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

Auteur
Dr. Manuel S. González Canché is an Associate Professor in the Policy, Organization, Leadership, and Systems Division of the University of Pennsylvania, where he holds a tenured appointment. Dr. González Canché also serves as affiliated faculty with the Human Development and Quantitative Methods division and the International Educational Development Program. In addition, he is a senior scholar in the Alliance for Higher Education and Democracy. In his research, Dr. González Canché employs econometric, quasi-experimental, spatial statistics, and visualization methods for big and geocoded data, including geographical information systems, representation of real-world networks, and text-mining techniques. In related work, he aims to harness the mathematical power of network analysis to find structure in written content. He is developing an analytic method (Network Analysis of Qualitative Data) that blends quantitative, mathematical, and qualitative principles to analyze text data. Similarly, he is also developing the implementation of geographical network analyses that merge network principles and spatial econometrics to model spatial dependence of the outcome variables before making inferential claims. Dr. González Canché is currently teaching courses that rely heavily on computer programming code for PhD students. The no-code tools included in the proposed book have translated into grant funding and peer-reviewed publications in The Journal of Mixed Methods Research, The International Journal of Qualitative Methods, Expert Systems with Applications, and Methodological Innovations. Additionally, he has been offering professional development workshops for the American Educational Research Association. Dr. González Canché has a PhD in Higher Education Policy with cognates in Sociology, Economics, and Biostatistics from the University of Arizona.

Texte du rabat

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.

Contenu

Part I. Introduction to Data Science and Interactive Visualization Tools for the Analysis of Qualitative Evidence

1. Truly Equal-Status Mixed Methods Design (TESM2D)
2. Textual and Relational data (TRD)
3. Digital Ethnography, Data Science, and Ethical Considerations
4. Bool Plan and Organization

Part II. Network modeling frameworks

5. Network Analysis of Qualitative Data (NAQD)
6. Graphical Retrieval and Analysis of Temporal Information Systems (GRATIS)
7. Visual Evolution, Replay, and Integration of Temporal Analytic Systems (VERITAS)
8. Relational Frameworks for Data Mining and Data Retrieval via Co-authorship Networks (CN)

Part III. Machine Driven Text Classification and Statistical Modeling frameworks

9. Latent Code Identification (LACOID)
10. Machine Driven Classification of Open-ended Responses (MDCOR)
11. Machine Driven Literature Classification (MDLC)

Part IV. Integration of Network and Text Classification Analyses

12. In what instances should or could we integrate the analyses and frameworks described in parts I and II?
13. Incorporating Spatial Context for Data StoryTelling: GeoStoryTelling
14. Sentiment Network Modeling
15. Closing thoughts and future work