Estimation Thinking in Network Analysis in Educational Leadership Research

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

Abstract

Objectives or Purposes:
This paper explores the application of estimation thinking in network analysis within educational leadership research. The objective is to demonstrate how probabilistic reasoning can be used to understand and predict relationships and patterns in social networks related to educational leadership.

Theoretical Framework:
The study is framed within the context of estimation thinking, which emphasizes the use of probabilistic models to account for the variability and dynamics of real-world networks. This approach is contrasted with dichotomous thinking, which categorizes relationships as either present or absent without considering their strength or variability.

Methods:
The study employs various probabilistic models, including exponential random graph models (ERGMs), stochastic actor-oriented models (SAOMs), latent space models, and Bayesian network analysis. These models are used to analyze the relationships and interactions within educational leadership networks, such as organizational members' advice-seeking networks and school-community networks.

Data Sources:
Data were collected from various educational leadership networks, including school building-level leaders, district-level leaders, and policymaking and implementation networks. The study focuses on quantitative data, utilizing probabilistic models to analyze the network structures and relationships.

Results and/or Conclusions:
The findings highlight the common mistakes in network analysis due to dichotomous thinking, such as categorizing relationships as either present or absent without considering their strength or variability, assuming relationships or outcomes are deterministic rather than probabilistic, and relying solely on point estimates without considering uncertainty or confidence intervals.

Scientific or Scholarly Significance:
This research emphasizes the importance of applying estimation thinking to network analysis in educational leadership. By using probabilistic models, researchers can develop more accurate models that reflect the variability and dynamics of real-world networks. This approach uncovers the intricacies of interconnected systems and improves the predictive power of quantitative network analysis models.
The study provides practical guidelines for researchers, peer reviewers, and journal editors, including encouraging the use of probabilistic models, promoting transparency in reporting, and emphasizing the importance of uncertainty intervals. By applying estimation thinking to network analysis, this research aims to enhance the methodological rigor and reliability of studies in educational leadership, ultimately improving decision-making processes and educational outcomes.

Author

• Yinying Wang, Georgia State University