{"id":2915,"date":"2015-07-17T13:34:29","date_gmt":"2015-07-17T20:34:29","guid":{"rendered":"http:\/\/nnlm.gov\/evaluation\/blog\/?p=2915"},"modified":"2019-09-24T10:13:30","modified_gmt":"2019-09-24T17:13:30","slug":"fast-track-interview-analysis-the-rita-method","status":"publish","type":"post","link":"https:\/\/news.nnlm.gov\/nec\/2015\/07\/17\/fast-track-interview-analysis-the-rita-method\/","title":{"rendered":"Fast Track Interview Analysis: The RITA Method"},"content":{"rendered":"

If you want a systematic way to analyze interview data, check out the Rapid Identification of Themes from Audio Recordings (RITA) method described in Neal et al. (2014). This method skips the time-consuming transcription process, because you conduct your analysis while listening to the recordings.\u00a0 Also, the process maintains nonverbal elements of your data (i.e., intonation), which are lost when interviews are transcribed. The authors presented a case in their article to demonstrate how to use the RITA method.<\/p>\n

The five-step RITA process, briefly described below, is meant to be used with multiple raters:<\/p>\n

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  1. Develop focused evaluation questions. Don\u2019t try to extract every detail from the recordings. Instead, write some focused evaluation questions to guide your analysis. For instance, you might want to know how participants applied lessons learn from a class on consumer health information or what services are desired by a specific type of library user.<\/li>\n
  2. Create a codebook. Develop a list of themes by talking with the project team, reviewing interviewer notes, or checking theories or literature related to your project. For their sample case, the authors used eight themes. That\u2019s probably is the upper limit for the number of themes that can be effectively used for this process. Once you have the list, create a codebook with detailed theme definitions.<\/li>\n
  3. Develop a coding form. (See the figure below.) This will be used by all coders to record absence or presence of a theme in time-specified (e.g., 3 minute) segments of the interview. They listen to a time segment, mark if any themes were present, and then repeat the process with the next segment. (The article describes the process for figuring out the most appropriate time segment length for your project.) If you want, you can also incorporate codes for \u201cvalance,\u201d indicating if comments were expressed positively, negatively, or in neutral tones.<\/li>\n
  4. Have the coding team pilot-test the codebook and coding form on a small subset of interviews. The team then should refine both documents before coding all recordings.<\/li>\n
  5. Code the recordings. At this stage, one coder per interview is acceptable, although the authors recommend that a subset of the interviews be coded by multiple coders and results tested for rater agreement.<\/li>\n<\/ol>\n

    \"RITA<\/a><\/p>\n

    While the RITA process may seem time consuming, it is much more efficient than producing verbatim transcripts. Once the authors finalized their coding form, it took a team member about 68 minutes to code a one-hour interview. Because coded data was expressed in numbers, it allowed the authors to assess inter-rater reliability (agreement), which demonstrated an acceptable level of agreement among coders. Rater agreement adds credibility to your findings and can be helpful if you seek to publish your results.<\/p>\n

    While the RITA method is used with qualitative data, it is essentially a quantitative analytic method, producing numbers from text. \u00a0That leads me to my main concern. By reducing the data to counts, you lose some of the rich detail and subtle nuances that are the hallmarks of qualitative data. However, most evaluation studies use mixed methods to provide a complete picture of their programs. \u00a0In that spirit, you can \u00a0simply \u00a0keep track of time segments that contain particularly great quotes and stories, then transcribe and include them in your project report. They will complement nicely the findings from your RITA analysis.<\/p>\n

    Here is the full citation for the Neal et al \u00a0article, which provides excellent instructions for conducting\u00a0the\u00a0RITA process.<\/p>\n

    Neal JW, Neal ZP, VanDyke E, Kornbluh M. Expediting the analysis of qualitative data in evaluation: a procedure for the rapid identification of themes from audio recordings (RITA). American Journal of Evaluation. 2015; 36(1): 118-132.<\/p>\n

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    If you want a systematic way to analyze interview data, check out the Rapid Identification of Themes from Audio Recordings (RITA) method described in Neal et al. (2014). This method skips the time-consuming transcription process, because you conduct your analysis while listening to the recordings.\u00a0 Also, the process maintains nonverbal elements of your data (i.e.,… Read More »<\/a><\/p>\n","protected":false},"author":2959,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[27],"tags":[],"class_list":["post-2915","post","type-post","status-publish","format-standard","hentry","category-blog"],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_shortlink":"https:\/\/wp.me\/p8ICUo-L1","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/news.nnlm.gov\/nec\/wp-json\/wp\/v2\/posts\/2915","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/news.nnlm.gov\/nec\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/news.nnlm.gov\/nec\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/news.nnlm.gov\/nec\/wp-json\/wp\/v2\/users\/2959"}],"replies":[{"embeddable":true,"href":"https:\/\/news.nnlm.gov\/nec\/wp-json\/wp\/v2\/comments?post=2915"}],"version-history":[{"count":1,"href":"https:\/\/news.nnlm.gov\/nec\/wp-json\/wp\/v2\/posts\/2915\/revisions"}],"predecessor-version":[{"id":6581,"href":"https:\/\/news.nnlm.gov\/nec\/wp-json\/wp\/v2\/posts\/2915\/revisions\/6581"}],"wp:attachment":[{"href":"https:\/\/news.nnlm.gov\/nec\/wp-json\/wp\/v2\/media?parent=2915"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/news.nnlm.gov\/nec\/wp-json\/wp\/v2\/categories?post=2915"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/news.nnlm.gov\/nec\/wp-json\/wp\/v2\/tags?post=2915"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}