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The effects of Q-Matrix misspecification on parameter estimates and classification accuracy in the DINA model

机译：Q-Matrix错误指定对DINA模型中参数估计和分类准确性的影响

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摘要

This article reports a study that investigated the effects of Q-matrix misspecifications on parameter estimates and misclassification rates for the deterministic-input, noisy '' and '' gate (DINA) model, which is a restricted latent class model for multiple classifications of respondents that can be useful for cognitively motivated diagnostic assessment. In this study, a Q-matrix for an assessment mapping all 15 possible attribute patterns based on four independent attributes was misspecified by changing one '' 0 '' or '' 1 '' for each item. This was done in a way that ensured that certain attribute combinations were completely deleted from the Q-matrix, and certain incorrect dependency relationships between attributes were represented. Results showed clear effects that included an item-specific overestimation of slipping parameters when attributes were deleted from the Q-matrix, an item-specific overestimation of guessing parameters when attributes were added to the Q-matrix, and high misclassification rates for attribute classes that contained attribute combinations that were deleted from the Q-matrix.
机译：本文报告了一项研究，该研究针对确定性输入，嘈杂的''和''门（DINA）模型（这是针对多种受访者分类的受限潜伏模型），研究了Q矩阵错误指定对参数估计和错误分类率的影响这对出于认知动机的诊断评估很有用。在这项研究中，通过为每个项目更改一个“ 0”或“ 1”，错误地指定了基于四个独立属性映射所有15种可能属性模式的评估Q矩阵。这样做是为了确保某些属性组合从Q矩阵中完全删除，并且表示了属性之间某些不正确的依赖关系。结果显示出明显的效果，包括当从Q矩阵删除属性时，特定项的滑动参数过高估计;当将属性添加到Q矩阵中时，对参数的特定项估计过高;以及对属性类别的错误分类率很高包含从Q矩阵中删除的属性组合。

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