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<p>There is a growing need to equip teachers with social emotional behavioral knowledge in schools through professional development. Yet, for developers of such professional development offerings, identifying a single adequate tool that can flexibly assess learning across topics and offerings presents a unique measurement challenge. In this study, we evaluated the psychometric characteristics of an adapted version of the Knowledge, Confidence, and Use Survey with teachers who engaged in a webinar series for managing student social, emotional, and behavioral health needs. Scores selected from two key webinars (<i>N</i>s of 136 and 214 respectively) displayed overall good internal consistency, with total scale <i>a</i> = 0.94 (subscale<i> a</i>s ranged from 0.85 to 0.94). Results from exploratory and confirmatory factor analyses indicated two- and three-factor structural models as common solutions. However, a modified model fit yielded a robust three-factor structure. Key findings and limitations are discussed.</p>

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A Psychometric Analysis of an Adapted Knowledge, Confidence, and Use Survey for a Webinar Series

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月16日

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  • Gerta Bardhoshi,
  • Qianyi Gao,
  • Derek B. Rodgers,
  • Allison Bruhn,
  • Ashley Rila

摘要

There is a growing need to equip teachers with social emotional behavioral knowledge in schools through professional development. Yet, for developers of such professional development offerings, identifying a single adequate tool that can flexibly assess learning across topics and offerings presents a unique measurement challenge. In this study, we evaluated the psychometric characteristics of an adapted version of the Knowledge, Confidence, and Use Survey with teachers who engaged in a webinar series for managing student social, emotional, and behavioral health needs. Scores selected from two key webinars (Ns of 136 and 214 respectively) displayed overall good internal consistency, with total scale a = 0.94 (subscale as ranged from 0.85 to 0.94). Results from exploratory and confirmatory factor analyses indicated two- and three-factor structural models as common solutions. However, a modified model fit yielded a robust three-factor structure. Key findings and limitations are discussed.