scholarly journals Investigating Interindividual Differences in Short-Term Intraindividual Variability

2011 ◽  
Author(s):  
Lijuan Wang
Gerontology ◽  
2016 ◽  
Vol 63 (3) ◽  
pp. 263-269 ◽  
Author(s):  
Anthony D. Ong ◽  
Nilam Ram

There is robust evidence linking interindividual differences in positive affect (PA) with adaptive psychological and physical health outcomes. However, recent research has suggested that intraindividual variability or fluctuations in PA states over time may also be an important predictor of individual health outcomes. Here, we report on research that focuses on PA level and various forms of PA dynamics (variability, instability, inertia, and reactivity) in relation to health. PA level refers to the average level of positive feelings. In contrast, PA dynamics refer to short-term changes in PA that unfold over time. We discuss how consideration of both PA level and PA dynamics can provide a framework for reconciling when high PA is conducive or detrimental to health. We conclude that more work on PA dynamics is needed, especially in combination with PA level, and suggest productive questions for future inquiry in this area.


2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Leslie Vaughan ◽  
Iris Leng ◽  
Dale Dagenbach ◽  
Susan M. Resnick ◽  
Stephen R. Rapp ◽  
...  

Intraindividual variability among cognitive domains may predict dementia independently of interindividual differences in cognition. A multidomain cognitive battery was administered to 2305 older adult women (mean age 74 years) enrolled in an ancillary study of the Women’s Health Initiative. Women were evaluated annually for probable dementia and mild cognitive impairment (MCI) for an average of 5.3 years using a standardized protocol. Proportional hazards regression showed that lower baseline domain-specific cognitive scores significantly predicted MCI (N=74), probable dementia (N=45), and MCI or probable dementia combined (N=101) and that verbal and figural memory predicted each outcome independently of all other cognitive domains. The baseline intraindividual standard deviation across test scores (IAV Cognitive Domains) significantly predicted probable dementia and this effect was attenuated by interindividual differences in verbal episodic memory. Slope increases in IAV Cognitive Domains across measurement occasions (IAV Time) explained additional risk for MCI and MCI or probable dementia, beyond that accounted for by interindividual differences in multiple cognitive measures, but risk for probable dementia was attenuated by mean decreases in verbal episodic memory slope. These findings demonstrate that within-person variability across cognitive domains both at baseline and longitudinally independently accounts for risk of cognitive impairment and dementia in support of the predictive utility of within-person variability.


2001 ◽  
Vol 23 (3) ◽  
pp. 222-244 ◽  
Author(s):  
Anthony J. Amorose

This study examined: (a) the prevalence of intraindividual variability (i.e., the degree to which individuals exhibit short-term fluctuations in their self-evaluations) of global self-worth, physical self-worth, and perceived physical competence; (b) the independent and combined influence of level and intraindividual variability of self-evaluations on students’ motivation; and (c) the relationship between social sources of evaluative information and intraindividual variability. Students (N = 167) ranging from 12 to 15 years of age (M = 13.48 yrs, SD = .56) completed questionnaires each day that they were in physical education class for 3 weeks (i.e., 6 occasions). Results revealed that most of the students exhibited fluctuations in their self-evaluations over the 3 weeks. Level of self-evaluations was the critical predictor of motivation; however, an interaction with intraindividual variability was also significant. Nonsignificant relationships were found between intraindividual variability and the importance that students placed on social sources of evaluative information. Overall, results indicated that intraindividual variability should be considered along with level as an important index of one’s self-perception profile.


1992 ◽  
Vol 136 (9) ◽  
pp. 1069-1081 ◽  
Author(s):  
Lloyd E. Chambless ◽  
Robert P. McMahon ◽  
Spencer A. Brown ◽  
Wolfgang Patsch ◽  
Gerardo Heiss ◽  
...  

1995 ◽  
Vol 151 (2) ◽  
pp. 406-411 ◽  
Author(s):  
P L Enright ◽  
J E Connett ◽  
R E Kanner ◽  
L R Johnson ◽  
W W Lee

Author(s):  
Eric S. Cerino ◽  
Karen Hooker

Intraindividual variability (IIV) refers to short-term fluctuations that may be more rapid, and are often conceptualized as more reversible, than developmental change that unfolds over a longer period of time, such as years. As a feature of longitudinal data collected on micro timescales (i.e., seconds, minutes, days, or weeks), IIV can describe people, contexts, or general processes characterizing human development. In contrast to approaches that pool information across individuals and assess interindividual variability in a population (i.e., between-person variability), IIV is the focus of person-centered studies addressing how and when individuals change over time (i.e., within-person variability). Developmental psychologists interested in change and how and when it occurs, have devised research methods designed to examine intraindividual change (IIC) and interindividual differences in IIC. Dispersion, variability, inconsistency, time-structured IIV, and net IIV are distinct operationalizations of IIV that, depending on the number of measures, occasions, and time of measurement, reflect unique information about IIV in lifespan developmental domains of interest. Microlongitudinal and measurement-burst designs are two methodological approaches with intensive repeated measurement that provide a means by which various operationalizations of IIV can be accurately observed over an appropriate temporal frame to garner clearer understanding of the dynamic phenomenon under investigation. When methodological approaches are theoretically informed and the temporal frame and number of assessments align with the dynamic lifespan developmental phenomenon of interest, researchers gain greater precision in their observations of within-person variability and the extent to which these meaningful short-term fluctuations influence important domains of health and well-being. With technological advancements fueling enhanced methodologies and analytic approaches, IIV research will continue to be at the vanguard of pioneering designs for elucidating developmental change at the individual level and scaling it up to generalize to populations of interest.


1991 ◽  
Vol 70 (6) ◽  
pp. 2432-2438 ◽  
Author(s):  
M. Rotger ◽  
R. Peslin ◽  
E. Oostveen ◽  
C. Gallina

Short-term intraindividual variability of the parameters derived from respiratory transfer impedance (Ztr) measured from 4 to 32 Hz was studied in 10 healthy subjects. The corresponding 95% confidence intervals (CIo) were compared with those computed from a single set of data (CIL) according to Lutchen and Jackson (J. Appl. Physiol. 62: 403-413, 1987). Ztr was analyzed with the six-coefficient model of DuBois et al. (J. Appl. Physiol. 8: 587-594, 1956), which includes airway resistance (Raw) and inertance (Iaw), tissue resistance (Rti), inertance (Iti), and compliance (Cti), and alveolar gas compressibility (Cg). The lowest variability was seen for Iaw (CIo = 11.1%), closely followed by Raw (14.3%) and Cti (14.8%), and the largest for Rti and Iti (24.6 and 93.6%, respectively). Using a simpler model, where Iti was excluded, significantly decreased the variability of Iaw (P less than 0.01) and Rti (P less than 0.05) but was responsible for a systematic decrease of Raw and Iaw and increase of Rti. Except for Raw with both models and Iaw with the simpler model, CIL was greater than CIo. Whatever the model, a high correlation between both sets of confidence intervals was found for Rti and Iaw, whereas no correlation was seen for Raw. This suggests that the variability of the former coefficients mainly reflects experimental noise, whereas that of the latter is largely due to biological variability.


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