TitleConstraints on general slowing: a meta-analysis using hierarchical linear models with random coefficients.
Publication TypeJournal Article
Year of Publication1998
AuthorsSliwinski, MJ, Hall, CB
JournalPsychol Aging
Date Published1998 Mar
KeywordsAged, Aging, Cognition, Humans, Least-Squares Analysis, Linear Models, Memory, Middle Aged, Reaction Time

<p>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.</p>

Alternate JournalPsychol Aging
PubMed ID9533199
Grant ListHD-01799 / HD / NICHD NIH HHS / United States
R29 AG12448 / AG / NIA NIH HHS / United States