On selecting indicators for multivariate measurement and modeling with latent variables: When "good" indicators are bad and "bad" indicators are good
On selecting indicators for multivariate measurement and modeling with latent variables: When "good" indicators are bad and "bad" indicators are good
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DOI：
10.1037/1082-989x.4.2.192
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发表时间：
1999-06-01
影响因子：
7
通讯作者：
Nesselroade, JR
中科院分区：
文献类型：
--
作者：
Little, TD;Lindenberger, U;Nesselroade, JR
对于研究设计的普遍性来说，选择指标与选择测量人员或测量场合同样重要。阐述了现存的知识基础上的指标选择，作者研究选择的有效性和可靠性的多变量表示的影响。一个模拟，系统地改变了4个关键维度的指标选择被用来调查其影响的保真度的结构表示和相对能力的探索性和验证性分析，以恢复内和之间的结构信息。例如，实证分析可以对结构之间的关系进行有效和无偏的估计，即使在内部一致性非常低的条件下也是如此。设计，程序和分析建议的基础上扩展分类的指标选择和模拟结果。
Selecting indicators is as important for the generalizability of research designs as selecting persons or occasions of measurement. Elaborating on the extant knowledge base regarding indicator selection, the authors examine selection influences on the validity and reliability of multivariate representations. A simulation that systematically varied 4 key dimensions of indicator selection was used to investigate their effects on the fidelity of construct representations and the relative ability of exploratory and confirmatory analyses to recover within- and between-construct information. Confirmatory analyses, for example, yielded valid and unbiased estimates of the relations between constructs, even under conditions of very low internal consistency. Design, procedural, and analysis recommendations based on an expanded taxonomy of indicator selection and the simulation results are provided.