A recent post on the LinkedIn NVivo users page inspired me to write this post on collaborating with NVivo. This is a topic that has been an important component of my work for the past 4 years. I’d surmise that pervasive communication technologies and increased interdisciplinary research mean that NVivo is being used on more research teams now than ever before. This post captures 6 key ideas when collaborating with NVivo. An early caveat is that this post does not discuss the implications for teamwork offered by NVivo Server. With the understanding that I’m excited to see how NVivo Server will develop, I’m not convinced it’s widely available enough to warrant an expanded discussion here.
1. NVivo 10’s collaborative functions
As it stands, only one team member at a time can edit an NVivo project file. However, team members’ identities are recorded when working in a project file at different times. Alternatively, each team member can work on a copy a project file that can later be imported back into a “master” file using the import project dialog box. The “master” file is the main copy of the NVivo 10 project file that contains the most up-to-date source data and data analysis. Crucially, the “master” file should be stored on a computer that is frequently backed up. Anyone who has ever suffered the loss of a project file would likely encourage multiple back-ups on your NVivo project. I use Dropbox as a solution for backing up my NVivo projects, but this solution isn’t perfect as I will expand on below.
Assuming that the work has come to a stage where different members have submitted contributions to the NVivo project file, make sure that the team uses easily identifiable standardized user profiles when they work with their respective parts. User profiles, represented by each individual user’s initials, are an important function when the time comes to compare coding.
Coding stripes can be a useful features for NVivo projects where multiple team members are coding data. Coding stripes allow a team to visually identify how data has been organized into Nodes. NVivo also allows coding stripes to be filtered by team member in order to visually identify how an individual coded the data.
When reporting data analysis findings, a more comprehensive summary of the variance in team members’ coding may be needed. In this case, Coding Comparison Queries allow multiple team members’ coding to be quantified in metrics of inter-rate reliability (e.g., Kappa coefficient).
2. Team roles & responsibilities
Beyond the technical features available for NVivo 10 users, team roles and responsibilities should include some attention to data analysis. On any research team on which I work, I advocate the team appoints an NVivo coordinator for the research project. The NVivo coordinator is the team member responsible for maintaining the team’s “master” project file. The coordinator role entails 4 primary duties:
◆ Ensuring that each team member’s independent data analysis is routinely imported into the “master” project;
◆ Backing up the”master” project;
◆ Importing new data into the “master” project;
◆ and Distributing up-to-date copies of the “master” project.
3. Team workflow plan
Create a team workflow plan that includes how to manage your team’s data and analytic findings. The team workflow plan is a document that capture the team’s shared understanding of filenames, read-only and read-write file access, storage and backup locations, and rules for file distribution and archiving. For example, when it comes to audio and video files, what file formats will the team use? Will those files be embedded items or linked as external items?
The workflow plan should include the team’s approach to creating and maintaining nodes. I always recommend that team members write ‘instructions’ in every Node’s Description field (max 512 characters). Unless using In Vivo Coding, creating a new node results in a new node dialog box that includes a blank description field. What better place for a researcher to capture their thinking at a given moment? A team member’s definitions or reflections on coding and nodes can also be written as a linked Memo, which is easier to write, read, print and code.
4. NVivo and cloud computing
I use cloud-based file sharing services like DropBox, SkyDrive and Google Drive as a working solution for backing up and sharing collaborative NVivo Project files. In theory, these services also allow changes to a NVivo project file to be made across several computers using the ‘cloud’.
I recommend you turn off the live syncing features of these programs while you are running the NVivo 10 software client. Most of these services allow you to toggle live-syncing on a folder by folder basis so that you can sync all other folders but the folder containing your NVivo project. I learned this the hard way by losing hours of coding time due to an error caused by simultaneously using NVivo and syncing its attendant .nvp file in a cloud-based utility. Other colleagues of mine have had similar experiences. Cloud-based utilities can be useful for team collaboration, but taking the proper precautions can avoid costly loss of analysis time due to software crashes.
5. Team meetings
Finally, add the NVivo project file as an agenda item team research meetings. While the meeting agenda will no doubt be packed with discussions of the research process, briefly talking about the tools of your is a good idea.
These insights and many more are contained my technical manual on QSR’s software, “NVivo 10 Essentials” (co-authored with Bengt Edhlund).