A Bayesian approach to modeling group and individual differences in multidimensional scaling

2016 ◽  
Vol 70 ◽  
pp. 35-44 ◽  
Author(s):  
Kensuke Okada ◽  
Michael D. Lee
Keyword(s):  
Individual Differences ◽  
Multidimensional Scaling ◽  
Bayesian Approach ◽  
Modeling Group

Multidimensional Scaling and Individual Differences

1971 ◽  
Vol 8 (1) ◽  
pp. 71-77 ◽  
Author(s):  
Paul E. Green ◽  
Vithala R. Rao
Keyword(s):  
Data Analysis ◽  
Individual Differences ◽  
Multidimensional Scaling ◽  
Synthetic Data

This article compares, via synthetic data analysis, the performance of five different methods for scaling averaged dissimilarities data under conditions involving individual differences in “perception.” All methods perform well when no “degradation” of the (simulated) ratings is entailed. When the data are transformed to zero-one values—a procedure sometimes followed in applied studies—all procedures perform poorly compared to the no-degradation case. Implications of these results for scaling applications involving group solutions are discussed.

scholarly journals A Bayesian Approach for Degradation Analysis with Individual Differences

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 175033-175040 ◽  
Author(s):  
Junyu Guo ◽  
Hualin Zheng ◽  
Binglin Li ◽  
Guo-Zhong Fu
Keyword(s):  
Individual Differences ◽  
Bayesian Approach ◽  
Degradation Analysis

Multidimensional Scaling as a Method for Probing the Conceptual Structure of State Categories: An Individual Differences Analysis

1987 ◽  
Vol 31 (1) ◽  
pp. 107-111 ◽  
Author(s):  
Monica C. Zubritzky ◽  
Bruce G. Coury
Keyword(s):  
Individual Differences ◽  
Multidimensional Scaling ◽  
Control Systems ◽  
Conceptual Models ◽  
Conceptual Structure ◽  
Individual Performance ◽  
Classification Task ◽  
System State ◽  
System Data ◽  
The Impact

Identifying the underlying decision criteria used by operators to classify system state, and revealing the way in which that information is internally represented is one of the challenges facing designers of control systems. This paper describes the use of multidimensional scaling (MDS) to probe the structure and composition of the internal conceptual models used by operators to identify system state. Specifically, the issue of individual differences in mental model is addressed, as well as the impact of these differences on individual performance in a classification task. Twenty subjects were trained as operators to classify instances of system data into one of four system state categories. After training, subjects were asked to rate the similarity between instances of system state. Results showed that the dominant dimensions used by an individual are related to his/her performance on the classification task.

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