Qualitative Coding and Business
Ricky Gettys
Penn State
Smeal College of Business
M&O DBA | October 2023
Introduction
Anthropologist (2012-2016)
Research on how business owners navigate changes in policies and culture in Thailand, Vietnam, China, & US.
Research Manager (2017-2020)
Helped a large multinational non-profit revamp its core strategy and engage Gen Z employees.
University Instructor (2015-2021)
Taught classes on applied qualitative methods and intercultural communication. Trained over 50 researchers to use applied qual. methods.
Ph.D. Student (2022-Present)
Studying deep-level diversity and how new ideas spread in teams and organizations
Agenda
Qualitative Coding
Qualitative Coding Types
| Grounded Coding | Top-down Coding |
Starting Point | Raw data (inductive) OR general lens (abductive) | Pre-defined codebook (deductive) |
Definitions of what “counts” as a code | Researcher gets to create the rules, typically starting with in-vivo quotes (direct quotes from the text) | Strict rules and definitions (see next slide for an example) |
Outcomes & Uses |
|
|
Top-down Codebook Template & Sample Entry
This codebook template and entry come from a project attempting to categorize all the kinds of pro-social responses companies made to the Covid-19 pandemic using corporate twitter data.
TEMPLATE | EXAMPLE |
Code Name Definition: (1-5 sentences that describe the “yes” criteria for this code) �Keywords: (3-10 unique words, separated by a comma, that would make this code instantly recognizable.) �Core Example:
�Edgecase, Yes:
�Edgecase, No:
| Access to Product & Services Definition: Free or discounted or more available for different groups �Keywords: Free access, donate, waive fees, extended free trial �Example:
�Edgecase, Yes:
�Edgecase, No:
|
Potential Products from Coding
Adapted from a table describing the products of grounded theory research. See O’Connor, M. K., Netting, F. E., & Thomas, M. L. (2008). Grounded Theory: Managing the Challenge for Those Facing Institutional Review Board Oversight. Qualitative Inquiry, 14(1), 28–45. https://doi.org/10.1177/1077800407308907
Grounded theory (Glaser & Strauss; Eisenhardt)
Constructivist Grounded theory
(Charmaz)
An Anthropologist Walks into a Bar
Other Business Cases for Qualitative Coding
Tech consulting firm. $900 million in revenues, 15,000 employees, recent merger and large growth.
B2B software company. $300 million revenue, 300 employees, growth-stage startup.
Big Non-profit. 60,000 full-time Gen Z volunteers, 7,500 full-time employees in 120 countries.
App company. $1 billion revenue, 1,400 employees.
Academic Case for Qualitative Coding
General Overview of the Coding Process
An iterative process between:
Figure 2.1 is from Saldaña, J. (2013). The coding manual for qualitative researchers (2nd ed). SAGE.
Part-Whole Analysis
Coding Example (MaxQDA)
Initial Steps:
Screenshot from MaxQDA 2020, a computer-assisted qualitative data analysis software (CAQDAS).
Memo: Initial Coding
"There is more money at stake, so it just changes the calculus,” (The End of Faking It in Silicon Valley - The New York Times, p. 2)
"when the easy money dries up, everyone parrots the Warren Buffett's proverb about finding out who is swimming naked when the tide goes out" (The End of Faking It in Silicon Valley - The New York Times, p. 2)
Memo: Initial Coding
"Start-ups have many of the conditions most associated with fraud, Mr. Dyck said. They tend to employ novel business models, their founders often have significant control and their backers do not always enforce strict oversight." (The End of Faking It in Silicon Valley - The New York Times, p. 4)
Memo: Initial Coding
Memo: Secondary Coding (Blue Codes)
Secondary Coding: What do laypeople think?
Screenshot from MaxQDA 2020, a computer-assisted qualitative data analysis software (CAQDAS).
Memo: Secondary Coding
“Based on this article two things are clear, the only people persecuted for being fraudsters are women and of color. White male privilege and the absolution of criminal activity.” (Comment 15, Paragraph 1)
“As a member of the privileged older white male demographic, I find it immensely gratifying that these interlopers finally get their comeuppance. Spectacular wealth gained from exploiting other people is an exclusive right we have reserved to ourselves for centuries and it has been most distressing to see it squandered on this undeserving rabble. It good to see the plebians’ hard earned tax dollars put to work fighting this terrible to my exclusive good. Go get ‘‘em tigers!”” (Comment 4, Paragraph 1)
Memo: Secondary Coding (Round 2)
Additional Questions to Review in the Secondary Coding:
Memo: Secondary Coding (Round 2)
Do demographic characteristics play a role in when people care who commits fraud?
