Advancing Estimation Thinking in Educational Leadership: Teacher Emotion Mediating School Leadership Effects

• In Event: Advancing Estimation Thinking in Educational Leadership Research: Methodological Innovations and Practical Implications

Abstract

Objectives or Purposes:
This study investigates how estimation thinking can be advanced in educational leadership research, focusing on teacher emotion as a mediator of school leadership effects on student achievement. The objective is to demonstrate the application of Bayesian methods to address challenges in educational leadership studies, such as small sample sizes and complex mediation models.

Theoretical Framework:
The research is framed within the context of estimation thinking, which emphasizes effect sizes, uncertainty quantification, and the use of Bayesian methods to enhance the robustness and reliability of research findings. The study contrasts this approach with traditional dichotomous thinking, which relies heavily on NHST and p-values.
Methods: The study employs Bayesian statistical methods to analyze data from a sample of schools. Bayesian estimation is used to model the mediation effects of teacher emotion on the relationship between school leadership and student achievement. Informative priors are incorporated to enhance the analysis's robustness, and sensitivity analyses are conducted to examine the influence of different prior specifications.

Data Sources:
Data were collected from a sample of schools, with information on teacher emotion, school leadership practices, and student achievement. The study focuses on quantitative data, utilizing Bayesian methods to address the complexities of the mediation model.

Results and/or Conclusions:
The findings highlight the importance of teacher emotion as a multi-component latent construct comprising commitment, collective efficacy, and trust, which significantly mediates the effects of school leadership on student achievement. The study demonstrates the value of Bayesian estimation for small samples, emphasizing the incorporation of prior knowledge, effect sizes, uncertainty quantification, and model comparison techniques.

Scientific or Scholarly Significance:
This research contributes to the broader discussion on advancing estimation thinking by offering a template for future studies. It highlights the importance of thoughtful prior specification, sensitivity analyses, and practical significance over binary decision-making. The study provides practical guidelines for researchers, reviewers, and editors, including encouraging Bayesian methods for small samples or complex models, promoting transparency in reporting, and emphasizing effect sizes and uncertainty intervals.
By sharing concrete examples from the study on teacher emotion mediating school leadership’s effect on student learning, this research aims to spark discussions on advancing rigorous quantitative research in educational leadership. The findings underscore the need for a paradigm shift towards estimation thinking to enhance the quality and relevance of research for more reliable and actionable insights in policy and practice.

Authors

• Kenneth A. Leithwood, University of Toronto - OISE

• Jingping Sun, University of Alabama

• Kaiwen Man, University of Alabama

• Cheng Hua, University of Montevallo

• Chunhua Cao, University of Alabama