Data Analysis in Qualitative Research PDF

Data Analysis in qualitative research B.M.Donda UKZN INSPIRING GREATNESS  Why bother ? “The lack of focus on rigorous and relevant [data] analysis has implications in terms of the credibility of the research process”. (Nowell, Norris, White and Moules ,20...

Data Analysis in qualitative research B.M.Donda UKZN INSPIRING GREATNESS  Why bother ? “The lack of focus on rigorous and relevant [data] analysis has implications in terms of the credibility of the research process”. (Nowell, Norris, White and Moules ,2017) UKZN INSPIRING GREATNESS Qualitative data is messy UKZN INSPIRING GREATNESS Key instrument of qualitative data analysis The researcher You must mould the clay of the data, tapping into your intuition while maintaining a reflective understanding of how your own previous knowledge is influencing your analysis, i.e., your preunderstanding- reflexivity UKZN INSPIRING GREATNESS  Tip Start analysing data as you are collecting it Get a “sense” of your data However, it is also common to collect all the data before examining it to determine what it reveals (Chamberlain et al., 2004). UKZN INSPIRING GREATNESS  Examples of data interview transcripts participant-observation field notes journals documents literature artifacts Photographs video websites email correspondence, et cetera. UKZN INSPIRING GREATNESS What shapes your data? Types of questions asked and the types of responses (Kvale, 1996; Rubin & Rubin, 1995) the detail and structuring of your field notes (Emerson, Fretz, & Shaw, 1995) the gender and race/ethnicity of your participants—and yourself (Behar & Gordon, 1995; Stanfield & Dennis, 1993), whether you collect data from adults or children (Greene & Hogan, 2005); Zwiers & Morrissette, 1999). UKZN INSPIRING GREATNESS How you interpret and code your data. DEPENDS ON: your level of involvement or participation with your subjects. UKZN INSPIRING GREATNESS Inductive and deductive approach to data analysis Inductive approach : Usually, when there are no previous studies dealing with the phenomenon, and therefore the coded categories are derived directly from the text data (Hsieh & Shannon, 2005). deductive approach: To test a previous theory in a different situation, or to compare categories at different periods (Hsieh & Shannon, 2005 ; Elo & Kyngäs, 2008). This form tends to provide a less rich description of the data overall, and a more detailed analysis of some aspect of the data (Braun & Clarke, 2006). UKZN INSPIRING GREATNESS  Types of data analysis Content analysis: Content analysis is a research tool used to determine the presence of certain words, themes, or concepts within some given qualitative data Thematic analysis: It emphasizes identifying, analyzing, and interpreting qualitative data patterns. To find themes UKZN INSPIRING GREATNESS Types of data analysis Narrative analysis: type of qualitative data analysis that focuses on interpreting the core narratives from a study group's personal stories. Using first-person narrative, data is acquired and organized to allow the researcher to understand how the individuals experienced something. Grounded theory analysis: Grounded theory sets out to discover or construct theory from data Discourse analysis: qualitative and interpretive method of analyzing texts (in contrast to more systematic methods like content analysis). You make interpretations based on both the details of the material itself and on contextual knowledge. UKZN INSPIRING GREATNESS Varying forms of patterns A pattern can be characterized by: Similarity (things happen the same way) Difference (they happen in predictably different ways) Frequency (they happen often or seldom) Sequence (they happen in a certain order) Correspondence (they happen in relation to other activities or events) Causation (one appears to cause another) (Hatch, 2002). UKZN INSPIRING GREATNESS  Tips As you identify patterns and construct categories in the coding process, keep in mind that, according to Tesch (1990), “data are within ‘fuzzy’ boundaries at best (pp. 135-8). Creswell (2007) notes that codes can emerge in response to not only expected patterning, but also what you find to be striking, surprising, unusual or conceptually captivating (p. 153). UKZN INSPIRING GREATNESS Example of a deductive approach How do NMFCMC manage the challenges that they face during assimilation in South Africa? Martin and Marsh five Cs of resilience confidence (self-efficacy), commitment (persistence), coordination (planning), control (how hard work and effective strategies impact achievement) composure (low anxiety) UKZN INSPIRING GREATNESS Participant A UKZN INSPIRING GREATNESS Participant B