IS428 AY2019-20T2 Assign RYAN WONG POH FAI

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Data Preparation

For Data Preparation, I referred to the following video for some guidance.

Data Cleaning and Transformation

Screenshots Steps
Data 1 - Pivot I01 - P27.jpg
1. Pivot I01-I26, P01-P27
Data 2 - Rename Columns.jpg
2. Rename Pivot Field Names and Pivot Field Values to "QuestionID" and "Score" respectively
Data 3 - Inner Join with Legend.jpg
3. Drag Legend and Inner Join with SMU tab (QuestionID = Code)
Data 4 - Rename Item Column to QuestionText.jpg
4. Rename Item Column to QuestionText
Data 5 - Hide Columns.jpg
5. Hide Columns: Code, Free text, 0-10, F15-F18
Data 6 - Create Dimension for Position.jpg
6. In a new Sheet, Duplicate Position under Measures > Rename it as Position (Category)
Data 8 - Create Aliases for Position.jpg
7. Create Aliases for Position
Data 7 - Aliases for Position.jpg
8. Rename Aliases for Position
Data 10 - Create Dimension for Library.jpg
9. Duplicate Library under Measures > Rename it as Library (Category)
Data 9 - Create Aliases for Library.jpg
10. Create Aliases for Library
Data 10 - Aliases for Library.jpg
11. Rename Aliases according to the screenshot
Data 12 - Duplicate and Rename Score Dimensions.jpg
12. Under Measures, duplicate Score and convert it to a dimension

13. Then, rename the Score dimension to "Score (Category)"

Comments on Survey

One issue with the Library Survey is that it does not assign a description to each score. The Survey only gave a description of Low for a score of 1, and a description of High for a score of 7. The Survey should improve on this by providing descriptions for each score. An example given:

Score Description
1
Not important at all / Not satisfied at all
2
Not important / Not satisfied
3
Slightly not important / Slightly unsatisfied
4
Neutral
5
Slightly important / Slightly satisfied
6
Important / Satisfied
7
Extremely Important / Extremely satisfied

By providing descriptions, survey respondents will have a better idea of what each score represents. Therefore, for the purposes of analysing this survey, I will assume that the scores follow the table above.


Analysis and Insights

1. Undergraduates

Importance

Performance

Net Promoter Score

2. Postgraduates

Importance

Performance

Net Promoter Score

3. Faculty

Importance

Performance

Net Promoter Score

4. Staff

Importance

Performance

Net Promoter Score