Week three notes
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This 7 page Class Notes was uploaded by Brittany Lopez on Monday October 17, 2016. The Class Notes belongs to Psy 7 RMH at LSBU taught by Dr Zoë Boden in Fall 2016. Since its upload, it has received 2 views. For similar materials see Research Methods for Mental Health in Psychology at LSBU.
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Date Created: 10/17/16
Qualitative Analysis & Thematic Analysis Week 3 Transcripts o The stage between data collection and data analysis o Turning audio (or visual) recordings into a text form Better developed for auditory than visual recordings Loss of some info from original source in the transcription process Different levels of transcriptions Features of talk o Some transcription techniques are better than others at coping with different levels of feature Prosodic features How a word is spoken (softly, loudly, emphasis) Paralinguistic features E.g. words spoken with a laugh or a sigh Extralinguistic features E.g. gestures and facial expressions Transcript o Verbatim=word for word, interviewer and participant o Basic transcription for qualitative research should include Stutters, 'erms' and 'ums' Nonverbal info Significant pauses Emphasis Places where you can't tell what is being said o Include a key to show which notation used The Jefferson System o One method of transcribing language data very detailed (See also Jeffersonlite) o Provides conventions for transcribing using a normal QWERTY keyboard Records info such as errors of speech, emphasis, pauses, nonverbal aspects and people talking over one another or speaking at the same time, and turn taking errors o Represents the material as a social exchange The perils of transcription o Decisions about how data are collected will affect what info is retained and what is lost Subtle nuances may be lost o Make sure you select the method best suited to the analysis that you want to carry out Jefferson system is very detailed and thus very timeconsuming Not worthwhile if you're only interested in the content, not how the words are spoken and/or gestures o Errors frequently occur in transcriptions Verbal additions, deletions, relocations and substitutions often occur o Always check the completed transcription against the original source o And/or a colleague to check the veracity of your transcriptions Data Analysis o Qualitative analysis is an involved and intensive process o There is often no clear separation between data collection and analysis Ongoing reflexive process involving adapting the data collection Reflecting both the emerging data (what the participants have said) and adapting to best answer the research question Research diaries o Most analysis involves some combination of Organizing and categorizing the data Linking what has been found to broader social processes or theoretical concepts Some types of Analyses o Grounded theory o Discourse analyses Foucauldian discourse analysis Discursive psychology o Dialogical analysis o Framework analysis o Template analysis o Conversation analysis o Narrative analyses o Thematic analysis o Psychoanalytic approaches o Descriptive phenomenology o Hermeneutic phenomenologies Lifeworld's phenomenology Interpretative phenomenological analysis QMethodology Doing data analysis o Some common stages/processes Immersion Intensive reading and/or listening to material Categorization Assigning codes or categories, or identifying meanings systematically Reduction/distillation Refining and distilling the categories/meanings. Going to a more conceptual or abstract level Broader interpretation Making sense of the data from a wider perspective. Making link between theory and data Thematic Analysis o A method for identifying, analyzing and reporting themes or patterns in data o Minimizing, organizes and describes your data set o But describe it in rich detail o It (usually) also allows for interpretation of the data o There are a range of different types of TA Perspectives on TA o Traumatizing meanings one of few shared generic skills across qualitative analysis (Holloway and Todres, 2003) o TA is a 'foundational method' o Thematic analysis is an analytic method in its own right (Braun and Clarke, 2006) o …others see it as a 'quick and dirty' method (Dallos and Vetere, 2005, p. 62) What is a theme? o how do we know what counts as a theme? o A theme is a significant strand/idea/concept within the data o It can be something that reoccurs frequently a motif o It can have special importance theoretically or seem particularly important for participant TA Approaches o Ontology/epistemology It can be more realist/essentialist or more relativist/constructionist o Approach Inductive or theoryled An inductive approach setsaside assumptions/beliefs/experiences and the theories available. 'bottomup', grounding all ideas in the data Theoryled works from specific theoretical frame, using this to influence the development of themes o Levels of analysis Descriptive (Semantic, surfacelevel) Latent (interpretative, critical, questioning) Step 1: Familiarize yourself with data o Transcribing data (if necessary) o Reading and rereading the data o Noting down initial ideas Step 2: initial coding o Data is coded line by line looking to identifying what is in the data o This is the first step in organizing the data o Codes need to be written next to the appropriate bit of text o You're attempting to paraphrase the content of the text o You may also start to use more psychological language o Codes tend to be quite specific, you may have quite a lot of them, even for a short piece of text Step 3: Searching for Themes o Look for connections between the codesthese are your themes o Themes are broader and less specific than codes o You are aiming for as few themes as possible (25) o But you are