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Predicting teacher commitment: The impact of school climate and social-emotional learning.

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Title: Predicting teacher commitment: The impact of school climate and social-emotional learning.
Authors: Collie, Rebecca J. (AUTHOR), Shapka, Jennifer D. (AUTHOR), Perry, Nancy E. (AUTHOR)
Source: Psychology in the Schools. Dec2011, Vol. 48 Issue 10, p1034-1048. 15p. 6 Charts.
Subjects: Teacher attitudes, Organizational commitment, School environment, Social emotional learning, Public school teachers, Internet questionnaires, Teacher-student relationships, Logistic regression analysis, Regression analysis
Geographic Terms: British Columbia, Ontario
Abstract: The aim of this study was to investigate whether school climate and social-emotional learning impact teacher commitment. The sample included 664 public schoolteachers from British Columbia and Ontario in Canada. Participants completed an online questionnaire about teacher commitment, school climate, and social-emotional learning. Binary logistic regression analyses showed that positive school climates significantly predicted three forms of teacher commitment: greater general professional commitment, future professional commitment, and organizational commitment. Of the school climate variables, student relations and collaboration among staff predicted commitment. In addition, stronger beliefs and integration of social-emotional learning predicted two types of teacher commitment: greater general professional commitment and organizational commitment. Of the social-emotional learning variables, the support and promotion of a social-emotional learning culture across the school and comfort with and regular implementation of social-emotional learning in the classroom predicted greater teacher commitment. Implications for practice and research are discussed. © 2011 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]
Copyright of Psychology in the Schools is the property of Wiley-Blackwell 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: Psychology and Behavioral Sciences Collection
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  Data: <searchLink fieldCode="JN" term="%22Psychology+in+the+Schools%22">Psychology in the Schools</searchLink>. Dec2011, Vol. 48 Issue 10, p1034-1048. 15p. 6 Charts.
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  Data: The aim of this study was to investigate whether school climate and social-emotional learning impact teacher commitment. The sample included 664 public schoolteachers from British Columbia and Ontario in Canada. Participants completed an online questionnaire about teacher commitment, school climate, and social-emotional learning. Binary logistic regression analyses showed that positive school climates significantly predicted three forms of teacher commitment: greater general professional commitment, future professional commitment, and organizational commitment. Of the school climate variables, student relations and collaboration among staff predicted commitment. In addition, stronger beliefs and integration of social-emotional learning predicted two types of teacher commitment: greater general professional commitment and organizational commitment. Of the social-emotional learning variables, the support and promotion of a social-emotional learning culture across the school and comfort with and regular implementation of social-emotional learning in the classroom predicted greater teacher commitment. Implications for practice and research are discussed. © 2011 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Psychology in the Schools is the property of Wiley-Blackwell 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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        Value: 10.1002/pits.20611
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              Text: Dec2011
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