Wang Wen-Chung, Chen Po-Hsi, Cheng Ying-Yao
Department of Psychology, National Chung Cheng University, Chia-yi, Taiwan.
Psychol Methods. 2004 Mar;9(1):116-36. doi: 10.1037/1082-989X.9.1.116.
A conventional way to analyze item responses in multiple tests is to apply unidimensional item response models separately, one test at a time. This unidimensional approach, which ignores the correlations between latent traits, yields imprecise measures when tests are short. To resolve this problem, one can use multidimensional item response models that use correlations between latent traits to improve measurement precision of individual latent traits. The improvements are demonstrated using 2 empirical examples. It appears that the multidimensional approach improves measurement precision substantially, especially when tests are short and the number of tests is large. To achieve the same measurement precision, the multidimensional approach needs less than half of the comparable items required for the unidimensional approach.
分析多次测试中项目反应的传统方法是分别应用单维项目反应模型，每次分析一个测试。这种单维方法忽略了潜在特质之间的相关性，在测试较短时会产生不精确的测量结果。为了解决这个问题，可以使用多维项目反应模型，该模型利用潜在特质之间的相关性来提高个体潜在特质的测量精度。通过两个实证例子证明了这种改进。多维方法似乎能显著提高测量精度，尤其是在测试较短且测试次数较多时。为了达到相同的测量精度，多维方法所需的可比项目数量不到单维方法的一半。
