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Ravalier PhD Theis.pdf - Anglia Ruskin Research Online

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167<br />

Along these lines the majority of the data were collected online which could then be copied directly into Microsoft<br />

Word and NVivo 8, making the transcription which plays such an important part in developing closeness to the data<br />

unnecessary. Similarly there was very little time between the closing of the log online collector and the beginning<br />

of the semi-structured interviews, a problem because the results of the log phase formed part of the questioning<br />

inherent within the interview schedule. Therefore content analysis, a quick method, was conducted to ensure that<br />

all of the data were adequately analysed in time for the beginning of the subsequent research phase.<br />

A difficulty inherent in the use of the daily log data collection method, and particularly with the use of online<br />

collectors, is that individual tone of speaking is often removed from individual responses in a way which does not<br />

occur with semi-structured interview techniques. With hand-written logs some tone was clear through the data<br />

collected in that respondents could capitalise, underline, highlight, italicise etc data that needed to be emphasised.<br />

This is much more difficult with online methods where capitalising was the only technique in which tone could be<br />

conveyed. Therefore during the data analysis methodology equal weight was given to each mention of a<br />

phenomenon, with scores represented in Table 23.<br />

As per the actual content analytical technique utilised, a simplified version of the methodology used by<br />

McIntyre, Horn and Matsuo (2008) in their study of daily hassles was implemented in order to complete the<br />

analysis. The process began therefore with each set of responses being read three times once the collector had<br />

closed in order to gain an understanding of some of the categories which may emerge from the data. Through a<br />

fourth reading a number of categories were developed (and coded in NVivo) to reflect the common themes, and a<br />

fifth more detailed reading meant any more sub-themes that would otherwise have been missed were picked out.<br />

Also within this fourth and fifth reading a tally was kept of each time each particular theme was mentioned, leading

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