Structural Equation Modeling for High School Principals' Data-Driven Decision Making: An Analysis of Information Use Environments

Mingchu Neal Luo

61 citations0/0 files storedEducational Administration Quarterly

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Educational Assessment and Improvement School Leadership and Teacher Performance Educational Leadership and Practices

Abstract

Background: Accountability demands are increasingly pushing school leaders to explore more data and do more sophisticated analyses. Data-driven decision making (DDDM) has become an emerging field of practice for school leadership and a central focus of education policy and practice. Purpose: This study examined principals' DDDM practices and identified the factors influencing DDDM using the theoretical frame of information use environments. Participants: Participants were 183 public high school principals in a Midwestern state. Research Design: The research design was cross-sectional survey research. Data Collection and Analysis: Survey instruments were developed and administered to principals. Structural equation modeling was conducted to determine what factors significantly affect principals' DDDM practices in different leadership dimensions. Findings: Principals used data more frequently in instructional and organization operational leadership than in the leadership dimensions of school vision and collaborative partnerships. Different contextual factors affected data use in different leadership dimensions. Human-related factors such as perceptions of data quality and data analysis skills seemed to have direct effects on data use in addressing administrative problems in instruction and organizational operation, in which data were used frequently. Organization-related factors such as school district requirement and accessibility of data tended to have more direct influence on data use in the leadership dimensions of school vision and collaborative partnerships, where data were used less often. Conclusions: This study supports the proposition that as information behavior, DDDM is situational, multidimensional, and dynamic. Results provide insights into practice, research, and theoretical foundation for the emerging topic of DDDM.

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References

1999Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives 2012Principles and Practice of Structural Equation Modeling 1998Principles and Practice of Structural Equation Modeling 1997Motivation and Personality 1984Research in Organizational Behavior 1998Working knowledge: how organizations manage what they know 1996Power analysis and determination of sample size for covariance structure modeling.1993Motivation and personality

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2012Conclusions and a Data Use Framework 2016Principals’ Data-Driven Practice and Its Influences on Teacher Buy-in and Student Achievement in Comprehensive School Reform Models 2016Conceptualizing Essential Components of Effective High Schools 2015Structuring Professional Learning to Develop a Culture of Data Use: Aligning Knowledge from the Field and Research Findings 2014Vergleichsarbeiten und schulische Personalentwicklung – Ausmaß und Voraussetzungen der Datennutzung

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