Statistical analyses

This section will describe the statistical techniques used to analyse the 2014 HBSC England data set. A variety of descriptive and inferential statistics were employed to achieve the research objectives. The variables listed in Section 4.2.1 were included in these analyses. It is possible to apply weights to the 2014 HBSC England data set to further improve its match to the larger population, up weighting cases from underrepresented groups and down weighting others. However, adding this complexity to the analysis is only worthwhile if it makes substantive differences to the results as it can restrict the analyses possible (the ability to use weights with some multilevel analyses is a subject of ongoing development). In all cases, exploratory analyses using weights showed only small differences to unweighted analyses and, as such, all analyses presented her were conducted without weights.

Descriptive statistics

Descriptive statistics are “procedures for organizing and summarizing data so that the important characteristics are described” (Heiman, 2004, p. 293); measures of central tendency, measures of dispersion, frequencies and percentages may be used to describe the basic characteristics of the data. Descriptive statistics were employed in relation to research objective no. 1 (see Section 1.3), to establish the prevalence of relational bullying and to situate relational bullying within the broader context of bullying behaviours by making comparisons with other measures of bullying contained within the 2014 HBSC England survey. Descriptive statistics were also used to establish a demographic picture of those experiencing relational bullying by looking at the frequency of this behaviour by gender, age, SES and ethnicity. All descriptive statistics were carried out using the software IBM SPSS Statistics. The results of the descriptive statistics are reported in Section 5.2 – Section 5.4 of Chapter 5.

Inferential statistics

Inferential statistics go beyond simply describing the data to make broader inferences based on the data being analysed. Inferential statistics often include tests of statistical significance, seeking to identify whether the findings are due to random chance or whether they are “representing a ‘real’ relationship found in nature” (Heiman, 2004, p. 126). However, statistical significance on its own does not imply practical significance, causation nor provide the size of the effect. Consequently, during the inferential analyses and presentation of findings, care has been taken to interpret statistically significant results within context.

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Cluster sampling was employed during the 2014 HBSC England study (see Section 3.4.2). Cluster sampling resulted in respondents being organised within classes and schools, these classes and schools inevitably comprised of different cultures and policies so it is likely respondents from the same class and/or school were more similar to each other (Field, 2009). The effects of clustering were acknowledged through the use multilevel modelling which took account of variation at the different levels – student, class and school levels. As such, all inferential modelling was conducted using the multilevel modelling software package MLwiN (Centre for Multilevel Modelling, University of Bristol).

Inferential statistics played a dominant role in facilitating research objectives no. 2 and no. 3 (see Section 1.3). The two research objectives, which a) sought to identify health and wellbeing outcomes of relational bullying and b) identify factors which young people perceive as helping them to navigate relational bullying, were regarded as building on each other. Successfully navigating relational bullying is likely to reduce the health and wellbeing outcomes associated with this behaviour. However, it was initially important to ascertain the health outcomes associated with relational bullying – especially considering the dearth of evidence from a UK-based perspective (see Section 2.4.5). As such, the inferential statistics examining health and wellbeing outcomes draw only on demographic factors in the social- ecological theory; however, the variables associated with the social-ecological theory (see Figure 4.6) are drawn upon heavily in the subsequent analysis identifying factors which may help with the navigation of relational bullying.

In response to research objective no. 2, three multilevel models were built in order to examine the association between young people’s experience of relational bullying and three measures of health and wellbeing:

1. HRQL as measured by KIDSCREEN-10. HRQL was a scale variable and consequently a regression model for a continuous outcome was computed (see Section 5.5).

2. General self-rated health was a categorical variable in which the response options formed a sequence, as such it was appropriate to fit an ordered multinomial regression model (see Section 5.6).

3. Life satisfaction, similar to general self-rated health, was a categorical variable with ordered response options and as such an ordered multinomial regression model was computed (see Section 5.7).

In all three multilevel models relational bullying was included as an explanatory variable, while demographic factors (age, gender, ethnicity and SES) and physical and verbal forms of bullying

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were included as potentially confounding variables irrespective of significance. The 5% level of significance was used to identify main effects. Random slopes and interactions between main effects were then considered using the stricter 1% level to reduce the risk of overfitting by including spurious terms. When modelling general self-rated health and life satisfaction a number of the main effects violated the proportional odds assumption and as such the effect of the variables differed across the outcome categories. For instance, the associated effect of relational bullying differed between the high, medium and low life satisfaction categories. The model building allowed for this variation by fitting separate coefficients for each outcome category.

Research objective no. 3, seeking to identify factors in the young person’s world which may help them to navigate the experience of relational bullying (see Section 1.3), was met through the integration of both inferential statistics and qualitative analysis. The quantitative analysis played an initial exploratory role. A multilevel model explored factors from the young person’s social-ecological system which were associated with high life satisfaction among those experiencing relational bullying, seeking to identify factors which help young people positively navigate relational bullying (see Section 5.8). Life satisfaction was a binomial outcome variable with either ‘low’ or ‘high’ life satisfaction, consequently a logistic regression model was created. A forward selection strategy was employed to identify main effects from the factors listed in Figure 4.6 which were associated with the social-ecological theory. Wald tests were used to judge significance at the 1% level. The 1% level of significance was used, as opposed to 5%, due to the fact multiple comparisons were being made which would have increased the chance of identifying spurious relationships. Random slopes and interactions between main effects were then considered using the stricter 0.1% level of significance to reduce the risk of overfitting. Demographic variables including age, gender, ethnicity and SES were retained in the model despite being non-significant to control for any minor effect they may have.