Attended a free webinar this morning via QSR the company that sells nVivo. The topic was on public consultation research presented by Patrick O'Neil rom Lincoln University and Dr. Lyn Lavery from academic consulting. Introduced by Kate from QSR.
This is the last of a series of 3 on working with different data came about through the public consultation process Chch city council ran after Chch earthquakes. with previous webinars available on research preparation (for the unknown) and rapidly synthesising and analysing data.
The projects was focused on the Share an idea project used to inform the Chch city plan. 14thMay and series of interactions also used with all data collected by July. 2 teams involved with city council And overseas consultants. Coding structure was set up to deal with multiplicity and amount of data. Needed to ensure data collected could be collated to be meaningful. Topics set up and sub themes emerged. Important to have shared understanding of what sub themes mean to all researchers.
All data, regardless of type, coded to nodes and subthemes on nVivo. large amount of data had to be analyzed within a short time frame. Need to ensure all data was analysable. Data gathered through expo, website, council forms, workshops.
Website collected tweets (over 20,000), emails, group ideas. Council forms included letters, emails, voicemails. Expo included post it notes, pictures and drawings, focus group transcripts etc.
NVivo allowed word, excel files, PDFs audio, video and images with all could be coded to the same topics and sub themes. Plus 5 researchers could all work on data at the same time using nVivo server.
Examples of data types from expo then described.
1) Let's hear it used written information on a form - with 4 questions - what missed, what to retain, how to make Chch city better, what is most important About vision for Chch. Transcripts entered as answers to questions and then coded to sub themes.
2) Web tweets with 4 themes - move, life, space and market. Engaged people before expo and then others could see their own and others and provide feedback and stimulated more ideas. Above coded with tweeters age And suburb and tweets coded into sub themes.
3) Post it notes from expo generated 17,000. Entered into an excel file and manually coded to theme. Then text search used on excel files deployed to create nodes. Merged into existing nVivo data. 1/3 of post it notes then coded using coding density functions.
4) YouTube videos used to capture interviews up to 3 minutes with people who preferred to provide oral feedback. Video transcribed within nVivo and again coded to sub themes. Selected parts were transcribed by firstly doing a first watch and then only transcribing relevant parts.
Children's art to help children articulate their ideas. Photo of art uploaded to nVivo, regions of the 5) picture can then be coded by identifying on the picture and labeling.
Outputs also used nVivo visualization tools and can be run using queries to generate tag clouds ( word frequency with stop word list to remove unnecessary words) and word trees (to illustrate words that are searched and other things associated to each word). Visualisations could be used to quickly display the most common ideas to inform good mass communication and quickly represent a particular topic and display a large amount of content.
All in a good opportunity to have an example of how to better use nVivo for qualitative analysis. Plus information on nVivo 10 which allows for social media data to be more easily used plus need for nVivo to be able to handle very large projects more efficiently.
Showing posts with label nVivo. Show all posts
Showing posts with label nVivo. Show all posts
Thursday, June 28, 2012
Monday, September 27, 2010
Shifting offices and getting re-acquainted with nVivo
I shifted offices four weeks ago from a shared office with the Adult Education team to another, with the elearning team and digital librarian. The office is just off to the side of the second level of the CPIT library. So a great place to be since I can now browse the stacks whenever I need a bit of time to do some contemplation.
Other Centre for Education Development (CED) staff are just around the corner and the main reason for the shift is to try to have all the CED staff offices in closer proximity to each other. Our manager designate from the UK, starts work at CPIT at the end of October. So it will be less daunting for him if all the CED staff are not scattered all over the campus.
I have enjoyed my first weeks (interrupted by a week away due to the earthquake) with the elearning team members. They have just about got used to seeing me in the office when they get in each morning as I am an early starter. I have now worked out all the security issues and liaised with security so that my early starts, once a week or so late finishes and study on Saturdays and on wet Sundays do not disrupt their security patterns.
It’s all also much quieter as the three others in the office beaver away at their computers and have few meetings whereas the adult ed. office was always a hive of activity with meetings and students and other visitors dropping in all the time. Therefore, I have made good progress with data analysis for the ‘first year apprentices’ project. The bulk of the data is now migrated to nVivo and various nodes (themes) have been established. Have also been able to make a start on the reports to each of the ITOs, due for end of this year. Also revised how to do ‘queries’ on nVivo, to compare demographic data and various coded text fragments. So far some patterns emerging as to ‘prior contact’ with a workplace or occupation and apprentices’ ‘stickability’. A common sense finding but well reflected in the data. While using the help feature on nVivo to work out various features of the software, came across the announcement on nVivo 9 launches October. Will need to re-evaluate nVivo9 for suitabitity for multimodal discourse analysis using video data once our version is updated.
