Guest Blog Authored by: Janet Sanchez Enriquez, Ph.D., BCBA-D, LBA
Shapers at Work, PLLC
Janet Sanchez Enriquez, PhD, BCBA-D, LBA, attended the University of North Carolina Charlotte as an OSEP scholar working under the mentorship of Dr. Rob Pennington. Her research interests include working alongside vulnerable and diverse populations, promoting applied behavior analysis for teaching and learning through research, and developing culturally and linguistically diverse verbal behavior assessments and interventions. She is a founding member and Secretary of the non-profit Mexican Organization of Practitioners of Applied Behavior Analysis (Organización Mexicana de Practicantes del Análisis Conductual Aplicado), serves on the Board of Directors for the World Behavior Analysis Day Alliance, sits on the Executive Council for the Texas Association of Behavior Analysis, and was previously the Council for Exceptional Children’s Division on Autism Developmental Disabilities DEI Student Liaison.
Merging Technology with Verbal Behavior
An exciting development of contemporary behavior science is merging conceptual systems with innovative technological frameworks, such as using statistical computation with leading-edge visual representations to analyze verbal behavior. This work can be especially significant for culturally and linguistically diverse (CLD)populations.
The verbal operant experimental analysis (VOX) is a systematic method for evaluating the functional sources of control over a speaker’s language. The VOX framework is adaptable to specific cultural and contextual variables, allowing for responses across languages, modalities, and topographies. Unlike traditional assessments, VOX focuses on function over form, offering a verbal-community-centered approach incorporating the unique characteristics of CLD speakers (Enriquez et al., 2023). VOX expands upon Lerman et al.’s (2005) functional analysis of the emerging, elementary verbal operants of children with speech, but is formatted to provide a comprehensive sample of the child’s functional language.
The VOX analysis categorizes language across the four primary operants:
- Tact – Labeling, influenced by environmental stimuli.
- Mand – Requesting, driven by specific wants or needs.
- Echoic (duplic) – Repeating, or echoing, prompted by verbal imitation.
- Intraverbal (sequelic) – Conversing, or replying, shaped by prior verbal interactions.
Mason and colleagues (2022) define stimulus overselectivity as “temporally extended patterns of restrictive stimulus control that have resulted in disproportionate populations of responding that cannot be attributed to chance alone” (p. 101), potentially interfering with language development. For example, overselectivity may result in a situation whereby a child may echo a specific word or phrase not because of its functional relevance, but because of narrow control by a familiar vocal pattern. As a result, that verbal response consistently fails to generalize across conditions. The Verbal Operant Experimental (VOX) analysis enables practitioners to detect such patterns by using metrics like SCoRE (which assesses verbal repertoire balance), Cochran’s Q (which quantifies overselectivity across operants), and Vβ Diversity (which measures resistance to extinction). Together, these tools provide insight into the balance, flexibility, and fluency of a learner’s verbal behavior.
The VOX Analysis Package integrates B.F. Skinner’s functional analysis of verbal behavior with modern data collection methods like the verbal operant experimental analysis (VOX). This tool enables behavior analysts to perform culturally responsive, data-informed language assessments that are both function-specific and contextually relevant.(Enriquez et al., 2023). Shiny was originally developed as an R package for developing web applications through an easy-to-use interface that lets users interact with data and analyses. Shiny apps are sleek, reactive, and powerful web applications that allow users to conduct complex data analysis without having to learn complex programming languages. Best of all, Shiny apps use reactive programming to allow for real-time analyses without the need for storing sensitive data.
The VOX Shiny Application enhances Skinner’s (1957) classification of verbal operants—mand, tact, echoic, and intraverbal—by offering an intuitive digital interface for analyzing the strength and balance of these elementary verbal operants. Developed using R’s Shiny framework (Chang et al., 2023), the app allows clinicians, researchers, and educators to input experimental data, to then generate in real-time, comprehensive summary displays and statistical metrics initiated by the Stimulus Control Ratio Equation (SCoRE; Mason & Andrews, 2019). The application’s utility is particularly evident in its capacity to support culturally responsive assessment and intervention by efficiently identifying the function of verbal responses instead of the form, i.e., the response typography, inclusive of diverse linguistic backgrounds, and individual and familial idiosyncratic speech.
Distribution of Verbal Operants as Calculated by the VOX Shiny Application. Image generated by the author using the online VOX analysis tool available at www.verbalbehavior.org.
Role of the VOX Shiny App
The VOX Shiny Application operationalizes the VOX framework into a real-time analysis tool:
- Interactive Data Analysis: The app functions as a “language calculator,” allowing users to input frequency data for each verbal operant and compute outcome measures.
- Reactive Programming: Built on Shiny’s reactive programming model, outputs automatically update with input changes. Notably, the app does not store data, ensuring user privacy.
- No PHI (Protected Health Information) Storage: Because the app runs locally on the client side, it supports compliance with HIPAA and FERPA regulations.
