Abstract
This pilot study applies Computer-Aided Text Analysis (CATA) to examine Language Style Matching (LSM) in counseling transcripts, offering a novel metric for counselor education and supervision. Analyzing 50 suicide-focused and 100 general counseling sessions using LIWC-22 software, we found high LSM scores in both contexts (suicide-focused: M = 0.906; general: M = 0.903). Bayesian analysis provided moderate evidence (BF01 = 5.048) in support of the null hypothesis, indicating no significant difference between the two session types. The consistently high LSM suggests counselors effectively build strong alliances and align with client goals across topics. LSM analysis provides an objective measure of verbal attunement. This technology can strengthen supervision practices and improve the development of verbal attunement skills, crucial in both general and crisis counseling. By advancing understanding of therapeutic alliance dynamics, this research underscores the potential of LSM to enhance counselor education and ultimately improve client outcomes.
Recommended Citation
Ni, Chung-Fan; Jacques, Justin; Silber, Charles; and Dykeman, Cass
(2025)
"Evaluating Counseling Skills Through Language Style Matching: A Computer-Aided Text Analysis of Suicide and General Counseling Transcripts,"
Journal of Technology in Counselor Education and Supervision: Vol. 6:
Iss.
1, Article 2.
DOI: https://doi.org/10.61888/2692-4129.1124
Available at:
https://digital.sandiego.edu/tces/vol6/iss1/2
Included in
Clinical Psychology Commons, Counseling Psychology Commons, Educational Psychology Commons, Educational Technology Commons, Experimental Analysis of Behavior Commons, Student Counseling and Personnel Services Commons
