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<noinclude>{{RECOUP_header|Data_management_&_analysis}}</noinclude>{{header}}
<noinclude>{{RECOUP_header|Data_management_&_analysis}}</noinclude>{{Template:RECOUP/Shortheader|title = Data management & analysis}}
 
= Session overview =
=Management and Analysis of Data (1)=
 


The basic presumption behind this session is that it is best to think about analysis before starting data collection, since failure to do so can leave a researcher with several major problems. Indeed, a common difficulty faced by new researchers is that they return from data collection and have little idea of what to do next; and when they do decide on a plan for analysis, they discover that they do not have all the material they need or have too much material which is of little relevance to the project.
The basic presumption behind this session is that it is best to think about analysis before starting data collection, since failure to do so can leave a researcher with several major problems. Indeed, a common difficulty faced by new researchers is that they return from data collection and have little idea of what to do next; and when they do decide on a plan for analysis, they discover that they do not have all the material they need or have too much material which is of little relevance to the project.
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==Management of Data==
=Management of data=


'''Time:''' 30-60 minutes
'''Time:''' 30-60 minutes
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If you have a project which needs to set basic ground-rules with respect to these data management issues, you might like to prepare a handout that can be given out at this point.
If you have a project which needs to set basic ground-rules with respect to these data management issues, you might like to prepare a handout that can be given out at this point.


'''Notes for facilitators:'''  <u>BJOERN: this would need to be in a box or something</u>
{{Template:RECOUP/Box|text='''Notes for facilitators:'''   


Main issues we suggest should be considered here are:
Main issues we suggest should be considered here are:
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* when and where to carry out back-ups (and how to name these)
* when and where to carry out back-ups (and how to name these)
* when to translate material into the project language (if necessary)
* when to translate material into the project language (if necessary)
}}


Translation is often a big issue for projects that have limited resources. There is a {{Template:RECOUP/HOA|handout on issues of translation}} used in the previous session which discusses some general principles. But in practice hard decisions may need to be taken about whether (or how much) to transcribe recorded interviews or focus group discussions; when (and how much) to translate; and who should translate, as well as what kinds of quality controls will be introduced to ensure the data are good enough for coding and eventual possible citation.


Translation is often a big issue for projects that have limited resources. There is a [[Handout]] which discusses some general principles. But in practice hard decisions may need to be taken about whether (or how much) to transcribe recorded interviews or focus group discussions; when (and how much) to translate; and who should translate, as well as what kinds of quality controls will be introduced to ensure the data are good enough for coding and eventual possible citation.
=Introduction to qualitative data analysis=
 
[[Link to HO_Issues of Translation]]
 
==Basic Introduction to Qualitative Data Analysis==


'''Time:''' Allow about 2-3 hours (half a day) for this part of the session. Many people find qualitative data analysis the hardest thing to understand, and the more they engage with practical examples, the better.  
'''Time:''' Allow about 2-3 hours (half a day) for this part of the session. Many people find qualitative data analysis the hardest thing to understand, and the more they engage with practical examples, the better.  
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You might want to bring in a couple of books with indexes, to show people the similarities between a familiar exercise – looking something up in an index – and basic descriptive coding.
You might want to bring in a couple of books with indexes, to show people the similarities between a familiar exercise – looking something up in an index – and basic descriptive coding.


We have provided some photos of non-computer-based coding – what is often called ‘cut and paste’. This helps people to understand some basic principles of collecting data by individual and by theme or topic (reading across the sheets to understand the variation in responses to a particular question or around a particular theme, and reading down the sheet to see how an individual’s responses fit into a larger picture of their life). If you have an example of your own, it would be worth bringing it in and talking participants through what you did. Alternatively, you could create an example, using invented quotes, cut and pasted onto large sheets of paper or card.
A popular non-computer-based coding method is the‘cut and paste’ method. Here the researcher cuts and pastes quotes from interviews under various themes/topics on a big sheet or chart paper. Each chart paper denotes a single theme/topic and all the quotes from various interviewees can be arranged in columns. When reading across the sheets you can understand the variation in responses to a particular question or around a particular theme, and when reading down the sheet you see how an individual’s responses fit into a larger picture of their life).  
 
