ISEOR-AoM QDAS Workshop

Improving the Impact of Qualitative Research: A Practical Perspective of a Study Supported by Qualitative Data Analysis Software from Inception to

Completion.

Dr. Joseph B. Baugh, PMPBaugh Group LTD

University of Phoenix

ISEOR-RMD Joint ConferenceLyon FranceJune, 2011

Agenda

• Why do Qualitative Research?• Who will benefit from Qualitative Data

Analysis Software (QDAS)• Data Collection and Analysis Processes• Describing QDAS• QDAS (Atlas.ti®) demonstration

– Assigning primary documents– Using codes and memos– Producing reports

• Q & AImproving the Impact of Qualitative Research - 2011 ISEOR-RMD, Lyon France 2© 2011 Joseph B. Baugh

Improving the Impact of Qualitative Research

• Coursework alone may not effectively prepare applied researchers.

• QDAS formerly disdained by many researchers (Morison & Moir, 1998).

• QDAS now acceptable and desirable• Frees up time once spent in data

management and encoding (Blank, 2004; Mangabeira, Lee & Fielding, 2004).

• Allows more time to understand the data, enhance analysis, improve the impact of the research.

Improving the Impact of Qualitative Research - 2011 ISEOR-RMD, Lyon France © 2011 Joseph B. Baugh 3

Why Use QDAS for Research?

• Unlike quantitative research with its heavy reliance on Likert surveys and other sterile instruments as primary data collection tools, qualitative research requires the use of a variety of field methods (Eaves, 2001; Robson, 2002) to capture rich data in its native context that will allow the researcher to adequately explore particular phenomena within the native environment and draw credible conclusions from the data.

• QDAS supports and enables exploration.Improving the Impact of Qualitative Research - 2011 ISEOR-RMD, Lyon France © 2011 Joseph B. Baugh 4

QDAS Researcher Populations

• Baugh, Hallcom, and Harris (2009) identified three potential user groups– Academic researchers

• Ex: Classic theory-driven researchers w/ extensive research experience

• Experienced in sound research methodologies, develop robust studies and results

– Market researchers• Ex: Applied researchers w/ some research experience• Studies driven by specific business constraints, which may

strain study credibility– Software adopters

• Ex: Project managers w/ little research experience• Adopters of specific tools, may not use scientifically.


Qualitative Research Continua


AcademicResearchers

MarketResearchers

SoftwareAdopters

Familiarity with QDAS

LowHigh

Familiarity with Qualitative Research Methodologies

HighLow

Qualitative Data Collection Tools & Techniques

• Interviews• Focus groups• Facilitated workshops• Group creativity techniques• Questionnaires • Surveys• Observations• Documentation


Data Collection: Field Studies

• Get out in the field to collect data and study social phenomena in native environment

• Develop rapport with participants• Be aware of researcher biases

– Researcher affects the case or the case affects the researcher (Miles & Huberman, 1994)

– Preconceived notions may skew analyses (Malterud, 2001; Robson, 2002)

– Seek disconfirming evidence (Creswell, 1998; Eisenhardt, 1989; Sadler, 1981)


Data Collection: Questionnaires

• Easy to study specific phenomena• Purposeful sampling• Easy to deploy• Cheaper than a field study

– Internet questionnaires don’t require transcription services

– No travel expenses– Minimal cost for Internet survey services

• Not as much fun as a field study• Response rates can be problematicImproving the Impact of Qualitative Research - 2011 ISEOR-RMD, Lyon France © 2011 Joseph B. Baugh 9

Data Collection Processes


Identify Potential Study Sites

Gain Access to Study Site

Build Rapport with Participants

Collect Data viaInterviews

Focus GroupsWorkshops

Group sessionsQuestionnaires

SurveysObservations

Documentation

Record Data(Audio & Written

Files)

Resolve FieldIssues

Preliminary Data Analysis

Activities

TranscribeRecordings

Verify and ValidateTranscripts

Track and ManageData

Formal DataAnalysisActivities

Study DesignActivities

Qualitative Data Collection Processes © 2009 Joseph B. Baugh

Identify Purposeful Samplings

Managing the Data

• "Qualitative data can easily become overwhelming, even in small projects . . . During and after data collection, you have to reduce the data mountain through the production of summaries and abstracts, coding, writing memos, etc." (Robson, 2002, p. 476)

• How can we accomplish this recommended reduction as we collect and analyze the raw data?


Qualitative Data Analysis Software

• Encode qualitative data• Allow multiple levels of encoding• Develop emerging themes and patterns• Attach memos to data segments• Develop reports• Use refined data to create narrative in

the words of the participants (Creswell, 1998) who lived the experiences (Denzin, 2001; Kvale, 1996).


Using QDAS in the Wild

• Use Qualitative Data Analysis Software (QDAS) to reduce the raw data to common themes and patterns through encoding.– Provisional start list

(Miles & Huberman, 1994)

– Create new codes as themes emerge

– Differentiate multiple levels of codes


Data Collection Activities

Organize and manage data

Gain a sense of the data

Encode data

Reflect on coded data

Categorize data,Create context,Create meaning

Analyze, evaluate, and interpret data

Place the data back into context

Write ReportQualitative Data Analysis Activities © 2009 Joseph B. Baugh

Represent refined data in participant

voices

Data Analysis Processes


QDAS is very useful

for these processes

QDAS Caveats

• QDAS can support well-designed and implemented studies:– QDAS is very effective at organizing, managing,

and tracking data, which allows the researcher to spend more time contemplating the data.

