<p class="MsoNormal" style="mso-margin-top-alt: auto; mso-margin-bottom-alt: auto; text-align: justify; line-height: normal;"><span style="font-size: 12.0pt; mso-ascii-font-family: Calibri; mso-fareast-font-family: 'Times New Roman'; mso-hansi-font-family: Calibri; mso-bidi-font-family: Calibri;">This open access book will guide qualitative researchers in the social sciences with little to no coding experience in leveraging large language models (LLMs). Responding to a lack of instructional materials that recognize the need to equip qualitative researchers with the most advanced tools, this book offers a research-focused guide to harness the power of LLMs.</span></p><p class="MsoNormal" style="mso-margin-top-alt: auto; mso-margin-bottom-alt: auto; text-align: justify; line-height: normal;"><span style="font-size: 12.0pt; mso-ascii-font-family: Calibri; mso-fareast-font-family: 'Times New Roman'; mso-hansi-font-family: Calibri; mso-bidi-font-family: Calibri;">The content is divided into two parts, beginning with an introduction to LLMs, natural language processing, and machine learning, as well as a historical and ethical perspective on the use of AI in research. The second part of the book serves as a hands-on guide, providing step-by-step instructions for the use of LLMs to analyze large datasets. It is written with practical cases, taken from management sciences, and emphasizes maintaining a close connection to the data throughout the process. It will be highly valuable to researchers in management studies, as well as in the wider social sciences.</span></p>

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AI for Qualitative Research

  • Diana Garcia Quevedo,
  • Josue Kuri

摘要

This open access book will guide qualitative researchers in the social sciences with little to no coding experience in leveraging large language models (LLMs). Responding to a lack of instructional materials that recognize the need to equip qualitative researchers with the most advanced tools, this book offers a research-focused guide to harness the power of LLMs.

The content is divided into two parts, beginning with an introduction to LLMs, natural language processing, and machine learning, as well as a historical and ethical perspective on the use of AI in research. The second part of the book serves as a hands-on guide, providing step-by-step instructions for the use of LLMs to analyze large datasets. It is written with practical cases, taken from management sciences, and emphasizes maintaining a close connection to the data throughout the process. It will be highly valuable to researchers in management studies, as well as in the wider social sciences.