ISSS608 2016-17 T1 Assign2 Shishir Nehete

Abstract

As the use of technology increases in data collection and storage in organizations, the demand for finding the insights from this data is a growing concern. Currently, most of the traditional business intelligence systems tend to confine to univariate and bivariate data analysis. The Project focuses on applying interactive data exploration and analysis techniques to discovery patterns in multivariate data to explore different relationships in the data. The topic used for exploring these techniques is “University faculty perceptions and practices of using Wikipedia as a teaching resource”. This is an ongoing research in which perception of colleagues and opinion about Wikipedia and the perceived quality of information in Wikipedia play a central role.

Theme of Interest and Motivation

The dataset used for this project is wiki4HE Data Set(https://archive.ics.uci.edu/ml/datasets/wiki4HE).
Identifying a theme of interest The dataset provides information of the survey providers on multiple variables such as: Age, Gender, Domain, PhD, Experience, University (Universitat Oberta de Catalunya, Universitat Pompeu Fabra), UOC_Position, Other, Other_Position, UserWiki

1. Perceived Usefulness
2. Perceived Ease of Use
3. Perceived Enjoyment
4. Quality
5. Visibility
6. Social Image
7. Sharing attitude
8. Use behaviour
9. Profile 2.0
10. Job relevance
11. Behavioural intention
12. Incentives
13. Experience

To define the scope of the assignment, I am considering 5 of the above list of variables. Limiting the scope will provide me a confined field of analysis which can be furthered to other variables too. These variables are Perceived Usefulness, Quality, Visibility, Experience and Sharing Attitude.

Tools Utilised

1. JMP – To explore and transform the data into usable data set. Also used to check distribution of the ratings for selected questions in scope of the assignment.
2. Tableau – To create interactive data visualizations for finding insights and relationships between multiple variables.
3. High-D – To create interactive visualization for analysing the quality criteria of the Wikipedia survey.