withinperson

Within-person refers to analysis, data, or phenomena that are observed or assumed to occur within the same individual over time or across different contexts, as distinct from within-group or between-person comparisons. In psychology and related fields, it describes how variables change within an individual rather than how different individuals compare to one another.

In research design, within-person approaches rely on repeated measurements of the same person, such as longitudinal studies, daily diaries, or experience sampling methods. These designs enable investigators to examine dynamic processes, temporal ordering, and causal mechanisms as they unfold within individuals.

Statistical methods for within-person analysis separate within-person variance from between-person variance. Multilevel or hierarchical linear models, fixed- and random-effects models, and time-series approaches are commonly used. A key concept is that a predictor can have a within-person effect (how deviations from a person’s typical level relate to deviations in the outcome) in addition to a between-person effect (how a person’s average level relates to average outcomes). The intraclass correlation coefficient (ICC) is often used to quantify the proportion of total variance that is between-person.

Applications include studying mood and affect, stress reactivity, cognitive performance, health behaviors, and interpersonal dynamics, especially when research questions concern processes that evolve within individuals over time.

Advantages include control for stable individual differences, finer-grained insight into processes, and greater power to detect within-person associations. Limitations involve greater data collection demands, potential measurement reactivity, and analytic complexity. Careful design and appropriate modeling are essential to interpret within-person effects reliably.