Quantitative approaches to content analysis: identifying conceptual drift across publication outlets

Quantitative approaches to content analysis: identifying conceptual drift across publication outlets

0.00 Avg rating0 Votes
Article ID: iaor2012113
Volume: 21
Issue: 1
Start Page Number: 49
End Page Number: 69
Publication Date: Jan 2012
Journal: European Journal of Information Systems
Authors: , ,
Keywords: datamining, information
Abstract:

Unstructured text data, such as emails, blogs, contracts, academic publications, organizational documents, transcribed interviews, and even tweets, are important sources of data in Information Systems research. Various forms of qualitative analysis of the content of these data exist and have revealed important insights. Yet, to date, these analyses have been hampered by limitations of human coding of large data sets, and by bias due to human interpretation. In this paper, we compare and combine two quantitative analysis techniques to demonstrate the capabilities of computational analysis for content analysis of unstructured text. Specifically, we seek to demonstrate how two quantitative analytic methods, viz., Latent Semantic Analysis and data mining, can aid researchers in revealing core content topic areas in large (or small) data sets, and in visualizing how these concepts evolve, migrate, converge or diverge over time. We exemplify the complementary application of these techniques through an examination of a 25‐year sample of abstracts from selected journals in Information Systems, Management, and Accounting disciplines. Through this work, we explore the capabilities of two computational techniques, and show how these techniques can be used to gather insights from a large corpus of unstructured text.

Reviews

Required fields are marked *. Your email address will not be published.