Critical Values for Yen's Q3: Identification of Local Dependence in the Rasch Model Using Residual Correlations
Critical Values for Yen's Q3: Identification of Local Dependence in the Rasch Model Using Residual Correlations
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DOI：
10.1177/0146621616677520
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发表时间：
2017-05-01
影响因子：
1.2
通讯作者：
Horton, Mike
中科院分区：
文献类型：
--
作者：
Christensen, Karl Bang;Makransky, Guido;Horton, Mike
局部独立性假设是所有项目反应理论（IRT）模型的核心。违规可能导致对可靠性的夸大估计和结构效度的问题。对于最广泛使用的拟合统计量Q（3），目前还没有关于临界值的有据可查的建议，这些临界值应用于指示局部依赖性（LD），因此，使用了各种任意的经验法则。在这项研究中，一个实证数据的例子和蒙特卡罗模拟被用来调查的不同因素，可以影响零分布的残差相关性，提出指导方针，研究人员和从业人员可以遵循时，作出决定，LD规模的发展和验证。参数自举过程应在每个单独的情况下实现，以获得适用于数据集的LD的临界值，并提供用于许多数据结构情况的示例临界值。结果表明，对于Q（3）拟合统计量，没有一个单一的临界值是适合于所有情况下，在经验零分布中的分布受项目的数量，样本大小，和响应类别的数量。此外，结果表明，LD应被视为相对于平均观察到的剩余相关性，而不是一个统一的值，因为这会导致更稳定的拟合统计的零分布的拟合。
The assumption of local independence is central to all item response theory (IRT) models. Violations can lead to inflated estimates of reliability and problems with construct validity. For the most widely used fit statistic Q(3), there are currently no well-documented suggestions of the critical values which should be used to indicate local dependence (LD), and for this reason, a variety of arbitrary rules of thumb are used. In this study, an empirical data example and Monte Carlo simulation were used to investigate the different factors that can influence the null distribution of residual correlations, with the objective of proposing guidelines that researchers and practitioners can follow when making decisions about LD during scale development and validation. A parametric bootstrapping procedure should be implemented in each separate situation to obtain the critical value of LD applicable to the data set, and provide example critical values for a number of data structure situations. The results show that for the Q(3) fit statistic, no single critical value is appropriate for all situations, as the percentiles in the empirical null distribution are influenced by the number of items, the sample size, and the number of response categories. Furthermore, the results show that LD should be considered relative to the average observed residual correlation, rather than to a uniform value, as this results in more stable percentiles for the null distribution of an adjusted fit statistic.