An Empirical Comparison of Two Individual Differences Multidimensional Scaling Models

1974 ◽  
Vol 34 (4) ◽  
pp. 823-828
Author(s):  
Richard Shikiar
Keyword(s):  
Individual Differences ◽  
Multidimensional Scaling ◽  
Empirical Comparison ◽  
Scaling Models

Restricted multidimensional scaling models

1978 ◽  
Vol 17 (2) ◽  
pp. 138-151 ◽  
Author(s):  
P.M Bentler ◽  
David G Weeks
Keyword(s):  
Multidimensional Scaling ◽  
Scaling Models

Multidimensional scaling models for reaction times and same-different judgments

Psychometrika ◽  
1983 ◽  
Vol 48 (3) ◽  
pp. 393-423 ◽  
Author(s):  
Yoshio Takane ◽  
Justine Sergent
Keyword(s):  
Multidimensional Scaling ◽  
Reaction Times ◽  
Scaling Models

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.

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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