Latent Change Score models (LCS) are a popular tool for the study of dynamics in longitudinal research. They represent processes in which the short-term dynamics have direct and indirect consequences on the long-term behavior of the system. However, this dual interpretation of the model parameters is usually overlooked in the literature, and researchers often find it difficult to see the connection between parameters and specific patterns of change. The goal of this paper is to provide a comprehensive examination of the meaning and interpretation of the parameters in LCS models. Importantly, we focus on their relation to the shape of the trajectories and explain how different specifications of the LCS model involve particular assumptions about the mechanisms of change. On a supplementary website, we present an interactive Shiny App that allows users to explore different sets of parameter values and examine their effects on the predicted trajectories. We also include fully explained code to estimate some of the most relevant specifications of the LCS model with the R-packages lavaan and OpenMx.

Accelerated longitudinal designs (ALD) allow studying developmental processes usually spanning multiple years in a much shorter time framework by including participants from different age cohorts, which are assumed to share the same population parameters. However, different cohorts may have been exposed to dissimilar contextual factors, resulting in different developmental trajectories. If such differences are not accounted for, the generating process will not be adequately characterized. In this paper, we propose a continuous-time latent change score model as an approach to capture cohort differences affecting the speed of maturation of psychological processes in ALDs. This approach fills an important gap in the literature because, until now, no method existed for this goal. Using a Monte-Carlo simulation study, we show that the proposed model detects cohort differences adequately, regardless of their size in the population. Our proposed model can help developmental researchers control for cohort effects in the context of ALDs.

The Bivariate Latent Change Score (BLCS) model is a popular framework for the study of dynamics in longitudinal research. Despite its popularity, there is little evidence of the ability of this model to recover latent dynamics when the latent trajectories are affected by stochastic innovations (i.e., dynamic error). The deterministic specification of the BLCS model does not account for the effect of these innovations in the system. In contrast, the stochastic specification of the BLCS model includes parameters that capture the effect of such innovations at the latent level. Through Monte Carlo simulation, we generated two developmental processes and examined the recovery of the parameters in the deterministic and stochastic BLCS models under a broad range of empirically relevant conditions. Based on our findings, we provide specific guidelines and recommendations for the application of BLCS models in developmental research.

Routine outcome monitoring (ROM) uses standardized measures to both track and inform mental health service delivery. Use of ROM has been shown to improve the outcome of psychotherapy when applied to different types of patients. The present research was designed to determine the reliability and validity of the Outcome Rating Scale (ORS) and the Session Rating Scale (SRS) in a sample of Spanish patients. After a controlled process of translation into the Spanish that is spoken and written in Spain (i.e., in Europe, as distinct from, e.g., Latin American Spanish), both measures were completed by patients of an outpatient mental health unit during eight sessions of psychotherapy. Sixty mental health patients filled out the ORS and 59 the SRS. In addition, the ORS was completed by 33 people who constituted the non-clinical sample. The cut-off of the ORS was 24.52 points, and the Reliable Change Index (RCI) was 9.15 points. ORS and SRS scores exhibited excellent internal consistency. The temporal stability of the SRS was adequate. The convergent and discriminant validity of the two measures were adequate. Regarding the factorial validity of the ORS and the SRS, in the third psychotherapy session, confirmatory factor analyses evidenced the existence of a unifactorial model. The predictive validity of SRS was acceptable. The ORS was sensitive to changes in patients’ symptoms. In conclusion, compared to the original English versions of the ORS and SRS measures, the Spanish versions of the measures are also reliable and valid.

The Bivariate Latent Change Score (BLCS) model is a popular framework for the study of dynamics in longitudinal research. Despite its popularity, there is little evidence of the ability of this model to recover latent dynamics when the latent trajectories are affected by stochastic innovations (i.e., dynamic error). The deterministic specification of the BLCS model does not account for the effect of these innovations in the system. In contrast, the stochastic specification of the BLCS model includes parameters that capture the effect of such innovations at the latent level. Through Monte Carlo simulation, we generated two developmental processes and examined the recovery of the parameters in the deterministic and stochastic BLCS models under a broad range of empirically relevant conditions. Based on our findings, we provide specific guidelines and recommendations for the application of BLCS models in developmental research. https://doi.org/10.1080/10705511.2022.2161906