The Process and Application of Phase Three: Semi structured

Informants

Phase Three of the research was initiated by a pilot study conducted with three clinicians to review the interview schedule. Results were then presented in a data matrix grid (Appendix 11) and the interview schedule revised accordingly (Appendix 11.1). Semi-structured interviews were then conducted with seven transport

clinicians from the initial reference group utilised in Phase One of the study. The data collecting instrument was carefully designed to establish face validity of the newly developed transport pain scale. This provided participants with the

opportunity to give their perceptions on the ‘face value’ of the scale, to review if ‘on the face of it’ the scale appeared to measure neonatal pain during transport. The management and analysis of qualitative data can be particularly challenging, primarily due to the immense amount of data which can be retrieved from qualitative methods, also due to the absence of standard analytical procedures in handling data and the difficulty in presenting data to ensure validity is transparent in the analysis (Polit and Beck 2010).

Qualitative content analysis was utilised in this Phase of the research, a method reported as being very flexible, requiring researchers to judge which variations are most appropriate for their particular study (Miles and Huberman 1994). Qualitative content analysis was utilized for the subjective interpretation of the content of text data and was applied through the systematic classification process of coding and identifying themes or patterns highlighted in the data (Hsieh and Shannon 2005). The method involved deriving codes from the data, which are read word for word and structured into categories (Miles and Huberman 1994).

Words were highlighted in the text capturing key concepts or thoughts which were consequently reflected in emerging labels or codes (Riley 1990), these codes reflected the primary ideas and eventually formed part of an initial coding system (Hseih and Shannon 2005). The codes were then arranged into categories utilized to organize and group sections of data into meaningful clusters, enabling categories to be structured into major themes. This method of conventional content analysis was therefore applied to this Phase of the study utilising open colour coding (Riley 1990) and identification of themes, with the aim of establishing face validity of the newly developed transport pain scale. A particular feature of qualitative research is that data collection and data analysis are carried out concurrently, encapsulating examination, categorisation, tabulation and combination of the evidence in order to draw conclusions (Parahoo 2006). Computer assisted software such as SPSS for the analysis of quantitative data is now widely available, however qualitative data analysis packages are still not universally accepted to the same extent as quantitative packages.

Within the context of the current research consideration was given to the use of computer assisted qualitative data software (CAQDAS, N-Vivo) which have been described as being useful in eliminating the labour intensive element of qualitative data analysis (Parahoo 2006, Bryman 2008).However as was highlighted by Parahoo (2006), the appropriate use of these software packages requires that the researcher is experienced and perceptive in the analysis of qualitative data. This view was also reflected by Webb (1999) who suggested that new researchers undertaking small- scale studies would be advised to use a manual approach in order to gain insight into the intuition aspects of analysis.As the essence of the data analysis within this study was to focus on the participants’ views and experiences on the pain

assessment scale it was necessary to remain close to the data at all stages in order to remain true to the study. An informed decision was therefore made to reject the use of software and therefore manual procedures were employed.

The first stage of open text analysis of semi-structured interviews conducted in Phase Three involved reading through transcripts of the interviews, key words or concepts were identified and assigned colour codes which facilitated the emergence of key concepts from the raw data, then all the open codes were listed and grouped manually. Initial Themes emerged during analysis, through word based techniques including word repetitions, indigenous categories or key- words- in- context, numerical codes were subsequently applied to statements in order to facilitate further analysis and facilitate confirmation of definitive Themes. For the purpose of analysis, each statement was allocated a number which was listed in sequence within each transcript.

- Audit Trail: Semi-Structured Interviews with Transport Clinicians

This section will provide an overview of the audit trail of data collection and analysis throughout Phase Three of the study.

1. A semi-structured interview schedule was developed to establish face validity of the pain scale based on the areas of focus highlighted during development of the Delphi questionnaire.

2. Three pilot interviews were conducted with volunteer participants from a dedicated transport team, any required amendments were made to address issues of ambiguity or wording.

3. Semi-structured interviews then were conducted in September 2011 with seven transport clinicians from the reference group in Phase One.

4. The researcher listened to each audio-recording and stored them in the researcher’s laptop computer, protected by a security password. In addition to this a backup of the audio–recording file was made.

5. Each audio-recording was transcribed verbatim by the researcher using computer word processing to allow computerised storage and organisation of data. To preserve anonymity of the participants no names were included with participants numerically identified on the transcriptions.

6. Transcribed copies of their interview were given to each participant for verification of content.

7. The researcher read through the transcriptions on several occasions to provide an overview of the information and gain familiarity with the content.

8. The researcher then read the transcriptions line by line to identify key words or meaningful concepts related to the research question and aims of the study, these sections were assigned codes to highlight a particular segment which is known as open coding. This method facilitated key concepts or words to emerge from the data.

9. Computer word processing (Track Changes) was applied at this stage to assist the process (Figure 16). At this point the coded transcriptions were cross checked by an outsourced neonatal education practitioner,

experienced in qualitative analysis.

Figure 16 Example of Open Coding using “Track Change” Word Processing

Programme

10. The open codes of the transcriptions were all listed, sorted and grouped manually into categories; overlap and redundancy among categories were therefore decreased. As a result of this process four main Themes were developed together with sub-themes. The list of Themes and sub-themes were then assigned numerical codes (see Figure 17 below for example of thematic framework assigned numerical codes).

Figure 17 Example: Thematic Framework assigned numerical codes

The thematic framework assigned codes were then carefully and systematically applied to all of the transcriptions. Item numbers were then allocated to each statement, which allowed the researcher to view and analyse data within all interviews under the themes developed (Figure 18).

Figure 18 Example: Semi-structured Interviews with Item Numbers and Codes

In order to reduce the data and make it more manageable without losing their substance, thematic charts were created applying the main themes and sub-themes from the thematic framework. The charts were structured to display each theme within its own chart with entries from all participants. In each chart the themes and sub-themes were in columns with the participant number, the itemised comments were placed in the appropriate column. The process culminated with data being combined into each appropriate theme. This process allowed the researcher to visualise and analyse the data under the developed themes, for an example of a thematic chart see Figure 19 below.