Other Centre for Education Development (CED) staff are just around the corner and the main reason for the shift is to try to have all the CED staff offices in closer proximity to each other. Our manager designate from the UK, starts work at CPIT at the end of October. So it will be less daunting for him if all the CED staff are not scattered all over the campus.
I have enjoyed my first weeks (interrupted by a week away due to the earthquake) with the elearning team members. They have just about got used to seeing me in the office when they get in each morning as I am an early starter. I have now worked out all the security issues and liaised with security so that my early starts, once a week or so late finishes and study on Saturdays and on wet Sundays do not disrupt their security patterns.
It’s all also much quieter as the three others in the office beaver away at their computers and have few meetings whereas the adult ed. office was always a hive of activity with meetings and students and other visitors dropping in all the time. Therefore, I have made good progress with data analysis for the ‘first year apprentices’ project. The bulk of the data is now migrated to nVivo and various nodes (themes) have been established. Have also been able to make a start on the reports to each of the ITOs, due for end of this year. Also revised how to do ‘queries’ on nVivo, to compare demographic data and various coded text fragments. So far some patterns emerging as to ‘prior contact’ with a workplace or occupation and apprentices’ ‘stickability’. A common sense finding but well reflected in the data. While using the help feature on nVivo to work out various features of the software, came across the announcement on nVivo 9 launches October. Will need to re-evaluate nVivo9 for suitabitity for multimodal discourse analysis using video data once our version is updated.
Monday, May 17, 2010
Comparing nVivo and alasti for video analysis
At present, the qualitative software analysis platform we use at CPIT is nVivo. I am now using nVivo with the ‘perspectives of first year apprentices’ project and the thematic analysis of interview data for the ‘perspectives of new trades tutors’ was completed using nVivo. I have discussed pros and cons of using Vivo before, Presently, I find it is easy to use and generally intuitive with regards to dealing with text analysis although I also take into account that nVivo does impose a certain way of thinking about qualitative analysis.
Discussions with a few other researchers who work with video data reveal several use atlasti. So I downloaded a free trial version of atlasti and have completed transcription of a short video clip with both sets of software. The free version of atlasti allows users to upload 10 primary documents, work with 50 codes, 100 quotations and 30 memos.
Here is a comparison of older versions of nVivo and Atlasti, As a comparison, of my own evaluation of the two, I have jotted down some notes.
The first task is to learn a whole set of new terms!! Atlasti – hermeneutic unit vs project on nvivo. Alasti quotes or quotations vs nodes in nVivo for themes. Networks in alasti vs models in nvivo etc etc.
Atlasti is more ‘windows like’ in layout but nVivo uses the concept of folders to store and navigate through the various layers of data. Atlasti is more intuitive to use for uploading ‘primary documents’ which are referred to as ‘sources’ in nVivo.
Importing videos into each programme meant we had to convert videos to the correct file format which could be read by each data analysis software package. We used any video converter which is a free download and easy to use. Converting between video formats using this tool is straightforward.
Time stamp on nVivo only allows for a minute intervals which are, at the moment, not fine enough with the transcripts we have been producing. Also only one column for timespan and another for comments. However, custom columns can be added.
Had to convert word table of transcripts into a rtf or txt file to upload as a ‘primary document’ in atlasti which mucked up the organisation of the word table. Assigning quotations (nodes in nVivo) was simple, similar to adding comments to a word document. Coding also straight forward using a drag and drop technique. Video coding using an editing technique to snip segments out of the video to code.
Coding video using nVivo also involves a snipping / selection process and then a drag and drop of the selected segment to the code required. The coding summary records the timespan or the transcript fragment.
On alasti, the coding is marked on the transcript on a side screen and the actual video clip comes up.
Will need to work with both nVivo and Atlasti for another couple of clips to become proficient at the technicalities of working with each tool. At the moment, they both complete similar tasks although for the moment, Atlasti provides a better method to access video segments which have been coded.
Discussions with a few other researchers who work with video data reveal several use atlasti. So I downloaded a free trial version of atlasti and have completed transcription of a short video clip with both sets of software. The free version of atlasti allows users to upload 10 primary documents, work with 50 codes, 100 quotations and 30 memos.
Here is a comparison of older versions of nVivo and Atlasti, As a comparison, of my own evaluation of the two, I have jotted down some notes.
