Measurement Burst Designs in Lifespan Developmental Research

Abstract

The study of human development across the lifespan is inherently about patterns across time. Although many developmental questions have been tested with cross-sectional comparisons of younger and older persons, understanding of development as it occurs requires a longitudinal design, repeatedly observing the same individual across time. Development, however, unfolds across multiple time scales (i.e., moments, days, years) and encompasses both enduring changes and transient fluctuations within an individual. Measurement burst designs can detect such variations across different timescales, and disentangle patterns of variations associated with distinct dimensions of time periods. Measurement burst designs are a special type of longitudinal design in which multiple “bursts” of intensive (e.g., hourly, daily) measurements are embedded in a larger longitudinal (e.g., monthly, yearly) study. The hybrid nature of these designs allow researchers to address questions not only of cross-sectional comparisons of individual differences (e.g., do older adults typically report lower levels of negative mood than younger adults?) and longitudinal examinations of intraindividual change (e.g., as individuals get older, do they report lower levels of negative mood?) but also of intraindividual variability (e.g., is negative mood worse on days when individuals have experienced an argument compared to days when an argument did not occur?). Researchers can leverage measurement burst designs to examine how patterns of intraindividual variability unfolding over short timescales may exhibit intraindividual change across long timescales in order to understand lifespan development. The use of measurement burst designs provides an opportunity to collect more valid and reliable measurements of development across multiple time scales throughout adulthood.

Depressive Symptoms, Perceived Stress, Self-Compassion and Nonsuicidal Self-Injury Among Emerging Adults: An Examination of the Between and Within-Person Associations Over Time

“…Ecological momentary assessment based approaches (i.e., EMA, assessing participants multiple times a day over several days) have also been shown to increase accuracy of self-reports, and represent an important extension for future research ( Russell & Gajos, 2020 ). Future research could combine EMA and longitudinal assessment together (a technique known as measurement burst sampling; Cho et al, 2019 ) to examine how these daily processes contribute to more enduring developmental change and stability in these constructs over time. However, it is important to note that a limitation of all self-report data is shared method variance (meaning that all measures were completed by the same individual, so other individual unmeasured factors could have contributed to participants’ scores on all the study measures; e.g., mood).…”

Suboptimal phenotypic reliability impedes reproducible human neuroscience

“…Given the challenges of collecting repeated measurements, recent advances in data collection strategies may enable such repeated assessments outside the laboratory setting (e.g., ecological momentary assessment and cognitive burst sampling via smartphone-or web-based assessments). These approaches can be leveraged to mitigate experimenter and participant burden, as well as to increase the accessibility of participation and reduce participant dropout [16][17][18][19][20] .…”

“…Improving reliability through development of novel assessments of phenotypic variation is needed, but it is not the sole solution. We emphasize the potential to improve the reliability of established phenotypic methods through aggregation across multiple raters and/or measurements [12][13][14][15] , which is becoming increasingly feasible with recent innovations in data acquisition (e.g., web-and smart-phone-based administration, ecological momentary assessment, burst sampling, wearable devices, multimodal recordings) [16][17][18][19][20] . We demonstrate that such aggregation can achieve better biomarker discovery for a fraction of the cost engendered by large-scale samples.…”

Suboptimal phenotypic reliability impedes reproducible human neuroscience

“…These approaches can be leveraged to mitigate experimenter and participant burden, as well as to increase the accessibility of participation and reduce participant dropout [16][17][18][19][20] .…”