UKZN INSPIRING GREATNESS Participant C UKZN INSPIRING GREATNESS Introduction to inductive approach UKZN INSPIRING GREATNESS  Thematic and content analysis Thematic analysis ((Braun & Clarke, 2006: 87) CONTENT ANALYSIS (Elo & Kyngäs, 2008: 110) Familiarising with data Preparation Being immersed in the data and obtaining the sense of Transcribing data, reading and rereading the whole, selecting the unit of analysis, deciding on the data, noting down initial ideas. analysis of manifest content or latent content. Generating initial codes: Coding interesting features of the data systematically across the entire data set, collating data relevant to each code. scribing data, Organising: reading and rereading the data, noting down initial ideas. Open coding and creating categories, grouping codes under higher order headings, formulating a general description of the Searching for themes research topic through generating categories and subcategories Collating codes into potential themes, gathering all data as abstracting. relevant to each potential theme. Reviewing themes Checking if the themes work in relation to the coded extracts and the entire data set, generating a thematic map. Defining and naming themes Ongoing analysis for refining the specifics of each theme and the Reporting overall story that the analysis tells, generating clear definitions and Reporting the analysing process and the results names for each theme. through models, conceptual systems, conceptual map or categories, and a story line. Producing the report The final opportunity for analysis. Selection of vivid, compelling extract examples, final analysis of selected extracts, relating back of the analysis to the research question and literature, producing a report of the analysis. UKZN INSPIRING GREATNESS  Thematic analysis “a method for identifying, analysing and reporting patterns (themes) within data” (Braun & Clarke,2006 : 79). thematic analysis provides a purely qualitative, detailed, and nuanced account of data (Braun & Clarke, 2006). involves the search for and identification of common threads that extend across an entire interview or set of interviews (DeSantis & Noel Ugarriza, 2000). UKZN INSPIRING GREATNESS  Thematic analysis As a method rather than a methodology (Braun & Clarke 2006; Clarke & Braun, 2013). This means that, unlike many qualitative methodologies, it is not tied to a particular epistemological or theoretical perspective. This makes it a very flexible method, a considerable advantage given the diversity of work in learning and teaching. UKZN INSPIRING GREATNESS  SEMANTIC AND LATENT ANALYSIS Braun & Clarke (2006) Semantic themes ‘…within the explicit or surface meanings of the data and the analyst is not looking for anything beyond what a participant has said or what has been written.’ (p.84). latent level looks beyond what has been said and ‘…starts to identify or examine the underlying ideas, assumptions, and conceptualisations – and ideologies - that are theorised as shaping or informing the semantic content of the data’ (p.84). UKZN INSPIRING GREATNESS Top down and bottom up thematic analysis Braun & Clarke (2006) distinguish between a top-down or theoretical thematic analysis, that is driven by the specific research question(s) and/or the analyst’s focus, and a bottom-up or inductive one that is more driven by the data itself. UKZN INSPIRING GREATNESS Braun & Clarke’s six-phase framework for doing a thematic analysis Step 1: Become familiar Step 4: Review themes, with the data, Step 5: Define Step 2: Generate initial themes, Step 6: codes, Write-up. Step 3: Search for themes, UKZN INSPIRING GREATNESS Step 1: Become familiar with the data reading, and re-reading the transcripts make notes and jot down early impressions UKZN INSPIRING GREATNESS make notes and jot down early impressions line-by-line coding to code every single line. open coding; no pre-set codes, but developed and modified the codes as you worked through the coding process. Theoretical thematic analysis : search for your interests in the data (some preliminary ideas about codes) UKZN INSPIRING GREATNESS make notes and jot down early impressions Can do this manualy or use nvivo ,Microsoft excel etc While it is very useful to have two (or more) people working on the coding it is not essential. UKZN INSPIRING GREATNESS Coding is a cyclical or iterative act With each subsequent coding cycle or iteration we are further managing, filtering, ordering, highlighting, and focusing the “salient features of the qualitative data record for generating categories, themes, and concepts, grasping meaning, and/or building theory” (Saldana, 2009, p. 8). UKZN INSPIRING GREATNESS First