OPTIMIZATION OF QUANTITATIVE RESEARCH METHODS IN SOCIAL SCIENCES IN THE ERA OF BIG DATA
Journal: Acta Informatica Malaysia (AIM)
Author: Yirui Song
This is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
As information technology continues to advance, the complexity of data is ever-increasing. Traditional quantitative research methods in the social sciences, such as basic visualization and traditional statistical models, are gradually becoming inadequate in meeting the demands of modern data analysis. Despite the challenges that big data presents, it also brings new opportunities – through its usage, the optimization of traditional methods can be achieved. More intricate graphing techniques such as mosaic plots, alluvial plots, slope charts, and area charts, alongside machine learning algorithms that are better adapted for big data analysis such as decision trees, random forests, and K-Means algorithm, are opening new avenues for quantitative analysis in social sciences. This will ultimately foster further development of the field by allowing new methods and ideas to emerge.