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More Techniques for Finding Themes

Other techniques for finding themes include pile sorting, making word counts, and producing key-word-in-context (KWIC) tables. To use the pile-sorting method—what Lincoln and Guba (1985:347-49) call cutting and sorting—look for real quotes from the interviews you do with informants that represent what you think are important topics in the data. Cut out each quote (making sure to leave some of the context in which the quote occurs) and paste it onto a 3 X 5 index card. On the back of the card, note who said it and where it appeared in the text. Then, lay out the cards on a big table, sort them into piles of similar quotes, and name each pile. These are the themes (Ryan and Bernard 2003:94).

You can check your reliability by asking several friends or colleagues (the more the better) to sort the cards into piles of‘‘what goes with what.’’ Sayles et al. (2007) analyzed 300 pages of transcripts from seven focus groups about the experience of stigma among people living with HIV. First, they read the transcripts and picked out 500 statements that represented what they thought were themes. Next, they printed the statements on slips of paper and sorted the slips into piles of more general themes, or domains. Then, other team members went through the material and decided, by consensus, if each of the slips belonged in its original pile or in one of the other piles—or a brand new pile.

Ryan (1995) used the pile-sort method to find themes in his study of household healthcare in Njinikom, Cameroon. As they did the pile sort, coder discussed with one another their reasons for putting items together or separating them. Ryan recorded the discussion and used the data to help identify the themes in his data.

Word lists and the KWIC technique are extensions of the philosophy behind in vivo coding in grounded theory: If you want to understand what people are talking about, look closely at the words they use. The method has a very, very long history. The classic KWIC method is a concordance, which is a list of every substantive word in a text with its associated sentence. Concordances have been done on sacred texts from many religions and on famous works of literature from Euripides (Allen and Italie 1954), to Beowulf (Bessinger and Smith 1969), to Dylan Thomas (Farringdon and Farringdon 1980). These days, KWIC lists are generated by asking a computer to find all the places in a text where a particular word or phrase appears and printing it out in the context of some number of words (say, 30) before and after it. You (and others) can sort these instances into piles of similar meaning to assemble a set of themes.

Ryan and Weisner (1996) told fathers and mothers of adolescents: ‘‘Describe your children. In your own words, just tell us about them.’’ In looking for themes in these rich texts, Ryan and Weisner did a word count. Mothers were more like than fathers to use words like as ‘‘friends,’’ and ‘‘creative,’’ and ‘‘honest’’; fathers were more likely than were mothers to use words like ‘‘school,’’ ‘‘student,’’ and ‘‘independent.’’ These counts became clues about the themes that Ryan and Weisner eventually used in coding the texts.

No matter how you actually do inductive coding—whether you start with paper and highlighters or use a computer to paw through your texts; whether you use in vivo codes, or use numbers, or make up little mnemonics of your own; whether you have some big themes in mind to start or let all the themes emerge from your reading—by the time you identify the themes and refine them to the point where they can be applied to an entire corpus of texts, a lot of interpretive analysis has already been done. Miles and Huberman say simply: ‘‘Coding is analysis’’ (1994:56).

 
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