Journal Article10.37745/ijqqrm13/vol11n1111
A Quantitative Analysis for Non-Numeric Data
Parente Frederick,John Christopher Finley,Christopher Magalis +2 more
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TL;DR: A quantitative analysis for non-numeric data using ARGAS is presented. This approach is applicable in exploratory research settings involving non-numeric data.
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Abstract: This study illustrates the use of an Association Rule General Analytic System (ARGAS) for analyzing non-numeric data. Previous research by Parente, Finley and Megalis (2021) showed how the ARGAS approach could be used to test hypotheses in conventional experimental designs. This study illustrates how ARGAS can be used in exploratory research settings such as single-case research, assessing organization in multi-trial learning experiments, analysis of social media, and case-oriented studies of individuals. This approach to analysis is appropriate in research settings where the units of measure are words, shapes, or other forms of non-numeric data.
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Citations
Performance validity testing: the need for digital technology and where to go from here
John-Christopher A. Finley
6
The influence of data categorization and attribute instances reduction using the gini index on the accuracy of the classification algorithm model
W.S.S. Fernando,Deny Jollyta,Dadang Priyanto,Dwi Oktarina +3 more
TL;DR: It is suggested that changing numerical data to categories data significantly improved the performance of the SVM model from 76.92% to 80.77% at a data splitting percentage of 95/5.
References
•Book
Data Mining: Concepts and Techniques
Jiawei Han,Micheline Kamber,Jian Pei +2 more
- 08 Sep 2000
TL;DR: This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.
Generalized Linear Models
TL;DR: This is the rst book on generalized linear models written by authors not mostly associated with the biological sciences, and it is thoroughly enjoyable to read.
14.7K
•Book
Applied Thematic Analysis
Greg Guest,Kathleen M. MacQueen,Emily Namey +2 more
- 09 Nov 2011
TL;DR: This chapter discusses themes and Codes, Validity and Reliability (Credibility and Dependability) in Qualitative Research and Data Analysis, and Integrating Qualitative and Quantitative Data.
7.2K
Statistical Pattern Recognition
E.R. Davies
- 01 Jan 1990
TL;DR: This chapter introduces the subject of statistical pattern recognition (SPR) by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier.
2K