Accounting

Business

Inference

Mathematics

Biology

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Causal model

Data mining

Statistics

Machine learning

Political science

Econometrics

Paleontology

Cognitive psychology

Artificial intelligence

Psychology

Causal inference

Multivariate statistics

Psychological research

Variance (accounting)

Data science

Representation (politics)

Context (archaeology)

Computer science

Longitudinal data

Social psychology

Multivariate Behavioral Research

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.

The Role of Time in the Quest for Understanding Psychological Mechanisms