Lena Bucatariu – ID95389 Qualitative Research Methods – Unit 4-1

DQ #1: What are the major challenges and advantages of using software for

qualitative data analysis?

1. Background

Computer-assisted qualitative data analysis software or CAQDAS is specialized software

which facilitates storing, indexing, and retrieving qualitative data for commercial and

academic research (Cambra-Fierro & Wilson, 2011). Software options vary in features,

usability, and price from QDA Miner which is highly compatible across formats for 238$

annual student subscription (Provalis , 2015) to MAXQDA for mixed methods research at

$51 for a 6-month student license (MAXQDA, 2015) and reputable code-based theory

builders (Bhowmik, 2006) ATLAS.ti and NVivo at $99 for a 2-year student license (atlas.ti,

2015) and $80 for a semester subscription, respectively (QSR International, 2015).

2. Advantages of using qualitative software

Developed to serve both research practitioners and academics, CAQDAS offers practicality,

transparency, and display benefits (Cambra-Fierro & Wilson, 2011; Kikooma, 2010).

From a practical dimension, qualitative software classifies, stores, and allows retrieval of

large amounts of virtual text data (Cambra-Fierro & Wilson, 2011) thus easing the

researcher’s physical task of data management.

Owing to inherent indexing and linking capabilities, software solutions facilitate the

mental processes associated with identifying key ideas or ‘nodes’ (QSR International,

2015), grouping them by themes, and assigning or ‘coding’ verbatims to them (Kikooma,

2010). These features becomes particularly important when the researcher works in a

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Lena Bucatariu – ID95389 Qualitative Research Methods – Unit 4-1

geographically dispersed project team with several members making updates and

annotations simultaneously (Cambra-Fierro & Wilson, 2011).

As Kikooma (2010) points out, software adds rigor and transparency to the qualitative

research process by enhancing saturation through modeling, keeping an electronic audit

and log trail to justify conclusions, and helping to rule out validity threats.

Finally, software solutions with display capabilities can visually organize data into

diagrams to facilitate analysis and be inserted into the final report (see Figure 1).

3. Barri ers

to

using qualitative software

Despite the contributions highlighted above, qualitative software is not yet widespread

(Cambra-Fierro & Wilson, 2011) which points to a few challenges to adoption. At the initial

stage, low penetration means that very few researchers have used the software and are

able to explain its benefits to others. Limited product knowledge thus deepens the risk

associated with making an initial investment and having to learn how to use the

software (Gibbs, 2011).

Among users, a common complaint is that CAQDAS is just a tool to aid the researcher in all

processes, but doesn’t do the thinking of actually creating nodes, identifying relations,

assigning verbatims or writing the report (Gibbs, 2011). Unlike CAQDAS, statistical

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Figure 1 Sample diagrams showing logical connections between ideas. Source: atlas.ti, 2015

Lena Bucatariu – ID95389 Qualitative Research Methods – Unit 4-1

packages such as SPSS, SAS, or Stata perform complex and often tedious calculations thus

making the investment easier to justify (Saunders, Lewis, & Thornhill, 2012).

Throughout the data processing stage, Cambra-Fierro & Wilson also mention ‘immediacy of

feedback, lack of non-verbal data, excessive detail or data fragmentation’ (2011, p. 19) as

further issues associated with the use of qualitative software.

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Lena Bucatariu – ID95389 Qualitative Research Methods – Unit 4-1

DQ #2: Using the thesis of Katja Louko in your course materials, evaluate the

effectiveness of the data analysis methods used in that dissertation.  How might the

latest technology change the process?

1. A review of the data analysis methods used

Concerned with investigating executive coaching at Sanoma Corporation, Louko (2013)

uses the stages of Punch (2005) as the theoretical foundation for data analysis. The phases

are Data Documentation and Collection Period, Data Reduction, Data Display, Conclusion

Drawing and Verification, and Data Analysis (Punch, 2005 p. 325, quoted by Louko, 2013).

After listing and briefly explaining the stages in the 3. Methodology subsection 3.4. Data

analysis, it would have been beneficial to also link each stage to the context of the study at

hand, e.g. by explaining what was done during the data documentation and collection about

executive coaching, how data reduction was conducted after interviewing, or how

conclusions were drawn. Such adapted illustrations would have shown rigor in the

methodology while also offering a useful preview of later sections.

