C13

Question 1

What is qual data analysis

Answer 1

Part mechanical, handling and sorting data, and part intellectual, thinking about and with data. In the same way we look for patterns and relationships in quant data, we examine qual for common themes and relationships.

Process of analysis is not a discrete phase as the end of fieldwork, but rather ongoing from the very stat of research. A lot of ideas will occur during fieldwork, when fieldwork is over and you have change to organise data, sort through it, you will be able to pull together ‘findings’

Question 2

are there Approaches to analysis? what will impact use of them?

Answer 2

No standard techniques / clearly defined procedures, as there are many different approaches. The approach you might take in analysing data depends on a range of factors and their interaction. These include:

Way your mind works to sort and think about things - influenced by learning style, training and perhaps left / right brain split
Level of experience
Level of knowledge of area under investigation
Availability of relevant theories or models
Type of projects e.g. groups, depths, workshops, F2F, online
Nature of research enquiry e.g. exploratory, descriptive or explanatory
Subject matter and how respondents approach it
End us of research
Resources available - time, money and number of people

With so many factors having potential influence it is unsurprising that qual data analysis is idiosyncratic - there are almost as many approaches as there are researchers.

Question 3

deductive reasoning

Answer 3

Deductive - speculate advance of fieldwork, about what it is we think we will find and set out research to tets this idea / hypothesis. Research is designed and analysis approach is designed in a way that allows this to happen. You move from general to specific in deductive reasoning - from general idea to general hypothesis or theory about what might be happening to specific observations to see if what we expect is happening. This can also be referred to as analytic induction.

IA works like this:

Defined research problem and idea about what you are looking for
Using understanding of issues and background to problem, you develop working hypotheses about matter under investigation
Fieldwork is started and throughout you assess with how what respondents are telling you fits in with initial ideas and hypothesis
Modify ideas about what is happening, explore some issues in greater depth and get more examples of things that fit with hypotheses and so on

Question 4

inductive reasoning

Answer 4

Induction - using this approach means that we do not go into fieldwork to test assumptions or existing theories / ideas. Data is collected and from data we identify general principles that apply to subject under study - move from specific to general. Theory building rather than theory testing.

It is difficult to use a purely inductive approach; difficult to keep all other ideas and to have completely open mind when tackling a problem, it is likely you will have some understanding of product field / area under investigation, or at least some understanding of general patterns of behaviour / attitudes (from previous research / literature). In real world research it is an iterative process involving both reasonings - ideas and hypotheses emerge from data and are tested out within data, you might revise or change them, collect more data in which to test and develop ideas on and so on.

Question 5

Grounded theory

Answer 5

Data is examined using constant comparative methods in order to identify themes and patterns; concepts and codes are developed in order to summarise what is in the data. These concepts and codes are used to build propositions / general statements about relationships within data. Codes and propositions are tested out in data to make sure that they hold up, to make sure that they fit categories to which they were assigned and that propositions help to understand what is being studied.

Question 6

Recognising own biases

Answer 6

Everyone has biases - they are owed to life experience and general knowledge as well as work on projects in the same area, briefing documents and background reading. Important that these are not allowed to skew analysis and interpretation of data / limit it in any way. Own thinking may mean that you only see what you want to see, or only what fits in with your view of the problem. It is therefore important in analysis to think about alternate hypotheses, to be open to different ways of looking at / interpreting evidence, question and challenge everything seen in data.

At the outset of the project you should examine what you know or assume, what preconceptions might be bringing into fieldwork or analysis. Remember to:

Keep open mind
Do not jump to conclusions
Separate how you see the issue from how respondents see it, to avoid imposing your views and ways of thinking on data
Do not force data to fit with what theory or model suggests

Question 7

Making use of theories and models

Answer 7

Can be used to help develop / expand your thinning, speed up analysis by giving it a framework and thus a coherence, suggest questions to ask abd lines of enquiry to follow, provide ideas for developing typologies. Use Alongside a systematic testing of ideas in data - looking for evidence that supports / refutes them - a model / theory can help produce more robust analysis. Use models / theories that are well researched and empirically based.

