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Illusory traits: Wrong but sometimes useful.

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Stable psychological traits may be illusory, emerging from omitted time-varying processes. Even when traits are not real, models assuming them can aid causal inference in longitudinal data analysis.

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Area of Science:

  • Psychology
  • Statistics
  • Developmental Science

Background:

  • Psychological measures often exhibit trait-like properties, prompting debate on the nature of stable psychological traits.
  • Existing theories emphasize dynamic psychological causes throughout development, which may influence trait perception.

Purpose of the Study:

  • To investigate how omitted causal dynamics can lead to illusory traits in psychological research.
  • To evaluate the impact of illusory traits on conclusions drawn from the random-intercept cross-lagged panel model (RI-CLPM).

Main Methods:

  • Simulations were used to demonstrate how linear systems with omitted processes can produce trait-like covariance matrices.
  • The study examined the performance of the RI-CLPM in detecting traits when none exist due to unmodeled dynamics.
  • Compared RI-CLPM with a cross-lagged panel model (CLPM) that does not assume stable traits.

Main Results:

  • Simulations showed that omitted processes can generate data with trait-like properties.
  • The RI-CLPM can falsely detect traits even when the data-generating process is purely dynamic.
  • In scenarios with illusory traits, RI-CLPM provided less biased estimates of autoregressive and cross-lagged effects than the CLPM.

Conclusions:

  • The presence of trait variance in data does not necessarily confirm time-invariant trait causes.
  • Statistical models assuming stable traits, like RI-CLPM, may still offer valuable causal inference even when traits are illusory.
  • Understanding the role of omitted dynamics is crucial for accurate interpretation of psychological trait research.