Investigating relationships between usage and

shared or individual ROS, or both, as described in Section 5.2. The number of respondents doing each activity at least once a week, in the warmer months, in each set and in the whole sample, was generated using SPSS 16. The number of respondents who never used their residential outdoor space for each activity and

123

the number of different activities done at least once a week were also examined. This enabled differences in usage between the different sets to be identified.

Using SPSS 16, frequencies of responses to the questionnaire questions were generated to explore these differences in more detail. For cross-tabulations against usage, a level of usage shown by at least 70% of respondents with access to individual residential outdoor space only was selected as representing a measure of usefulness. This level was doing at least four different activities at least once a week, in their residential outdoor space, in the warmer months, and is close to the mean number of activities done at least once a week for the whole sample. Residents who were doing this number of different activities are defined by the researcher as getting reasonable usage from their space. This definition of usage is used throughout the analysis. Cross-tabulations of this level of usage against all the variables of interest were generated, to establish which variables are linked with variations in usage (Achen, 2002, cited by Aarts, 2007).

Frequencies of each variable were also generated for each set, to identify significant variations between the sets, which might be associated with differences in usage. This analysis enabled a list of variables linked with differences in usage between the different sets to be produced.

To establish the importance of different combinations of variables case-based analysis was done using Ragin’s fsQCA software

(http://www.u.arizona.edu/~cragin/fsQCA). The variables are dichotomised for this analysis. This was done in two ways. For variables, such as age, for which the outcome across the whole range is required, a series of dichotomised

124

variables was produced by entering each age band as a variable either present or not present. For other variables, such as number of dwellings sharing the residential outdoor space, the cross-tabulation with usage enabled a cut-off point to be defined between high and low rates of usage.

Case based analysis was done for small sets of associated variables. For example, age of respondent and number of adults and children in the household were analysed together. The fsQCA software generates all the possible logical combinations of selected variables into a table of sets of cases with the values of

each variable the same. The number of sets generated = 2n, where n is the

number of binary variables represented, and therefore increases rapidly with the number of variables. The software treats each questionnaire response as a separate case and allocates it to the appropriate set. Many of the combinations of variables are not represented in the sample. For example, only one of the age band variables can equal 1 (i.e. is present), so all the sets with more than one age band variable equal to 1 are impossible and can have no cases in them. The software also calculates the proportion of cases in each set that show the outcome

(usage ≥ 4 activities, at least once a week). This is given as a table. An example

table showing the first twenty-one most populated sets only, is given in Appendix 4.6. The proportion of the cases in each set that have the positive outcome is

shown in the column headed raw consistency. (PRI consistency is an alternative

measure of consistency, which is not used in this analysis. It is only relevant for

fuzzy sets, and equals raw consistency in this analysis, where the sets are crisp.

That is, each case completely belongs to one set and one set only. Product is the

125

(Ragin, www.u.arizona.edu/~cragin/fsQCA ). The set with the largest number of cases (52) consists of established couples with a child or children in the household and 75% of them do four different activities in their ROS at least once a week. This table can be sorted according to raw consistency as shown in Appendix 4.7. The top nine rows have 100% positive outcome, but only represent a small proportion of the sample (13 cases). Visual inspection of the sets with 75% or more of the cases with a positive outcome immediately underlines the significance of particular variables (having children in the household and visiting children).

The fsQCA software leaves the outcome column (headed high usage in

Appendices 4.6 and 4.7) blank so that the researcher can enter the outcome as 1 (true) or 0 (false) according to their judgement of how consistent the cases in a set should be to represent the outcome. For example, outcome = 1 if raw

consistency > 60%. The table is then minimised by combining sets with similar levels of consistency that display a difference in only one variable.

