Data Science and Interactive Visualization Tools for the Analysis of Qualitative Evidence

Despite the expansion of open-source data science software, the knowledge-based benefits of, and access to artificial intelligence (AI), machine learning (ML), and data science and visualization (DSV) tools in scientific research continues to require computer programming literacy. Data Science and Interactive Visualization Tools for the Analysis of Qualitative Evidence empowers qualitative and mixed methods researchers in the data science movement by offering no-code, cost-free software access so that they can apply cutting-edge and innovative methods to synthetize qualitative data. This book builds on the idea that qualitative and mixed methods researchers should not have to learn to code to benefit from rigorous open-source, cost-free software that uses artificial intelligence, machine learning, and data visualization tools—just as people do not need to know C++ or TypeScript to benefit from Microsoft Word. The applied conceptual understanding of these procedures is not a problem for the majority of researchers. The real barrier is the hundreds of R code lines required to apply these concepts to their databases. By removing the coding proficiency hurdle, this book will empower their research endeavors and help them become active members of and contributors to the applied data science community. The book offers a comprehensive explanation of data science and machine learning methodologies along with access to software application tools to implement these techniques without any coding proficiency. The book addresses the need for innovative tools that enable researchers to tap into the insights that come out of cutting-edge data science tools with absolutely no computer language literacy requirements. Provides access to no-code software that enables the dynamic analysis of knowledge production and democratizes access to data science toolsDiscusses analytic frameworks that overcome aggregation bias in text classification via machine learningCovers the integration of qualitative and quantitative methods in a fully equal status mixed methods design