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Multilevel Factor Analysis and Student Ratings of Instructional Practice.

机译：多层次因素分析和学生对教学实践的评价。

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摘要

Student surveys of classroom climate can provide teachers and researchers with valuable information about instruction. These surveys are becoming a critical component in policy efforts to assess and improve teaching. Advocates note that students are natural observers of their classroom environments, have extensive and rich knowledge of their teachers, and that student ratings of teacher practice can be predictive of important outcomes, such as student academic and socio-emotional development. Inferences about a teacher's instructional practice are often based on aggregated student survey responses, and a key step in assessing the appropriate uses of the information collected from student surveys is to understand the dimensions of classroom climate or instructional practice that are discernible when looking at student responses aggregated by classroom.;This dissertation proposes a new approach for exploring the dimensionality of aggregated student ratings. This approach also has the potential to provide validity evidence supporting the use of student surveys as measures of instructional practice in both formative and summative evaluation. Specifically, this dissertation applies a non-parametric cluster bootstrap technique to a multilevel covariance structure analysis framework that allows researchers to evaluate and investigate psychometric models when data is collected from students and teachers are the objects of measurement. This approach is extended to applications where teachers are clustered within schools. The cluster bootstrap technique is demonstrated on a realistic dataset to illustrate how the methods may be used to investigate the dimensions of teacher professional practice that are discernible on a state-wide student survey of instructional practice.;The results of this dissertation demonstrate that the proposed cluster bootstrap technique can be used in conjunction with maximum likelihood estimation to yield accurate parameter estimates, and that for sufficiently large sample sizes, test statistics and standard errors based on the cluster bootstrap technique will yield valid inferences about the psychometric properties of aggregated survey responses.
机译：学生对教室气候的调查可以为教师和研究人员提供有关教学的宝贵信息。这些调查正在成为评估和改进教学的政策工作中的重要组成部分。倡导者指出，学生是课堂环境的自然观察者，对教师具有广泛而丰富的知识，并且学生对教师实践的评价可以预测重要的结果，例如学生的学术和社会情感发展。关于教师教学实践的推论通常基于汇总的学生问卷调查回答，评估从学生问卷调查中收集到的信息的适当用途的关键步骤是了解在看学生的回答时可以辨别的课堂气氛或教学实践的范围。本论文提出了一种探索学生综合评分量纲的新方法。这种方法还可能提供有效性证据，以支持在形成性评估和总结性评估中使用学生调查作为教学实践的度量。具体而言，本文将非参数聚类自举技术应用于多级协方差结构分析框架，该框架允许研究人员在从学生和老师收集数据作为测量对象时评估和研究心理测量模型。这种方法扩展到教师聚集在学校内部的应用程序。在一个实际的数据集上演示了集群引导技术，以说明如何使用这些方法来调查教师专业实践的维度，这在全州学生对教学实践的调查中是可以看出的。群集引导程序技术可以与最大似然估计结合使用，以产生准确的参数估计值；对于足够大的样本量，基于群集引导程序技术的测试统计信息和标准误差将得出有关汇总调查响应的心理计量特性的有效推论。

著录项

  • 作者

    Schweig, Jonathan David.;

  • 作者单位

    University of California, Los Angeles.;

  • 授予单位 University of California, Los Angeles.;
  • 学科 Educational evaluation.;Educational tests measurements.
  • 学位 Ph.D.
  • 年度 2014
  • 页码 224 p.
  • 总页数 224
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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