Michael Braun and Ettore Recchi
Michael Braun and Camelia Arsene
Michael R. Braun and Scott F. Latham
Christopher R. Dishop, Michael T. Braun, Goran Kuljanin and Richard P. DeShon
In this chapter we explore how to think about patterns contained in longitudinal or panel data structures. Organizational scientists recognize that psychological phenomena and processes unfold over time; and, to better understand them, organizational researchers increasingly work with longitudinal data and explore inferences within those data structures. Longitudinal inferences may focus on any number of fundamental patterns, including construct trajectories, relationships between constructs, or dynamics. Although the diversity of longitudinal inferences provides a wide foundation for garnering knowledge in any given area, it also makes it difficult for researchers to know the set of inferences they may explore with longitudinal data, which statistical models to use given their questions, and how to situate their specific inquiries within the broader set of longitudinal inferences. Moreover, the diversity of statistical models that can be applied to longitudinal data requires that researchers understand how one inference category differs from another.