Importance of Teamwork in Mixed Method Research Projects

With the implementation of survey instruments there is little movement of quantitative data, and minimal opportunity for varying interpretation of responses, as well as questions items (Bryman & Burgess, 1994).  With qualitative instruments integrated into mulimethod studies, the case is not as pronounced and therefore difficulties may arise in interpreting and evaluating qualitative data (Maderson, Kelaher, & Woelz-Stirling, 2011).  Hence in constant data collection phases, the management of information can become problematic when data are qualitative, collected by more than one researcher, and are intended for multiple users (Bryman & Burgess, 1994).

As two researchers working on individual projects are compounding data within a single research endeavor, the aspect of teamwork becomes crucial to the success of data analysis. Teamwork paired with reflexivity leads to improved productivity, effectiveness, and more robust research – overall higher quality (Barry et al., 1999). At the qualitative stage specifically, West (1994) reports that teamwork enhances the rigor of the methodological design, analysis, and interpretive elements of a research project.

Additionally teams can foster deeper conversations and higher levels of conceptual thinking than researchers working alone hence enriching the coding and analysis process at each stage (Barry et al., 1999).  This will include: integrating differing perspectives and ease at identifying bias (Liggett et al., 1994); a better standardization for coding and improving accuracy in theme creation and application (Delaney & Ames, 1993); and advancing the overall analyses to a higher level of abstraction (Olesen, Droes, Hatton, Chico & Schatzman, 1994).  In an effort to have a more rigours data analysis process and the reduction of personal bias, teamwork is crucial to the multiphase research model.

During the analysis phase of both the quantitative data and the qualitative information, the team aspect is crucial to the development of coding schemes and information interpretations.  The multidisciplinary discussions will act as a mindset for the two main analysis phases, sharpening the researchers to code of themes they might not have individually considered.

 

References

Barry, C. A., Britten, N., Barber, N., Bradley, C., & Stevenson, F. (1999). Using reflexivity to optimize teamwork in qualitative research.Qualitative health research,9(1), 26-44.

Bryman, A., & Burgess, B. (Eds.). (1994).Analyzing qualitative data. New York, NY: Routledge.

Delaney, W., & Ames, G. (1993). Integration and exchange in multidisciplinary alcohol research. Social Science and Medicine, 37, 5-13.

Friedman, T. (2005). The world is flat. New York, NY: Farrar, Straus & Giroux.

Liggett, A. M., Glesne, C. E., Johnston, A. P., Hasazi, B.,&Schattman, R. A. (1994). Teaming in qualitative research: Lessons learned. Qualitative Studies in Education, 7, 77-88.

Manderson, L., Kelaher, M., & Woelz-Stirling, N. (2001). Developing qualitative databases for multiple users.Qualitative health research,11(2), 149-160.

Olesen, V., Droes, N., Hatton, D., Chico, N.,&Schatzman, L. (1994). Analyzing together: Recollections of a team approach. In R. G. Burgess (Ed.), Analyzing qualitative data (pp. 111-128). London, UK: Routledge.

West, M. A. (1994). Effective teamwork. Leicester, UK: BPS Books.

 

What package do you pick?

My research partner and I were having a conversation about what programs we should use to code our open-ended survey data and interviews. So we began listing out all our options from Google documents to complex CAQDAS packages and then some. We soon discovered an issue…we didn’t have a shared skill set, meaning that software my partner had experience with I did not and vice versa.  So instead of trying to find common ground we started looking into what packages offered trial versions long enough for us to complete data coding over the course of a semester.

That narrowed down our list some…only then we ran into another issue. I am a window/linux user and my partner is an apple user that works off their mobile device more often than not. In the end we couldn’t really make a decision because of our differences…we decided that we would have to compromise.  And then for the third time we rewrote our list and began a conversation anew. This time it was guided by functionality and a discussion by Taylor, Lewins and Gibbs (2005).

Since the size of our data set is fairly small this wasn’t too much of an issue, so that didn’t knock any off our list.  The next topic was collaboration: we want to be able to either send documents back and forth via email/cloud or collaborate directly without much hindrance. Surprisingly, we didn’t think about our data type until the third conversation.  Since we have text, audio, and pdf artifacts we needed a CAQDAS package that could support coding directly in the platform for audio elements.  Our fourth criteria was based on using frequency and comparative matrices across our data types.  Because we are doing a mixed methods study, we are very concerned about convergence (or even divergence) across themes.  Additionally, we are very interested in working with quantitative data (for minimal descriptive statistics) within a single package as well. Finally we settled on two possible software options.

What a hassle? There has to be a simpler, comparative chart that would have allowed us to check properties across various CAQDAS packages and cross off options that didn’t meet our criteria.  Turns out that these charts are already floating around the web, we just didn’t look hard enough. Here is an example from UNC that compares ATLAS.ti, MAXQDA, NVivo and Dedoose, and another Stanford site that compares Nvivo, HyperResearch, Studiocode, Atlas.ti, and Tams Analyzer.

 

References:

Taylor, C., Lewins, A., & Gibbs, G. (2005, December 12). Debates about the software. Retrieved from http://onlineqda.hud.ac.uk/Intro_CAQDAS/software_debates.php