Question 8

Aim of analysis

Answer 8

Extract meaningful insights from data to produce valid and reliable findings to help answer research problems. To achieve this analysis should be disciplined and rigorous; does not mean it should be entirely mechanical or prescriptive. Should be thorough, consistent, comprehensive, systematic without being rigid - open to possibilities and insights that emerge as a result. Intuition and creativity are vital.

Question 9

Planning analysis at research design stage - questions to ask self

Answer 9

List of questions to think through what implications each part of research process and decision has on analysis:

Problem:
Issues clear? Problem clearly defined?
Task to explore, describe, explain or evaluate
What output is expected? How will information be used?
What are your working hypotheses or ideas?
Using theory to drive or inform analysis?
Any previous research / relevant literature that might be useful?

Simple:

Who do you need to interview? How many?
Identified different kinds of respondent?
Expect to see different responses from different types of respondent?
Useful to compare responses among similar groups / diff groups?

Methods:

Observation? Depth? Group discussions? How will this affect analysis process?

Questions:

Topics covered in interview / discussion?
Questioning techniques? Projective / enabling
Implications these questions have for analysis?

Question 10

why plan analysis at research design phase?

Answer 10

Process of analysis will be easier and outcome of better data quality if analysis is thought of up front, and how research design decisions will implicate analysis on the other end. May involve reviewing any relevant literature on topic / reviewing findings of other research projects etc. objectives of research drive research design and choice of sample, method and questions which all determine analysis strategy. Thinking of these at early stage will give way into analysis, ideas of how to tackle it, helping to develop strategy and framework for interrogating data and presenting findings.

Question 11

Planning analysis at fieldwork stage

Answer 11

Overlap between fieldwork and analysis; collect data, think about them, collect more - perhaps using slightly amended discussion guide or reworked stimulus material as fieldwork sheds light on issues. Whole time your thinking about issues is developing - ideas, hunches, insights, hypotheses you may want to test and explore further.

Question 12

Planning analysis at research design stage :Making fieldnotes

Answer 12

Due to this it is worth keeping a detailed log of thoughts and insights as they occur; written down ASAP as you may not remember, or remember correctly at the main phase of analysis. Make detailed notes or maps about what is emerging, what picture is a beginner to build up, write down particularly relevant or interesting questions.

Ask yourself what was unexpected or surprising in order to examine and challenge your own assumptions. Consider what is to be explored in further depth, what new areas need to be probed, consider implications of these for further fieldwork and for analysis / interpretation and make changes if necessary. Note down key themes, relevant quotations - anything that may be useful when analysis is in full swing.

Question 13

Planning analysis at research design stage: reviewing fieldwork with colleagues / clients

Answer 13

Review fieldwork together in detail as soon as it is over and make detailed notes - if you have client observers ask them what they thought, and note down what they say

Question 14

Planning analysis at research design stage: writing up summary

Answer 14

Write up a summary of main points made by participants under each of the questions / topics on interview guide or on contact summary form. Another approach is to mind map them.

Whichever is chosen should help settle and fix things in memory that will be useful later in analysis. Having a summary record of some sort will help you think about and develop ideas about data / decide on an analysis strategy. May also be a useful source to reference when it comes to writing up findings in detail; particularly if more than one person is involved in analysis, where other members can read them in order to get to grips with data across the whole sample

Question 15

Developing analysis strategy - importanc e/ why

Answer 15

Having thought through research problems and partially completed fieldwork, you should have in mind /notes the basis of an analysis strategy or plan to tackle analysis. This should be formalised and made explicit.

Possible lines of enquiry in most qual studies are numerous and resources are limited. Analysis strategy should set out a way of approaching data, ensuring it is tackled in a systematic and rigorous way. Strategy that has been developed to suit aims and objectives should help you make the most out of resources, especially by helping you prioritise lines of enquiry. Strategy should be flexible - can and should be adapted and modified to fit circumstances.

