Leveraging Bayesian Statistics in Educational Leadership and Policy Studies

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

Abstract

Objectives or Purposes:
This paper introduces Bayesian statistical approaches to address the limitations of traditional frequentist methods in educational leadership and policy studies. The objective is to illustrate the application of Bayesian methods through a case study and propose a workflow for their use in educational research.

Theoretical Framework:
The study contrasts Bayesian statistics with frequentist methods, highlighting Bayesian inference's flexibility and informative nature. Bayesian statistics view probabilities as degrees of belief and treat parameters as random variables with probability distributions, allowing for the incorporation of prior knowledge and the progressive accumulation of evidence.

Methods:
The study employs Bayesian linear regression to examine the relationship between teachers’ perception of school leadership and their job satisfaction at U.S. middle schools. The analysis addresses missing data issues and applies weights to ensure national representation. Markov chain Monte Carlo (MCMC) sampling methods are used to estimate the posterior distribution of the target parameter.

Data Sources:
Data from the Teaching and Learning International Survey (TALIS) 2018 were used. The survey provides comprehensive information on teachers' and school leaders' perceptions, allowing for a detailed analysis of the relationship between leadership practices and job satisfaction.

Results and/or Conclusions:
The findings illustrate the advantages of Bayesian statistics in educational leadership research. Bayesian approaches allow for incorporating past research to update knowledge, support direct estimation of uncertainty, and promote robust assessment through posterior predictive checks, convergence diagnostics, and sensitivity analysis. The case study demonstrates the practicality and flexibility of Bayesian methods in addressing complex educational research questions.

Scientific or Scholarly Significance:
This paper highlights the potential of Bayesian statistics to enhance estimation thinking in educational leadership and policy studies. By promoting the use of Bayesian methods, the research aims to improve the validity and reliability of quantitative studies, addressing the limitations of traditional frequentist approaches. The study provides practical guidelines for researchers, including the importance of thoughtful prior specification, transparency in reporting, and the use of sensitivity analyses.
The adoption of Bayesian statistics represents a significant advancement in educational research methodology, offering a robust framework for understanding complex relationships and improving decision-making processes. This research contributes to the ongoing dialogue on methodological innovation in educational leadership, emphasizing the need for a paradigm shift towards estimation thinking.

Authors

• Lei Jiang, University of Kansas

• Jeff Harring, University of Maryland