Los marginales

The section details how the quantitative and qualitative data was entered, verified and analysed.

3.10.1. Questionnaires

For the pupil questionnaires, each pupil was asked to indicate the extent to which they would like to work (Q1) and play (Q2) with each of the other pupils in their class using a 3-point scale indicated by a smiley face, neutral face and sad face (see Appendix G). The number of nominations for the most liked category (smiley face) was converted into a proportion by dividing the number of nominations by the number of pupils that completed the questionnaire (excluding the participant) and multiplying by 100. A pupil would receive a score of 100% if they were nominated by every pupil in the class as someone they would ‘like to play/work’ with. These proportions are referred to as ‘work acceptance’ and ‘social acceptance’ for the work and play questions respectively. A corresponding process was followed to achieve ‘work rejection’ and ‘social rejection scores’ whereby the least liked nominations (sad face) were added up for each pupil, divided by the number of participants and multiplied by 100.

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The decision was made by the researcher to convert the data into proportions in this way, to account for the different class sizes that were used in the study. By converting the data into proportions as opposed to presenting the data as frequencies of responses, it effectively normalises the data, allowing for the ‘acceptance’ and ‘rejection’ data for each participant to be compared against pupils from different class sizes in a meaningful way. In addition, this technique has been used in previous research in the field, (e.g. Frederickson and Furnham 2004; Pinto 2015), meaning that the results of the present study are relatable to past research on the peer relations of PSEN. An alternative approach to calculating measures of acceptance and rejection would have been to convert the smiley, neutral and sad faces into a number (e.g. 1-3), before finding the median response given for each participant. However, by finding the median as opposed to the mean, some of the variability between the data would be lost, i.e. the difference between a 1 and 2 and 2 or 3 would not be captured. Therefore, by calculating measures of peer acceptance by using the mean, it allows for greater variability of the data to be captured within the study.

Work and social preference scores were calculated by finding the difference between levels of peer acceptance and peer rejections for all pupils for the ‘would like to play with’ and ‘would like to work with’ questions in the peer rating scale questionnaires.

Question 3 of the questionnaires asked pupils to nominate up to 3 pupils that they deemed to be their closest friends. The number of times that each of the focus pupils received a ‘closest friend’ nomination was totalled to produce a unilateral friendship measure. The literature refers to this measure as another indicator of peer acceptance. A reciprocal friendship score was calculated by finding the number of times that the pupil’s nominated friend also nominated them.

For questions 4 and 5 which asked pupils to rate how frequently they worked and played with each of the other pupils in the class, the pupil responses given were converted into the following codes: Everyday= 5, Most days= 4, At least once a week= 3, At least once a term=2, Never= 1. The

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codes reported by all participants were then totalled for each pupil so that means and standard deviations for the different groups in the study could be calculated and analysed. This present study uses the terms ‘perceived peer contact in class’ and ‘perceived peer contact at break’ to describe these measures of reported peer contact.

For the teacher questionnaires, a similar process was followed whereby the responses given were allocated a numerical code to allow comparisons to be made between pupil groups. For question 1, the codes were: All of the time=5, Most of the time= 4, Some of the time= 3, Not very often= 2, Never= 1, and for question 2, the codes were: Always= 5, Nearly always= 4, Sometimes= 3, Not very often= 2, Never= 1.

3.10.2. Socio cognitive mapping

In this study, peer group analysis was conducted using the SCM software by Leung (1994). Where pupils were identified through the software as being a multi-member, the group to which they said they belonged was prioritised for the analysis. The SCM measures selected for this study were: ‘social network size’ (measured by the number of members), ‘centrality of the group’ (as measured by the SCM software) and 2 measures for the individual position of the pupil within the group which are labelled as ‘position in group’ (measured by the SCM software), and ‘nominations’ (measured by the number of nominations given to each individual). Only pupils who completed the peer group question (Q3) within the questionnaires were included within the analysis.

The SCM software assigns individuals and peer groups with a label of: nuclear, secondary and peripheral. These labels were converted into the following codes: nuclear = 3, secondary = 2, peripheral = 1. These codes were then totalled for each of the participants to allow for comparisons to be made between the groups.

3.10.3. SPSS Data analysis

The quantitative data was manually entered by the researcher into Excel (2014) spreadsheets before the data was screened and cleaned. Initially, 3

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separate data sets were created for: (1) breaktime observation data (2) classroom observation data and (3) teacher and pupil questionnaire data. As the focus pupils were observed several times, the number of observed interactions for each coding sub-category (e.g. help-seeking behaviours) were totalled for each pupil and were converted into a percentage (proportion) of the total interactions for the entire observation category (e.g. playground behaviour). This proportional data from the classroom and breaktime observations were then collated with the teacher and pupil questionnaire data to form an overall ‘child level’ data set which allowed for cross tabulations. The original observation level data sets were used to generate the descriptive statistics, whereas the child level data set was used to carry out the additional statistical analyses.

Following this, SPSS (version 25) was used to analyse the data. Checks for normality (using Kolmogorov Smirnov) and homogeneity of variance (using Levene’s test) were carried out on all variables to help decide the statistical tests to be used. Where parametric tests were deemed to be appropriate, a one-way Analysis of Variance (ANOVA) or t-tests were used. Games Howell post hoc tests were used to further explore the differences when there were unequal variances and the Bonferroni test was used where the variances were equal. Where parametric tests were not appropriate, the Kruksall- Wallis non-parametric test was used to explore the differences between subgroups and where significant, Mann-Whitney tests, were used to further examine the differences.

When non-parametric tests were deemed to be significant, parametric tests were also used in conjunction with these. Where the results of the parametric and non-parametric tests were consistent, parametric results are reported as these are more powerful and make fuller use of the data. Where appropriate, eta squared (2_{) and effect sizes (r) are presented.}

3.10.4. Interview Analysis

The qualitative interviews for all of the 10 focus PSEN were analysed together. The interviews were transcribed and analysed thematically because it enables flexibility (Braun & Clarke, 2006). Narrative analysis was

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also considered for the present study, however, it was rejected because the present study seeks primarily to use the qualitative interview data to help draw light on the quantitative results as opposed to using the interview data to derive a personal and detailed narrative around the playground experiences of PSEN in mainstream primary schools. Furthermore, Grounded Theory was ruled out because the present study does not seek to derive a theory, but instead seeks to derive a descriptive understanding of the breaktime experiences of PSEN.

The thematic analysis consisted of the following 6 stages: (1) Familiarisation of the data by examining transcripts, (2) Coding the data by highlighting points of interest, (3) Collecting codes into themes and subthemes,

(4) Creating a thematic map to indicate the relationships between the themes,

(5) Naming the themes to best describe the data, and

(6) Reporting the data in a logical and coherent way, (Braun and Clarke 2006).

Nvivo software was used as a tool to code the data and organise the codes into subthemes and themes. The coded transcripts were then shared and discussed during formal supervisions and during peer- supervision with a fellow trainee educational psychologist (TEP). During peer supervision sessions, the peer read and coded a selection of transcripts (10% of total transcripts) which allowed for comparisons to be made between the interpretations of the codes by the 2 different researchers. Overall there was a high level of overlap between the 2 researchers. Where differences were observed between the coded transcripts, this was discussed between the two researchers and this led to some adjustments in relation to the naming of codes and ways in which the codes were grouped into themes.

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