Demery Jason A, Pedraza Otto, Hanlon Robert E
Department of Clinical and Health Psychology, University of Florida Health Sciences Center, Gainesville, Florida 32610-0165, USA.
J Clin Exp Neuropsychol. 2002 Sep;24(6):818-27. doi: 10.1076/jcen.24.6.818.8400.
Several recent investigations have utilized cluster analytic procedures to elucidate profiles of verbal learning on the California Verbal Learning Test following traumatic brain injury (TBI). Although the results of these studies have contributed to our understanding of verbal learning following TBI, limitations in sample composition and methodology render the results difficult to evaluate. The current study provides an analysis of verbal learning clusters in the most comprehensive sample (n = 160) of TBI patients reported thus far. Results obtained from multiple hierarchical agglomerative clustering procedures suggested the presence of two distinct clusters, the first consisting of performance patterns falling within normal limits and the second consisting of moderate-to-severe impairment. Two iterative partitioning analyses further suggested a reliable solution with better-than-chance agreement (kappa coefficients >.85, p <.001). Thus, it is concluded that a two-cluster classification solution provides a parsimonious understanding of verbal learning profiles after TBI.
最近的几项研究利用聚类分析程序来阐明创伤性脑损伤（TBI）后在加利福尼亚言语学习测验上的言语学习概况。尽管这些研究的结果有助于我们理解TBI后的言语学习，但样本构成和方法学上的局限性使得结果难以评估。当前的研究对迄今为止报告的最全面的TBI患者样本（n = 160）中的言语学习聚类进行了分析。从多个层次凝聚聚类程序获得的结果表明存在两个不同的聚类，第一个聚类由落在正常范围内的表现模式组成，第二个聚类由中度至重度损伤组成。两项迭代分区分析进一步表明存在一个可靠的解决方案，其一致性优于随机水平（卡帕系数>.85，p <.001）。因此，得出结论，两聚类分类解决方案为TBI后的言语学习概况提供了一种简洁的理解。