Cycle Coding Processes The magnitude of the data coded can range from a single word to a full sentence to an entire page of text, to a stream of moving images. First cycle can be repeated numerous times before proceeding to Second Cycle coding. » TIP If higher-level categories, concepts, or themes pop out at you, that is fine, just make note of them in a separate analytic memo (as it can sometimes inform later analysis) and don’t muddle higher-level analysis with the first cycle (a kind of ‘pre/trans fallacy’ in Wilber’s terms). UKZN INSPIRING GREATNESS Second Cycle Coding Processes Generally, beginner researchers may want to consider erring on the side of conserving (and not deleting) data while there “data sense” is developing. In subsequent coding cycles, initial codes can be refined, relabeled, subsumed by other codes, or dropped all together. Abbott (2004) likens the cycles of coding to the process of decorating a room: “you try it, step back, move few things, step back again, try a serious reorganization, and so on” (p. 215). UKZN INSPIRING GREATNESS Second Cycle Coding Processes In terms of portions, you can code the exact same units coded in First Cycle, longer passages of text, or even a reconfiguration of the first cycle codes. can be repeated numerous times, since coding demands that researchers pay meticulous attention to the nuances of language and reflect deeply on the emergent patterning embedded within the data. can code every detail of the raw field data or aspects of the data that are deemed to be salient Take caution when deleting data, as what appears in one coding cycle to be irrelevant may turn out to hold keys to unlocking the larger emergent pattern that may come forth in subsequent coding cycles. UKZN INSPIRING GREATNESS  Coding method Simultaneous Coding: the application of two or more codes within a single datum (e.g., when one code refers to an embedded or interconnected part of the single datum). Values Coding: captures and labels subjective-value perspectives. Holistic or “Lumper” Coding: a broad-brush stroke representation of a (relatively large) datum, intended to capture the essence (e.g., using one code to represent an entire 200-word excerpt from an interview transcript). Process Coding: a word or phrase that captures action/behaviour. UKZN INSPIRING GREATNESS  Coding Method Descriptive Coding: summarizes the primary topic of the excerpt/datum. In Vivo Code: when a code is taken verbatim directly from the data and placed in quotation marks. (e.g., I really feel inspired around him. Code “INSPIRED”). Initial Coding: an open-ended approach to coding in which the researcher codes for their first ‘hit’ or impression words or phrases in response to engaging the datum. Splitter Coding: “splitter” coding can be contrasted with so-called Holistic or “lumper” coding—splitting the data such that more codes can be applied (e.g., applying 10 codes to a 200-word excerpt from an interview transcript). Lumping or splitting, can both be appropriate relative to the research context. UKZN INSPIRING GREATNESS Questions to code for One general approach to coding is to code for your central research question/research concern. UKZN INSPIRING GREATNESS  Questions to code for In one exemplary approach, Emerson, Fretz, and Shaw (1995) propose a general list of questions to consider in coding field notes: What are people doing? What are they trying to accomplish? How exactly, do they do this? What specific means and/or strategies do they use? How do members talk about, characterize, and understand what is going on? What assumptions are they making? What do I see going on here? What did I learn from these notes? Why did I include them? (p. 146). UKZN INSPIRING GREATNESS Example: Codes in data analysis UKZN INSPIRING GREATNESS Codifying and Categorizing Codify: “to arrange things in a systematic order, to make something part of a system or classification, to categorize” (Saldana, 2009, p. 8). Codification helps to group and link data so as to enact and consolidate meaning and explanatory interpretation. Such analysis or codification “is the search for patterns in the data and for ideas that help explain why those patterns are there in the first place” (Bernard, 2006, p. 452). Categories are generated by grouping similarly coded data based on shared characteristics. Categories can be descriptive or speak to more conceptual processes, depending on your method. UKZN INSPIRING GREATNESS Step 3: Search for themes. As Braun & Clarke (2006) explain, there are no hard and fast rules about what makes a theme. A theme is characterised by its significance UKZN INSPIRING GREATNESS Step 4: Review themes. At this point it is