Daniel McNeish
Intensive longitudinal data (ILD) are commonly used to study within-person processes, and psychometric methods for computing within-person reliability with ILD have recently appeared in the methodological literature. The dense, frequent nature of data collection in ILD often leads to copious amounts of missing data-recent reviews find that 30%-40% missing data rates are typical. However, given the nascent state of psychometrics for ILD, the intersection of psychometrics and missing ILD has yet to be explored despite the pervasiveness of missing ILD in empirical studies. In addition, given that ILD are frequently used to study sensitive topics such as mental health and substance use, there is a heightened risk of missing not at random (MNAR) data. That is, the reason data are missing is associated with what the value would have been (e.g., depression responses are missing when a person is momentarily too depressed to respond). The goal of this article is to (a) clarify how missing values affect estimates of within-person reliability with ILD and (b) to extend one recently proposed method (the measurement error autoregressive model) with a Diggle-Kenward selection process to evaluate sensitivity to a potential MNAR mechanism. Simulations in the article find that the accuracy of within-person reliability estimates from standard approaches can deteriorate when missing data are present, but the proposed model can better recover population within-person reliability-even with a large amount of MNAR data-under the conditions and missingness mechanisms studied. Limitations and extensions to other methods for computing within-person reliability are also discussed. (PsycInfo Database Record (c) 2026 APA, all rights reserved).