– Word frequency counts are not data analyses, they are just hints of the themes and patterns hidden in the raw data, the researcher still must dig them out.

– Remember that QDAS is just a tool, it can’t - and shouldn’t - do your thinking for you. (Morison & Moir, 1998).


QDAS Packages

• Several popular programs are available to support qualitative data analysis:– Atlas.ti from www.atlasti.com– Ethno 2 from www.indiana.edu/%7Esocpsy/ESA/– Ethnograph from www.qualisresearch.com– NVivo from www.qsrinternational.com– Qualrus from www.ideaworks.com/qualrus/index.html

• Most 3rd Generation QDAS packages have similar features, perhaps different naming conventions

• This demonstration will focus on Atlas.tiImproving the Impact of Qualitative Research - 2011 ISEOR-RMD, Lyon France © 2011 Joseph B. Baugh 16

http://www.atlasti.com/

http://www.indiana.edu/~socpsy/ESA/

http://www.qualisresearch.com/

http://www.qsrinternational.com/

http://www.ideaworks.com/qualrus/index.html

QDAS Example: Atlas.tiAssigning Primary

Documents


QDAS Example: Atlas.ti®Encoding Data


QDAS Example: Atlas.ti®Managing Memos


QDAS Example: Atlas.ti®Generating Code Reports






QDAS Example: Atlas.ti®Developing Networks


Using QDAS in the Wild

• Develop a thick description of the phenomena under study (Denzin, 2001; Geertz, 1973).

• Use participant quotes to place the findings back into context in the words of the participants (Creswell, 1998; Stake, 1995)

• Develop the final report.


Conclusion

• Qualitative research studies can help uncover complex social phenomena that are beyond the reach of classic quantitative research methodologies– Require a scholarly approach to develop

credible results– Use multiple tools and techniques to collect

data– Use QDAS to ease the pain of analyzing a

mountain of data, which can improve the impact of qualitative research

Improving the Impact of Qualitative Research - 2011 ISEOR-RMD, Lyon France 25© 2011 Joseph B. Baugh

Questions


drjoe(at)baughgroup.com

ReferencesBaugh, J. B., Hallcom, A. S., & Harris, M. E. (2010a). Computer assisted qualitative data analysis software: A practical

perspective for applied research. Revista del Instituto Internacional de Costos, (6), 69-81.

Baugh, J. B., Hallcom, A. S., & Harris, M. E. (2010b, June). Changes that make a difference: Attaining a PhD while maintaining an active life. Proceedings of the ISEOR – Academy of Management International Conference and Doctoral Consortium on Organizational Development and Change (Vol. 1, CD-ROM). Lyon France: ISEOR.

Blank, G. (2004). Teaching qualitative data analysis to graduate students. Social Science Computer Review, 22(2), 187-196.

Creswell, J. W. (1998). Qualitative inquiry and research design: Choosing among the five traditions. Thousand Oaks, CA: Sage.

Denzin, N. K. (2001). Interpretive interactionism (2nd ed.). Thousand Oaks, CA: Sage.

Eaves, Y. D. (2001). A synthesis technique for grounded theory data analysis. Journal of Advanced Nursing, 35, 654-663.

Eisenhardt, K. M. (1989). Building theories from case study research. In A. M. Huberman & M. B. Miles (Eds.), The qualitative researcher's companion (pp. 5-35). Thousand Oaks, CA: Sage.

Geertz, C. (1973). The interpretation of cultures. New York: Basic Books.

Kvale, S. (1996). Interviews: An introduction to qualitative research interviewing. Thousand Oaks, CA: Sage.

Malterud, K. (2001, August 11). Qualitative research: Standards, challenges, and guidelines. The Lancet, 358, 483-488.

Mangabeira, W. C., Lee, R. M., & Fielding, N. G. (2004). Computers and qualitative research: Adoption, use, and representation. Social Science Computer Review, 22(2), 167-178.

Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: An expanded sourcebook (2nd ed.). Thousand Oaks, CA: Sage.

Morison, M., & Moir, J. (1998). The role of computer software in the analysis of qualitative data: Efficient clerk, research assistant, or Trojan horse? Journal of Advanced Nursing, 28, 106-116.

Robson, C. (2002). Real world research: A resource for social scientists and practitioner-researchers (2nd ed.). Malden MA: Blackwell Publishing.

Sadler, D. R. (1981). Intuitive data processing as a potential source of bias in naturalistic evaluations. In A. M. Huberman & M. B. Miles (Eds.), The qualitative researcher's companion (pp. 123-135). Thousand Oaks, CA: Sage Publications.

Stake, R. E. (1995). The art of case study research. Thousand Oaks, CA: Sage.


ISEOR-AoM QDAS Workshop

Technology

research experiencestudies

quantitative research

raw data

data management

rich data

data mountain

dataqualitative data

sound research methodologies