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Area of Science:

  • Organizational Psychology
  • Computational Social Science
  • Team Dynamics Research

Background:

  • Traditional measurement methods struggle to capture multilevel and dynamic organizational processes.
  • Existing approaches limit the modeling and understanding of team interactions over time.

Purpose of the Study:

  • To develop computer-aided text analysis (CATA) techniques for measuring team processes.
  • To utilize individuals' communications as valid, unobtrusive indicators of collective constructs.
  • To apply CATA to score team communications into valid team process measures based on Marks et al.'s (2001) model.

Main Methods:

  • Developed CATA dictionaries using a deductive content validity-based approach.
  • Demonstrated convergent validity with subject matter expert (SME) codings and behavioral ratings.
  • Employed multitrait-multimethod analyses across three samples, including paramedics in a mass casualty incident exercise.
  • Used longitudinal dual-discontinuous change growth modeling to test criterion-related validity.
  • Integrated wearable sensor badge data to model dynamic individual-level interaction networks.

Main Results:

  • CATA techniques demonstrated convergent validity with SME assessments of team communications and behaviors.
  • Modeled relationships among team processes across different episodes using multiple samples.
  • Established criterion-related validity by linking CATA-scored team processes to dynamic performance criteria.
  • Showcased the potential of CATA, integrated with sensor data, for modeling dynamic team interaction networks.

Conclusions:

  • Computer-aided text analysis (CATA) provides a valid and unobtrusive method for measuring dynamic team processes.
  • This approach enhances the ability to model and understand complex organizational phenomena over time.
  • The developed CATA techniques offer practical applications for research and management in team dynamics.