Version 1
: Received: 24 October 2024 / Approved: 24 October 2024 / Online: 25 October 2024 (11:32:55 CEST)
How to cite:
Montgomery, R. M. Generative and Descriptive Methods: A Comparative Analysis of Creation and Observation Paradigms. Preprints2024, 2024102000. https://doi.org/10.20944/preprints202410.2000.v1
Montgomery, R. M. Generative and Descriptive Methods: A Comparative Analysis of Creation and Observation Paradigms. Preprints 2024, 2024102000. https://doi.org/10.20944/preprints202410.2000.v1
Montgomery, R. M. Generative and Descriptive Methods: A Comparative Analysis of Creation and Observation Paradigms. Preprints2024, 2024102000. https://doi.org/10.20944/preprints202410.2000.v1
APA Style
Montgomery, R. M. (2024). Generative and Descriptive Methods: A Comparative Analysis of Creation and Observation Paradigms. Preprints. https://doi.org/10.20944/preprints202410.2000.v1
Chicago/Turabian Style
Montgomery, R. M. 2024 "Generative and Descriptive Methods: A Comparative Analysis of Creation and Observation Paradigms" Preprints. https://doi.org/10.20944/preprints202410.2000.v1
Abstract
This paper examines the fundamental distinctions and complementary relationships between generative and descriptive methods in research and analysis. Through a systematic review of their applications across various fields, we explore how descriptive methods excel in capturing and characterizing existing phenomena, while generative methods enable the creation of new instances based on learned patterns. The analysis reveals that while these approaches serve different primary purposes, their integration often leads to more robust and comprehensive research outcomes. Our findings suggest that understanding the strengths and limitations of both methodologies is crucial for researchers and practitioners in choosing appropriate approaches for their specific contexts.
Keywords
Keywords Generative methods; Descriptive analysis; Data modeling; Pattern recognition; Research methodology; Data synthesis; Observational techniques; Analytical frameworks; Computational methods; Knowledge representation
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
