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Clustering, seriation, and subset extraction of confusion data
Michael J Brusco1, Douglas Steinley
1Marketing Department, College of Business, Florida State University, Tallahassee, FL 32306-1110, USA. Mbrusco@cob.fsu.edu
Researchers developed MATLAB programs to analyze confusion data, offering optimal solutions for stimulus clustering, ordering, and subset extraction. These tools provide pragmatic alternatives for applied researchers studying psychological similarity and proximity data.
Area of Science:
- Psychology
- Data Analysis
- Computational Science
Background:
- Confusion data analysis is a common practice in psychology.
- Existing analytical approaches include stimulus subset extraction, partitioning, and ordering.
- Standard software may not guarantee optimal solutions for these analyses.
Purpose of the Study:
- To present a suite of MATLAB programs for analyzing confusion data.
- To provide optimal solutions for common confusion data analysis tasks.
- To offer pragmatic alternatives for applied researchers.
Main Methods:
- Development of MATLAB *.m files for preprocessing confusion matrices.
- Implementation of programs for fitting the similarity-choice model.
- Creation of programs for optimal stimulus clustering, ordering, and subset extraction.
Main Results:
- The presented MATLAB programs offer optimal solutions for analyzing confusion data.
- The software facilitates preprocessing, similarity-choice model fitting, and stimulus organization.
- These tools provide practical alternatives to standard commercial software.
Conclusions:
- The developed MATLAB programs provide effective and optimal methods for analyzing confusion data.
- The programs are versatile and applicable to various types of proximity data.
- These tools enhance the capabilities of applied researchers in data analysis.
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