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Citation |
Gates KM, Liu S. Assessment 2016; 23(4): 459-471. |
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Affiliation |
University of California-Davis, CA, USA. |
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Copyright |
(Copyright © 2016, SAGE Publishing) |
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DOI |
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PMID |
27084837 |
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Abstract |
Individuals in both clinical and research settings increasingly provide data across numerous time points. Examples include measurements collected from wearable technology (e.g., accelerometers), psychophysiological measures, coded observations, social media behaviors, and daily diary data. When numerous observations are available for each individual, the data fall under the class of time series data and can be examined from within a dynamic systems perspective. We provide a broad overview of current analytic methods for quantifying relations among dyads using this data type. The techniques include those from within a linear modeling framework, approaches that include a measurement model, and methods for examining cyclical relations. We also discuss some special topics, such as methods that allow for models to shift across time and that accommodate heterogeneity across individuals. Finally, methods that account for similar shapes of nonlinear curves across time are described. From this breadth of options, we hope to help guide practitioners, clinicians, and researchers in choosing the optimal method for their data and line of questions. To further aid in this choice, we indicate programs available for each technique. Example dyads presented here range from mother-infant, patient-caretaker, and husband-wife; however, the analytic methods can be applied to any type of dyad. Language: en |