The first task is to learn a whole set of new terms!! Atlasti – hermeneutic unit vs project on nvivo. Alasti quotes or quotations vs nodes in nVivo for themes. Networks in alasti vs models in nvivo etc etc.
Atlasti is more ‘windows like’ in layout but nVivo uses the concept of folders to store and navigate through the various layers of data. Atlasti is more intuitive to use for uploading ‘primary documents’ which are referred to as ‘sources’ in nVivo.
Importing videos into each programme meant we had to convert videos to the correct file format which could be read by each data analysis software package. We used any video converter which is a free download and easy to use. Converting between video formats using this tool is straightforward.
Time stamp on nVivo only allows for a minute intervals which are, at the moment, not fine enough with the transcripts we have been producing. Also only one column for timespan and another for comments. However, custom columns can be added.
Had to convert word table of transcripts into a rtf or txt file to upload as a ‘primary document’ in atlasti which mucked up the organisation of the word table. Assigning quotations (nodes in nVivo) was simple, similar to adding comments to a word document. Coding also straight forward using a drag and drop technique. Video coding using an editing technique to snip segments out of the video to code.
Coding video using nVivo also involves a snipping / selection process and then a drag and drop of the selected segment to the code required. The coding summary records the timespan or the transcript fragment.
On alasti, the coding is marked on the transcript on a side screen and the actual video clip comes up.
Will need to work with both nVivo and Atlasti for another couple of clips to become proficient at the technicalities of working with each tool. At the moment, they both complete similar tasks although for the moment, Atlasti provides a better method to access video segments which have been coded.
Monday, September 21, 2009
Video analysis software for multimodal data analysis
While browsing through literature on multi-modal analysis, I came across studiocode as a video data analysis software tool. I then asked around CPIT and found out the sports science people were using a NZ developed product called silicon coach which is developed specifically to analyse sports performances but also provides capabilities for comparison of individual performances and to build up resources based on annotations made on videos. Many video analysis software has a sports orientated slant as this is where it is most useful for analysing the performance of athletes.
I then did the usual google search and came up with a short article which recommended four video data analysis tools including studiocode (Mac OS only). The other three are annotation which is only Mac based but seemed to have an attractive, user friendly interface for US$299; ecove with a byline of software for gathering data while observing behaviour, runs also on Palm OS & Pocket PCs for US$189; & Observer xt which has both Mac & Windows versions plus a version for mobile devices, seems to have all the bells & whistles (like studiocode).
A comprehensive list of qualitative data analysis software provided over thirty examples with about a third capable of video and audio analysis. These include:-
Transana which is developed by the University of Wisconsin and open source and cost US$50 for single user and US$500 per project.
Dart fish which provides a free download trial for 30 days
Atlasti which is a standard qualitative data analysis provides for multimedia coding and supposed to be similar to nVivo.
Hyperresearch $399 as another alternative to the more expensive sports based video analysis tools.
A couple which are freeware to have a look at include Elan and signstream.
So plenty of choice for the moment, the most likely ones will need to be evaluated against nVivo. I am looking for one which will be easy to use as one of the goals of implementing multi-modal analysis protocols for observing teaching & learning at CPIT is to devolve the analysis to tutors.
I then did the usual google search and came up with a short article which recommended four video data analysis tools including studiocode (Mac OS only). The other three are annotation which is only Mac based but seemed to have an attractive, user friendly interface for US$299; ecove with a byline of software for gathering data while observing behaviour, runs also on Palm OS & Pocket PCs for US$189; & Observer xt which has both Mac & Windows versions plus a version for mobile devices, seems to have all the bells & whistles (like studiocode).
A comprehensive list of qualitative data analysis software provided over thirty examples with about a third capable of video and audio analysis. These include:-
Transana which is developed by the University of Wisconsin and open source and cost US$50 for single user and US$500 per project.
Dart fish which provides a free download trial for 30 days
Atlasti which is a standard qualitative data analysis provides for multimedia coding and supposed to be similar to nVivo.
Hyperresearch $399 as another alternative to the more expensive sports based video analysis tools.
A couple which are freeware to have a look at include Elan and signstream.
So plenty of choice for the moment, the most likely ones will need to be evaluated against nVivo. I am looking for one which will be easy to use as one of the goals of implementing multi-modal analysis protocols for observing teaching & learning at CPIT is to devolve the analysis to tutors.
