and VINI-P versions used the same 12 items, scored along a 5-point Likert-type scale (e.g., Never to Very Often). The VINI-O instructions were “Rate the instructor on each of the following items,” whereas the VINI-P instructions were “Rate your preference for how often you would like your instructor to use each of the following items”.
3.2 Statistical analyses
Survey data were analyzed using JASP 0.95.3 and Mplus v8.11. First, we explored the factorial structure of the VINI Scale using exploratory factor analysis (EFA). Kaiser-Meyer-Olkin (KMO > 0.60) and Bartlett’s test of Sphericity (p < 0.01) were used to determine support for factorability (Kaiser, 1974). EFA was conducted in Mplus based on maximum likelihood estimation (ML) and Geomin Oblique rotation. A Bifactor EFA was also conducted using ML estimation and Bi-Geomin Orthogonal rotation to explore the influence of a general factor (Muthén and Asparouhov, 2016). The EFA was specified to extract two factors (Physiological Immediacy, Virtual Immediacy), and the Bifactor EFA was specified to extract a general factor and two specific factors, with criteria including Eigenvalues > 1, item loadings > 0.40, and overall percent of variance ≥ 50 (Hurley et al., 1997; Muthén and Muthén, 2017; Streiner, 1994; Watkins, 2018).
Second, a competing measurement modeling strategy was employed in Mplus using confirmatory factor analysis (CFA) to investigate construct validity. Models were estimated using the robust maximum likelihood (MLR) estimator to address potential non-normality in the 5-point Likert scale data (Li, 2016; Tóth-Király et al., 2018). For CFA models, items were loaded onto their theoretical factors, cross-loadings were constrained to zero, and error terms were allowed to correlate (Wang and Wang, 2020). The following models were tested and compared for the VINI-O and the VINI-P:
Model 0: Unidimensional CFA model of overall VINI.
Model 1: Independent cluster model (ICM) CFA comprised of two correlated first-order factors (physiological and virtual immediacy).
Model 2: Hierarchical CFA (H-CFA) model with a single second-order VINI factor and two first-order factors.
Model 3: Bifactor CFA model of overall VINI.
Models were evaluated for acceptable fit using the following criteria: χ2/df < 3, CFI > 0.90, TLI > 0.90, RMSEA < 0.08, and SRMR < 0.08 (Chen, 2007; Wang and Wang, 2020). The best-fitting model was determined by examining parameter estimates, including standardized factor loadings (> 0.35), item uniqueness (0.10 to 0.90), and factor correlations (Morin et al., 2016, 2020; Tóth-Király et al., 2020; Van Zyl and Ten Klooster, 2022).
Descriptive statistics included item means, standard deviations, skewness, kurtosis (< 2 for normality; Kim, 2013), and corrected item-total correlations (CITC > 0.30; Zijlmans et al., 2019). Item-level reliability was assessed with Cronbach’s alpha (> 0.70; Nunnally and Bernstein, 1994), and factor-level reliability was determined by McDonald’s omega (> 0.70; Morin et al., 2020) and average variance extracted (AVE > 0.50; Kline, 2015).
Finally, measurement invariance of the optimal model was assessed across participant gender (female vs. male), instructor gender (female vs. male), and instructional method (face-to-face vs. online) using a series of increasingly restrictive models: configural, metric, scalar, and strict invariance (Millsap, 2012; Rudnev et al., 2018). Evidence for invariance was established by examining model fit and comparing nested models using robust chi-square difference tests (Δχ2; Satorra and Bentler, 2010) and changes in fit indices (ΔCFI ≤ 0.010, ΔRMSEA ≤ 0.015, and ΔSRMR ≤ 0.03; Chen, 2007; Fisher et al., 2020; Wang and Wang, 2020).
