 # Chapter 12 Analysing quantitative data

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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 Defining the data type

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

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

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

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