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Leading with Data: An Increasingly Important Feature of School Leadership.

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Title: Leading with Data: An Increasingly Important Feature of School Leadership.
Authors: Jingping Sun1 jsun22@ua.edu, Johnson, Bob2 bljohnson7@ua.edu, Przybylski, Robert2 rpcrimsonforever@gmail.com
Source: International Studies in Educational Administration (Commonwealth Council for Educational Administration & Management (CCEAM)). 2016, Vol. 44 Issue 3, p93-128. 36p.
Subject Terms: *Educational leadership, *School principals, *Academic achievement, *Decision making, Confirmatory factor analysis
Geographic Terms: United States
Abstract: Large-scale efforts to improve student achievement have increasingly encouraged principals to use student data to inform their decision-making and change initiatives in schools. However, research on the nature and best practices of such data use remains very thin. The aim of this study was to develop an incipient theoretical model to understand school leaders' practices in the effective use of data to lead schools. The study, conducted in the state of Alabama in the USA, provides evidence to show that data-driven school leadership (DDSL) is an important feature of today's principalship. Scale analysis indicates a high reliability of the measure of our model. Confirmatory factor analysis was used to refine the model until a good fit was achieved. The refined model can be used to better understand and measure school leaders' DDSL and can be used to inform leadership preparation in this regard. [ABSTRACT FROM AUTHOR]
Copyright of International Studies in Educational Administration (Commonwealth Council for Educational Administration & Management (CCEAM)) is the property of Commonwealth Council for Educational Administration & Management (CCEAM) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: *<searchLink fieldCode="DE" term="%22Educational+leadership%22">Educational leadership</searchLink><br />*<searchLink fieldCode="DE" term="%22School+principals%22">School principals</searchLink><br />*<searchLink fieldCode="DE" term="%22Academic+achievement%22">Academic achievement</searchLink><br />*<searchLink fieldCode="DE" term="%22Decision+making%22">Decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Confirmatory+factor+analysis%22">Confirmatory factor analysis</searchLink>
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  Data: Large-scale efforts to improve student achievement have increasingly encouraged principals to use student data to inform their decision-making and change initiatives in schools. However, research on the nature and best practices of such data use remains very thin. The aim of this study was to develop an incipient theoretical model to understand school leaders' practices in the effective use of data to lead schools. The study, conducted in the state of Alabama in the USA, provides evidence to show that data-driven school leadership (DDSL) is an important feature of today's principalship. Scale analysis indicates a high reliability of the measure of our model. Confirmatory factor analysis was used to refine the model until a good fit was achieved. The refined model can be used to better understand and measure school leaders' DDSL and can be used to inform leadership preparation in this regard. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of International Studies in Educational Administration (Commonwealth Council for Educational Administration & Management (CCEAM)) is the property of Commonwealth Council for Educational Administration & Management (CCEAM) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – SubjectFull: Academic achievement
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              Text: 2016
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