Modeling life-span growth curves of cognition using longitudinal data with multiple samples and changing scales of measurement.
Modeling life-span growth curves of cognition using longitudinal data with multiple samples and changing scales of measurement.
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DOI：
10.1037/a0015857
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发表时间：
2009-06
影响因子：
7
通讯作者：
Meredith, William
中科院分区：
文献类型：
--
作者：
McArdle, John J.;Grimm, Kevin J.;Hamagami, Fumiaki;Bowles, Ryan P.;Meredith, William
关键词：
本研究使用多个样本的纵向数据，从不同的测试电池来检查命题的变化，在结构的寿命。这些数据来自三项关于智力的经典研究，其中，N=441人在70年内重复测量多达16次。词汇和记忆的认知结构进行了测量，使用八个不同的年龄相适应的智力测试电池，我们探讨这些规模使用项目反应理论（IRT）可能的联系。我们基于联合模型似然方法（即，NLMIXED和WINBUGS）。模型中包括组差异，以检查水平和变化的潜在个体间差异。由此产生的纵向IRT（LIRT）分析导致了一些新的方法论建议，用于处理重复的结构的基础上不断变化的测量发展研究。
This research uses multiple-sample longitudinal data from different test batteries to examine propositions about changes in constructs over the lifespan. The data come from three classic studies on intellectual abilities where, in combination, N=441 persons are repeatedly measures as many as 16 times over 70 years. Cognitive constructs of Vocabulary and Memory were measured using eight different age-appropriate intelligence test batteries, and we explore possible linkage of these scales using Item Response Theory (IRT). We simultaneously estimate the parameters of both IRT and Latent Curve Models (LCM) based on a joint model likelihood approach (i.e., NLMIXED and WINBUGS). Group differences are included in the model to examine potential inter-individual differences in levels and change. The resulting Longitudinal IRT (LIRT) analyses leads to a few new methodological suggestions for dealing with repeated constructs based on changing measurements in developmental studies.
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影响因子：
2.4
作者：
Brooks, SP;Gelman, A
通讯作者：
Gelman, A
影响因子：
4.6
作者：
Bell, Richard Q.
通讯作者：
Bell, Richard Q.
影响因子：
4.6
作者：
BAYLEY, N
通讯作者：
BAYLEY, N
影响因子：
4.6
作者：
Bell, Richard Q.
通讯作者：
Bell, Richard Q.
影响因子：
4.9
作者：
BRADWAY, KP;THOMPSON, CW
通讯作者：
THOMPSON, CW