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 Distinguishing different modes of growth using single-cell data

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Prathitha Kar ◽  
Sriram Tiruvadi-Krishnan ◽  
Jaana Männik ◽  
Jaan Männik ◽  
Ariel Amir
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Statistical Methods ◽  
Synthetic Data ◽  
Cellular Growth ◽  
Biological Mechanisms ◽  
E Coli ◽  
High Throughput Data ◽  
Consistent Method ◽  
Cell Data

Collection of high-throughput data has become prevalent in biology. Large datasets allow the use of statistical constructs such as binning and linear regression to quantify relationships between variables and hypothesize underlying biological mechanisms based on it. We discuss several such examples in relation to single-cell data and cellular growth. In particular, we show instances where what appears to be ordinary use of these statistical methods leads to incorrect conclusions such as growth being non-exponential as opposed to exponential and vice versa. We propose that the data analysis and its interpretation should be done in the context of a generative model, if possible. In this way, the statistical methods can be validated either analytically or against synthetic data generated via the use of the model, leading to a consistent method for inferring biological mechanisms from data. On applying the validated methods of data analysis to infer cellular growth on our experimental data, we find the growth of length in E. coli to be non-exponential. Our analysis shows that in the later stages of the cell cycle the growth rate is faster than exponential.

Iranian EFL Learners' Cognitive Styles and Their Explanations of Conceptual Metaphors

2018 ◽  
Vol 8 (1) ◽  
pp. 68-78
Author(s):  
Somaye Piri ◽  
Dara Tafazoli
Keyword(s):  
Data Analysis ◽  
Individual Differences ◽  
Test Data ◽  
Cognitive Styles ◽  
Target Domain ◽  
Conceptual Metaphors ◽  
Efl Learners ◽  
Source Domain ◽  
Metaphor Processing

The current study aims to investigate Iranian EFL learners' cognitive styles and their explanations of conceptual metaphors, offering a possible range of individual differences in metaphor processing. 71 participants were asked to explain some established conceptual metaphors that are commonly used in English. Then, their cognitive styles were classified into “analytic” or “holistic” and “imager” or “verbalizer” by means of cognitive styles test. Data analysis revealed that 29 participants (40.85%) explained the three conceptual metaphors by making structural correspondences between source and target domain. Moreover, 20 participants (28.17%) explained at least one of the metaphors by applying elements which were not part of the source domain. The results of the experiment revealed that learners with “holistic” cognitive styles were more likely to blend their conception of the target domain with the source domain in comparison to participants with “analytic” styles; also, “imagers” were more likely than “verbalizers” to refer to stereotypical images to explain the metaphors.

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.

A Granular Approach to Analyze Spatiotemporal Data

2013 ◽  
Vol 380-384 ◽  
pp. 2876-2879
Author(s):  
Ming Li Song ◽  
Shu Juan Wang
Keyword(s):  
Data Analysis ◽  
Everyday Life ◽  
Synthetic Data ◽  
Spatiotemporal Data ◽  
Information Granules

Spatiotemporal data are widely visible in everyday life. This paper proposes an algorithm to represent them in a granular wayinformation granules. Information granules can be regarded as a collection of conceptual landmarks using which people can view the data and describe them in a semantic way. The key objective of this paper is to introduce a new granular way of data analysis through their granulation. Several experiments are done with synthetic data and the results show a clear way how our algorithm performs.

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