Instruments to Assess Doctoral Supervisory Styles: Development and Validation in Spanish-Speaking Samples
To develop and validate psychometric scales to assess supervisory styles in doctoral education – directive, guiding, and hands-off – from both supervisor and student perspectives (SSS-S and SSS-s).
Doctoral supervision critically shapes students’ progress, well-being, and completion; yet, most studies rely on qualitative or conceptual typologies, and there is no standardized, validated instrument to quantify supervisory styles. Addressing this gap can help programs diagnose practices and align expectations between supervisors and candidates.
Two complementary studies were conducted with multi-country, Spanish-speaking doctoral programs. Study 1 established content validity with the assistance of expert judges. Study 2 used online, non-probability sampling to survey 333 supervisors and 510 doctoral students. Instruments were administered, and factor-analytic models (oblique and bifactor) and reliability coefficients were used to evaluate internal structure and consistency.
This study introduces the first psychometrically validated instruments to quantify directive, guiding, and hands-off supervisory styles through brief, parallel supervisor and student scales, providing a standardized, scalable approach for programs to diagnose and improve doctoral supervision.
Our results indicate that supervisory styles are one-dimensional. The internal consistency coefficients estimated for each style were satisfactory. Two instruments were developed, each tailored to assess the three supervisory styles, comprising three subscales with twelve items in each.
Use the instruments to (a) map supervisory-style profiles across programs or departments, (b) target professional development for supervisors (e.g., strengthen guiding behaviors), and (c) facilitate expectation-alignment conversations between supervisors and students.
Employ the scales to examine links between supervisory styles and outcomes (e.g., student satisfaction, time-to-degree, well-being), test invariance across disciplines or countries, and evaluate changes in style following training or feedback interventions.
By enabling evidence-based supervision, these instruments can help universities enhance doctoral learning environments, potentially improving candidate well-being and completion, and thus strengthening research capacity.
Validate the instruments in additional languages and contexts; assess predictive validity for academic and psychosocial outcomes; and investigate dyadic (supervisor–student) convergence or divergence and its consequences over time.
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