Qualitative Analysis: Coding Project Report of a Virtual Interview Question

The virtual interview question: Explain what being a doctoral student means for you? How has your life changed since starting your doctoral journey?

Description of your coding process

The steps I followed in this coding process were to read the responses once, at least one week before this individual project assignment was due.  This allowed me to think of generic themes, and codes at a super high level throughout the week.  Then after the week was over, I quickly went to wordle.net to create a word cloud on the top 50 most used words in this virtual interview and found out the results below.

wordle

Figure 1: Screenshot for wordle.net results which were used to help develop sub-codes and codes, words that bigger appear more often in the virtual interview than those words that are smaller.

The most telling themes from Figure 1 are: Time, Family, Life, Work, Student, Learning, Frist, Opportunity, Research, People, etc.  This helped create some codes and some of the sub-codes like prioritization, for family, etc.  Figure 1 has also helped me to confirm my ideas for codes that I have been thinking already in my head for the past week, thus I felt ready to begin coding.  After, deciding on the initial set of codes, I did some manual coding, while asking the questions: “What is the person saying? And how they are saying it? And could there be a double meaning in the sentences?”  The last question helped me identify if each sentence in this virtual interview had multiple codes within it.  I used QDA Miner Lite as my software of choice for coding, it is an open-source product and there are plenty of end-user tutorials made by different researchers from many fields on how to effectively use this software effectively on YouTube.  After the initial manual coding, I revisited the initial coding book.  Some of the subcodes that fell under betterment, were moved into the future code as it better fit that theme than just pure betterment. This reanalysis of coding went on for all codes.  As I re-read the responses for the third time, some new subcodes got added as well.  The reason for re-reading this virtual interview a third time was to make sure not many other codes could be created or were missing.

Topical Coding Scheme (Code Book)

The codebook that was derived is as follows:

  • Family
    • For Family
    • Started by Family
    • First in the Family
  • Perseverance
    • Exhausted
    • Pushing through
    • Life Challenges
    • Drive/Motivation
    • Goals
  • Betterment
    • Upgrade skills
    • Personal Growth
    • Maturity
    • Understanding
    • Priority Reanalysis
  • Future
    • More rewarding
    • Better Life
    • Foresight
  • Proving something
    • To others

 

Diagram of findings

Below are images developed through the analytical/automated part QDA Miner Lite:

fig2

Figure 2: Distribution of codes in percentages throughout the virtual interview.

Figure 3: Distribution of codes in frequency throughout the virtual interview.

fig4

Figure 4: Distribution of codes in frequency throughout the virtual interview in terms of a word cloud where more frequent codes appear bigger than less frequent codes.

Brief narrative summary of finding referring to your graphic diagram

Given figures 2-4, one could say that the biggest theme for going into the doctoral program is the prospect of a better life and hoping to change the world, as they more frequently showed up in the interview.  One student states that their degree would open many doors, “Pursuing and obtaining this level of degree would help to open doors that I may not be able to walk through otherwise.” While another student says that hopefully, their research will change the future lives of many “The research that I am going to do will hopefully allow people to truly pursue after their dreams in this ever-changing age, and let the imagination of what is possible within the business world be the limit.” Other students are a bit more practical with their responses stating things like “…move up in my organization and make contributions to the existing knowledge” and finally “More opportunities open for you as well as more responsibility for being credible and usefulness as a cog in the system”

Another concept that kept repeating here is that this is done for family, and because of family work, and school, the life of a doctoral student in this class has to be reprioritized (hence the code priority reanalysis).  This is primarily seen as all forms of graphical output show that these are the two most significant things that drive towards the degree.  One student went to one extreme, “Excluding family and school members, I am void of the three ‘Ps’ (NO – people, pets, or plants). I quit my full-time job and will be having the TV signal turned off after the Super Bowl to force additional focus.”  Another student said that time was the most important thing they had and that it has changed significantly, “The most tangible thing that has changed in my life since I became a doctoral student has been my schedule.  Since this term began I have put myself on a strict schedule designating specific time for studies, my wife, and time for myself.”  Finally, another student says balance is key for them: “Having to balance family time, work, school, and other social responsibilities, has been another adjusted change while on this educational journey. The support of my family has been very instrumental in helping me to succeed and the journey has been a great experience thus far.”  There are 7 instances in which these two codes overlap/included within each other, which apparently happen 80% of the time.