trying to cluster all codes into themes o You can develop this process visually (mindmaps) or by creating lists of things that seem to go together (excel, word, by hand) o Sometimes a code doesn’t seem to fit anywhereit's a good idea to create a miscellaneous theme those codes may well fit later on, or a small number be discarded Step 4: Review of Themes o Examine your themes against the original data does the theme have enough data to support it? o Draw all the evidence (extracts from the transcripts) relating to the same theme and paste it into one document o Give page and line numbers for each extract o A theme may need to be split or subdividedeach theme may have 23 subthemes o A theme should aim to fully fit the data (although in large datasets this is difficult) Step 5: Theme Definition and Labelling o Can your theme be successfully differentiated from other themes? o Choose a title/label that is punchy and concise. It should immediately help reader understand what theme is about o Can you say exactly what theme is about and what it's not about… in just couple of sentences? o Best share you ideas about what a theme is with other to check for validity What not to do o You should not use your interview questions to create themes o A weak analysis may be indicated by themes which overlap too much or where there is a lack of consistency or coherence o Make sure your themes capture what is in the data, not your assumptions about the topic Evidencing your themes o It's typical to include verbatim quotes from the participant/s o A pseudonym or number is used to avoid identifying the participant (for ethical reasons) o Numbers are sometimes seen to be dehumanizing, so many researcher prefer to pick appropriate pseudonyms for their participants o Quotes shouldn't be too long, or too short o Your analysis should be longer than the quote o But quote needs enough context that it provides good evidence and allows the reader to understand what is happening How to DO analysis o Two approaches With a computer Specialist programs Excel, word By hand Postits Diagrams Lists Or a mixture of the two Computer analysis o One tool that can help with analysis CAQDAS (computerassisted qualitative data analysis software) o Cuts down on amount of paper Much textual material to keep under control Linenumbered transcripts, coding categories, evolution of these categories o Useful for making and changing links within (and between) documents o Quick recording of data, allowing for changes to categories and additional data Writing up Qualitative Research o Title o Intro o Methods E.g. participants and recruitment, ethics, reflexivity data collection, data analysis o Findings o Discussion o References o Appendices Writing up Qualitative Research: Intro o Similar to quantitative reports Aims Why does this matter? o Literature review What do we know about this area already? o Rational for this study Sometimes justifies the use of a qualitative methodology or introduces the approach a little o Research question(s) NO hypothesis! Writing up Qualitative Research: Method o Sections typically include Participants (and recruitment) Ethical considerations Data collectionhow you collected your data, which approach and why, details of the procedure involved Data analysis how you did your analysis, which method you used and why details of the procedures involved (steps) Writing up Qualitative Research: Findings o For a thematic analysis Intro to the themes Subsections with each of the theme titles as the headings In each theme section, a short intro to the theme including any subthemes Analysis written up as a narrative commentary under each theme A selection of quotations to illustrate your analytic comments (with pseudonyms, page and line numbers as appropriate) A brief concluding paragraph or short summary This can review the themes and point out any links between them Writing as Analysis o Writing up is the final step o Connections are made o Contradictions are spotted o Themes are refined and may be integrated or collapsed further Tips for writing up the analysis o Ensure you have sufficient extracts from the data to support your themes o Provide clear descriptions of each theme when you introduce it o Make your thematic analysis go beyond simple description and show how the themes address the research question o Choose rich data examples to illustrate each theme o Don't oversample extracts from one participant make sure everyone's voice is heard Include a table or diagram of your crosscase analysis o Evidencing your themes o It's typical to include verbatim quotes from the participant/s o A pseudonym or numbers is used to avoid identifying the participant (for ethical reasons) o Numbers are sometimes seen to be dehumanizing, so many researcher prefer to pick appropriate pseudonyms for their participants (or have them pick) o Quotes shouldn't be too long or too short o Your analysis should be longer than the quote, but quote needs enough context that it provides good evidence and allows reader to understand what is happening When to use literature to support your claims o Two ways of doing this Save all discussion of literature for discussion section Integrate literature with the findings and then have a General Discussion at the end Writing up Qualitative Research: Discussion Much like an APA quantitative report o Start with a summary of the findings o Make links with previous literature, including empirical and theoretical work o Can introduce new studies hereas your research is exploratory it may have gone in new and unexpected directions. The discussion is the time to explore these in more depth o Reflexive statementhow has the researcher shaped the data? o Clinical and research implications Limitations/evaluation o o conclusion
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