“The REAL FAKING happens in the east coast BANKS. The Silicon Valley faking is from naïveté and obsessive ‘optimism’.” (Comment 3, Paragraph 1)
“Based on this article two things are clear, the only people persecuted for being fraudsters are women and of color. White male privilege and the absolution of criminal activity.” (Comment 15, Paragraph 1)
Memo: Secondary Coding (Round 2)
Two emerging views of fraud: Lack of common sense and bad intent.
“The REAL FAKING happens in the east coast BANKS. The Silicon Valley faking is from naïveté and obsessive ‘optimism’.” (Comment 3, Paragraph 1)
One is tempted to be a cranky old timer and say, see, these kids are getting their comeuppance. On the other hand, one hundred years ago, young entrepreneurs building the 20th century economy were doing the same dumb, dishonest, experimental stuff in their own new world. They had to crash, then learn lessons and rebuild their economy for the new century. So these kids need to try and fail and hopefully create something substantial at some point. (Comment 10, Paragraph 1)
Memo: Secondary Coding (Round 2)
Do commentors view investors and VCs as more culpable than CEOs?
I explore this by:
Memo: Secondary Coding (Round 2)
Word Frequency Table
Rank | Word | Documents | Documents % | Frequency | % | Word length |
1 | fraud | 126 | 15.24 | 178 | 0.86 | 5 |
2 | investor | 114 | 13.78 | 153 | 0.74 | 8 |
3 | company | 112 | 13.54 | 159 | 0.77 | 7 |
4 | work | 107 | 12.94 | 123 | 0.60 | 4 |
5 | business | 98 | 11.85 | 132 | 0.64 | 8 |
6 | valley | 76 | 9.19 | 96 | 0.46 | 6 |
7 | fake | 74 | 8.95 | 96 | 0.46 | 4 |
8 | silicon | 74 | 8.95 | 86 | 0.42 | 7 |
9 | time | 74 | 8.95 | 84 | 0.41 | 4 |
10 | look | 70 | 8.46 | 82 | 0.40 | 4 |
11 | only | 69 | 8.34 | 77 | 0.37 | 4 |
12 | come | 68 | 8.22 | 77 | 0.37 | 4 |
13 | tech | 66 | 7.98 | 83 | 0.40 | 4 |
14 | take | 62 | 7.50 | 69 | 0.33 | 4 |
15 | founder | 61 | 7.38 | 81 | 0.39 | 7 |
Table created in MaxQDA 2020.
Memo: Secondary Coding (Round 2)
Comments By Key Terms (151 comments with either “Founder” or “Investor”)
Screenshot from MaxQDA 2020.
Memos: Secondary Coding (Round 2 summary)
Analysis Notes:
Tentative developments for Round 3
Next steps:
Tentative developments for Round 3
Next steps (in the data we have):
Tentative developments for Round 3
Next steps (other data we might collect):
Bias and coding
| Positivism | Constructivism |
Beliefs about truth | There is one underlying truth that the researcher is trying to discover | There are multiple valid interpretations that a researcher can justify. |
Intended Outcomes |
|
|
Views of researcher | Researchers can and should be free from bias. The more detached from the topic, the better. | Biases research at every stage of research, by their initial research questions, methods they use, data they select, way they code, etc. |
Relationship of a Researcher’s Bias to study outcomes | Undesirable, a threat to validity. | Valuable to uncover special insights that computers can not. |
What should be done to bias | Eliminate or control for biases by using controls, interrater reliability, statistical methods, random sampling. | Transparency. Researchers should take notes of how they think their biases are influencing questions, responses, and interpretations. Triangulation. Seek other data to uncover other ways of seeing a phenomenon. |
Thank You!
PennState
Smeal College of Business
Ricky Gettys
Appendix: Coding Software Comparison Rubric
| | | Uploading Data | Theme Finding and coding | Analysis Ease | Reports & Visualizations | Overall Score |
Name | Subscription Costs | Perpetual Cost | Avg Score | Avg Score | Avg Score | Avg Score | Overall Avg Score |
MaxQDA Plus | $55 (110 for 2 years) | $740.00 | 7 | 9 | 8 | 9.5 | 8.4 |
Nvivo 12 Pro | $118/ year (Student license) | $1,068 | 8.2 | 8.25 | 9 | 8.5 | 8.5 |
QDA Miner | $238.00 | $595.00 | 3.8 | 5 | 7.5 | 5.5 | 5.5 |
QDA Miner W/ Wordstat | $398.00 | $995.00 | 3.8 | 6.25 | 9 | 7.5 | 6.6 |
Excel | N/A | N/A | 5.6 | 4.75 | 4 | 2.5 | 4.2 |
*Rubric created in 2019, and does not list all options like Dedoose, Atlas TI, R with Tidy text, etc.
Feel free to reach out if you are considering a software, I can provide further insights.
MaxQDA Resources
*Note, I don’t get a commission from MaxQDA. I just like it, and it is cost-effective.
MAXQDA