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A modular, extensible approach to massive ecologically valid behavioral data.

Mark VanDam1, Paul De Palma2

  • 1Speech & Hearing Sciences, Elson S. Floyd College of Medicine, Washington State University, Spokane, WA, USA. mark.vandam@wsu.edu.

Behavior Research Methods
|November 18, 2018
PubMed
Summary

Modern computing enhances behavioral data collection and analysis, improving scientific rigor and outcomes. This approach is applicable across diverse research, from child speech to clinical populations and data sharing.

Keywords:
Automatic speech recognition ASRBig dataBiosensorEcological validityNatural languageSpeechVery large database

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Area of Science:

  • Behavioral Science
  • Computer Science
  • Data Science

Background:

  • Traditional behavioral data collection faces challenges in efficiency and scalability.
  • Advancements in computing offer new possibilities for data analysis.

Purpose of the Study:

  • To explore the application of modern computer hardware and software for behavioral data.
  • To demonstrate the utility of computational approaches in behavioral research.

Main Methods:

  • Discussed issues: ecological validity, storage, processing, data permanence, automation, validity, and algorithmic determinism.
  • Presented four proof-of-concept research projects.
  • Detailed projects on child-directed speech, clinical populations (hearing loss), quality control, and data sharing.

Main Results:

  • Demonstrated the viability and utility of computational methods in behavioral data analysis.
  • Successfully applied automated methods to clinical populations.
  • Established quality control and validity assessment protocols.
  • Initiated a public database for data sharing.

Conclusions:

  • Computational approaches enhance the quality and scope of behavioral science research.
  • Methodology is extendable to interdisciplinary projects for improved scientific outcomes.
  • Potential for better outcomes in behavioral, social, and health sciences.