IS428 AY2019-20T2 Assign LIM PAO LING RACHEL

SMU Library Services

Overview

Every 2 years, SMU libraries will conduct a comprehensive survey to provide an opportunity for faculty, students and staff to rate and comment various aspects of SMU library services. The survey aims at creating input that could possibly enhance existing services as well as to anticipate the emerging needs of SMU students, graduates, faculty and staff.
On the contrary, past library survey reports were not as comprehensive as intended, since the report consisted of numerous tables of data that could be better depicted with visualisations. The large amount of textual data collated poses as an issue when it comes to summarising data into a single visualisation, hence, appropriate categorising of charts and tables by performance metrics, importance metrics and other valuable metrics is key for an organised presentation of data. Visual Analytics is used to provide insight into the various level of services provided by SMU libraries that is perceived by 4 main groups:

1. Undergraduate Students
2. Postgraduate Students
3. Faculty Lecturers
4. School staff

Data Description

Data Attribute	Data Description
Survey Statistic Questions
Campus	Which library is more frequented
StudyArea	The major area of study / research / teaching
Position	Either an undergraduate, postgraduate, faculty or school staff
ID	If the student is an international or not (applicable to non-exchange students)
HowOftenL	The frequency of visiting the library
HowOftenC	The frequency of visiting the campus
HowOftenW	The frequency of accessing library resources (online articles, databases, ebooks)
Survey Rating Questions
NPS1	Likelihood of recommending library services to other students
I01 – I26	Importance factors that are rated by survey respondents
P01 – P27	Performance factors that are rated by the survey respondents
NA01 – NA26	Survey questions that are not applicable to the survey respondents
Comment1	Free text for survey respondents to suggest improvements or any other comments

Data Preparation

By referencing the Legend tab in the Raw Data excel file (Raw data 2018-03-07 SMU LCS data file – KLG), a reorganisation of data columns and rows were done to convert the coded data into textual representations. This is to ensure that there are readable headers when doing up the visualisations in Tableau. A general use of excel coding functions such as ‘VLOOKUP’, ‘HLOOKUP’ were used to match the legends to the specific header codes.

Screen Shot	Data Cleaning Description
	For dimensions:[ Campus, Position, Study Area, ID ] the use of HLOOKUP was done to create aliases for coded categorical data =HLOOKUP(B3,'Legend (Categorised)'!$A$1:$R$8,2,FALSE) For dimensions: [ I01 - I26 ] the use of VLOOKUP was done to match the legend aliases for the survey question codes (Importance factors) =VLOOKUP(J1,'Legend (Categorised)'!$A$10:$B$35,2,FALSE) For dimensions: [ P01 - P27 ] the use of VLOOKUP was done to match aliases for the survey question codes (Performance factors) =VLOOKUP(AJ1,'Legend (Categorised)'!$A$36:$B$62,2,FALSE) For dimensions: [ NA01 - NA26 ] the use of VLOOKUP was dones to match aliases for the survey question codes (NA factors) =VLOOKUP(BO1,'Legend (Categorised)'!$A$63:$C$89,3,FALSE)
image	description
image	description
image	description