Why the study?
New data collection methods necessitate evaluating circumplex models with multivariate time series data, which is complicated by correlation between nearby time points.
Population
An individual with daily affect ratings and simulated data
Comparison
Adapted circumplex model accommodating time series data vs a method treating time series data as cross-sectional
Design
Methodological adaptation with an empirical illustration and simulation study
Follow-up
70 days
Key result
Adapting Browne's circumplex model for multivariate time series data provided more satisfactory confidence intervals and test statistics than treating the data as cross-sectional.
Authors
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Caution against cross-sectional assumptions in time-series cardiovascular modeling; extends options but requires prospective validation.
Adapting Browne's circumplex model for time series data improves the accuracy of confidence intervals and test statistics compared to cross-sectional methods.
Lee et al. (2023) studied Affect and personality traits (n=1). Adapted Browne's circumplex model for time series data vs. Method treating time series data as cross-sectional was evaluated on Statistical properties (confidence intervals and test statistics). Adapting Browne's circumplex model for multivariate time series data provided more satisfactory confidence intervals and test statistics than treating the data as cross-sectional.