4 Results
4.1 Exploratory factor analysis
Analysis of the VINI Scale began with exploring its factorial structure using EFA. The KMO measure of sampling adequacy (VINI-O = 0.799, VINI-p = 0.800) and Bartlett’s test of sphericity (VINI-O = p < 0.001, VINI-P = p < 0.001) supported the extraction of meaningful factors. For both the VINI-O and VINI-P, two factors emerged with Eigenvalues > 1. We labeled these factors Physiological Immediacy (PI) and Virtual Immediacy (VI). Table 1 left panel shows the item loadings and percent of variance for the VINI-O two-factor EFA model, and Table 1 right panel shows the item loadings and percent of variance for the VINI-P two-factor EFA model. Items that significantly loaded onto their respective a priori factors are shown in bold font (p < 0.05).
Table 1. EFA and bifactor EFA of the VINI-O and VINI-P scales.
EFA results for the VINI-O found the two factors accounted for 54.4% of the overall variance. The factorial structure of PI (λ = 0.555 to 0.800) and VI (λ = 0.484 to 0.737) factors was supported. In contrast to the EFA, the Bifactor EFA results found 10 of the 12 VINI Scale items significantly loaded onto a general factor (λ1 = 0.173 to 0.729) but did not maintain the expected pattern of loadings in the two specific factors (i.e., λ2 and λ3). EFA results for the VINI-P found the two factors accounted for 46.5% of the overall variance. The factorial structure of PI (λ = 0.505 to 0.685) and VI (λ = 0.403 to 0.735) factors was supported. In contrast to the EFA, the Bifactor EFA results found 3 of the 12 VINI Scale items significantly loaded onto a general factor (λ1 = 0.196 to 0.685) and did not maintain the expected pattern of loadings in the two specific factors (i.e., λ2 and λ3). These initial results suggested the EFA supported the hypothesized two-factor structure, leading next to an analysis using CFA.
4.2 Measurement models
Table 2 presents the goodness-of-fit indexes and information criteria associated with the competing CFA measurement models for the VINI-O (top panel) and the VINI-P (bottom panel). Each model was evaluated using the following goodness of fit criteria for acceptable model fit: χ2/df < 3, CFI > 0.90, TLI > 0.90, RMSEA < 0.08, SRMR < 0.08 (Chen, 2007; Wang and Wang, 2020). The Unidimensional CFA solution (Model 0) and the Bifactor CFA solution (Model 3) did not fit the data well. In contrast, the ICM CFA (Model 1) and the H CFA (Model 2) solutions provided an acceptable degree of fit to the data. While both models met all fit criteria across samples, Model 1 demonstrated greater parsimony with one fewer degree of freedom and lower information criteria values (VINI-O: AIC = 15016.56, BIC = 15172.46; VINI-P: AIC = 8866.85, BIC = 9006.95) compared to Model 2 (VINI-O: AIC = 15018.56, BIC = 15178.56; VINI-P: AIC = 8868.85, BIC = 9012.64). The marginal differences in fit indices between models (e.g., CFI difference of 0.002 in VINI-O) did not justify the additional complexity of the hierarchical structure. Consequently, Model 1 was retained as the optimal factor structure for the VINI Scale.
Table 2. Goodness of fit statistics for the models estimated on the VINI Scale.
4.3 Factor structure and measurement quality
The measurement quality of the two-factor CFA solution (Model 1) was inspected via standardized parameter estimates (Table 3). Both the VINI-O and VINI-P produced well-defined factors with significant loadings (> 0.40) in the expected pattern and acceptable item uniqueness. Model-based omega coefficients were comparable across both samples for the Physiological Immediacy (ω = 0.828 vs. 0.739) and Virtual Immediacy (ω = 0.728 vs. 0.763) factors. All factor loadings were statistically significant (p < 0.01) and ranged from 0.400 to 0.803 for VINI-O and 0.426 to 0.714 for VINI-P. For Physiological Immediacy, the strongest indicators were Eye Contact (λ = 0.803, 0.714) and Smiles (λ = 0.768, 0.704) across both samples. For Virtual Immediacy, the strongest indicators were Use of Colors/Visuals (λ = 0.799, 0.630) and Use of Wiki/Collabs (λ = 0.400, 0.686). Notably, Use of Wiki/Collabs showed substantial variation across contexts, functioning as the weakest indicator in VINI-O (λ = 0.400) but the strongest in VINI-P (λ = 0.686), suggesting that collaborative tools may be more salient when considering preferences than when observing actual instructor behaviors.
