ISSS608 2016-17 T1 Assign2 XU Qiuhui

Data Sources

Dataset from UCI, Survey of faculty members from two Spanish universities on teaching uses of Wikipedia

Source: E. Aibar, J. Lladós, A. Meseguer, J. Minguillón (jminguillona[at]uoc[dot]edu), M. Lerga. Universitat Oberta de Catalunya, Barcelona, Spain.

Theme of Interest and Motivation

This Analysis aims to find out overall impressions of different user segments on Wikipedia and their use behavior according to high dimensional survey question answers. Then propose recommendations for Wikipedia's future development. In this analysis, we'll mainly answer the following questions：

1. Relationships between user impressions and user behaviors.
2. Relationships user behaviors and external environments.

Data Preparation

Transfer Data Type

Variables	Original Data Type	Transferred Data Type	Reason
Gender	Numeric	Categorical	According to dataset dictionary, gender is meaningless while using numeric value to do analysis.
PhD	Numeric	Categorical	According to dataset dictionary, PhD is meaningless while using numeric value to do analysis.
University	Numeric	Categorical	According to dataset dictionary, University is meaningless while using numeric value to do analysis.
YearsExp	Categorical	Numeric	Years of experience should be continuous data, so that we can firstly bin them into several groups, then use groups to classify them.

Bin Numeric Data

Variables	Original	Transferred Variables	Formula
Age		Age(bin)	If(:AGE <= 30,"20~30",If(:AGE <= 40,"30~40",If(:AGE <= 50,"40~50",If(:AGE <= 60,"50~60","60~70"))))
YearsExp		YearsExp(bin)	If( :YEARSEXP <= 10,"0~10",If( :YEARSEXP <= 20,"10~20",If( :YEARSEXP <= 30,"20~30","more than 30")))

Group Categorical Data

Transform all survey question answers with 1-5 scores to “High, Mid, Low” degree.

Scores	Degree
1	Low
2	Low
3	Mid
4	High
5	High

Visualization

Parallel Set

Tools Utilized

1. High-D - For initial data exploration and analysis
2. JMP 12, MS Excel – For data preparation
3. d3.js - For data visualization