Questionnaire data

There were two incomplete questionnaires, the decision was taken to remove these completely from the data analysis, as the relationship between questions for those respondents could not be ascertained and this was an important factor of the data analysis.

The questionnaire data from the 73 fully completed responses was entered into IBM SPSS and the data was coded as described, to enable statistical analysis to be carried out. For the 16 closed ended questions, where the respondents were asked to provide a yes, no, don’t know response the data was coded as follows Yes = 1

No = 2

Don’t know = 3 Not applicable = 4

Some of the questions requested the response to a time scale or period, the years were grouped to enable more effective analysis and to ensure there were adequate number of responses per group so valid data analysis could take place.

When asking how long the respondent and the family had farmed around the common, the responses were coded as per Table 1.

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1 Ten years or less

2 11 to 50 years

3 51 to 100 years

4 101 to 150 years

5 151 years plus

Table 1 Coding for responses to time period a respondent had farmed around the common

For the question relating to the period of time a family had livestock grazing the common, this was coded as Table 2.

1 One to 30 years

2 31 years plus

3 Not applicable

Table 2 Coding from responses to how long a family had grazed livestock around the common

Where respondents were asked if they would consider using their common rights to graze in the future, and if so, how long into the future, the responses were coded as per Table 3.

1 In the next five years

2 In the next ten years

3 Not considering grazing in the future.

4 Not applicable

Table 3 When respondents would consider grazing the common

The Likert data for the three parts were coded in SPSS as a scale of one to five with one representing the ‘strongly disagree’/’very difficult’ option through to five representing ‘strongly agree’/’very easy’. However there was a problem with the data analysis as there were insufficient numbers of responses per category for successful data analysis. It was therefore decided to re-group the responses from a five point scale to a three point scale with the responses for ‘strongly disagree’/’very difficult’ being incorporated with ‘disagree’/’difficult’ and ‘strongly

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agree’/’very easy’ incorporated with ‘agree’/’easy’, leaving a singular response group in the middle representing the no opinion or do not know group.

There has been much debate as to the analysis of Likert scale data, as the data is considered ordinal and the distance between the points on the scale not to be measurable so as to create an interval scale. Carifio and Perla (2008) indicate it is acceptable to use the means whereas other researchers warn against using such methods as it would not provide an accurate picture of the results within the mean score (Robbins and Heiberger, 2011). Likert scale data is accepted as most suited to non-parametric statistical analysis, however more recent research does suggest that with a large enough sample size, the data can be analysed using parametric statistical analysis, providing robust results (Sullivan and Artino, 2013; Wadgave, 2016; Carifio and Perla, 2008). The Likert data was also displayed as a diverging stacked bar chart (Robbins and Heiberger, 2011)Recognising the smaller sample size within this research, non- parametric tests were therefore employed; Chi square goodness of fit, Pearson’s Chi square and Fisher’s exact test. A diagrammatic representation of the methodology employed with the data is shown in Figure 9.

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Figure 9 Diagrammatic representation of data analysis

Questionnaire Data Interview Data Coded Transcribed Entered in to SPSS Coded

Likert Non Likert

Diverging stacked bar chart Cross Tabulation Chi square Re-coded Descriptive Statistics Bar charts Results Chi square goodness

of fit

Cross Tabulation

Chi square

Data extracted - statements

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Initially, descriptive frequencies were looked at, with presentation in simple bar charts. All of the data, including the Likert style questions were analysed by this method. A diverging stacked bar chart was considered most appropriate for displaying the responses from the Likert style questions.

The questionnaire data was then subjected to cross tabulation. The Likert data was analysed by the same methods, but the Likert sections of the questionnaire were analysed as separate data.

As the Likert data met the four following assumptions  There was at least one categorical variable  An independence of observations

 Mutually exclusive responses  At least five expected frequencies

Chi square goodness of fit could be carried out on the Likert question data. The data within SPSS for these questions were not weighted before the analysis, as it was not necessary to do so. The goodness of fit test would measure if the actual responses to the Likert questions varied significantly from the expected responses.

One way Chi square analysis was carried out on all of the questionnaire data. Where the statistical data for the Chi square analysis indicated there were more than 2 cells with an expected count of less than five, then Fisher’s exact test of

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independence was used, as this was more appropriate than the Pearson’s Chi square analysis.