Thus, from this virtual interview, I am able to conclude that family is mentioned with priority reanalysis in order to meet the goal of the doctoral degree and that time management a component of priority reanalysis is key.  There are students that take this reanalysis to the extreme as aforementioned, but if they feel that is the only way they could accomplish this degree in a timely manner, then who am I to judge.  After all, it is the job of the researcher, when coding to be non-biased.  However, the family could drive people to complete the degree, it is the prospects of a better life and changing the world for the better is what was mentioned most.

Appendix A

An output file from qualitative software can be generated by using QDA Miner Lite.

 

Qualitative Methods: Questions distinctions

Qualitative Research Questions distinctions

Usually, qualitative research methods start off with an open-ended central question or two with the words “what” or “how” on a single phenomenon or concept, in order to suggest an exploratory design.  The rest of the question uses exploratory verbs (report, describe, discover, seek, explore, etc) in a non-directional manner as not to suggest causation.  The research question could also be asked in a way to suggest what qualitative research methodological tool you will use to analyze the data, i.e. using the words opinions could mean interviews. Finally, the research question could include the key defining features of the participants in the study (teen, women, men, veterans, people with disabilities, etc.) (Creswell, 2014).

Central research question:

So, an example of a central question could be to do a follow on study on the results of my doctoral research.  So:

What are the opinions on the results from the use of text analytics on tropical discussions to discover weather constructs that positively and negatively affecting hurricane forecast skills perceived and used by the hurricane specialist at the National Hurricane Center?

References:

Quasi-experimental

In the Quantitative Methodology, there are experimental (deals with the impact of an outcome, while having a controlling variable to see if the tested variable does have an impact), quasi-experimental (deals with a non-random sample but still measures the impact of an outcome) and non-experimental (deals with generalizing/inferring about a population) project designs.

For a non-experimental project design, surveys are used as an instrument to gather data and help produce quantitative/numeric data to help identify trends and sentiment from a sample of a total population (Creswell, 2013).  The Pew Research Center (2015), wanted to analyze the changing attitudes on Gay Marriage a few days after the Supreme Court struck down the bans as unconstitutional, have asked:

Do you oppose/favor allowing gays and lesbians to marry legally? What is your current age? What is your Religious Affiliation? What is your Political Party?  What is your Political Ideology? What is your Race? What is your gender?

Pew found that overall, since they were conducting this survey since 2001, they have seen that in every descriptive variable classifying people has shown an increase in acceptance for marriage, with an overall 55% approval rating to 39%.  This example is not trying to explain a relationship but rather a trend.

For an experimental project design, it usually follows the following steps: Identification of participants, gathering of materials, draft and finalize procedures and setting up measures so that you can conduct the experiment and derive some results from it (Creswell, 2013).

When a participant in a study is randomly assigned to a control group or in other groups in an experiment it is considered a true experiment, if the participants in a study are not randomly assigned then it is considered a quasi-experiment (Creswell, 2013).  In the famous Milgram Obedience Experiment (1974), an ad was posted to collect participants for a study on memory, but in fact, they were there to see if the presence of authority would compromise their internal morals to cause pain and sometimes delivering fatal shocks to another participant (an actor).  About 2/3 of people were willing to administer the deadly shock because they had the presence of authority (a man in a white coat) telling them to continue to the study.  Though this study will be hard to replicate today (due to IRB considerations), it wasn’t fully random, thus it’s a quasi-experiment, but it challenged and shocked the world.  This is a pivotal paper/experiment that defined behavioral science.

Resources

Internal validity in qualitative studies

Internal validity is determining the accuracy of the findings in qualitative research from the viewpoints of the researcher, participants or reader (Creswell, 2013). There are many validity strategies like: Triangulation of different data sources, member checking, rich thick description of the findings, clarifying any bias, presenting negative or discrepant information, prolong the time in the field, peer debriefing, external auditor to review the project, etc.

Triangulation of different data sources for observational work is an idea where I would examine evidence from multiple sources of data to justify the themes that I create through coding.  Converging themes from multiple sources of data and/or perspectives from participants would add to the validity of the study.  Thus, in order to increase the validity of the thematic codes would be to present the thematic codes from analysis of multiple sources like:

  • Interviews from N number of participants (until data saturation is reached)
  • Observations of the participants
    • Repeated observations will be taken, during multiple different types of shifts, with or without the same participants and during different random days of the week over a one-month period.
    • Observational Goals: Tracking what information is used (type and time stamps, instrumentations, etc.)
    • Observational Goals 2: Through videotaping, I hope to track conversations between participants sharing the same shift. Field notes would contain: “Why the conversation was initiated?”, “What was discussed?”, “Were there decisions made regarding the area of study”, “What is the bodily-based behavior portrayed by the specialists in the discussion?”, and “What was the outcome of that discussion?”
  • Document Analysis

The aforementioned, in particular, will help ensure internal validity in quiet a few studies.

