A comparison of traditional content analysis and a web based visual analytical tool to analyse qualitative data

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2 A comparison of traditional content analysis and a web based visual analytical tool to analyse qualitative data Authors: Dr Sherrill Snelgrove, Dr Fiona Murphy Ms Dawn Snow

3 Presentation aim The aim of this presentation is to contrast two methods of coding and categorising qualitative data with a view to propagating discussion rather than advocating a new way of working.

4 Content Analysis The objective, systematic and quantitative description of the manifest content of communication (Berelson, 1952, p.18)

5 Content Analysis - a coding operation and data interpretation operation (Morse and Richards, 2002) - Systematic examination and interpretation of a body of material so as to identify patterns, themes, biases and meanings (Berg and Latin, 2007, Leedy and Ormrod, 2005; Neuendorf, 2002) - any technique for making inferences by objectively and systematically identifying specified characteristics of messages (Holsti, 1969)

6 Word Cloud technique TagCrowd web application Steinbock, Free to use. A word cloud is a visualisation technique that identifies the mostfrequently used words in any text. Objective and systematic. Useful for large qualitative datasets. The researcher can then contextualise the most-used words (e.g., 10% of word cloud ) to understand how the words are used - their contexts explain this

7 Background: Attrition Study Student Attrition Questionnaire ( The Attrition Risk prediction Tool; Fowler & Norris, 2009) An opportunistic sample of all pre- registration nursing and midwifery students attending the College of Human and Health Science January March 2011 N = 227 free text responses Analysed using traditional form of content analysis and then re-analysed using Tag Crowd (Steinbock, 2011)

8 Traditional Content Analysis: What we did Determined basic units of analysis a coding scheme meaning units/coding unit Each piece of text was individually coded, counted and categorised. Categories examined for meaningful patterns Built themes based on patterns (A sequential and iterative process)

9 Reasons for leaving: Categories/Sub categories External Issues Travel Car parking costs & inadequate bursary Childcare Costs & other responsibilities Personal reasons (Home/work balance) Clinical placements Reality of clinical work Lack of preparation Length of stay and repetition of experience Mentorship Academic Feedback and supervision Perceived relevance of course content Perceived challenge of course Personal tutor support Course organisation Timetabling Course requirements and logistics

10 Reasons for Staying: Categories /Sub categories External Issues Bursary Clinical placements Supportive Mentors Enjoy placements Feedback and supervision Teaching quality Tutor support Academic Course organisation Course organisation Timetabling Course requirements and logistics Personal Factors Motivation Friendship and support

11 Using Word Cloud The two questionnaire columns containing reasons for staying on the course and leaving the course were blocked, copied, and pasted separately into the TagCrowd software text box. Option settings were: (1) English for the Language of text, (2) 60 for the Maximum number of words to show (3) 1 for the Minimum frequency, which means that potentially any word, even if only used once, could appear in the word cloud ; (4) yes to Show frequencies (5) yes to Group similar words, and (6) etc. for Don't show these words We contextualised the 6 most frequently used words (e.g., 10% of the word cloud ) (See Osborne et al., 2012)

12 Results: Reasons for leaving

13 Results: reasons for staying

14 The Challenges of Word Cloud Technique Setting the criteria for showing the most common words Cleaning up the data Omitting important issues Blending manifest and latent analysis

15 Comparing traditional Content Analysis procedure and using Word Cloud (Steinbock, 2006) Traditional Analysis - Read Text Meaning Units Coding Categories Themes /trends Present themes/ quotes Word Cloud - Block text Word Cloud Individual word analysis in context Categories Trends across samples

16 Word Cloud Techniques STRENGTHS objective/detached freely available an almost instant visual representation and counting of key words in the text Time saving Useful for large amounts of qualitative data Easy for identifying overarching trends across the sample comparison WEAKNESSES A priori coding (one word rather than units of meaning) Cleaning the data -Researcher may miss out on important data because of limits set Manifest content analysis rather than latent content of the text Reductionist Online anonymity?

17 Conclusion Word Cloud is useful in a quantitative surveys that includes an open- ended question Word Cloud is a rapid, visually striking supplementary tool but has limitations. The use of Word Clouds in qualitative content analysis deserves further consideration.