If you have an example of your own, it would be worth bringing it in and talking participants through what you did. Alternatively, you could create an example, using invented quotes, cut and pasted onto large sheets of paper or card.


You’ll also need a sample (perhaps one page) from an interview transcript, fieldnotes or other form of qualitative data, one copy for each participant and an electronic version that can be shown on a projector to help in the feed-back session.
You’ll also need a sample (perhaps one page) from an interview transcript, fieldnotes or other form of qualitative data, one copy for each participant and an electronic version that can be shown on a projector to help in the feed-back session.
In the [[PowerPoint]] we’ve provided a worked example from the PhD thesis of one of the authors of this manual. However, it would be useful to work with an example that you feel more comfortable with, in which case you can use this PowerPoint as a template.




'''Process:'''
'''Process:'''


You can use the PowerPoint as the basis for this session. We suggest that you break up the flow with a series of full-group brainstorms or small-group tasks. For example,
You can use the PowerPoint {{Template:RECOUP/PRA|Presentation on Qualitative Data Analysis}} as the basis for this session. We suggest that you break up the flow with a series of full-group brainstorms or small-group tasks. For example:


* Ask people what they see as the main differences and similarities between the analysis of quantitative and of qualitative data.  
* Ask people what they see as the main differences and similarities between the analysis of quantitative and of qualitative data.  
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* Use the sample extract as a basis for getting people to generate codes, and to think about the differences between descriptive and analytic codes.
* Use the sample extract as a basis for getting people to generate codes, and to think about the differences between descriptive and analytic codes.


[[Link to PPT_11_2_QDanalysis]]
=Basic introduction to the use of computers to help with qualitative data analysis=
 
 
==Introduction to the use of computers to help with qualitative data analysis==  


'''Time:''' 45 minutes
'''Time:''' 45 minutes
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'''Process:'''
'''Process:'''


If you want to run this just as a general introduction to CAQDAS (Computer Assisted Qualitative Data Analysis Software), we suggest you use the attached [[PowerPoint]] with worked examples from projects with which you are familiar.
If you want to run this just as a general introduction to CAQDAS (Computer Assisted Qualitative Data Analysis Software), we suggest you use the attached {{Template:RECOUP/PRA|Presentation on CAQDAS}} with worked examples from projects with which you are familiar.
 
[[Link to PPT_11_3_CAQDAS]]
 
There is a [[Handout]]: you could give this out at the end of this session


[[Link to HO_CAQDAS]]
There is a {{Template:RECOUP/HOA|Handout on CAQDAS}}: you could give this out at the end of this session




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We are not recommending any particular CAQDAS package: to our minds, each has its strengths and weaknesses. For specific reasons, Atlas.ti is the package that is being used in the RECOUP project. Some members of the team prefer one of the other packages on the market, such as HyperRESEARCH V.2.06, MAXqda, N6, NVivo, Qualrus
We are not recommending any particular CAQDAS package: to our minds, each has its strengths and weaknesses. For further information on Computer Assisted Qualitative Data Analysis, see [[RECOUP/CAQDAS|CAQDAS]].
 
Each program offers a Tutorial to help you get started, but they are not always very easy to use in a group.
 
If you want to use NVivo 7, http://www.lynrichards.org/HQD_Tutorials.htm offers access to self-teaching tutorials and access to a global network of people using the package
 
If you want to use Atlas.ti then you might find the attached [[PowerPoint]] by Laura Jeffery useful. This has been used in workshops to intensively train people in the basics of the program (in 2 days) or teaching the program and developing the codes and doing some practice coding on a data-set collected by the participants (5 days).
 
[[Link to PPT_Atlas_Introduction]]
<noinclude>{{RECOUP_footer|Data_management_&_analysis}}</noinclude><noinclude>[[Category:RECOUP]]</noinclude>
<noinclude>{{RECOUP_footer|Data_management_&_analysis}}</noinclude><noinclude>[[Category:RECOUP]]</noinclude>
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