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If computer programs were smarter, they would, like people, recognize sequences of events, form models of their environment, and formulate rules based on experience. This paper describes the development of a program designed to address the difficult computational problems involved in integrating the inductive and deductive reasoning necessary to perform such tasks. “Theme One” is a prototype program composed of “Index”, a learning algorithm for sequential data, and “Study”, an algorithm for building logical models. The project goal is an interactive research tool that assists students and investigators in the exploration of qualitative data using artificial intelligence.
The goal of this project is to improve the power and scope of computer routines that search for structure in a data base. IDEA (Inductive Data Exploration and Analysis) is a computer program that detects and represents inherent structure in multi-variage data. The IDEA program is designed either to run in an automatic mode or to allow the investigator to intercede at each major decision in the analysis. At each such juncture he is presented with information that permits him to concur or override a computer decision before the program continues to the next major decision. In addition, IDEA has two other distinguishing features: (1) Heuristic computational procedures are used for those cases where the combinational aspects of the analysis would require extensive computations, and (2) heuristics are selectively used for different types of data, enabling IDEA to operate on a mixture of nominal (categorical), ordinal (ranked), and interval- or ratio-scaled measurements.
2002 •
Lensu, Anssi Computationally Intelligent Methods for Qualitative Data Analysis Jyväskylä: University of Jyväskylä, 2002, 57 p. (+included articles) (Jyväskylä Studies in Computing ISSN 1456-5390; 23) ISBN 951-39-1374-0 Finnish summary Diss. This study focuses on computationally intelligent methods, which are applied to the analysis of survey data in educational research. The methods can be used with complex data sets, which contain several data types. Each data type is analyzed in a separate subanalysis, and the results from these subanalyses can be combined. The methodology makes it possible to locate groups of similar answers from the subanalyses, and to identify these groups using background information. It also allows one to compare groups that are selected from different subanalyses, from different populations, and to locate and identify similar textual answers. In connection to this study, a software application has been created to test the developed methods.
Advances in Intelligent Systems and Computing
Empowering Qualitative Research Methods in Education with Artificial Intelligence1992 •
We believe the problem of automating the process of building models from empirical data is a critical issue for both Artificial Intelligence and other scientific computing researchers. Although both fields require models of the behavior of complex systems, as AI researchers we may more directly address our particular needs. AI researchers require models that let us determine the influence of design decisions and environmental factors on the performance of AI programs so as to inform the design of the next generation of intelligent agents. Our research includes the complementary projects of building a blackboard-based automated modelbuilding assistant and analyzing the efficacy of heuristics used in function finding programs.
2012 •
Journal of Business Research
AI research without coding: The art of fighting without fighting: Data science for qualitative researchers2020 •
In this tutorial, we show how to scrape and collect online data, perform sentiment analysis, social network analysis, tribe finding, and Wikidata cross-checks, all without using a single line of programming code. In a step-by-step example, we use self-collected data to perform several analyses of the glass ceiling. Our tutorial can serve as a standalone introduction to data science for qualitative researchers and business researchers, who have avoided learning to program. It should also be useful for experienced data scientists who want to learn about the tools that will allow them to collect and analyze data more easily and effectively.
2012 •
Research and industry increasingly make use of large amounts of data to guide decision-making. To do this, however, data needs to be analyzed in typically non-trivial refinement processes, which require technical expertise about methods and algorithms, experience with how a precise analysis should proceed, and knowl-edge about an exploding number of analytic approaches. To alleviate these problems, a plethora of different systems have been proposed that “intelligently” help users to analyze their data.
The Qualitative Report
Ηow to Use Artificial Intelligence (AI) as a Resource, Methodological and Analysis Tool in Qualitative Research2023 •
Artificial Intelligence (AI) has had far-reaching effects in research and the academic world. It has been used in many ways by the scientific community within the context of qualitative research, such as literature and systematic reviews, for conceptualization purposes, thematic and content analysis. It has however prompted concerns and questions about the potential for unreliable research, bias, and unethical behavior in the outcomes of AI-produced research. The purpose of this paper is to examine the current use of AI in research, its strengths and limitations, dilemmas and ethical considerations from theoretical critical perspective principles, while delivering five key considerations for the appropriate, rigorous, and reliable use of AI in research practice. The first step is to become acquainted with the data generated by AI systems. The second is concerned with removing biased content and addressing ethical concerns when using AI, while the third is concerned with cross-referencing information generated by AI. The fourth step is to control the analysis process. The fifth and most important key consideration is the demonstration of cognitive input and skills by the researcher throughout the process of using AI in any qualitative research study and in reaching conclusions.
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