Chapter 4 common features of qualitative data analysis

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SQQS5034Qualitative Data AnalysisMohd. Noor Abdul HamidCommon Features of Qualitative Data AnalysisChapter 3

IntroductionQualitative analysis which is interpretive aim to go further than descriptive analysis, unpicking the accounts that are give and asking questions like What is going on here? and How can we make sense of these account?It tries to gain a deeper understanding of the data that have been gathered, and often looks beneath the surface of the data, as it were, try to understand how and why the particular accounts were generated and to provide a conceptual account of the data, and/or some sort of theorising around this.Mohd Noor Abdul Hamid, Universiti Utara Malaysia

Reading & FamiliarizationThe analysis of qualitative data essentially begins with a process of immersion in the data to become intimately familiar with your datasets content, and to begin to notice thins that might be relevant to your research question.This normally involve:- textual data: reading and re-reading each data item- audio data: repeated listeningBy doing so, you may start to notice things of interest e.g. loose overall impression of the data, a conceptual idea you have about the data, or more concrete and specific issues.

Mohd Noor Abdul Hamid, Universiti Utara Malaysia

Reading & FamiliarizationFamiliarisation is not a passive process of just understanding the words (or images). It is about starting to read data as data not simply absorbing the surface meaning of the words, but reading the word actively, analytically and critically, starting to think about what the data mean.This involves asking questions like:- How does participant make sense of their experiences?- Why might they be making sense of their experiences in this way (and not in the other way?)- In what different ways do they make sense of the topic discussed?- How common-sense is their story?- What assumptions do they make in talking about the world?- What kind of world is revealed through their account?An analytic sensibility is essential for moving beyond a surface, summative reading of the data and questions like the above will help in developing an analytic sensibility.Mohd Noor Abdul Hamid, Universiti Utara Malaysia

Reading & FamiliarizationIt is good to keep a record of these noticing and record them in a place you can refer back to (often called a analytical memo or research diary). What to write in analytical memo?Reflect on:- what the data is telling you- reflects on the words or phrases- record your ideas about an emergent concept or theme- your research question, note your assumptions, issues for further investigation & hunches to check out- your analysis, eg. codes & their operational definition- possible relationships among the codes, patterns, categories, themes, concepts and assertion problems with the study- personal or ethical dilemas- emergent or related existing theory - personal relation with the participant or the phenomenon - and anything that helps you with your analysis.Mohd Noor Abdul Hamid, Universiti Utara Malaysia

CodingCoding an abstract representation of an object or phenomenon.In other words, coding is a way of indexing or categorizing the text in order to establish a framework of thematic ideas about it.A code is a word of brief phrases that captures the essence of why you think a particular bit of data maybe useful. Codes provide a building blocks of analysis. Coding is not an exclusive process any data extract can an should be coded as many ways as fit the purpose.However, the way you code will depend on the analytical approach that you adopted.In broad terms, code can either reflect the semantic content of the data (called data-derived or semantic codes) or more conceptual or theoretical interpretations of the data (called researcher-derived or latent codes).New qualitative researchers tend to initially generate mostly data-driven codes as they are easier to identify, and rely less on having conceptual and theoretical knowledge through which to make sense of data.The ability to generate researcher-derived codes develops with experience, as they require deeper level of engagement with the data and with fields of scholarship and theorising.Mohd Noor Abdul Hamid, Universiti Utara Malaysia

Two Types of CodeData-derived codesResearcher-derived codesProvide a succinct summary of the explicit content of the data Semantic codes based on semantic meaning of the data.They mirror participants language & concept we havent put an interpretative frame around their words.Example: Code: modern technology facilitate obesityPassage: I think modern technology, like allows you to be lazy as well cos you dont have to do things for yourself. You get machines and stuff to do things for youGo beyond the explicit content of the data.They are latent codes invokes researchers conceptual and theoretical frameworks to identify implicit meaning within data (i.e. the assumptions an frameworks that underpin what is said in the data).Example:Code: human as naturally lazyPassage: ..erm you know we didnt all have cars. So like my mum used to walk two to three miles to go to the train station to go another ten miles to work, you know, it was like there wasa lot more impact. There was no bus for her so she had to walk. Nowadays we think oh I cant do that cant miles to go and do that. Yeah take the cars

