Analyzing longitudinal multirater data with individually varying time intervals

Numerous models have been proposed for the analysis of convergent validity in longitudinal multimethod designs. However, existing multimethod models are limited to measurement designs with equally spaced time intervals. We present a new multirater latent state-trait model with autoregressive effects (MR-LST-AR) for designs with structurally different raters and individually varying time intervals. The new model is illustrated using the German Family Panel pairfam. By means of stochastic differential equations, we show how key coefficients of convergent and discriminant validity can be examined as a function of time. We compare the results from continuous and discrete time analysis and provide code to fit the new model in ctsem. Finally, the advantages and limitations of the model are discussed, and practical recommendations are provided

The role of time in the quest for understanding psychological mechanisms

The lead-lag structure of multivariate time-ordered observations and the possibility to disentangle between-person (BP) from within-person (WP) sources of variance are major assets of longitudinal (panel) data. Hence, psychologists are making increasing use of such data, often with the intent to delineate the dynamic properties of psychological mechanisms, understood as a sequence of causal effects that govern psychological functioning. However, even with longitudinal data, psychological mechanisms are not easily identified. In this article, we show how an adequate representation of time may enhance the tenability of causal interpretations in the context of multivariate longitudinal data analysis. We anchor our considerations with an example that illustrates some of the main problems and questions faced by applied researchers and practitioners. We distinguish between static versus dynamic and discrete versus continuous time modeling approaches and discuss their advantages and disadvantages. We place particular emphasis on different ways of addressing BP differences and stress their dual role as potential confounds versus valuable sources of information for improving estimation and aiding causal inference. We conclude by outlining an approach that offers the potential of better integration of information on BP differences and WP changes in the search for causal mechanisms along with a discussion of current problems and limitations.Peer Reviewe