Foundation Session Taught at Beijing Foreign Studies University in 2011 drawn extensively from Saunders et al

1. Slide 1.1
Analysing quantitative data

2. Slide 1.2
Quantitative data analysis
Key points
Data must be analysed to produce information
Computer software analysis is normally used for this
process (Microsoft Excel, SPSS etc.)
Present, explore, describe & examine relationships

3. Slide 1.3
Examples of basic chart
Pie chart
Saunders et al. (2009)
Figure 12.8 Pie chart

4. Slide 1.4
More advanced work requires Statistical analysis
Establishing the statistical relationship between two
variables (e.g. If I am in this group I am have a %
probability of doing X).
If you need to do this then see:
http://www.statsoff.com/textbook
http://oli.web.cmu.edu/openlearning/forstudents
/freecourses/statistics

5. Slide 1.5
Quantitative data analysis: Main Concerns
Preparing, inputting and checking data
Choosing the most appropriate statistics to
describe the data
Choosing the most appropriate statistics to
examine data relationships and trends

6. Slide 1.6
Type of Data: category data
 Example: Number of cars hatchback / saloon /
estate
 Can’t measure it, just simply count occurrences
 Focus on one discrete variable (i.e. Hatchback)
 Dichotomous data (e.g. either Male or Female)
 Ranked data (how strongly you agree with
statement X)

7. Slide 1.7
Type of Data: numerical data
 Example: temperature in Celsius
 Quantifiable data that can be measured
 Interval data e.g. Degrees Celsius [zero
degrees is not actually ZERO]
 Ratio (calculate the difference) data e.g.
Profits up 34% for a year

8. Slide 1.8
Type of Data: continuous data
 Example: height of students
 Can be any value [within a range]

9. Slide 1.9
Level of Precision
LESS MORE
Precise data can be grouped to make it less precise
(e.g. Mark of 85% grouped into a ‘Very Good’ category but
Not the other way round)

10. Slide 1.10
Exploring Data: Tukey’s (1977) exploratory data
analysis approach focus on tables & diagrams
Great Tables & Diagrams Need:
Clear & Distinctive Title
Clearly stated units of measurement
Clearly stated source of data
Abbreviations explained in notes
Size of the sample is stated “n = 43”
Column / Row / Axis Labels
Dense shading for smaller areas
Logical Sequence of columns & rows

11. Slide 1.11
Exploratory Analysis: Individual unit of data
Highest and lowest values
Trends over time
Proportions (relative size)
Distributions (number in a group)
Sparrow (1989)

12. Slide 1.12
What Do You Want To Show?
Highest / Lowest: Bar Chart / Histogram for Categories
You can reordered it for Non-continuous data


13. Slide 1.13
What Do You Want To Show?
Frequency: Again a Histogram / Bar Chart (reorder it
to make it clearer)
Perhaps a pictogram


14. Slide 1.14
What Do You Want To Show?
Trend: Line Chart or histogram


15. Slide 1.15
What Do You Want To Show?
Proportion: Pie chart or bar chart


16. Slide 1.16
Distribution of values

17. Slide 1.17
Normal Distribution
Sample of 100+ people should
produce a normal curve.
Standard deviation shows how wide
the spread of results are.
Low standard deviation shows a narrow range of values
High standard deviation shows a wide range of values

18. Slide 1.18
How to calculate it:
Consider a population consisting of the following eight values:
2, 4, 4, 4, 5, 5, 7, 9
Calculate the Mean (2, 4, 4, 4, 5, 5, 7, 9) / 8 = 5
Calculate the difference between each individual data point and
the mean. Then square each one
Calculate the average of these values (i.e. 32 / 8 = 4)
Find the sqaure root of this number (square root of 4 is 2)
http://www.statsoff.com/textbook
http://oli.web.cmu.edu/openlearning/forstudents/freecourses/statistics

19. Slide 1.19
Negative skew / Positive skew

20. Slide 1.20
What to do with your distribution?
Try to understand what is the story behind the data:
Is the data ‘unrepresentative’?
Are the categories the wrong width?
Is there something going on we did not know
about at the start?

21. Slide 1.21
Comparing variables to show
Totals
Proportions and totals
Distribution of values
Relationship between cases for variables

22. Slide 1.22
Multiple bar chart: Totals & Highest / Lowest

23. Slide 1.23
Comparing proportions
Would you buy this product again? – products 1 to 6

24. Slide 1.24
Compare Trends

25. Slide 1.25
A final word of caution
GI-GO
Garbage In – Garbage Out