Data analysis

The analysis of the data collected is an integral part of all research. Swetnam (2004:83) advises to ‘consider first the amount of data that has been collected and secondly the level of measurement involved’.

According to Hardy and Bryman (2004:3) analysis is a process used to answer the research question by:

‘…identifying certain patterns, noting their frequency, determining the contexts under which they occur always, sometimes, or never, (we) make sense of the data’.

Mason and Dale state that analysis involves ‘...reading data (e.g. texts of interview transcripts) with a critical analytic attitude’ (Mason and Dale, 2011:21). The analysis of the data, according to Swetnam (2004), will need to be summarised and the method selected will depend on the amount of data that has been collected. Swetnam details four basic scales in analysing data (Swetnam. 2004:84) which are shown here at Table 3.11:

Qualitative Quantitative

Nominal Scales: naming or categorising scales used for classifying. Whatever codes are used the scale can only be used for counting from such questions as: Are you in full-time employment? Yes/no.

Interval Scales: have the properties of ordinal scales but the points on the scale are equal. The researcher sets the units and origin of the scale and must be careful not to make too many assumptions about the intervals. Ordinal Scales: place data in some order, the

relative positions of people or things, for example a scale ranks them from the highest to the lowest without specifying the distance between positions. A typical ordinal scale would use a code such as: strongly agree 1 to strongly disagree 5. Only a limited range of

Ration Scales: are common in physical sciences as they have equal intervals and an actual zero point. Used for measuring

characteristics such as length, time and weight they have higher mathematical and statistical potential than others but limited relevance to social scientists whose area of interest involve

statistics may be applied and such scales should not really be averaged.

human behaviour.

Table 3.11: Four basic scales in analysing data (Adapted from Swetnam, 2004:84)

The empirical data gathered for this research was obtained through the mixed

qualitative and quantitative methods selected to answer the research questions. This data was analysed through thematic coding and partly through the use of SPSS and Excel Software. These two methods were selected because in the social sciences ‘…There is a regrettable lack of tools available for the analysis of qualitative material’ Attride-Stirling, 2001:385).

Coffey et al. state that ‘Postmodernism, in recognizing and celebrating the diversity of types and representations, encourages a variety of genres. It also encourages the blurring and mixing of genres’ (Coffey et al., 1996:6.2) which lends support to the use of a mixed methodology approach undertaken for this research.

3.6.1 Thematic Coding

Saldana describes a code as ‘most often a word or short phrase that symbolically assigns a summative, salient, essence-capturing, and/or evocative attribute for a portion of language-based or visual data’ (Saldana, 2009:3). Saldana continues to explain that ‘coding is not a precise science; it’s primarily an interpretive act’ (Saldana, 2009:4). According to Bowling ‘coding is a method of conceptualising research data and classifying them into meaningful and relevant categories ...’ (Bowling, 2009:364).

The coding of the data should be carried out as soon as it is collected which can also involve categorising the data if not already done in the original questionnaire, for example, in relation to location or job title (Wisker, 2001). For example, interview data could be coded by the interviewer while the interview is taking place (Bowling, 2009).

For the purposes of this research, coding was used to identify themes. A theme is ‘a phrase or sentence that identifies what a unit of data is about and/or what it means’ (Saldana, 2009:139). In the first analysis of the data collated from the questionnaires the themes were identified and a range of variables were created and entered into the SPSS Software package. However, the software package was unable to produce required data sets therefore the data was re-coded and the number of variables

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reduced. The main purpose of this recoding was to facilitate the limitations of the software package.

Hardy and Bryman (2004:7) state that:

‘The techniques of analysis should be sufficiently transparent that other researchers familiar with the area can recognize how the data are being collected and tested, and can replicate the outcomes of the analysis process.’

The use of the analysis procedures utilised in this research by other researchers familiar with the research topic would produce reproduce the results and findings (see Chapter 6).

This research underwent two cycles of coding. The first cycle of coding involved coding the data by reviewing each response to identify the main word or phrase and attribute a number for input into the SPSS software to carry out the analyse. For example,

question 5 of the questionnaire completed for the Health and Planning Survey asked the respondent what they understood by the concept of Healthy Urban Planning. This generated the following coding/variables (Table 3.12):

Code Description

1 Public transport 2 Walking

3 Cycling

4 Improve health and wellbeing 5 Health built into plan-making 6 Support for allotments 7 Provision of leisure facilities

8 Environments that encourage healthy lifestyles Table 3.12: Example of first coding/variables

These variables were then input into the SPSS software. However, the SPSS software does not recognise more than one selection in the variable box. This was resolved by grouping together common variables and this resulted in following code/variables being drawn up (Table 3.13):

Code Description

1 No comment

2 Promoting public transport, walking and cycling 3 Planning that influences health and wellbeing 4 Design for active lifestyles

5 Integrating planning and health policies Table 3.13: Example of second coding/variables

3.6.2 Statistical Package for the Social Sciences (SPSS) and Excel

Dunleavy (2003) advocates the use of graphs to present data as they are simpler to analyse and also provide the reader with a better understanding as long as they are clear and comprehensible. Dunleavy describes eight main types of charts with a brief description of their use and points to watch out for. These are displayed in Table 3.14.

Type of chart Use

Vertical bar chart For simple over-time data;

You have other appropriate comparative data & the labels for each bar are short enough to fit underneath it

Horizontal bar chart

There is comparative data where the labels for each bar are too long to fit underneath columns easily

Pie chart To show the shares of something or percentages Percentage

component chart

To show the shares of something or percentages vary across a number of different cases or areas

Grouped bar chart To show how the levels of several indices vary across a number of different cases or areas or time periods

Line graph To show continuous over-time data

Layer chart To show how the relative size of two positively associated variables varies across time

Scatterplot or ‘X and Y’ graph

To show how the level of a dependent variable (shown on the vertical Y axis) varies depending on the level of an independent variable (shown on the horizontal X axis)

Table 3.14: Chart types (Adapted from Dunleavy, 2003:173-180)

There are a number of computer software packages available to produce the graphs mentioned above, namely Excel and the Statistical Package for the Social Sciences (SPSS) computer software, previously known as Predictive Analytics SoftWare (PASW) and Excel.

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These software packages were selected as the tools that would be used to enhance and contribute to the visualisation and presentation of the data.

However, it soon became apparent that the SPSS software was unable to undertake the production of a visual diagram using the initial codes that had been identified and recorded. Therefore, the data were re-coded in order to address the limitations of the software analysis capabilities (See 3.6.1 above).

Excel, a part of the Microsoft package, was used to record the findings of the FOI survey, the first collection of empirical data relating to the use of Health Impact Assessments (HIA) by local planning authorities in England. This software is very similar to the SPSS software but is a more basic tool.

It was decided that due to the low numbers of responses and the time required to become proficient in the use of the computer aided software packages to analyse the empirical data collected it became apparent that the analysis of the data clerically and then through the Excel software was the most appropriate method for the coding and analysis in this instance.