Table 3. Standardized parameter estimates from the first-order factor CFA solution (model 1) for the VINI-O and VINI-P.
Corrected item-total correlations ranged from 0.405 to 0.686 for VINI-O and 0.377 to 0.578 for VINI-P, indicating adequate item discrimination. Average variance explained was 0.445 and 0.338 for Physiological Immediacy and 0.315 and 0.347 for Virtual Immediacy in VINI-O and VINI-P, respectively. Overall, the results support the two-factor structure of the VINI Scale, with both Physiological Immediacy and Virtual Immediacy factors demonstrating adequate psychometric properties across observation and preference samples. The consistent factor structure and acceptable measurement quality provide a foundation for testing measurement invariance across the VINI-O and VINI-P samples to determine whether the scale functions equivalently in both contexts.
4.4 Measurement invariance
Finally, given that Model 1 was the optimal model, we estimated the measurement invariance of the VINI Scale across key grouping variables. Establishing invariance is essential for determining whether observed score differences reflect true differences in the construct or measurement artifacts. Invariance was tested hierarchically across three comparisons: participant gender, instructor gender, and instructional method (online vs. face-to-face). Configural invariance tests whether the factor structure is equivalent across groups. Metric invariance tests whether factor loadings are equivalent, permitting correlational analyses. Scalar invariance tests whether item intercepts are equivalent, allowing latent mean comparisons. Invariance criteria included nonsignificant chi-square difference tests and changes in fit indices below established thresholds (ΔCFI < 0.010, ΔRMSEA < 0.015, ΔSRMR < 0.030). Results are presented in Table 4.
Table 4. Measurement invariance for model 1.
4.4.1 Measurement invariance across participant gender groups
Measurement invariance testing examined whether the VINI Scale functions equivalently across participant gender groups (Table 4, top panel). For VINI-O, the configural model demonstrated acceptable fit (CFI = 0.939, RMSEA = 0.057, SRMR = 0.058), establishing that the two-factor structure holds across groups. However, metric invariance was not supported, as the chi-square difference test was significant (Δχ2 = 27.18, p = 0.002) and CFI decreased beyond the acceptable threshold (ΔCFI = −0.014). For VINI-P, both configural (CFI = 0.923, RMSEA = 0.053, SRMR = 0.069) and metric invariance (ΔCFI = −0.002, ΔRMSEA = −0.002, ΔSRMR = 0.012) were supported, with a nonsignificant chi-square difference test (Δχ2 = 11.28, p = 0.336). Scalar invariance was not achieved (Δχ2 = 20.51, p = 0.025, ΔCFI = −0.017), indicating that item intercepts differ across participant gender groups in the preference sample. These results suggest that while male and female participants interpret immediacy preferences similarly (equivalent factor loadings), they differ in their baseline levels of preference for specific immediacy behaviors.
4.4.2 Measurement invariance across instructor gender groups
Measurement invariance was tested across instructor gender groups for both samples (Table 4, middle panel). In VINI-O, the configural model fit adequately (CFI = 0.889, RMSEA = 0.889, SRMR = 0.058), but metric invariance failed, with a significant chi-square difference (Δχ2 = 39.32, p < 0.001) and substantial CFI decline (ΔCFI = −0.023). For VINI-P, configural invariance was established (CFI = 0.950, RMSEA = 0.043, SRMR = 0.066), and metric invariance was supported (Δχ2 = 17.13, p = 0.071, ΔCFI = −0.010). However, scalar invariance was not achieved (Δχ2 = 30.74, p = 0.001, ΔCFI = −0.033), suggesting that item intercepts vary across instructor gender groups in the preference sample. The lack of metric invariance in observations indicates that students may weight or interpret specific immediacy behaviors differently depending on instructor gender, which has implications for making direct comparisons of observed immediacy scores across instructor gender groups.
