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.

 

Pictures are worth a 1000 words, but you don’t get that many when coding.

Mitchell (2011) claims that there is no roadmap when trekking through visual data, and there is no set way to engage in fieldwork or analyzing multimedia information (Pink, 2007).  Through the course of my development as a budding researcher I have often felt lost when trying to carry out carefully constructed research projects. Things don’t always seem to go as planned, and interpretations of procedures and information can sometimes become muddled.

In an attempt to explore how to best carry out such tasks as interpreting visual data, engaging in field work, and analyzing information I have turned to several texts and scholars.

One of the first courses, taken during my doctoral studies was grounded in evaluation research and needs analysis.  My journey through the semester allowed our research team to work with a client to establish goals that needed to be investigated. Surprisingly this course was very much a cookbook style that prescribed specific steps and assessments be taken in precise order.

Interestingly enough, none of my following methods courses truly provided the same structure.  During one qualitative inquiry course we worked through the Merriam (2009) and Seidman (2013) texts.  While these are great resources that provide starting points they provided too much variety in approaches.  It seemed that every unique project had a different approach to engaging in field work and analyzing information.  To further explore qualitative research designs, methods, and approaches, several students turned to Creswell’s (2012; 2013) texts.  While these cookbook resources are a great base to understand and compare qualitative, and mixed method, approaches to fieldwork, they don’t cover too many approaches to visual data analysis.

While text such as Emmison’s (2010) chapter on visual data offer great insight into the history of visual data and analysis approaches, they could be improved upon by commenting on best practices, and providing guidance for budding researchers. Other book chapters (i.e. Cohen, Manion & Morrison, 2011) provide an introductory discussion about visual data interpretation; but the larger lesson is that ‘it depends’ on the research questions and context.

 We seem to hear, “it depends” a lot in our field, no?

 

References

Cohen, L., Manion, L., & Morrison, K. (2011). Visual media in educational research. In Research Methods in Education (7th ed., pp. 526-534). New York, NY: Routledge.

Creswell, J. W. (2012). Qualitative inquiry and research design: Choosing among five approaches. Thousand Oaks, CA: Sage.

Creswell, J. W. (2013). Research design: Qualitative, quantitative, and mixed methods approaches. Thousand Oaks, CA: Sage.

Emmison, M. (2010). Conceptualizing visual data. In D. Silverman (Ed.), Qualitative Research (3rd ed., pp. 233-249). San Francisco, CA: Sage.

Merriam, S. B. (2009). Qualitative research: A guide to design and implementation. San Francisco, CA: John Wiley & Sons.

Mitchell, C. (2011). Doing visual research. London, UK: Sage.

Pink, S. (2013). Doing visual ethnography. London, UK: Sage.

Seidman, I. (2012). Interviewing as qualitative research: A guide for researchers in education and the social sciences. New York, NY: Teachers college press.

 

 

 

New Media Information Display

Throughout this discussion of possible new tools for data interpretation and display remember that the purpose is to communicate not to impress; don’t get caught up in how cool something looks. Think critically about if the options below truly represent the best means of communicating your meaning.
With the rapid centralization of journal article to interactive databases there has been a steady push for incorporating new media in research articles as novel forms of data representation. Often researchers consider tables and other graphical displays completing the discourse, report, or narrative. Typically information is represented in graphs and charts that include: bar charts, pie charts, line graphs (Minter & Michaud, 2003). Other data types can include realistic artifact such as, diagrams, maps, drawings, illustrations, and photographs.
Cidell (2010) took this idea of content analysis and incorporated word clouds into the mix. While word clouds can be effective displays and allow viewers to see what terms are prominent, they don’t allow researchers to display complete phrases. This is where poetic representations (Cahnmann, 2003) can be useful in place of word clouds (for more information check out MacNeil, 2000; Sparkes & Douglas, 2007).
Russ-Eft and Preskill (2009) discuss some very interesting information analysis that includes drama, cartoons, photography, checklists, and videos. These image based constructs of data analysis are further discussed by (Banks, 1998, as cited by Prosser, 1998).
Some interesting approaches to data analysis and display can include the following: cartoons and photo stories, such as graphic novel representation, using recreative images to synthese meaning and convey dialogue; enhanced audio elements as elaborated by Silver & Patashnick (2011); and interpretative live action, as described by Carter (2004), can include dance, plays, and other stage performances. Recently there has also been a push for multimedia video reports, much like this Africa Climate Change Resilience Alliance (ACCRA) project report.  Additionally, other interactive elements such as infographics and webpages are becoming more common place. And lastly, reflective blogs have also proved to be a useful tool (Paulus, Lester, & Dempster, 2013).

 

References

Bank, M. (1998). Visual anthropology: Image, object, and interpretation. In J. Prosser (Ed.), Image-based research: A sourcebook for qualitative researchers. (1st ed., pp. 6-19). Psychology Press.
Cahnmann, M. (2003). The craft, practice, and possibility of poetry in educational research. Educational researcher, 32(3), 29-36.
Carter, P. (2004). Material thinking : the theory and practice of creative research. Carlton, Australia: Melbourne University Press
MacNeil, C. (2000). The prose and cons of poetic representation in evaluation reporting. American Journal of Evaluation, 21(3), 359-367.
Minter, E., & Michaud, M. University of Wisconsin – Extension, Program Development and Evaluation. (2003). Using graphics to report evaluation results. Retrieved from: http://learningstore.uwex.edu/Assets/pdfs/G3658-13.pdf
Paulus, T. M., Lester, J. N., & Dempster, P. (2013). Digital tools for qualitative research. London, UK: Sage.
Russ-Eft, D. F., & Preskill, H. (2009). Communicating and reporting evaluation activities and findings. In Evaluation in organizations: A systematic approach to enhancing learning, performance, and change (2nd ed., pp. 399-442). New York, NY: Basic Books.
Silver, C., & Patashnick, J. (2011, January). Finding Fidelity: Advancing Audiovisual Analysis Using Software. In Forum: Qualitative Social Research (Vol. 12, No. 1). Retrieved from: http://www.qualitative-research.net/index.php/fqs/article/view/1629/3148
Sparkes, A. C., & Douglas, K. (2007). Making the Case for Poetic Representations: An Example in Action. Sport psychologist, 21(2), 170-189