Correction to “The many reliabilities of psychological dynamics: An overview of statistical approaches to estimate the internal consistency reliability of intensive longitudinal data” by Castro-Alvarez et al. (2025).

, Advanced Online Publication, Oct 20, 2025, np; see record 2026-77126-001). The authors discovered an error in the computation of the reliability coefficients according to the 2RDM. Upon correcting this, several results required minor updates. Specifically, the corrections affect a couple of sentences in the Results and Discussion sections, Figure 3, and the corresponding parts of the Supplementary Material. While the overall conclusions of the paper remain unchanged, the article has been updated to accurately reflect the corrected computations. The article was published with the impact statement from an unrelated paper. The impact statement has been replaced with correct version. All versions of this article have been corrected (The following abstract of the original article appeared in record 2026-77126-001.) Reliability is a key concept in psychology that has been broadly studied since the introduction of Cronbach's α, which is a measure of internal consistency. Despite its importance, reliability has been relatively understudied when dealing with intensive longitudinal data. Although intensive longitudinal measurements are often considered more ecologically valid and less prone to recall bias than survey data collected using traditional methods, there is no warranty that they are more reliable. Hence, empirical researchers need tools to study and report the reliability of the scales used in intensive longitudinal research. In recent years, psychologists have proposed different approaches to estimate the reliability of scales and items used when studying psychological dynamics. However, it is unclear how these approaches compare to one another, making it difficult to determine what options researchers have given a particular data set and specific research questions. Specifically, these approaches estimate reliability indices based on different statistical models, such as linear multilevel analysis, vector autoregressive models, and dynamic factor models. Furthermore, while some methods involve estimating one reliability index for the scores that applies to the whole sample, others estimate person-specific reliability indices. This wide variety of approaches can provoke some confusion. In this article, we aim to bridge this gap by reviewing and highlighting the similarities and differences of different methods used to estimate the reliability of intensive longitudinal data. We also showcase their application with empirical data. (PsycInfo Database Record (c) 2026 APA, all rights reserved).