Analogy of Codes, Theme, Theory/Framework

Theme 1(wall)Theme 2(roof)Theme 3(wall)Code(each individual brick)Theory/FrameworkMohd Noor Abdul Hamid, Universiti Utara Malaysia

Two Main Approaches of CodingInvolves identifying a corpus of instances of phenomenon that you are interested in, and then selecting those out.The purpose here is one of data reduction.Often seen as a pre-analytics process, the pragmatic selection of your data corpus, rather than part of your analysis.However it does inevitably have an analytic element, in that you need to work out what counts as an instance of what you are looking for, and where the instance starts and finishes.It also requires pre-existing theoretical and analytic knowledge that enables you to identify the analytic concepts that you are looking for.The process of reading and familiarisation may be more involved and take longer with this approach than for a complete coding approach.Selective coding is normally use for narrative, discursive and conversation analytic approaches as well as pattern base discourse analysis to build a corpus of instances of the phenomenon you are interested in. a) Selective CodingMohd Noor Abdul Hamid, Universiti Utara Malaysia

Involves identifying a corpus of instances of phenomenon that you are interested in, and then selecting those out.a) Selective CodingTwo Main Approaches of Coding


Two Main Approaches of CodingAim to identify anything and everything of interest or relevance to answering your research question, within the entire dataset.You code all the data that is relevant to your research question, and its only later in the analytic process that you become more selective.

b) Complete Coding


Doing Complete CodingBegin with your first data item, and systematically work through the whole item, looking for chunks of data that potentially address your research question.If you are starting with a very broad research question, which you may refine during the analytic process, you want to code widely and comprehensively.If you have a very specific research question, you may fin that large section of the data are not relevant and dont need to be coded.The process continues in the same way for the rest of the data item. For each new bit of text you code, you have to decide whether you can apply a code you have already used, or whether a new code is needed in order to capture what it is you have identified.Basically, every time you identify something potentially relevant, code it.What important is that coding is inclusive, thorough and systematic.As your coding progresses and you start to understand the shape and texture of your data a bit more, you will likely modify existing codes to incorporate new material.Once you have finished the first coding of the dataset, it is worth revisiting the whole thing, as your codes will probably have developed during coding.Mohd Noor Abdul Hamid, Universiti Utara Malaysia

Doing Complete CodingWhat makes a good code:- Codes should be as concise as possible - Codes should work when separated from data. - Codes should be distinct in some way.

Ultimately you want a comprehensive set of codes that differentiates between different concepts, issues and ideas in the data, which has been applied consistently to the dataset.The final stage of complete coding is collating the coded data (the instances of text where that codes appear in the dataset).Mohd Noor Abdul Hamid, Universiti Utara Malaysia

Doing Selective CodingTo do selective coding, you need to know what you are looking to code before you begin.Data familiarisation is thus particularly vital.The basic elements of selective coding include:a) identifying what youre coding for normally focus on researcher-derived codes (define it, look for it and mark it)b) determining the boundaries of instances deciding when an instance begins and ends.c) collating instances compile all instances into a single file.Often additional coding occurs throughout the development of analysis, as the shape of analysis take form. This means some instances will be rejected as no longer relevant, and other data may need to be collated to fully develop and complete the analysis.


