Faculty

Overview

Every two years, SMU Libraries conduct a comprehensive survey in which faculty, students and staff have the opportunity to rate various aspects of SMU library's services. The survey provides SMU libraries with input to help enhance existing services and to anticipate emerging needs of SMU faculty, students and staff. The latest survey is currently on going and will be ended by 17th February 2020.

The 2018 Survey Results can be found here and the full report is available by following this link. The past reports are mainly made-up of pages of tables, which are very difficult to comprehend. In view of this, your task is to apply appropriate data visualisation to transform these tables into visual representation that allow SMU libraries to gain useful insights.

Data Cleaning & Preparation

About
An overview of the library 2018 survey results found that there are 2638 respondents in total that responded to various parts of the 83 survey questions with respect to the performance and importance level of the library and other general questions to understand the patron's sentiments of the SMU Library. Here is the breakdown of the users that frequent the SMU library

Figure 1

As seen from figure 1, predominantly, most of the survey respondents comes from undergraduates with some from other areas such as graduates, faculty, staff and others. In order to gain more insights from the survey results, we will first do some pre-processing and cleaning of the data.

Cleaning

Step	Screen shot	Explanation
1	Img 1 Img 2	Based on the initial dataset given, the datset is aggregated by values for various fields as seen from img 1. The column names are labelled by its code number and there is no name given in the raw data. The given names are represented in img 2 where the labelling of each code are given in the legend. To re-label the data into a readable dataset for users to further analyse, we will then process the data and labelling through excel.
2	Img 3	Through using lookup and concatenate function, we can combine the id code and the name into the column headers. We then transfer the values into a new sheet and re-arrange the values such that the general questions are in front as seen in Img 3.
3	Insert Image	After further inspection of the survey questions, there is 26 survey questions that are irrelevant towards the insights that will be generated later on. The survey questions that fall under the ID code 'NA' to filtered and removed under the 'Datasource' tab.