Abstract

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.

Full Text
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