Question 16

Developing analysis strategy - considerations

Answer 16

Practical considerations
How many will be involved in analysis
Will client / sponsor be involved
How long do you have for analysis
Going to work from transcripts, recordings, notes or combination?
Using a computer analysis package?
Research considerations
Decisions to be made on basis of research findings
How detailed analysis needs to be
Outputs required - presentation, summary report, full report
Are findings to be published?
How is it going to be tackled
By country?
Interview by interview or gorup by group
Question by question
Respondent type

Question 17

Developing analysis strategy - how to

Answer 17

Use research briefs / proposals, write down big research questions set out to answer (objectives of research). List Qs and the type of respondents that might help to throw light on each of these and write down what it is you will be looking for in data generated by the questions / respondents that will help address research objectives. This is an analysis strategy,

As analysis / ideas develop may find body of knowledge that supports them or that will give you ideas or alternative ways of looking at data. Night find this knowledge in previous reports of research in topic, or literature about substantive topic you are investigating. Often worthwhile to make use of these models and theories, as they can help you to structure analysis, suggesting lines of enquiry and help develop thinking.

Question 18

Doing analysis - 5 stages?

Answer 18

Organising data
Getting to know data
Getting to grips with what is going on in data
Making Links, looking for relationships
Pulling together findings

Question 19

Doing analysis - organising data

Answer 19

Ordering materials in order to get on with analysis. Depending on size / complexity of the project, you may have accumulated a lot of raw materials e.g. recordings, filedonotes, transcriptions.

Spend time sorting materials into files, labelling and generally making it easy to retrieve. Particularly useful to make several copies of transcripts, unadulterated master copy, copy for cutting and pasting and copy to make notes. Once this is complete you can review field notes, listen to / watch recordings, read transcripts and plan how to tackle analysis.

Once these have been reviewed it is likely story will start to emerge and you will recognise themes and patterns.

Question 20

Doing analysis -Getting to know data

Answer 20

Listen / review fieldwork recordings / transcripts. Will allow you to learn alot about your own interviewing technique, and will give you a chance to get into data and know it thoroughly. Data collected is spoken in discourse, so it is important to hear / see it in that form more than once, otherwise you may lose nuances or richness. If you are not able to prepare your own transcripts, as it is a time-consuming process, make sure you listen to or watch recordings at least once and read transcripts in full. Make notes as you do this, about how things were said, what was not said, what interpretations occur to you as you go through etc.

It is important not to jump to conclusions as you enter this intensive phase; may find that until reviewing all materials that all groups merge into one. Danger of misremembering things, give some things more importance than is actually the case. Need to protect against selectivity and decay of memory. This is why notes made at time are important; when reading notes and reviewing materials, write down any analytic ideas / impressions that occur to you and make notes about testing them out across all data to see if they hold up. Will need to systematically review to make sure you see the whole picture, not just bits stick in mind. Test ideas by looking at and comparing data from other respondents. Keep mind open to alternative explanations / ideas,

Question 21

Doing analysis -Getting to grips with what’s going on

Answer 21

Pulling apart the stage of analysis. You may start to recognise patterns and themes after eviewing materials. Some may crop up more often, or less and there may be discernible patterns in attitudes, behaviour, opinions, experiences. Patterns in way people express themselves and language used - these should all be recorded for future reference.

Question 22

Getting to grips with what’s going on: coding and summarising

Answer 22

Need to dissect data, pull apart and scrutinize it bit by bit. Involves working through data, identifying themes, patterns and labelling them / placing them under rheadings / brief descriptions summarising what they mean. This is known as categorising or coding data.

Coding process is not just mechanical one of naming things and assigning them to categorize - it is also a creative and analytic process involving dissecting and ordering in a meaningful way that helps to think about and understand research problems. Coding is a useful data handling tool that allows you to bring similar bits of data together and by reducing them to summary codes, making mass of data more manageable and easier to get to grips with, enabling you to see what is going on relatively quickly and easily.

Process of developing codes and searching for examples, instances or occurrences of material that relate to code ensures that you take a rigorous and systematic approach.