Table 4.5 Example of how the table of sets is minimised Young 18-29 yrs Estab- lished 30-41 yrs Middle aged 42-53 yrs Ma- ture 54-65 yrs Older 66-77 yrs Elderly 78 plus yrs Have child /chil- dren Sin- gle Pair Mul- tiple Child -ren visit Number of cases Raw consist. 0 0 0 1 0 0 1 0 1 0 1 2 1 0 0 0 1 0 0 1 0 0 1 1 2 1 0 0 1 0 0 0 1 0 0 1 0 2 1 0 0 0 1 0 0 1 0 1 0 0 2 1 0 1 0 0 0 0 1 0 0 1 1 1 1 0 0 0 0 1 0 1 1 0 0 1 1 1 0 0 0 0 1 0 0 0 0 1 1 1 1 0 0 0 1 0 0 1 0 0 1 0 1 1 0 0 0 0 1 0 1 0 1 0 1 1 1

126

For example, the shaded sets of Table 4.5 all contain mature respondents with children at home and raw consistency equal to 1. Rows 1 and 2 can be combined as one set with visiting children and more than one adult. Rows 4 and 8 can be combined into a similar set without visiting children. These two sets can then be combined into a set where visiting children is not significant as shown in Table 4.6.

Table 4.6 Minimising the table of sets Young 18-29 yrs Estab- lished 30-41 yrs Middle aged 42-53 yrs Ma- ture 54-65 yrs Older 66-77 yrs Elderly 78 plus yrs Have child /chil- dren Sin- gle Pair Mul- tiple Child -ren visit Number of cases Raw consist. 0 0 0 1 0 0 1 0 7 1 0 0 1 0 0 0 1 0 0 1 0 2 1 0 1 0 0 0 0 1 0 0 1 1 1 1 0 0 0 0 1 0 1 1 0 0 1 1 1 0 0 0 0 1 0 0 0 0 1 1 1 1 0 0 0 0 1 0 1 0 1 0 1 1 1

This variable is not influential on the outcome in the combination of variables in the two combined sets and is eliminated from these sets only. The variable remains in all other sets, as it may be influential in sets with different combinations of variables. Rows 4 and 6 in Table 4.6 may be similarly combined. Minimisation was done by the researcher in Excel (and not using the fsQCA software) so that decisions about which sets to combine were made using the researcher’s observations of how variables were relating to each other, rather than by the logical rules embedded in the software, which do not recognise connections between the variables.

Table 4.7 lists the combinations of variables analysed in this way. Repeated application of this analysis to different groups of variables allowed combinations of variables associated with high and with low probabilities of usage to be identified.

127

Table 4.7 Combinations of variables analysed using fsQCA software

Attributes of the dwelling Dwelling type

Tenure

Lowest storey of dwelling,

Whole sample Stage of life Age Length of residence Socio-economic group Employment status

Number of adults in household Number of children in household Number of visiting children

Attributes of the dwelling and location Dwelling type Era

Tenure Settlement size Maximum number of floors Residential location Attributes of the development

Age of the development Layout type

Number of dwellings sharing outdoor space Area per dwelling

Greenness Tree cover Boundary

Inequality of outdoor space Maximum number of storeys

Number of parking spaces per dwelling Number of garages per dwelling

Stage of life Age

Number of adults in household Number of children in household Number of visiting children Age

Socio-economic status Employment status

SROS set only

Barriers to usage

Noise Unattractiveness Lack of privacy Neighbours Poor maintenance Fear

Final analysis SROS set only

Dwelling type Greenness

Tenure Maximum number of storeys Lowest storey of dwelling Privacy

Area per dwelling Noise

Number of dwellings sharing space Residential location Employment status Settlement size, Number of children in the household

128

This analysis was done for the whole sample initially and then just for the set of respondents with access to shared space only. When the key combinations of variables for high usage had been identified, the relationships were explored in more detail for nine cases of shared space, with different levels of usage, to identify the features that may account for these differences.

4.4.2 Thematic analysis of qualitative questionnaire data

Respondents to the questionnaire were given the opportunity to expand on their views about their residential outdoor space by answering two open questions:

A4. What do you like most about your outdoor space?

A5. What would you change about your outdoor space if you could?

These open questions were answered by 78% and 72% of the respondents respectively. Space was also made available at the end of the questionnaire for additional comments and this space was used, sometimes at some length by 213 (16%) respondents. Recurrent themes were identified from these

comments by visual inspection.