NDLI: Interactive Statistics with $\textit{Illmo}$

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Interactive Statistics with $\textit{Illmo}$

Content Provider	ACM Digital Library
Author	Martens, Jean-Bernard
Copyright Year	2014
Abstract	Progress in empirical research relies on adequate statistical analysis and reporting. This article proposes an alternative approach to statistical modeling that is based on an old but mostly forgotten idea, namely Thurstone modeling. Traditional statistical methods assume that either the measured data, in the case of parametric statistics, or the rank-order transformed data, in the case of nonparametric statistics, are samples from a specific (usually Gaussian) distribution with unknown parameters. Consequently, such methods should not be applied when this assumption is not valid. Thurstone modeling similarly assumes the existence of an underlying process that obeys an a priori assumed distribution with unknown parameters, but combines this underlying process with a flexible response mechanism that can be either continuous or discrete and either linear or nonlinear. One important advantage of Thurstone modeling is that traditional statistical methods can still be applied on the underlying process, irrespective of the nature of the measured data itself. Another advantage is that Thurstone models can be graphically represented, which helps to communicate them to a broad audience. A new interactive statistical package, Interactive Log Likelihood MOdeling $(\textit{Illmo}),$ was specifically designed for estimating and rendering Thurstone models and is intended to bring Thurstone modeling within the reach of persons who are not experts in statistics. $\textit{Illmo}$ is unique in the sense that it provides not only extensive graphical renderings of the data analysis results but also an interface for navigating between different model options. In this way, users can interactively explore different models and decide on an adequate balance between model complexity and agreement with the experimental data. Hypothesis testing on model parameters is also made intuitive and is supported by both textual and graphical feedback. The flexibility and ease of use of $\textit{Illmo}$ means that it is also potentially useful as a didactic tool for teaching statistics.
Starting Page	1
Ending Page	28
Page Count	28
File Format	PDF
ISSN	21606455
e-ISSN	21606463
DOI	10.1145/2509108
Volume Number	4
Issue Number	1
Journal	ACM Transactions on Interactive Intelligent Systems (TiiS)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-04-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Multilevel models Thurstone Maximum likelihood
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Human-Computer Interaction

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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