Monday, August 17, 2009
Tools for analysing qualitative research data
Jane Hart is compiling a list of most useful / most used tools for research. Many of the ones listed are unfamiliar to me!! Most of these are search engines, although there are also bibliographical archiving tools. I have been trawling the databases the last couple of weeks. Basically doing the ground work for my Ako Aoteoroa funded ‘teaching craftsmanship’ research project. So it was a good opportunity to try out a few of these tools. I find Google Scholar to be a good way to source ‘key words’ & starting material which I can then use in the standard academic search database to find references pertinent to the topic I am researching. Due to the nature of the topic ‘new trades tutors & their perceptions of teaching’ I have to rely on journal articles. A few books are available on Google books but only as previews so I have had to request interloans via our helpful CPIT library.
I have also started data anaylsis, learning my way through nVivo. I need to use NVivo so that I am familiar with the software in order to be able to support other staff in using this research analysis tool. Will provide more feedback later in the year on how I feel about using nVivo to replace my usual mixture of iterative data analysis using ‘copy & paste’ & find features on Word, along with summarising, collating and reorganising themes / threads into tables. My first impressions are that nVivo imposes a way of doing (& thinking) which is slightly out of synch with how I usually approach my data. I need to have a handle on the ‘big picture’ & how other themes relate to the overall scheme of things. An article by Elaine Welsh (2002) presents both the advantages & disadvantages of using nVivo for qualitative analysis.
Helen Colley & Kim Diment’s article on ‘holistic research for holistic practice: making sense of qualitative research data’ is part of the UK’s teaching & learning research programme ‘ building research capacity’ site. Their interpretation and recommendations on qualitative data analysis does resonate with my approach to undertaking research. I am often after the ‘whole picture’ of why things happen & am constantly trying to work out how the various themes that immerge from the data ‘fits into the whole’. It is also important to view the data as a holistic ‘narrative’ rather than just focus on its atomistic parts. Telling the story which research participants try to impart via their involvement in interviews etc. is an important role of the research process. I need to distil rather than filter & at the moment, I feel a software tool like nVivo is causing me to filter & sieve rather than to distil, refine and sharpen themes. It could be because I am working solely with digital sources instead of a mixture of digital & hard copy. Will persevere for the moment with nVivo & evaluate what eventuates in a couple of months. I can always do a manual collation as well after the nVivo process to see if anything different comes through.
The TLRP site is one I constantly dip into for interesting, informative and mostly practical / applicable suggestions on doing research. The Teaching & Learning Programme itself has been a source of many articles and research reports of relevance to the work I am doing towards by PhD thesis. In particular the projects completed under the ‘further & post 16 education’, higher education, workplace learning, professional learning, lifelong learning and technology enhanced learning sections.
I have also started data anaylsis, learning my way through nVivo. I need to use NVivo so that I am familiar with the software in order to be able to support other staff in using this research analysis tool. Will provide more feedback later in the year on how I feel about using nVivo to replace my usual mixture of iterative data analysis using ‘copy & paste’ & find features on Word, along with summarising, collating and reorganising themes / threads into tables. My first impressions are that nVivo imposes a way of doing (& thinking) which is slightly out of synch with how I usually approach my data. I need to have a handle on the ‘big picture’ & how other themes relate to the overall scheme of things. An article by Elaine Welsh (2002) presents both the advantages & disadvantages of using nVivo for qualitative analysis.
Helen Colley & Kim Diment’s article on ‘holistic research for holistic practice: making sense of qualitative research data’ is part of the UK’s teaching & learning research programme ‘ building research capacity’ site. Their interpretation and recommendations on qualitative data analysis does resonate with my approach to undertaking research. I am often after the ‘whole picture’ of why things happen & am constantly trying to work out how the various themes that immerge from the data ‘fits into the whole’. It is also important to view the data as a holistic ‘narrative’ rather than just focus on its atomistic parts. Telling the story which research participants try to impart via their involvement in interviews etc. is an important role of the research process. I need to distil rather than filter & at the moment, I feel a software tool like nVivo is causing me to filter & sieve rather than to distil, refine and sharpen themes. It could be because I am working solely with digital sources instead of a mixture of digital & hard copy. Will persevere for the moment with nVivo & evaluate what eventuates in a couple of months. I can always do a manual collation as well after the nVivo process to see if anything different comes through.
The TLRP site is one I constantly dip into for interesting, informative and mostly practical / applicable suggestions on doing research. The Teaching & Learning Programme itself has been a source of many articles and research reports of relevance to the work I am doing towards by PhD thesis. In particular the projects completed under the ‘further & post 16 education’, higher education, workplace learning, professional learning, lifelong learning and technology enhanced learning sections.
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