Constraints on general slowing: A meta-analysis using hierarchical linear models with random coefficients

Martin J. Sliwinski, Charles B. Hall

Research output: Contribution to journalArticle

49 Scopus citations

Abstract

General slowing (GS) theories are often tested by meta-analyses 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. Hierarchial 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.

Original languageEnglish (US)
Pages (from-to)164-175
Number of pages12
JournalPsychology and aging
Volume13
Issue number1
DOIs
StatePublished - May 1 1998

All Science Journal Classification (ASJC) codes

  • Social Psychology
  • Aging
  • Geriatrics and Gerontology

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