4.4.3 Measurement invariance across instructional method groups
Invariance testing across method groups (online vs. face-to-face instruction) yielded differing results for the two samples (Table 4, bottom panel). For VINI-O, configural invariance was supported (CFI = 0.895, RMSEA = 0.075, SRMR = 0.061), as was metric invariance (Δχ2 = 13.46, p = 0.199, ΔCFI = −0.002). Scalar invariance failed (Δχ2 = 73.89, p < 0.001, ΔCFI = −0.047), indicating that item intercepts differ across method groups. For VINI-P, configural (CFI = 0.924, RMSEA = 0.053, SRMR = 0.069), metric (Δχ2 = 15.41, p = 0.118, ΔCFI = −0.010), and scalar invariance (Δχ2 = 13.39, p = 0.202, ΔCFI = −0.006) were all achieved. Strict invariance was not supported (Δχ2 = 69.33, p < 0.001, ΔCFI = −0.089), indicating that residual variances differ across method groups in the preference sample. The achievement of scalar invariance for preferences supports meaningful comparisons of latent means across online and face-to-face contexts, while the lack of scalar invariance for observations suggests that baseline levels of observed immediacy behaviors vary systematically by instructional method.
Despite the absence of full scalar invariance across all comparisons, the VINI Scale demonstrates several strengths that support its research utility. Configural invariance was consistently achieved across all group comparisons in both samples, confirming that the two-factor structure is conceptually equivalent across participant gender, instructor gender, and instructional method. The establishment of metric invariance in multiple comparisons permits correlational and regression analyses across groups, which represent the most common analytical approaches in immediacy research. Notably, the VINI-P achieved scalar invariance across method groups, enabling meaningful latent mean comparisons between online and face-to-face contexts—a critical capability for intervention studies and program evaluation. These findings indicate that the VINI can be reliably used for examining relationships between immediacy and student outcomes across diverse instructional contexts.
5 Discussion: implications for theory, practice, and research
The findings from this study provide strong psychometric support for the 12-item VINI Scale. The results confirm that the instrument is a reliable and structurally valid measure of a modernized, two-dimensional immediacy construct that is applicable across the spectrum of contemporary learning environments. This section interprets these findings, discussing their implications for communication theory, pedagogical practice, and future research.
5.1 The VINI Scale as a valid measure of a modernized immediacy construct
The primary contribution of this research is the development and validation of a new instrument to measure instructor immediacy. The comprehensive analytical strategy, comparing four different factor structures, demonstrated that a first-order, two-factor CFA model provided the best representation of the data. This empirical validation of a two-factor structure, distinguishing between Physiological and Virtual Immediacy, represents a critical advancement for the field. The rejection of higher-order and bifactor models indicates that the two immediacy domains are best understood as distinct but related constructs, and researchers should utilize the separate factor scores rather than a single total VINI score. The establishment of scalar measurement invariance across learning modalities for the VINI-P is a particularly crucial finding; it confirms that the preference version of the scale can support meaningful latent mean comparisons between face-to-face and online contexts. While full scalar invariance was not achieved across all group comparisons, the consistent configural invariance and metric invariance in multiple comparisons confirm that the VINI is a unified instrument capable of measuring immediacy as a coherent construct across diverse instructional contexts.
5.2 Theoretical implications: the distinction and interrelation of physiological and virtual immediacy
The confirmation of a two-factor structure carries significant theoretical implications. The moderate correlation between the two factors suggests that while they are related, they represent distinct sets of communicative competencies. This relationship may be explained by a higher-order latent trait, such as an instructor’s general student-centeredness, teaching presence, or motivation to connect with students. However, the fact that they are empirically distinct indicates that being skilled in one domain does not guarantee skill in the other. An instructor may be a charismatic and engaging face-to-face lecturer but may struggle to project presence in an asynchronous online course, and vice versa.
It is important to note that Virtual Immediacy in the VINI Scale focuses specifically on asynchronous, design-based behaviors. Synchronous digital behaviors represent a distinct category of online immediacy that warrants separate investigation. These behaviors include gestures during live video conferencing, real-time chat responsiveness, and the use of breakout rooms. Theoretically, these synchronous behaviors may function as a bridge between Physiological Immediacy, defined as embodied real-time actions, and Virtual Immediacy, defined as persistent design choices. Future research should investigate whether synchronous digital immediacy behaviors load onto these existing factors or constitute a third distinct dimension of contemporary instructor immediacy.