useful to gather together all the data that is relevant to each theme. You can easily do this using the ‘cut and paste’ function in any word processing package, by taking a scissors to your transcripts or using something like Microsoft Excel (see Bree & Gallagher, 2016). (code book) UKZN INSPIRING GREATNESS Step 4: Review themes. Do the themes make sense? Does the data support the themes? Am I trying to fit too much into a theme? If themes overlap, are they really separate themes? Are there themes within themes (subthemes)? Are there other themes within the data? UKZN INSPIRING GREATNESS  Step 5: Define themes. final refinement of the themes aim is to ‘..identify the ‘essence’ of what each theme is about.’.(Braun & Clarke, 2006, p.92). What is the theme saying? If there are subthemes, how do they interact and relate to the main theme? How do the themes relate to each other? You can create a thematic map UKZN INSPIRING GREATNESS  Step 6: Writing-up. Usually the end-point of research is some kind of report, often a journal article or dissertation. UKZN INSPIRING GREATNESS Content Analysis Used to determine trends and patterns of words used, their frequency, their relationships, and the structures and discourses of communication (Mayring,2000; Pope et al.,2006 ; Gbrich, 2007). examining who says what, to whom, and with what effect (Bloor & Wood2006, ). By using content analysis, it is possible to analyse data qualitatively and at the same time quantify the data (Gbrich,2007). Content analysis uses a descriptive approach in both coding of the data and its interpretation of quantitative counts of the codes (Downe-Wamboldt, 1992; Morgan, 1993) UKZN INSPIRING GREATNESS TIPS : content analysis If in content analysis only the frequency of codes is counted to find significant meanings in the text, there is the danger of missing the context (Morgan,1993). The problem is that a word or coding category may occur more frequently in the speech of one person or group of people than another for different reasons. Frequent occurrence could indicate greater importance. OR it might simply reflect greater willingness or ability to talk at length about the topic (Loffe & Yardley,2004 ; Shields & Twycross,2008 ). UKZN INSPIRING GREATNESS Content analysis : tip A common pitfall is to use the main interview questions as the themes (Clarke & Braun, 2013). Typically, this reflects the fact that the data have been summarised and organised, rather than analysed. UKZN INSPIRING GREATNESS How to conduct content analysis familiarize oneself with the data and the hermeneutic spiral, divide the text into meaning units and subsequently condensing these meaning units, formulate codes develop categories and themes. UKZN INSPIRING GREATNESS The initial step: Get a sense of your data: To read and re- read the interviews (at a glance to gain a general understanding of what your participants are talking about. get ideas of what the main points expressed by your participants. UKZN INSPIRING GREATNESS Step 2 : formulating codes Start dividing up the text into smaller parts, namely, into meaning units called codes Definition A code can be thought of as a label a name that most exactly describes what this particular condensed meaning unit is about. Usually one or two words UKZN INSPIRING GREATNESS Step 2 : formulating codes are descriptive labels for the condensed meaning units. Codes concisely describe the condensed meaning unit and are tools to help researchers reflect on the data in new ways. Codes make it easier to identify connections between meaning units. very limited interpretation of content. You may adjust ,re-do, re-think ,and re-code until you get to the point where you are satisfied that your choices are reasonable. write notes during coding on your impressions and reactions to the text. UKZN INSPIRING GREATNESS  Activity 1: create codes Exploring ‘‘Patient’s experience of being admitted into the emergency centre” Participant : “They pushed me into the middle of the room and then walked away... they just left me”. UKZN INSPIRING GREATNESS  Activity 1 Exploring ‘‘Patient’s experience of being admitted into the emergency centre” “They pushed me into the middle of the room and then walked away... they just left me”. Code: Left alone; feeling /abandoned UKZN INSPIRING GREATNESS Definition of a category Describe the different aspects, similarities or differences, of the text’s content that belong together UKZN INSPIRING GREATNESS Example: Codes in data analysis UKZN INSPIRING GREATNESS  Category Codes might be grouped together according to similarity or if they pertain to the same topic or general concept (content or context) Easy to follow natural skin