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A methodology for preprocessing structured big data in the behavioral sciences.

Paul A Brown¹, Ricardo A Anderson²

¹Department of Basic Medical Sciences, Faculty of Medical Sciences Teaching and Research Complex, The University of the West Indies, Mona, Kingston 7, Jamaica. paul.brown02@uwimona.edu.jm.

Behavior Research Methods

|June 29, 2022

Summary

This study introduces the Big Data Quality & Statistical Assurance (BDQSA) model to help behavioral scientists preprocess big data. The BDQSA model ensures data quality for reliable analysis in behavioral research.

Keywords:

Behavioral science research Behavioral sciences Big data Data preprocessing Personality big data

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Area of Science:

Behavioral Science
Data Science
Computer Science

Background:

Big data's volume, variety, and velocity challenge traditional analysis methods.
Computational methodologies are essential for handling big data, but may be unfamiliar to non-specialists.
Existing data analytics pipelines offer a foundation for big data quality assurance.

Purpose of the Study:

To describe the Big Data Quality & Statistical Assurance (BDQSA) model for behavioral science researchers.
To provide methodological guidance for preprocessing big data in behavioral science.
To ensure acceptable data quality prior to analysis.

Main Methods:

The BDQSA model employs a sequential pipeline of data preprocessing tasks.
Includes data screening, cleaning, transformation, and understanding.
Statistical quality phase involves data subset extraction, type conversion, representativeness checks, and assumption assessment.

Main Results:

The BDQSA model offers a structured approach to managing big data challenges in behavioral science.
Provides practical steps for data preparation and quality assurance.
Includes sample R code for practical application.

Conclusions:

The BDQSA model enhances the reliability of behavioral science research using big data.
Facilitates the application of computational data analytics in behavioral science.
Promotes rigorous data quality standards for big data analysis.