The substantial variation in Use of Wiki/Collabs loadings across VINI-O and VINI-P samples provides additional theoretical insight. This item functioned as the weakest indicator in observations (λ = 0.400) but the strongest in preferences (λ = 0.686), suggesting a recognition gap between what students observe instructors actually doing versus what they wish instructors would do. Collaborative technologies require substantial instructor effort to implement effectively, and students may observe relatively infrequent use in practice (Zheng et al., 2015). However, when asked about preferences, students highly value such tools for their potential to facilitate peer learning and social interaction in online environments (Zhang et al., 2013). This pattern suggests that collaborative tools represent an underutilized opportunity for enhancing virtual immediacy.
The lack of full scalar invariance across participant gender in both samples warrants discussion. This finding suggests potential biological or socialization differences in baseline preferences for specific immediacy behaviors, consistent with broader research on gender differences in communication preferences and nonverbal decoding (Hall et al., 2019). For example, research indicates that women and men may differ in their baseline preferences for certain types of social interaction and may interpret nonverbal cues differently due to socialization patterns (Guerrero and Miller, 1998). However, this lack of scalar invariance does not undermine the scale’s research utility. The achievement of metric invariance in the VINI-P indicates that the underlying factor structure and the meaning of the constructs are equivalent across genders, permitting correlational and regression analyses. Researchers can confidently examine relationships between immediacy and outcomes within gender groups or control for gender in their analyses. Future research should investigate whether these gender differences in baseline preferences translate to differential effects of immediacy behaviors on learning outcomes.
This finding provides an empirical foundation for refining theories of instructional communication, such as Social Presence Theory (Gunawardena and Zittle, 1997). It is also important to clarify what the VINI Scale does and does not measure within the social presence literature. The scale focuses on one specific dimension of teaching presence. It targets the communicative behaviors instructors use to reduce psychological distance rather than the full multidimensional social presence construct (Kreijns et al., 2024). This focused scope represents both a strength in its behavioral specificity and a limitation regarding its coverage of the broader construct. The VINI Scale contributes to scholarship by identifying specific actions within teaching presence. Other instruments take a different approach. The Community of Inquiry survey assesses students’ perceptions of teaching presence holistically (Garrison et al., 1999). The Networked Minds Social Presence Inventory captures cognitive and emotional dimensions of co-presence (Biocca et al., 2003). In contrast, the VINI Scale uniquely itemizes discrete instructor behaviors that can be directly modified through professional development. This specificity complements existing measures by offering practitioners concrete targets for intervention. Consequently, researchers investigating comprehensive social presence should use the VINI Scale in conjunction with instruments measuring cognitive presence and community outcomes.
The VINI Scale offers researchers a tool to move beyond the general concept of “social presence” and investigate the differential impact of specific types of immediacy behaviors. For example, future research can now ask more nuanced questions: Do Physiological and Virtual Immediacy contribute equally to students’ sense of belonging? Does the relative importance of each factor in predicting student satisfaction change depending on the learning modality? The VINI Scale provides the operationalized variables necessary to test these and other important theoretical hypotheses about communication in modern learning environments.
5.3 Practical applications: a dual-framework for pedagogy
Perhaps the most significant contribution of this work lies in its direct applicability to teaching practice, a result of the innovative dual-data (Observations and Preferences) design. This design positions the VINI Scale not just as a research instrument, but as a powerful tool for pedagogical improvement.
First, the VINI-O (Observations) scale serves as a straightforward diagnostic tool. Instructors can use it for self-assessment, or it can be integrated into peer or institutional evaluation systems. The results can provide concrete, behavior-based feedback, helping educators identify specific strengths and areas for growth in both their synchronous and asynchronous teaching practices. It effectively demystifies the often-amorphous concept of “instructor presence” by breaking it down into a discrete set of 12 observable behaviors (Gunawardena and Zittle, 1997; Tu and McIsaac, 2002).
