1. Chapter 12 Analysing quantitative data

2. Quantative data analysis (1)
Key points
Data must be analysed to produce information
Computer software analysis is normally used for this process
Data should be carefully prepared for analysis
Researchers need to know how to select and use different charting and statistical techniques

3. Quantative data analysis (2)
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

4. Preparing, inputting and checking data (1)
Main considerations
Type of data (scale of measurement)
Data format for input to analysis software
Impact of data coding on subsequent analyses
Case weighting
Methods for error checking

5. Preparing, inputting and checking data (2)
Defining the data type
Saunders et al. (2009)
Figure Defining the data type

6. Preparing, inputting and checking data (3)
Defining the data type
Saunders et al. (2009)
Figure Defining the data type (Continued)

7. Preparing, inputting and checking data (4)
A simple data matrix
Saunders et al. (2009)
Table A simple data matrix

8. Preparing, inputting and checking data (5)
Main data categories for coding
Numerical data
Categorical data
Missing data

9. Preparing, inputting and checking data (6)
Final stages of the process
Entering data – rubbish in = rubbish out!
Weighting cases
Always take time to check for errors – including illegitimate codes, illogical relationships and that rules were followed in filter questions

10. Exploring and presenting data (1)
Exploratory analysis can include:
Specific values
Highest and lowest values
Trends over time
Proportions
Distributions
Sparrow (1989)

11. Exploring and presenting data (2)
Checklist Box 12.8
Complete the Checklist in Box 12.8
to help you design diagrams and tables
Saunders et al. (2009)

12. Exploring and presenting data (3)
Showing aspects of individual variables
Specific values
Highest and lowest values
Trends
Proportions
Distribution of values

13. Examples of diagrams (1)
Bar Chart
Source: adapted from Eurostat (2007) © European Communities, 2007 Reproduced with permission
Figure Bar chart

14. Examples of diagrams (2)
Histogram
Saunders et al. (2009)
Figure Histogram

15. Examples of diagrams (5)
Pie chart
Saunders et al. (2009)
Figure Pie chart

16. Exploring and presenting data (4)
Comparing variables to show
Specific values and independence
Highest and lowest values
Proportions
Trends and conjunctions

17. Exploring and presenting data (5)
Comparing variables to show
Totals
Proportions and totals
Distribution of values
Relationship between cases for variables

18. Describing data using statistics (1)
Statistics to describe a variable focus on two aspects
The central tendency
The dispersion

19. Describing data using statistics (2)
Describing the central tendency
To represent the value occurring most frequently
To represent the middle value
To include all data values

20. Describing data using statistics (3)
Describing the dispersion
To state the difference between values
To describe and compare the extent by which values differ from the mean

21. Examining relationships, differences and trends
Using statistics to
Test for significant relationships and differences
Assess the strength of relationship
Examine trends

22. Summary: Chapter 12
Data for quantitative analysis can be collected and then coded at different scales of measurement
Data type constrains the presentation, summary and analysis techniques that can be used
Data are entered for computer analysis as a matrix and recorded using numerical codes
Codes should be entered for all data values
Existing coding schemes enable comparisons

23. Summary: Chapter 12 Data must be checked for errors
Initial analysis should use both tables and diagrams
Subsequent analyses involve describing data and exploring relationships by using statistics
Longitudinal data may necessitate different statistical techniques