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Extending reliability to intensive longitudinal data with the Kalman filter

Abstract

Reliability is central to how researchers approach measurement in standard, group-based analyses of single-time-point data, yet this critical aspect is often overlooked in the analysis of repeated observations. Since its inception, reliability has been a between-person concept, but we redevelop this notion for within-person designs by proposing a new coefficient κ$$ kappa $$ of reliability for single-subject designs. This coefficient shares the same general definition of reliability as former coefficients—the ratio of the true score variance to the total variance—but applies to time-dependent within-person variability rather than independent between-person variability. Coefficient κ$$ kappa $$ begins with a latent variable time series model called a state space model, and is then extended to a state space model for multiple subjects with continuous or discrete variation across people. Using analytic methods, we derive coefficient κ$$ kappa $$ and prove its relations to other coefficients of reliability.

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Posted in: Journal Article Abstracts on 04/06/2026 | Link to this post on IFP |
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