Idiographic Interrater Reliability Measures for Intensive Longitudinal Multirater Data

Interrater reliability plays a crucial role in various areas of psychology. In this article, we propose a multilevel latent time series model for intensive longitudinal data with structurally different raters (e.g., self-reports and partner reports). The new MR-MLTS model enables researchers to estimate idiographic (person-specific) rater consistency coefficients at both the dynamic and momentary level. Additionally, the model allows rater consistency coefficients to be linked to external explanatory or outcome variables. It can be implemented in Mplus as well as in the newly developed R package mlts. We illustrate the model using data from an intensive longitudinal multirater study involving 100 heterosexual couples (200 individuals) assessed across 86 time points. Our findings show that relationship duration and partner cognitive resources positively predict momentary, but not dynamic, rater consistency. Results from a simulation study indicate that the number of time points is critical for accurately estimating idiographic rater consistency coefficients, whereas the number of participants is important for accurately recovering the random effect variances. We discuss advantages, limitations, and future extensions of the MR-MLTS model.