What Can be CodedItemExampleSpecific acts, behaviours what people do or sayAvoiding the question. Getting the opinion of friendsEvent- these are usually brief, one-off events or things someone has done.Being rejected at job interview.Movie into a homeless hostel.Activities these are of longer duration than acts & often take place in a particular setting & may have several people involved.Going dancing.Taking a training course.Working a part time job.Strategies, practices or tactics activities aimed towards some goal.Using word-of-mouth to find jobs.Getting divorced for financial reasons.States general conditions experienced by people or found in organizations.Resignation.Working extra hours to get the job done.Meanings a wide range of phenomena at the core of much qualitative analysis.What concepts do participants use to understand their world? What norms, values, rules guide their actions?What meaning or significance does it have for participants? How do they construe event? What are their feeling?What symbols do people use to understand their situation? What names do they use for objects, events, persons roles, settings or equipment?The idea of on-sight climbing amongst rock climbers to describe a climb without inspection, artificial aids, pre-placed protection.Blame. E.g. His letter made me feel I was to blame

Delivery van referred to as the old busTeaching referred to as work at the chalkface

Source: Gibbs, G. (2011). Analyzing Qualitative Data. London: Sage Publication Ltd.

What Can be CodedItemExampleParticipation peoples involvement or adaptation to a setting.Adjusting to a new job. E.g. I find I have to be careful with what I say now because I know about things before they are finalizedRelationship or interaction between people, considered simultaneously.Enjoying the family. E.g. theyre 26 and 21 and most boys of that age are married, but mine arent and they like to come home, have friends to stay. I like that.Conditions or constraints the precursor to or cause of events or actions, things that restrict behaviour or actions.Firms loss of market (before lay off)Divorce (before financial difficulties)Consequences what happens if.Experience gets job. E.g. So what you get is, people that havent got no qualification, but have got few months experience are walking into jobs.Settings the entire context of the events under studyHostel for the homeless.Day care centre.Reflexive the researchers role in the process. How intervention generated the data.Expressing sympathy, e.g. it must be hard for you in that situation.

Source: Gibbs, G. (2011). Analyzing Qualitative Data. London: Sage Publication Ltd.

Strategies for Identifying and Naming Codesa) Seeing as generating conceptual codesSource: Bazeley, P. (2007). Qualitative Data Analysis with Nvivo. London: Sage Publications Ltd.Identify: What is interesting? Highlight the passageAsk: Why is it interesting? This may generate a useful descriptive code or perhaps interpretive code. It may also warrant a comment in a memo.Then ask: Why am I interested in that? This will lift you off the page to generate a more abstract and generally applicable concept. Moving from description to analysis.


Strategies for Identifying and Naming Codesb) A priori (or Theoretically derived codes)Source: Bazeley, P. (2007). Qualitative Data Analysis with Nvivo. London: Sage Publications Ltd.Codes derived from literature, past experiences or background knowledge.While having a list of a priori codes can be useful (especially in focused or time-limited study), it can confine your reading of the text.So it is advisable to hang loose, feel free to change or develop what you have set up, as you delve into the data.


Strategies for Identifying and Naming Codesc) In vivo (or Indiginous codes)Source: Bazeley, P. (2007). Qualitative Data Analysis with Nvivo. London: Sage Publications Ltd.Codes derived directly from the data capturing an actual expression of a participant as the title for a code.Look for local terms, especially those that may sound unfamiliar or are used in unfamiliar ways.Use the language of the participants to label the typological concepts.


Strategies for Identifying and Naming Codesd) Repetition or RegularitiesSource: Bazeley, P. (2007). Qualitative Data Analysis with Nvivo. London: Sage Publications Ltd.People repeat ideas that are of significance for them. Repetition therefore suggest useful concepts to use as a basis for nodes.

e) Ask questionsUse questions of the text to generate codes who, what, when, why, how, how much, what for, what if, or with what results?Each aspect then warrants a separate code.


Strategies for Identifying and Naming Codesf) Compare and ContrastSource: Bazeley, P. (2007). Qualitative Data Analysis with Nvivo. London: Sage Publications Ltd.Compare one segment of text with another think about the ways in which they are both similar and different.Comparative technique help move attention from factual description to increase sensitivity to the dimension of the concepts being derived from the data, as well as to overcome analytic block.One consequence of doing so is that you may create codes (nodes in Nvivo) simply to hold ideas. If these nodes never acquire any data, then that is of interest too.


