Literature DB >> 29029201

A Comprehensive Comparison of Quantifications of Intraindividual Variability in Response Times: A Measurement Burst Approach.

Robert S Stawski1, Stuart W S MacDonald2,3, Paul W H Brewster2,3, Elizabeth Munoz4, Eric S Cerino1, Drew W R Halliday2,3.   

Abstract

OBJECTIVES: To formally identify and contrast the most commonly-employed quantifications of response time inconsistency (RTI) and elucidate their utility for understanding within-person (WP) and between-person (BP) variation in cognitive function with increasing age.
METHOD: Using two measurement burst studies of cognitive aging, we systematically identified and computed five RTI quantifications from select disciplines to examine: (a) correlations among RTI quantifications; (b) the distribution of BP and WP variation in RTI; and (c) the comparability of RTI quantifications for predicting attention switching.
RESULTS: Comparable patterns were observed across studies. There was significant variation in RTI BP as well as WP across sessions and bursts. Correlations among RTI quantifications were generally strong and positive both WP and BP, except for the coefficient of variation. Independent prediction models indicated that slower mean response time (RT) and greater RTI were associated with slower attention switching both WP and BP. For selecting simultaneous prediction models, collinearity resulted in inflated standard errors and unstable model estimates. DISCUSSION: RTI reflects a novel dimension of performance that is a robust and theoretically informative predictor of BP and WP variation in cognitive function. Among the plenitude of RTI quantifications, not all are interchangeable, nor of comparable predictive utility.
© The Author(s) 2017. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Entities:  

Keywords:  Central tendency; Cognitive aging; Inconsistency; Intraindividual variability; Measurement burst design; Response time

Mesh:

Year:  2019        PMID: 29029201      PMCID: PMC6377057          DOI: 10.1093/geronb/gbx115

Source DB:  PubMed          Journal:  J Gerontol B Psychol Sci Soc Sci        ISSN: 1079-5014            Impact factor:   4.077


  14 in total

1.  Modeling long-term changes in daily within-person associations: An application of multilevel SEM.

Authors:  Jonathan Rush; Philippe Rast; David M Almeida; Scott M Hofer
Journal:  Psychol Aging       Date:  2019-02-07

2.  The Ups and Downs of Cognitive Function: Neuroticism and Negative Affect Drive Performance Inconsistency.

Authors:  Elizabeth Munoz; Robert S Stawski; Martin J Sliwinski; Joshua M Smyth; Stuart W S MacDonald
Journal:  J Gerontol B Psychol Sci Soc Sci       Date:  2020-01-14       Impact factor: 4.077

3.  Associations Between Control Beliefs and Response Time Inconsistency in Older Adults Vary as a Function of Attentional Task Demands.

Authors:  Eric S Cerino; Robert S Stawski; G John Geldhof; Stuart W S MacDonald
Journal:  J Gerontol B Psychol Sci Soc Sci       Date:  2020-10-16       Impact factor: 4.077

4.  Reliabilities of Intra-Individual Mean and Intra-Individual Variability of Self-Reported Pain Derived From Ecological Momentary Assessments: Results From the Einstein Aging Study.

Authors:  Jinshil Hyun; Jiyue Qin; Cuiling Wang; Mindy J Katz; Jelena M Pavlovic; Carol A Derby; Richard B Lipton
Journal:  J Pain       Date:  2021-11-13       Impact factor: 5.820

5.  Ambulatory Assessment in Concussion Clinical Care and Rehabilitation.

Authors:  R J Elbin; Melissa N Womble; Daniel B Elbich; Christina Dollar; Sheri Fedor; Jonathan G Hakun
Journal:  Front Digit Health       Date:  2022-06-23

6.  The influence of social support and perceived stress on response time inconsistency.

Authors:  Sandi Phibbs; Robert S Stawski; Stuart W S MacDonald; Elizabeth Munoz; Joshua M Smyth; Martin J Sliwinski
Journal:  Aging Ment Health       Date:  2017-11-24       Impact factor: 3.658

7.  Cognitive dispersion and ApoEe4 genotype predict dementia diagnosis in 8-year follow-up of the oldest-old.

Authors:  Tam Watermeyer; Jantje Goerdten; Boo Johansson; Graciela Muniz-Terrera
Journal:  Age Ageing       Date:  2021-05-05       Impact factor: 10.668

8.  Individual Differences and Features of Self-reported Memory Lapses as Risk Factors for Alzheimer Disease Among Adults Aged 50 Years and Older: Protocol for a Coordinated Analysis Across Two Longitudinal Data Sets.

Authors:  Jacqueline Mogle; Nikki L Hill; Jennifer R Turner
Journal:  JMIR Res Protoc       Date:  2021-05-14

9.  Intra-Individual Variability Across Fluid Cognition Can Reveal Qualitatively Different Cognitive Styles of the Aging Brain.

Authors:  Sara De Felice; Carol A Holland
Journal:  Front Psychol       Date:  2018-10-16

10.  Heterogeneous Indicators of Cognitive Performance and Performance Variability Across the Lifespan.

Authors:  Lauren A Rutter; Ipsit V Vahia; Brent P Forester; Kerry J Ressler; Laura Germine
Journal:  Front Aging Neurosci       Date:  2020-03-06       Impact factor: 5.750

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.