在序列系数模型中,基于参数不稳定的得分测试
Franz Classe1, Rudolf Debelak2, Christoph Kern3
1Deutsches Jugendinstitut e.V., Munich, Germany.
概括
一种新方法有效计算使用有限信息估计的顺序因子模型的参数不稳定性测试. 这种方法为复杂模型的完整信息估计提供了更快,更强大的替代方案.
科学领域:
- 心理测量 心理测量 心理测量
- 统计建模 统计建模
- 计算统计学 计算统计学
背景情况:
- 顺序系数模型,特别是分级响应模型 (GRMs),对于分析各种领域的分类数据至关重要.
- 评估参数稳定性对于这些模型的可靠性和有效性至关重要.
- 在GRM中进行参数不稳定性测试的现有方法,特别是使用完整信息 (FI) 估计的方法,可能是计算密集的.
研究的目的:
- 引入一种新的,计算效率高的方法,用于在序列因子模型中计算模型得分.
- 为了使得这些模型中的参数不稳定性能够开发基于分数的测试.
- 为了促进对多维物件响应理论 (MIRT) 模型的参数不稳定性测试的快速执行.
主要方法:
- 开发一种用于计算模型得分的新方法,适用于GRM中的有限信息 (LI) 估计器.
- 使用拟议的LI估计方法,实施基于得分的参数不稳定性测试.
- 与使用完整信息 (FI) 估计的既定方法进行比较性性能分析.
主要成果:
- 建议的基于分数的测试显示出良好的I型错误率和高的统计能力.
- 有限信息 (LI) 估计方法在计算上比传统的全信息 (FI) 估计更快.
- 该方法的有效性通过使用复杂模型和现实数据的应用程序来验证.
结论:
- 基于LI的新型得分计算方法提供了一种高效和有效的工具,用于在序列因子模型中测试参数不稳定性.
- 这种方法显著加快了模型稳定性的评估,特别是在复杂的多维物件响应理论 (MIRT) 模型中.
- 在R包"lavaan"中的实施使得这种先进的统计技术可用于更广泛的研究应用.
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