Chapter 12
Analysing quantitative data
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
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
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
Preparing, inputting and checking data (2)
Defining the data type
Saunders et al. (2009)
Figure 12.1 Defining the data type
Preparing, inputting and checking data (3)
Defining the data type
Saunders et al. (2009)
Figure 12.1 Defining the data type (Continued)
Preparing, inputting and checking data (4)
A simple data matrix
Saunders et al. (2009)
Table 12.1 A simple data matrix
Preparing, inputting and checking data (5)
Main data categories for coding
• Numerical data
• Categorical data
• Missing data
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
Exploring and presenting data (1)
Exploratory analysis can include:
• Specific values
• Highest and lowest values
• Trends over time
• Proportions
• Distributions
Sparrow (1989)
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)
Exploring and presenting data (3)
Showing aspects of individual variables
• Specific values
• Highest and lowest values
• Trends
• Proportions
• Distribution of values
Examples of diagrams (1)
Bar Chart
Source: adapted from Eurostat (2007) © European Communities, 2007
Reproduced with permission
Figure 12.2 Bar chart
Examples of diagrams (2)
Histogram
Saunders et al. (2009)
Figure 12.4 Histogram
Examples of diagrams (5)
Pie chart
Saunders et al. (2009)
Figure 12.8 Pie chart
Exploring and presenting data (4)
Comparing variables to show
• Specific values and independence
• Highest and lowest values
• Proportions
• Trends and conjunctions
Exploring and presenting data (5)
Comparing variables to show
• Totals
• Proportions and totals
• Distribution of values
• Relationship between cases for variables
Describing data using statistics (1)
Statistics to describe a variable focus on two aspects
• The central tendency
• The dispersion
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
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
Examining relationships, differences and trends
Using statistics to
• Test for significant relationships and differences
• Assess the strength of relationship
• Examine trends
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
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
