Data Analysis Quantitative Approach

In undertaking the quantitative part of the research, it was important to consider the best way of analysing the data collected. Quantitative data is described by Babbie (2010) as:

73

‘The numerical representation and manipulation of observations for the purpose of describing and explaining the phenomena that those observations reflect’. (p422) Greene & D’Oliveira (1999) highlight how in undertaking quantitative research predictions are made and that quantitative data analysis is about developing and testing theories. Neuman (2006) stated that in considering research design there requires important decisions to be made about what data is going to be collected and how it is going to be measured.

There are many strengths and weaknesses of the quantitative data analysis approach. An advantage of quantitative data analysis is that the data collected is measurable and numerical statistical tests can be used to understand and interpret findings. Another advantage of a quantitative approach is that the analysis is more objective and descriptive. Quantitative data can also help to identify and establish connections and correlations between different variables and outcomes. Choy (2014) identifies some of the strengths and weaknesses of the quantitative approach, stating that its strengths are its reliability and replicability, whereas there are weaknesses in that this approach is not as in depth or subjective. The objective and measureable nature of this approach would contrast well with the subjective nature of the qualitative interviews and provide a broader analysis of the risk assessment process.

In considering how to analysis the quantitative data a computer-based software package (SPSS) was considered most useful. There were several types of analysis that needed to be undertaken on the case file information; these being the analysis of the risk ratings of the case files across the five different risk assessments as well as being able to undertake an analysis of the risk assessment tools themselves in terms of their content.

In considering the risk ratings of the case files, the first analysis undertaken was to see if there were similarities in the way the different tools assessed the risk presented within the case file information. As each risk assessment tool rated risk differently, using different weightings, it was important to find a way to compare the different tools. This was achieved by giving numerical ratings to each of the overall risk ratings (i.e. Low risk = 1, medium low= 2 through to very high risk= 6); this allowed the different tools to be compared.

A simple line graph was used to highlight if there were any trends in the ways the risk assessments rated risk. This allowed for a visual representation to be provided that not only identified if risk assessments rated the case files at the same level but also was able to recognise whether, even if they did not rate the risk the same, they followed the same trend.

74 This was a rudimental initial analysis of the data and a more comprehensive method of analysis was needed. A means analysis was thought to be useful in order to measure internal consistency, in this case whether the risk assessments rated risk similarly. Tavakol & Dennick (2011) describe internal consistency as ‘the extent to which all the items in a test measure the same concept or construct and hence it is connected to the inter-relatedness of the items within the test’. This test was able to look at the average risk rating each tool gave the case file information and compare them. It was also able to consider the standard deviation; the amount that each risk assessment deviated from the mean rating. This test allowed there to be a comparison as to how consistent the tests were in comparison to each other.

To expand this analysis further a correlation analysis was undertaken. A Spearman’s Rhowas identified as a suitable test. The Spearman's Rank correlation coefficient is a statistical test that can be used to look at the correlation of variables, both in terms of strength and direction. This test looks at how the risk assessment tools correlate with each other in terms of their risk ratings.

The next section of the quantitative analysis focused on the content of the different risk assessment tools, considering whether they were taking account of the same factors. Similarly, to the start of the previous quantitative analysis a simple analysis was initially undertaken. There were six key domains identified in the literature review, these were developmental, behavioural, relational, environmental, attitudinal and aspirational. Each of the risk assessments were considered individually, looking at the questions that were asked and categorising them in terms of identifying which of the six categories they best fitted in to. To provide further information, the behavioural section was divided in to sexual and non- sexual behaviour. With all six risk assessments questions categorised this data was translated into simple pie charts, allowing there to be a comparison of the risk assessment tools focus and content.

75

Chapter Four

Pilot

1. Introduction

Chapter Four focuses on the pilot undertaken to explore the design of the research and its findings. There will also be an outline of the profiles of the research participants provided before concluding with a summary of the challenges. It was important to undertake a pilot study prior to starting the full research so that the research design could be tested to see whether there were any difficulties with the administration of the interviews, whether the questionnaires provided an appropriate focus to elicit information needed to explore the research questions and provided the opportunity to analyse the data. This section overviews the pilot study and the final data collection process.

2. Method