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首页> 外文期刊>Educational and Psychological Measurement >The Influence of Between-Dimension Correlation, Misfit, and Test Length on Multidimensional Rasch Model Information-Based Fit Index Accuracy
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The Influence of Between-Dimension Correlation, Misfit, and Test Length on Multidimensional Rasch Model Information-Based Fit Index Accuracy

机译：维度之间的相关性，失配和测试长度对基于多维Rasch模型信息的拟合指数精度的影响

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摘要

Most research on confirmatory factor analysis using information-based fit indices (Akaike information criterion [AIC], Bayesian information criteria [BIC], bias-corrected AIC [AICc], and consistent AIC [CAIC]) has used a structural equation modeling framework. Minimal research has been done concerning application of these indices to item response models, especially within the framework of multidimensional Rasch analysis with an emphasis of the role of between-dimension correlation on index accuracy. We investigated how sample size, between-dimension correlation, model-to-data misfit, and test length affect the accuracy of these indices in model recovery in dichotomous data using a multidimensional Rasch analysis simulation methodology. Results reveal that, at higher values of between-dimension correlation, AIC indicated the correct two-dimension generating structure slightly more often than the BIC or CAIC. The results also demonstrated that violations of the Rasch model assumptions are magnified at higher between-dimension correlations. We recommend that practitioners working with highly correlated multidimensional data use moderate length (roughly 40 items) instruments and minimize data-to-model misfit in the choice of model used for confirmatory factor analysis (multidimensional random coefficient multinomial logit or other multidimensional item response theory models).
机译：使用基于信息的拟合指数（Akaike信息标准[AIC]，贝叶斯信息标准[BIC]，偏差校正的AIC [AICc]和一致的AIC [CAIC]）进行的验证性因素分析的大多数研究都使用了结构方程建模框架。关于将这些指标应用于项目响应模型的研究很少，特别是在多维Rasch分析框架内，着重强调了维度间相关性对指标准确性的作用。我们使用多维Rasch分析模拟方法研究了样本大小，维度间相关性，模型到数据的失配以及测试长度如何影响二分类数据中模型恢复中这些指标的准确性。结果显示，在较高的维度间相关值下，AIC指示正确的二维生成结构的频率要比BIC或CAIC稍微高一些。结果还表明，在较高的维度间相关性下，违反Rasch模型假设的情况被放大。我们建议处理高度相关多维数据的从业人员使用中等长度（约40项）的仪器，并在选择用于验证性因子分析的模型（多维随机系数多项式logit或其他多维项响应理论模型）时，尽量减少数据与模型的不匹配）。

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