Sliwinski M J, Hall C B
Department of Neurology and Rose F. Kennedy Center for Mental Retardation and Human Development, Albert Einstein College of Medicine, Bronx, New York 10461, USA.
Psychol Aging. 1998 Mar;13(1):164-75. doi: 10.1037//0882-7974.13.1.164.
General slowing (GS) theories are often tested by meta-analysis that model mean latencies of older adults as a function of mean latencies of younger adults. Ordinary least squares (OLS) regression is inappropriate for this purpose because it fails to account for the nested structure of multitask response time (RT) data. Hierarchical linear models (HLM) are an alternative method for analyzing such data. OLS analysis of data from 21 studies that used iterative cognitive tasks supported GS; however, HLM analysis demonstrated significant variance in slowing across experimental tasks and a process-specific effect by showing less slowing for memory scanning than for visual-search and mental-rotation tasks. The authors conclude that HLM is more suitable than OLS methods for meta-analyses of RT data and for testing GS theories.
一般减速（GS）理论通常通过元分析来检验，这种元分析将老年人的平均反应时建模为年轻人平均反应时的函数。普通最小二乘法（OLS）回归不适用于此目的，因为它没有考虑多任务反应时（RT）数据的嵌套结构。分层线性模型（HLM）是分析此类数据的另一种方法。对21项使用迭代认知任务的研究数据进行OLS分析支持了GS；然而，HLM分析表明，不同实验任务的减速存在显著差异，并且通过显示记忆扫描任务的减速比视觉搜索和心理旋转任务少，体现了特定过程效应。作者得出结论，对于RT数据的元分析和检验GS理论，HLM比OLS方法更合适。