Second, and most innovatively, the VINI-P (preferences) scale functions as a prescriptive tool. As the user’s initial notes suggested, it acts as a “ready-made checklist for course design.” By aggregating VINI-P data, institutions and faculty developers can create an evidence-based profile of what their specific student population values most in instructor communication. This data provides a clear roadmap for where instructors should invest their time and effort to maximize their impact on student engagement. For example, if preference data show that students highly value the use of embedded video but care little for varied fonts, this provides actionable guidance for faculty development workshops.
Finally, the true power of this dual framework lies in comparing an instructor’s VINI-O scores against their students’ VINI-P scores. A significant gap between observation and preference on a specific item (e.g., low observed use of interactive polls but high student preference for them) identifies a precise, high-leverage target for instructional improvement. This moves faculty development beyond generic advice and toward personalized, data-driven coaching.
5.4 Recommendations for practitioners
The VINI Scale provides actionable guidance for practitioners across educational and professional development contexts. For instructors, the VINI-O can serve as a self-assessment tool to identify areas for growth in both face-to-face and online teaching (Dixson et al., 2017). Comparing personal VINI-O scores against student VINI-P data reveals gaps between current practice and student preferences, enabling targeted improvement efforts. Instructors should recognize that competence in one immediacy domain does not guarantee competence in the other; deliberate skill development in both Physiological and Virtual Immediacy is necessary for effective multi-modal teaching (Carrell and Menzel, 2001).
For instructional designers and faculty developers, the VINI-P provides an evidence-based framework for prioritizing professional development initiatives. By collecting preference data from specific student populations, institutions can identify which immediacy behaviors yield the highest return on investment for faculty training time (Godsk and Møller, 2025). The scale can also guide the development of course design rubrics and quality standards that explicitly incorporate both physiological and virtual immediacy criteria (Garcia and Lee, 2020).
For coaches and facilitators in corporate and health training contexts, the VINI framework extends beyond academic settings. Professional trainers can use the Physiological Immediacy items to assess their in-person facilitation skills, while the Virtual Immediacy items apply to virtual workshops, telehealth consultations, and remote coaching sessions (Witt et al., 2004). The dual-factor structure acknowledges that building rapport and psychological safety requires different competencies depending on the communication medium, with implications for training program design across industries (Tu and McIsaac, 2002).
5.5 Limitations and future directions
While this study provides a strong foundation for the VINI Scale, certain limitations should be acknowledged. The data were collected institutions in the United States, which may limit the generalizability of the findings to other cultural and institutional contexts. Additionally, a methodological limitation concerns our reliance on student self-reports of instructor behavior. While student perceptions represent the psychologically meaningful reality that drives learning outcomes (Witt et al., 2004), self-report measures are susceptible to recall bias, halo effects, and social desirability. Students may systematically over-report behaviors of instructors they like or under-report behaviors they find irrelevant. An additional limitation concerns content validity. Although our item selection relied on a systematic literature review and theoretical analysis, we did not employ formal expert review panels or conduct cognitive interviews during item development. The exclusion of synchronous digital behaviors and individualized communication practices from the current VINI Scale represents a deliberate limitation in scope.
Future cross-cultural validation studies are needed, as research has shown that the interpretation and value of immediacy behaviors can vary across cultures (Matsumoto and Hwang, 2013; McCroskey et al., 1995). Future research should triangulate VINI-O scores with objective measures of instructor behavior. For Physiological Immediacy, this could include systematic video coding of recorded class sessions using established protocols (Toivanen et al., 2025). For Virtual Immediacy, learning management system (LMS) analytics offer promising objective indicators. Relevant metrics might include the frequency of embedded multimedia, the variety of activity types, color palette diversity, and the usage of collaborative tools (Deng et al., 2025). Such multi-method designs would clarify the relationship between student perceptions and actual instructor behavior while also revealing potential blind spots in student awareness of instructor effort. Future research should strengthen content validity through more rigorous procedures. These might include expert judgment studies with larger panels representing diverse disciplinary and institutional contexts. Such procedures would provide quantitative evidence of item relevance and clarity while potentially expanding the item pool to capture a broader range of immediacy behaviors.
