用一块石头杀死两只鸟:使用展开物品响应树模型计算展开物品响应过程和响应风格
Zhaojun Li1, Lingyue Li2, Bo Zhang2,3
1Department of Psychology, The Ohio State University, Columbus, OH, USA.
Multivariate behavioral research
|August 31, 2024
概括
新的展开项目响应树 (UIRTree) 模型准确地区分了在利克特类项目中的展开响应和响应风格. 这种模型改善了参数估计,并且比现有方法更好地适应人格数据.
科学领域:
- 心理测量 心理测量 心理测量
- 统计建模 统计建模
- 人格评估 人格评估 人格评估 人格评估
背景情况:
- 对于利克特类项目,有两个平行研究流,即展开模型和个人响应风格 (RSs).
- 准确地理解利克特类项目响应需要区分展开的响应和RSs.
研究的目的:
- 提出和评估展开的项目响应树 (UIRTree) 模型.
- 将UIRTree的性能与Samejima的分级响应模型,通用分级展开模型和主导项目响应树模型等现有模型进行比较.
- 评估UIRTree能够准确地恢复模型参数并适应实证数据的能力.
主要方法:
- 蒙特卡洛模拟研究将UIRTree与其他三种模型进行比较.
- 经验数据集的分析,特别是人格数据.
- 使用R代码估计模型参数.
主要成果:
- 当数据包括展开过程和RSs时,AIC准确地选择了UIRTree模型.
- 在UIRTree中的模型参数在现实的条件下被准确地恢复.
- 与竞争型号相比,UIRTree证明了对人格数据集的优越适应性,并提供了比竞争型号更合理的参数估计,揭示了RSs的强烈存在.
结论:
- 该UIRTree模型有效地从Likert类型项目中的响应样式分析展开的响应.
- 错误地指定项目响应过程或忽略RSs会对参数估计产生负面影响.
- UIRTree提供了一个有价值的工具,用于建模利克特类型的项目响应,并了解响应行为的个体差异.
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