Date of Award

Summer 8-11-2020

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Counseling and Psychological Services

First Advisor

Joel Meyers

Second Advisor

Kenneth Rice

Third Advisor

Ann Kale Kruger

Fourth Advisor

Jeffrey Ashby

Abstract

School climate has been recognized as an opportunity to foster student success due to its demonstrated links to desirable academic, social/emotional, and behavioral outcomes and its critical role in the school improvement process. The significance of school climate and the value of its study have been made clear both in educational literature and educational policy. As reflected in its inclusion in the Every Student Succeeds Act (ESSA) of 2015, more and more states are reporting school climate indicators alongside more traditional academic outcomes within their accountability systems. Accordingly, the stakes attached to the accurate measurement of school climate are greater than ever. Unfortunately, the complexity of school climate presents an array of challenges when attempting to measure it accurately. It is typically measured using survey data, from which several analytic issues arise. In particular, the clustered nature of survey data confounds the effects of school climate at individual and school levels. It is important that researchers clearly define the level of school climate being investigated and use appropriate statistical techniques to measure it. In addition, survey items and constructs may have different meanings for various groups of individuals within schools and across schools with differing characteristics – leading to invalid comparisons. Researchers should investigate the equality of school climate surveys for diverse student and school populations. This dissertation systematically reviews the techniques school climate researchers employ to address these issues during scale development. Then, it employs a bioecological framework to investigate the clustered nature and invariance of a school climate survey using multilevel confirmatory factor analysis and multilevel structural equation modeling procedures.

DOI

https://doi.org/10.57709/18737788

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