Enhancing Culturally Responsive Practice
As Atherkode and Mason (2024) demonstrated, the VOX methodology supports culturally responsive interventions by allowing bilingual and multilingual speakers to switch freely between languages, thus respecting the functional dynamics of their home verbal communities. Standardized language assessments often fail CLD learners by emphasizing topography and failing to adapt to linguistic diversity (Esch et al., 2010). In contrast, the VOX approach centers the speaker’s natural language environment and enables assessors to reinforce culturally relevant responses. This aligns with Rosales and colleagues’ (2023) call for behavior analysts to deliver services that integrate family culture, language preferences, and values to reduce disparities in access and outcomes for marginalized populations.
Radar chart of the verbal operants as calculated by the VOX Shiny Application. Image generated by the author using the online VOX analysis tool available at www.verbalbehavior.org.
Looking Ahead: Expanding Opportunities for Application
The VOX Application represents the future of interdisciplinary collaboration, drawing on principles from ABA, data science, and inclusive education. This convergence expands our capacity to:
- Tailor early intensive behavioral interventions (EIBI) for diverse learners.
- Build individualized treatment plans.
- Foster equitable access to evidence-based services.
Recent literature underscores that culturally and linguistically responsive assessment and service delivery are no longer optional but essential for ethical and effective practice (Rosales et al., 2023). VOX is uniquely positioned to meet this demand by offering practitioners practical tools that align with both scientific rigor and cultural humility. We encourage professionals from all sectors, clinicians, graduate faculty, researchers, and school-based professionals to consider how VOX can be adapted to their contexts, particularly those serving multilingual and multicultural populations.
Invitation to Test and Collaborate
The VOX Shiny App was recently deployed, and we invite individuals to explore, test, and contribute to its ongoing development. We are particularly interested in use cases involving:
- Functional language assessments for bilingual learners.
- Integration into IEP development and progress monitoring.
- Graduate-level training in culturally responsive ABA.
- Research projects on stimulus control and verbal operant variability.
VβDiversity of the verbal operants as calculated by the VOX Shiny Application. Image generated by the author using the online VOX analysis tool available at www.verbalbehavior.org
Learn More
- www.verbalbehavior.org
- VOX Analysis Package Website: https://free-state-analytics.github.io/voxanalysis/
- Shiny Application: https://free-state-analytics.shinyapps.io/voxanalysis/
References
Atherkode, S., & Mason, L. (2023). Assessing the verbal behavior of a linguistically diverse speaker with autism. The Analysis of Verbal Behavior, 1-9. https://doi.org/10.1007/s40616-023-00196-x
Chang, W., Cheng, J., Allaire, J. J., Sievert, C., Schloerke, B., Xie, Y., Allen, J., McPherson, J., & Dipert, A. (2023). Shiny: Web application framework for R (Version 1.7.4) [Computer software]. https://CRAN.R-project.org/package=shiny
Enriquez, J., Arechiga, N., Atherkode, S., Otero, M., Andrews, A., & Mason, L. (2023). Culturally responsive language assessment through a verbal operant experimental analysis. Behavior Analysis: Research and Practice, 23(2), 165–178. https://doi.org/10.1037/bar0000269
Esch, B. E., LaLonde, K. B., & Esch, J. W. (2010). Speech and language assessment: A verbal behavior analysis. The Journal of Speech and Language Pathology—Applied Behavior Analysis, 5(2), 166–191. https://doi.org/10.1037/h0100270
Lerman, D.C., Parten, M., Addison, L.R., Vorndran, C.M., Volkert, V.M., Kodak, T. A. (2005). Methodology for assessing the functions of emerging speech in children with developmental disabilities. Journal of Applied Behavior Analysis, 38(3), 303–316. https://doi.org/10.1901
Mason, L. L., & Andrews, A. (2014). Referent-based verbal behavior instruction for children with autism. Behavior Analysis in Practice, 7, 107-111. https://doi: 10.1007/s40617-014-0018-zz
Mason, L. L., & Andrews, A. (2019). The verbal behavior stimulus control ratio equation: A quantification of language. Perspectives on Behavior Science, 42, 323-343.https://doi.org/10.1007/s40614-018-0141-1
Mason, L. L., & Andrews, A. (2021). Referent-Based Instruction to Strengthen the Verbal Behavior of Early Learners with Autism and Related Language Disorders. Behavior Analysis in Practice, 14, 660-672. https://doi.org/10.1007/s40617-020-00491-2
Mason, L., Otero, M., & Andrews, A. (2022). Cochran’s Q test of stimulus overselectivity within the verbal repertoire of children with autism. Perspectives on Behavior Science, 1-21. https://doi.org/10.1007/s40614-021-00315-w
Rosales, R., León, I. A., & León-Fuentes, A. L. (2023). Recommendations for working with culturally and linguistically diverse families: A report from the field. Behavior Analysis in Practice, 16(1), 1255–1269. https://doi.org/10.1007/s40617-023-00870-5
Skinner, B. F. (1957). Verbal behavior. Appleton-Century-Crofts.