Also useful data thinking tool, that allow you to see fairly quickly what similarities, differences, patterns, themes, relationships and so on exist in data - helps you develop bigger picture by bringing together material related to ideas, hunches enabling you to put them in a conceptual order and make links and generate findings.

Question 23

Generating codes

Answer 23

Can use topics or questions from interview or DG as general codes or headings. You may have asked respondents to decrease ideal airline flight - develop general code called ‘ideal flight’ and during coding bring together all relevant data under this heading. Some people may have talked about a particular topic / answered questions later or earlier than the topic was mentioned, so it is necessary to search the data record for incidences of it.

Rather than imposing codes from outside the data you can go into data (a bottom up, data driven approach) and see what terms, words, concepts respondents use to describe things and use these as codes.

Question 24

Coding process

Answer 24

Can be tackled in a number of ways, different researchers will have different approaches - using pen and paper or computer. Easy way of doing so is to create a new document for each heading / code, and copy / paste pieces of text that relate to code into the document. This way you build up a store of relevant material related to code. Take care to label the source of each bit e.g. respondent details, fieldwork details, place in transcript, so that you can know the context from which it came and refer back if necessary. One piece of data may fit into multiple code.

Likely you will take several coding passes through data - two. First pass may be fairly general and kept to a minimum number. E.g. 4 or 5 key themes. As you work through data second time you can divide these themes under several specific topic areas. May group data extracts under each of the revenant codes as follows, in ideal flight example these may be:

Emotional aspects
Physical aspects
Facilities
service

Question 25

Bottom up coding process

Answer 25

Thinking about what individuals said / did not say
Examine words / phrases used
Note frequency / strength of these
Examine how they said things as well as what they did say
Look at it in context in which they said it
Think about what is meant
Think about what these things are example of
Create headings / code / categories to label these things
Highlight or colur code these bits of transcript
Cut and paste things under headings
Build up code frame or list of headings

Question 26

Doing analysis Making links and looking for relationships

Answer 26

Should now have a good grasp of data; story should be emerging, and it is likely that you will have tentative ideas or explanations for what is going on. As you have read through and coded you will have made notes about links between different themes and codes that overlap, asking questions of data, testing out ideas and looking at relationships.

You may be able to develop typologies, categorising respondents in terms of similarities in their characteristics. Questions you may ask of the data and the way you develop data will be driven by research objectives.

As you make links, connections, see relationships - think about what might explain them and think of alternate explanations. Once you have generated possible explanations, look for evidence to support ideas and interpretations as well as evidence that may refute them. At this stage you may well be coding data, refining or detailing codes. At the same time you may also find out that you can move from specific codes developed to more abstract concepts to a greater degree of generalisation about what is going on in data.

Question 27

Making links and looking for relationships - Using charts, diagrams and maps

Answer 27

Using these to present data can help you think and uncover patterns and relationships. Some people can think in and / or express ideas better in pictures and diagrams than they can in words. Reducing data to fit in one of these can help to focus thinking on relationships that exist in data. Use a suitable format for what you are trying to understand - perceptual map for to show how different brands lie in relationship to one another. Flow chart to show and understand chronology of events e.g. steps in process. Table might be useful for summarising reactions of different groups to stimulus material e.g. product concepts or mood boards.

Question 28

Pulling together the findings - helpful ways to help thinking

Answer 28

As you immerse yourself in data, pulling them apart and building them up, questioning, testing ideas and hypotheses you are likely to reach a point where it suddenly seems to fit together and make sense / produce a story. When all data / ideas are in head, you may want to take a break from analysis to let things ferment and give things a chance to gestate. Another way is to talk about findings aloud to someone not directly involved in the project, often in articulating ideas in mind to speak them out loud and explain them to someone else you will make connections or see pictures that have not been seen before. The other person may help by asking questions that make you explain thinking and reasoning, or questions you have not considered yourself. Anyother way is to read a literature review relevant to the project, which may spark off fresh ideas, suggest further lines of questioning or help you make a useful connection.