routine. UKZN INSPIRING GREATNESS How to formulate a category First assimilate smaller groups of closely related codes in sub-categories. Sub-categories related to each other through their content can then be grouped into categories A category answers questions about who, what, when, or where? In other words, categories are an expression of manifest content, i.e., what is visible and obvious in the data Category names are factual and short UKZN INSPIRING GREATNESS Activity one continued Look for a categories from this data Exploring ‘‘Patient’s experience of being admitted into the emergency centre” Participant : “They pushed me into the middle of the room and then walked away... they just left me”. UKZN INSPIRING GREATNESS  Activity 1 Exploring ‘‘Patient’s experience of being admitted into the emergency centre” “They (who) pushed me staff action: assistance; service; care) into the middle of the room (to now where) and then walked away (unacceptable Staff action (abandon)... they (who) just left me” (lack of care) non-action (no communication) feeling neglected: {complaint} unhappy, dissatisfaction. Unmet need: (lack of communication) Category: Staff action and non-action; unmet need ; lack of communication; lack of professionalism Code: Left alone UKZN INSPIRING GREATNESS  themes A theme captures a common, recurring pattern across a dataset, organised around a central organising concept. A theme tends to describe the different facets of a pattern across the dataset. Natural protection and replenishing of the skin UKZN INSPIRING GREATNESS  Theme A theme can be seen as expressing an underlying meaning, i.e., latent content, found in two or more categories. Themes are expressing data on an interpretative(latent) level. A theme answers questions such as why, how ,in what way, or by what means? A theme is intended to communicate with the reader on both an intellectual and emotional level. Theme names are very descriptive and include verbs, adverbs and adjectives UKZN INSPIRING GREATNESS  Activity 1 continued Create a theme from the data They pushed me into the middle of the room and then walked away … they just left me” UKZN INSPIRING GREATNESS  Activity 1 Exploring ‘‘Patient’s experience of being admitted into the emergency centre” “They (who) pushed me staff action: assistance; service; care) into the middle of the room (to now where) and then walked away (unacceptable Staff action (abandon)... they (who) just left me” (lack of care) non-action (no communication) feeling neglected: {complaint} unhappy, dissatisfaction. Unmet need: (lack of communication) How the [patient sees the experience : “They got me out of their way or sight and left me in the middle of one of the hospital rooms without a care in the world “ Overarching theme : The emergency centre through patients' eyes- Alone and cold in chaos Theme: Not a person, just a body in the hectic EC , dehumanizing care in the EC Category: Staff action and non-action Code: Left alone UKZN INSPIRING GREATNESS Keeping a Codebook or Code List As you code, Saldana (2009) recommends that you keep a record of your emergent codes, their content descriptions, and a brief data example in a codebook, separate file, or via a qualitative analysis software program. It can be quite useful in the analysis process to be able to see all your codes together in one place, without having to sort through your raw data documentation. UKZN INSPIRING GREATNESS Collaborative Coding and Member Checks researchers can bring additional researchers into the process Multiple researchers may then code the same raw data and then attempt to bridge or synthesize the spheres of divergence and/or move towards interpretive convergence. Researcher can also bring their research subjects into the coding/analytical process to varying degrees, actually inviting them to collaboratively code the data, or to simply crosscheck your interpretations with theirs (so-called “member checks”). Such member checks, or triangulation of interpretations in the coding process, will tend to increase the validity of your knowledge claims. UKZN INSPIRING GREATNESS Individual Activity 2: 10 minutes Chose two pictures or two phrases or words that best capture who you are and write a paragraph or two expressing who you are? UKZN INSPIRING GREATNESS ndividual activity : 10 minutes Highlight 6 lines that strike you as express who you are. UKZN INSPIRING GREATNESS Data analysis: Pantoum poem From your data chose 6 utterances that are striking Give your poem a title. 15 minutes individual presentation UKZN INSPIRING GREATNESS Data presentation: How to write a pantoum poem UKZN INSPIRING GREATNESS Thank you !! UKZN INSPIRING GREATNESS