Strategies for Identifying and Naming Codesg) Record narrative structure & mechanismsSource: Bazeley, P. (2007). Qualitative Data Analysis with Nvivo. London: Sage Publications Ltd.How things are said and the way in which text was structured by the interviewee (discourse & narrative features o the text) are also revealingParticular features that you might note include:Transition and turning point in the narrative, signifying a change of theme or a subject to be avoided.Inconsistencies, endings, omissions, repetitions and silencesDenotation in time and tenses in verbsThe use of metaphors and analogiesRepetitive use of a word or phraseStructural aspects of turn taking and other rules naturally occurring in conversation.The broader discursive construction or framework within which the discourse is set, eg. Biomedical, romantic, gender etc.Narrative (story) components within a longer non-narrative text.Use of particular articles or pronouns pointing to particularized or generalized referents.Mohd Noor Abdul Hamid, Universiti Utara Malaysia

Different Ways to Codea) Manually Pen & PaperMohd Noor Abdul Hamid, Universiti Utara Malaysia

Different Ways to Codeb) Word processor annotation/comments

Different Ways to Codea) CAQDAS e.g. Nvivo, Atlas TI etc.

Identifying patterns across dataFrom codes to themesA theme capture something important about the data in relation to the research question, represents some level of patterned response or meaning within the dataset.It is typically broader than a code in that it contains many facets.A good code will capture one idea, a theme has a central organising concept, but will contain lots of different ideas or aspects related to the central organising concept meaningful, something about how, and in what way, that concept appears in the data.Theme tells us something meaningful in relation to our research question.Mohd Noor Abdul Hamid, Universiti Utara Malaysia

Identifying patterns across dataFrom codes to themesTheme: Modern life is rubbishi.e. modern lifestyles encourage obesity

Availability of convenient foodNo time for cookingUbiquitous advertising of unhealthy foodAvailability of fast food chain


Identifying patterns across dataHow to identify themes?Developing theme from coded data is an active process the researcher examines the codes and coded data, and start to create potential patterns.Involves reviewing the codes and collated data relating to each code, with the aim of identifying similarity and overlap between codes. Basically you want to identify a number of themes that capture the most salient patterns in the data relevant to answering your research question.


Identifying patterns across dataHow to identify themes?Good questions to ask in developing theme:Is it a theme (is it a code or just a subtheme)?Is there a central organising concept that unifies the data extract?What is the quality of this theme? Does the central organising concept tell me something meaningful about a pattern in the data, in relation to my research question?Can I identify the boundaries of this theme? What does it include and exclude?Are there enough (meaningful) data to support this theme? Is the theme too thin?Is there too much going on in the theme, so that it lacks coherence? Are the data too diverse and wide-ranging? Would using subthemes resolves this problem? Or should it be better split into two or more themes, each with their own central organising concept?How does this (potential)theme relate to other (potential) themes? Is the relationship between (potential) themes hierarchical or linear?Whats the overall story of my analysis? How does this theme contribute to overall story?Is the central organising concept reflected in the title I have given to the theme?Mohd Noor Abdul Hamid, Universiti Utara Malaysia

Identifying patterns across dataHow to identify themes?Important things to remember:After initial analysis, you normally come up with some candidate themes (provisional). These themes will be revised or refined through the developing analysis.It is also important to remember that themes are not determine in some quantitative fashion and there is no magical equation or cut-off point to determine what counts as a theme across dataset, and what doesnt. Your theme doesnt have to cover everything in the data they should be about addressing the research question, and since you are reporting patterned meaning, some less patterned or irrelevant codes will be excluded.If you are doing your analysis with anyone else involved (supervisor or co-researcher), it is important to realise that some coding and analytic differences are likely. The key is to work out whether the differences are problematic, and if so, work out where they are coming from (perhaps different theoretical perspective) and how to resolve them.


Chapter 4 common features of qualitative data analysis

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