List of other helpful things:

Checking what connections and patterns mean in context of individual interview, whole sample, theme / concept / idea, and in relation to bigger picture
Re-read transcripts from holistic view
Question whether there could be alternative explanations or interpretations

Question 29

Managing self

- assumptions prejudices

Answer 29

Take through the process of assessing assumptions you have made on respondents and topic, make these explicit to yourself so that you can go into analysis with an open mind. Important at this point as you do not want to let assumptions, prejudices, views intrude on interpretation of data. Skill of a researcher is to be aware of respondents’ stance in relation to topic, but to be aware of own stance, and be able to stand back and not impose opinion and remain non-judgemental.

Question 30

Managing self - objectives of research

Answer 30

Important to bear in mind objectives of research and not lose sight of them as you become immersed in data. Can be helpful after completing the coding stage to write things down in detail, and to be constantly asking yourself how it all ties in with research objectives. Think about what light the evidence you uncovered sheds on research objectives, implications the finding have.

Question 31

Managing self -quality of findings

Answer 31

Plausibility
Whether they make sense
Are they intuitive? Surprising or what you expect?
How much evidence is there to support them
How credible and plausible is this evidence
Does it fit in with evidence gathered elsewhere - from other research in this area, from theory, literature
Has data been examined for refuting evidence
Checked that other explanations do not fit data better
Accounted for contradictions, oddities or outliers
Introduced in bias?
Given more weight to articulate members of sample at expense of others
Been systematic and rigorous in looking for evidence and taking into account all views and perspectives
Seeing data that you want to see?
Overinterpreting things?
Anything may have misseD?

Question 32

Analysis and interpretation relevant MRSCOC

Answer 32

Ensure that conclusions disseminated by them are clearly and adequately supported by data
Comply with reasonable requests to make available technical information necessary to assess validity of published findings from project
Allow client to arrange checks on quality of fieldwork and data preparation provided client pays any additional costs involved in this
Provide client sufficient technical details to enable client to assess validity of results of projects carried out on their behalf
Ensure reports include sufficient information to enable reasonable interpretation of the validity of results
Ensure reports and presentations clearly distinguish between facts and interpretation
Ensure that in interpreting they make clear which data is being used to support this
Ensure qual reports and presentations accurately reflect findings of research in addition to interpretations and conclusions

Question 33

Using computers in qual data analysis , why how pros and cons

Answer 33

Programs can be used for mechanical aspects of the process including storing and managing data, searching for and retrieving text, coding and mapping or charting data. Use of software tends to imply systematic approach, added rigour in analysis process and a transparent and traceable route through data.

Using packages can be expensive and take time, and depend on full transcripts of interviews or discussions - which are not always produced. Use of packages effectively requires training.

Main functions inmost packages are:

Search and retrieve function - key word search, frequency counts, alternative words with similar meanings
Coding and labelling facilities
Note-making facilities
Content analysis
Visual mapping or charting

Good way of storing and handling data and making analysis accessible. Allow reworking of coding schemes as new insights emerge, by revisiting segments of data quickly and easily. Search and retrieve functions allow easy interrogation of data and more thoroughly than you might with paper transcripts - enabling a more in-depth understanding of data and greater confidence in findings.

Question 34

Data storage - relevant MRSCOC

Answer 34

Ensure that completed recruitment qnn, incentives, attendance lists, transmissions / recordings and any tiger information that identify respondents are not passed to clients or third parties without explicit permission from respondents: reasonable steps should be taken to ensure information / outputs are only used for purpose agreed at time of data collection
Ensure that any material handed to clients or included in reports without consent from respondents is anonymised
Members should take reasonable steps to ensure that all hard copy / electronic lists containing personal data are held, transferred and stored securely in accordance with relevant data protection retention policies
Take reasonable steps to ensure that all parties involved in the project are aware of data security obligations.
Take reasonable steps to ensure destruction of data is adequate for confidentiality of data being destroyed e.g. destroyed in a manner which safeguards confidentiality.