Traditional psychometric models assume that psychological traits and
attributes are stable over time; yet, growing evidence suggests they are
dynamic, context-sensitive, and interactive. Current assessment tools
struggle to capture these fluctuations. This issue directly engages with
the shift from in vitro (static, trait-based) to in vivo (process-oriented)
measurement. It challenges the reliance on classical test theory (CTT)
and item response theory (IRT) by emphasizing ecological, time
sensitive, and personalized assessments. Discussion points will focus on
the promise and challenges of intensive longitudinal assessments (e.g.,
EMA, ESM), computational and dynamical systems modeling in
psychological assessment, and applications of machine learning in
detecting state-based fluctuations in psychological constructs.
Some challenges of this shift are methodological (e.g., data demands).
Others are conceptual - these being more burdensome, e.g.: trait vs.
state confusion (how to differentiate between core tendencies and
contextual expressions?), generalizability (how can within-person
findings be generalized to populations?), and validity (what does
reliability and validity mean in a dynamic model where consistency
across time is not expected?).
This shift is not just technical - the technical and methodological issues,
while difficult, can be solved (multilevel modeling, time-series analysis,

dynamic structural equation models). The shift is also philosophical,
challenging core assumptions about what it means to "measure" a
person, leading us from traits to trajectories, from scores to systems of
scores, from snapshots to streams.
From a practical perspective, this shift may align with a broader move in
psychology toward idiographic science, where the goal is not just to
compare people, but to understand them in context, over time, and in
motion. It suggests new applications, such as personalized mental
health tracking, dynamic risk patterns, and just-in-time adaptive
interventions. However, it is uncertain how much predictive power it
brings beyond classical approaches for classical problems (e.g.,
personnel selection or admission testing).