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首页> 外文期刊>Psychometrika >Bayes Factors for Evaluating Latent Monotonicity in Polytomous Item Response Theory Models
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Bayes Factors for Evaluating Latent Monotonicity in Polytomous Item Response Theory Models

机译：贝叶斯因子评估多种物品响应理论模型中的潜在单调性

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摘要

The assumption of latent monotonicity is made by all common parametric and nonparametric polytomous item response theory models and is crucial for establishing an ordinal level of measurement of the item score. Three forms of latent monotonicity can be distinguished: monotonicity of the cumulative probabilities, of the continuation ratios, and of the adjacent-category ratios. Observable consequences of these different forms of latent monotonicity are derived, and Bayes factor methods for testing these consequences are proposed. These methods allow for the quantification of the evidence both in favor and against the tested property. Both item-level and category-level Bayes factors are considered, and their performance is evaluated using a simulation study. The methods are applied to an empirical example consisting of a 10-item Likert scale to investigate whether a polytomous item scoring rule results in item scores that are of ordinal level measurement.
机译：潜在单调性的假设是由所有常见的参数和非参数多种多种物品响应理论模型进行的，并且对于建立项目分数的序数测量至关重要。 可以区分三种形式的潜在单调性：累积概率的单调性，延续比率和相邻类别比率。 推导出这些不同形式的潜在单调性的可观察结果，并提出了用于测试这些后果的贝叶因子方法。 这些方法允许量化有利于和对测试性质的证据。 考虑项目级和类别级别的贝叶斯因子，并使用模拟研究评估其性能。 该方法应用于由10项李克特标度组成的经验示例，以调查多种物品评分规则是否导致序数测量的项目分数。

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