I am an assistant professor of communication studies at Sam Houston State University, with a focus on interpersonal and instructional communication, as well as quantitative research methods. I research romantic partners’ experiences of relational turbulence, relational load, and supportive communication. Additionally, I investigate how teacher (mis)behaviors influence student learning and motivation within higher education. I employ structural equation modeling, path analysis, and repeated measures within-subjects designs to investigate these communication processes.
As an assistant professor, I take what I have learned about teaching and support from my studies and apply them in the classroom. I have taught over 10 different communication studies courses in several modalities (face-to-face, flipped classroom, asynchronous online, and hybrid) at Northern Illinois University, West Virginia University, and Sam Houston State University.
For more information about my research and teaching, check out my vita and downloadable full-text publications.
Using a latent variable mediation model, we tested proposition 7 of relational turbulence theory (RTT) predicting that individuals in romantic relationships experiencing more relational turbulence would receive less emotional support from their partner because of a disruption to dyadic synchrony. A sample of 319 dating partners reported on their current relational turbulence, comfort and ease in communicating with their romantic partner, and emotional support received from their partner. Results of a structural regression model revealed a negative direct effect of relational turbulence on received emotional support and a negative indirect effect through dyadic synchrony as a mediator. Our study provided evidence of a theoretical process consistent with proposition 7 of RTT.
Using dynamic structural equation modeling (DSEM; Asparouhov et al., 2018), this study tests how partner disruptions of daily routines create a chaotic relational state through intensified emotions directed at partners, as posited by relational turbulence theory (RTT; Solomon et al., 2016). To test this affective process, individuals in dating relationships (N = 130) completed daily surveys for 30 days (T = 30; 3,478 total observations), measuring that day’s interference from their partner, anger experienced while interacting with their partner, and their relational turbulence. DSEM accounted for the intensive longitudinal aspects of the data while modeling three types of person-specific random effects: random intercepts to account for subject-specific averages; random slopes to account for subject-specific effects; and random variances to account for subject-specific volatility. RTT processes were supported, as greater than typical interference of routines in daily life predicted more relational turbulence that day via increased daily anger (controlling for the previous day’s levels). The use of DSEM allowed us to further test RTT by modeling person-specific inertia and volatility (for levels of interference, anger, and relational turbulence throughout a month). The use of a multilevel “location-scale” DSEM with random intercepts and random variances revealed that attachment avoidance and anxiety predicted a variety of person-specific features of the studied longitudinal processes: averages, inertia, and volatility over time. We provide our data and a supplemental primer to illustrate how to test communication theory with DSEM and model the intensive dynamics of daily life.
“Bekah was wonderful, her attitude made learning the information exciting. The workbook went right along with the powerpoints which made studying easy! She was overall my favorite professor and COMM 104 was my favorite and most successful class!”
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“I loved learning about this course and have been teaching my family too! I won’t even bring it up, and they will be so interested to learn more that they will ask me to teach them which has been really fun. I also wanted to say that you provided really good information and examples to keep the online self-learning, a motivating and engaging experience, compared to many other online classes I have taken, and I was very thankful for that.”