The validation of the VINI Scale opens up numerous exciting avenues for future research. The most immediate next step is to use the scale to predict key student outcomes. Studies should investigate the relative predictive power of Physiological and Virtual Immediacy on student satisfaction, motivation, relatedness, persistence, and academic achievement across different learning modalities. Experimental research is also needed, where specific VINI behaviors are systematically manipulated in controlled settings to establish causal links to student learning.
Additionally, the validation of the VINI Scale opens up numerous avenues for future scale development. Future research could explore additional Virtual Immediacy behaviors, including synchronous digital behaviors (e.g., breakout room facilitation, real-time polling, live video gestures) and individualized communication practices (e.g., personalized feedback timeliness, email tone, individual student check-ins), to develop a more comprehensive taxonomy of online instructor immediacy. The current instrument’s focus on asynchronous, design-based behaviors represents one important dimension but does not exhaust the full range of ways instructors can build connection in digital environments. Future scale development efforts should also employ more comprehensive item generation procedures to capture the full breadth of the social presence construct.
Finally, the VINI Scale provides a tool for longitudinal research into the evolution of teaching and learning. As instructional technologies continue to advance, the nature of Virtual Immediacy will undoubtedly change. As the user’s notes presciently suggest, the construct may evolve to become “solely an online-virtual construct.” The VINI Scale provides a baseline against which these future changes can be measured, allowing researchers to track the shifting landscape of instructional communication and its impact on education.
6 Conclusion
The transition to multi-modal educational delivery has necessitated a parallel evolution in our understanding and measurement of foundational pedagogical constructs. This study introduced and validated the Virtual and Interpersonal Nonverbal Immediacy (VINI) Scale, a 12-item instrument that modernizes the concept of immediacy for the 21st-century educational landscape. The robust psychometric evidence confirms that the VINI Scale is a reliable and valid measure of a two-factor structure, Physiological and Virtual Immediacy, that functions consistently across diverse student groups and learning environments. More than just a research tool, the VINI Scale’s dual-framework design offers a practical, evidence-based resource for educators and instructional designers. By providing a clear language to diagnose observed behaviors and a student-centered guide to prioritize preferred behaviors, the VINI Scale bridges the gap between communication theory and classroom practice, empowering instructors to build stronger connections and foster deeper engagement with students, regardless of the modality.
Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Ethics statement
The studies involving humans were approved by Lawrence Technological University Institutional Review Board, Southfield, MI, United States. The studies were conducted in accordance with the local legislation and institutional requirements. The ethics committee/institutional review board waived the requirement of written informed consent for participation from the participants or the participants’ legal guardians/next of kin because data were collected from an online survey containing an informed consent question.
Author contributions
AG: Writing – original draft, Investigation, Data curation, Conceptualization, Writing – review & editing. MC: Methodology, Writing – review & editing, Data curation, Writing – original draft, Formal analysis, Software.
Funding
The author(s) declared that financial support was not received for this work and/or its publication.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.
Generative AI statement
The author(s) declared that Generative AI was used in the creation of this manuscript. Anthropic Claude Sonnet 4.5 was used to assist with the interpretation of the measurement invariance.
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Keywords: nonverbal immediacy, virtual immediacy, psychometric properties, instructional communications, online learning
Citation: Gordon A and Cole ML (2026) Measuring nonverbal immediacy across learning modes: psychometric properties of the 12-item VINI Scale (virtual and interpersonal nonverbal immediacy). Front. Educ. 10:1726842. doi: 10.3389/feduc.2025.1726842
Edited by:
Kuan-Yu Jin, Hong Kong Examinations and Assessment Authority, Hong Kong SAR, ChinaReviewed by:
Ahmed Mohamed Fahmy Yousef, Sultan Qaboos University, OmanDavid Mykota, University of Saskatchewan, Canada
Copyright © 2026 Gordon and Cole. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Matthew L. Cole, bWNvbGVAbHR1LmVkdQ==