Question

Describe the parameters for visual analysis of case study data. How does analysis through single case effect size statistics effect the confidence in our predictions?    

Answer 1

The various case studies help in investigating visual parameter space analysis could help in supporting the validation of simulation models. To guide and structure research endeavors in this area, these parameters for visual analysis of case study data are needed to be followed.

This presentation of the conceptual framework of structured analysis of the visualization literature based on my own experiences. It contains three major components:

(1) A data flow model can help in describing the visual parameters of space analysis problems which are independent of the applications of the domain.

(2) The analysis of visualization is the set of four navigation strategies which shows that of how visualization of tools can support parameter space analysis.

(3) It is the characterization of six analysis tasks.

Based on the conceptual framework, we will investigate and try to classify the present body of literature, by identifying three open research gaps in visual parameter space analysis.

This framework will help to support visualization, designers, and researchers in order to characterize parameter space analysis problems so that it could be guided in their strategies and evaluation.

Due to rapid progressions in the investigation of data from single-case research designs, the multiple behavior-change indices says that effects and size of the various statistics can lead to the production of confusing results.

However, to reduce such types of confusion regarding single case effect size statistics, we need to describe and compare various effect-size indices. Each of these indices will help to examine the data to prevent overlapping between the phases. Most importantly here we have to highlight similarities and differences, which should be both conceptual and computational.

Various indices are needed to be applied to these samples of different series, in order to examine their distributions. For combining nonoverlapping indices we can use a generic meta-analytic method which can be used to display multiple data series within complex designs.