基于大规模评价数据的多层次模型的可信值生成。

IF 1.5 3区 心理学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Xiaying Zheng
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引用次数: 0

摘要

大规模评估(LSAs)通常使用潜在回归来产生可信值(pv),以无偏估计考生背景变量与表现之间的关系。为了处理LSA数据中常见的聚类效应,多层建模是一种流行的选择。然而,大多数lsa采用单层次条件作用的方法,导致了imputation模型与多层分析模型之间的不匹配。虽然一些lsa在单水平潜在回归中实施了特殊技术来支持随机截距建模,但这些技术并不支持随机斜率模型。为了解决这一差距,本研究提出了两种新的单水平方法来支持随机斜率估计。在支持多水平模型的能力方面,将现有方法和提出的方法与理论上无偏的多水平潜在回归方法进行了比较。结果表明,现有的两种单级方法可以支持随机截取模型。多水平潜回归方法提供了足够的估计,但受计算量的限制,并不是在所有条件下都有最好的性能。我们提出的一种单水平方法提供了一种有效的替代多水平潜在回归的方法,并且能够恢复所有参数的可接受估计。我们为每种方法都可以应用的情况提供了建议,但有一些注意事项。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On generating plausible values for multilevel modelling with large-scale-assessment data

Large-scale assessments (LSAs) routinely employ latent regressions to generate plausible values (PVs) for unbiased estimation of the relationship between examinees' background variables and performance. To handle the clustering effect common in LSA data, multilevel modelling is a popular choice. However, most LSAs use single-level conditioning methods, resulting in a mismatch between the imputation model and the multilevel analytic model. While some LSAs have implemented special techniques in single-level latent regressions to support random-intercept modelling, these techniques are not expected to support random-slope models. To address this gap, this study proposed two new single-level methods to support random-slope estimation. The existing and proposed methods were compared to the theoretically unbiased multilevel latent regression method in terms of their ability to support multilevel models. The findings indicate that the two existing single-level methods can support random-intercept-only models. The multilevel latent regression method provided mostly adequate estimates but was limited by computational burden and did not have the best performance across all conditions. One of our proposed single-level methods presented an efficient alternative to multilevel latent regression and was able to recover acceptable estimates for all parameters. We provide recommendations for situations where each method can be applied, with some caveats.

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来源期刊
CiteScore
5.00
自引率
3.80%
发文量
34
审稿时长
>12 weeks
期刊介绍: The British Journal of Mathematical and Statistical Psychology publishes articles relating to areas of psychology which have a greater mathematical or statistical aspect of their argument than is usually acceptable to other journals including: • mathematical psychology • statistics • psychometrics • decision making • psychophysics • classification • relevant areas of mathematics, computing and computer software These include articles that address substantitive psychological issues or that develop and extend techniques useful to psychologists. New models for psychological processes, new approaches to existing data, critiques of existing models and improved algorithms for estimating the parameters of a model are examples of articles which may be favoured.
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