A multivariate, multilevel Rasch model with application to self-reported criminal behavior
A multivariate, multilevel Rasch model with application to self-reported criminal behavior
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DOI：
10.1111/j.0081-1750.2003.t01-1-00130.x
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发表时间：
2003-01-01
期刊：
影响因子：
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通讯作者：
Sampson, RJ
中科院分区：
文献类型：
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作者：
Raudenbush, SW;Johnson, C;Sampson, RJ
在研究社会行为、态度和信念的相关性时，需要一个测量模型来整合大量项目反应中的信息。多重结构通常是感兴趣的，协变量通常是多层次的（例如，在个人和邻里水平上测量）。一些项目级别的数据缺失是可以预料的。本文提出了一个具有随机效应的多变量、多层Rasch模型，并举例说明了它在犯罪行为自我报告中的应用。在条件独立性和可加性的假设下，该方法使研究者能够在间隔尺度上校准项目和人员，评估个人和社区水平上的可靠性，研究每一级犯罪类型之间的相关性，评估每一级犯罪类型的变异比例，纳入每一级的协变量，并适应随机缺失的数据。利用芝加哥196个人口普查区2842名9至18岁青少年的20项回答数据，我们说明了如何检验关键假设，如何根据诊断分析调整模型，以及如何解释参数估计。
In studying correlates of social behavior, attitudes, and beliefs, a measurement model is required to combine information across a large number of item responses. Multiple constructs are often of interest, and covariates are often multilevel (e.g., measured at the person and neighborhood level). Some item-level missing data can be expected. This paper proposes a multivariate, multilevel Rasch model with random effects for these purposes and illustrates its application to self-reports of criminal behavior. Under assumptions of conditional independence and additivity, the approach enables the investigator to calibrate the items and persons on an interval scale, assess reliability at the person and neighborhood levels, study the correlations among crime types at each level, assess the proportion of variation in each crime type that lies at each level, incorporate covariates at each level, and accommodate data missing at random. Using data on 20 item responses from 2842 adolescents ages 9 to 18 nested within 196 census tracts in Chicago, we illustrate how to test key assumptions, how, to adjust the model in light of diagnostic analyses, and how to interpret parameter estimates.