Although section 3.Methodology has a special subsection dedicated to Punch (2005) as the

backbone of data analysis, it is rather surprising that section 5. Discussion and Data Analysis

does not make reference to these stages again. A thorough scan of chapter 5 shows

narration and interpretation of respondents’ answers, without any direct statement of

when and how the Punch (2005) stages were applied. On this note, it is felt that listing out

the phases and briefly illustrating the type of work supporting each step would have added

transparency. For enhanced credibility, the author could also consider following the

framework approach (Smith & Firth, 2011) with its structured coding and analysis stages

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Lena Bucatariu – ID95389 Qualitative Research Methods – Unit 4-1

or the repertory grid (Hair, Rose, & Clark, 2009) where triangulation could yield interesting

results on executive coaching benefits and motivations.

2. Qualitative software use

Considering the areas of improvement highlighted above, the use of qualitative data

analysis software could help primarily with transparency of data management as well as

display of results.

First, the software would keep track of the actions involved at each stage, creating an audit

trail for retrieval during data analysis (Kikooma, 2010). Clear tasks with supporting

documentation would be first introduced in the Methodology section, each logged under the

corresponding Punch (2005) stage. The primary concern here would be to demonstrate the

validity and rigor of the methodology employed without necessarily previewing findings.

The Data Analysis chapter could be re-structured following the Punch (2005) stages (or any

other method chosen) with implementation details and corresponding screen shots from

the QDA software. For instance, screens could include the result of data reduction, a list of

NVivo ‘nodes’ or mind maps of interrelated concepts drawn from respondents.

Lastly, the insertion of computer enhanced displays could improve the visual appeal of the

report, especially in the area of presentation of findings. It is hoped that modern computer

graphics would encourage the researcher to prioritize key motivational factors (e.g. by

color-coding and/or graphical arrangement into semantic areas) instead of simply listing

itemized findings – see Louko (2013) esp. pp. 63-65.

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Lena Bucatariu – ID95389 Qualitative Research Methods – Unit 4-1

References atlas.ti. (2015). Students . Retrieved March 7, 2015, from atlasti.com:

http://atlasti.com/students/

Bhowmik, T. (2006). Building an Exploratory Visual Analysis Tool for Qualitative Researchers. Retrieved March 7, 2015, from http://www.geovista.psu.edu/publications/2006/Bhowmick_AutoCarto_QualRes_06.pdf

Cambra-Fierro, J., & Wilson, A. (2011). Qualitative data analysis software: will it ever become mainstream? Evidence from Spain. International Journal of Market Research, 53(1), 17-24.

Cresswell, J. (2009). Research Design. Qualitative, Quantitative and Mixed Methods Approaches (3rd ed.). California: Sage Publications.

Gibbs, G. R. (2011). Learning Qualitative Data Analysis on the Web. Retrieved March 7, 2015, from http://onlineqda.hud.ac.uk: http://onlineqda.hud.ac.uk/Intro_CAQDAS/What_the_sw_can_do.php

Hair, N., Rose, s., & Clark, M. (2009). Using qualitative repertory grid techniques to explore perceptions of business-to-business online customer experience. Journal of Customer Behavior , 8(1), 51-65.

Kikooma, J. F. (2010). Using Qualitative Data Analysis Software in a Social Constructionist Study of Entrepreneurship. Qualitative Research Journal, 10(1), 40-51.

Louko, K. (2013, January 21). A Case Study of the Sanoma Corporation - The Motives and Benefits of Executive Coaching From a Leader’s Perspective. Zug, Switzerland.

MAXQDA. (2015). Order information . Retrieved March 7, 2015, from maxqda.com: http://www.maxqda.com/licenses/students/order-information

Provalis . (2015). Order . Retrieved March 7, 2015, from provalisresearch.com: http://provalisresearch.com/order/

Punch, K. F. (2005). Introduction to Social Research–Quantitative & Qualitative Approaches. SAGE.

QSR International. (2015). Quick Order Listing. Retrieved March 7, 2015, from QSRInternational.com: http://www.qsrinternational.com/quick-order_listing.aspx

Saunders, M. N., Lewis, P., & Thornhill, A. (2012). Research Methods for Business Students. Harlow: Pearson.

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Smith, J., & Firth, J. (2011). Qualitative data analysis: the framework approach. Nurse Researcher, 18(2), 52-62.

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