对正确指定的模型和分布错误指定的模型的基于模型隐含模拟的功率估计:对非线性和线性结构方程模型的应用
Julien P Irmer1, Andreas G Klein2, Karin Schermelleh-Engel2
1Institute of Psychology, Department of Research Methods and Evaluation, Goethe University Frankfurt, Theodor-W.-Adorno-Platz 6, 60629, Frankfurt am Main, Germany. irmer@psych.uni-frankfurt.de.
本研究引入了一种新的基于模拟的方法,用于统计测试中的功率估计. 模型隐含的基于模拟的功率估计 (MSPE) 准确地确定各种模型中所需的统计功率的样本大小.
科学领域:
- 统计 统计 统计 统计
- 统计建模 统计建模
背景情况:
- 分析功率估计仅限于特定模型,需要正确的规范.
- 基于模拟的功率估计是广泛适用的,但缺乏一个一般的样本大小计算框架.
研究的目的:
- 为z-test提出一种新的基于模拟的功率估计方法 (MSPE).
- 为计算特定功率率的样本大小提供一个一般框架.
主要方法:
- 开发了一种基于模型暗示模拟的功率估计 (MSPE) 方法,利用M估计器及其非对称的正常性.
- 使用参数模型将功率与样本大小联系起来,以确定所需的样本大小.
- 在正确和错误指定的条件下,在线性和非线性结构方程模型 (SEM) 中评估性能.
主要成果:
- 该MSPE方法在根平均二次误差和I型误差率方面表现出公正性和良好的表现.
- 预测的样本大小和功率率显示出高准确度.
- 超越了线性插值和逻辑回归等替代方法的性能.
结论:
- 该MSPE方法提供了一个广泛适用的和准确的方法来估计功率,特别是对于缺乏分析解决方案的模型.
- 它为确定实现所需统计功率所需的样本大小提供了有价值的工具.
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