 References:

Observational protocol and qualitative documentations

As a researcher, you could be a non-participant to a full-on participant when observing your subjects in a study.  Thus, the observed/empathized behavioral and activities of individuals in the study are jotted down in field notes (Creswell, 2013).  Most researchers use an observational protocol to jotting down these notes as they observe their subjects.  According to Creswell (2013), this protocol could consist of: “separate descriptive notes (portraits of the participants, a reconstruction of dialogue, a description of the physical setting, accounts of particular events, or activities) [to] reflective notes (the researcher’s personal thoughts, such as “speculation, feelings, problems, ideas, hunches, impressions, and prejudices), … this form might [have] demographic information about the time, place, and date of the field setting where the observation takes place.”

Whereas, observational work can be combined with in-depth interviewing, and sometimes the observational work (which can be an everyday activity) can help prepare the researcher for the interviews (Rubin, 2012).  Doing so can increase the quality of the interviews because the interviewers know what the researcher has seen or read and can provide more information on those materials.  This can also allow the researcher to master the terminology before entering the interview. Finally, Rubin (2012) also states that cultural norms become more visible through observation rather than just a pure in-depth interview.

In Creswell (2013), Qualitative Documents are information contained within documents that could help a researcher out in their study that could be either public (newspapers, meeting minutes, official reports) and/or private (personal journals/diaries, letters, emails, internal manuals, written procedures, etc.) documents.  This can also include pictures, videos, educational materials, books, files. Whereas, Artifact Analysis is the analysis of the written text, usually are charts, flow sheets, intake forms, reports, etc.

The main analysis approach of this document would be to read the document to gain a subject matter understanding.  Document analysis would aid in quickly grouping, sorting and resort the data obtained for a study.  This manual will not be included in the coded dataset, but will help provide appropriate codes/categories for the interview analysis, in other words give me suggestions about what might be related to what.   Finally, one way to interpret this document would be for triangulation of data (data from multiple sources that are highly correlated) between the observation, interviews and this document.   

References

Differences between Quantitative and Qualitative Intros and Lit Reviews

Simply put, quantitative methods are utilized when the research contains variables that are numerical, and qualitative methods are utilized when the research contains variables that are based on language (Field, 2013).  Thus, each methods goals and procedures are quite different. This difference in goals and procedures drives differences in how a research paper’s introduction and literature review are written.

Introductions in a research paper allow the researcher to announce the problem and why it is important enough to be explored through a study.  Given that qualitative research may not have any known variables or theories, the introductions tend to vary tremendously (Creswell, 2014; Edmondson & MacManus, 2007).  Creswell (2014), suggested that qualitative methods introductions can begin with a quote from one the participants; stating the researchers’ personal story from a first person or third person viewpoint, or can be written in an inductive style.  There is less variation in quantitative methods introductions because the best way to introduce the problem is to introduce the variables, from an impersonal viewpoint (Creswell, 2014).  It is through gaining further understanding of these variables’ influence on a particular outcome is what’s driving the study in the first place.

The purpose of the literature review is for the researcher to share the results of other studies tangential to theirs to show how their study relates to the bigger picture and what gaps in the knowledge they are trying to solve (Creswell, 2014).  Edmondson and MacManus (2007) stated that when the nature of the field of research is nascent, the study becomes exploratory and qualitative in nature.  Given their exploratory nature, in qualitative methods, the researchers write their literature review in the form that is exploratory and in an inductive manner (Creswell, 2014).  Edmondson and MacManus (2007) stated that when the nature of the research is mature, there are plenty of related and existing research studies on the topic, a more quantitative approach is more appropriate.  Given that there is a huge body of knowledge to draw from when it comes to quantitative methods, the researchers tend to have substantially large amounts of literature at the beginning and structure it in a deductive fashion (Creswell, 2014).  Framing the literature review in a deductive manner allows the researcher at the end of the literature review to state clearly and measurably their research question(s) and hypotheses (Creswell, 2014; Miller, n.d.).

To conclude, understanding which methodological approach best fits a research study can help drive how the introduction and literature review sections are crafted and written.

References

  • Creswell, J. W. (2014) Research design: Qualitative, quantitative and mixed method approaches (4th ed.). California, SAGE Publications, Inc. VitalBook file.
  • Edmondson, A. C., & McManus, S. E. (2007). Methodological fit in management field research. Academy of Management Review, 32(4), 1155–1179. http://doi.org/10.5465/AMR.2007.26586086
  • Field, A. (2013) Discovering Statistics Using IBM SPSS Statistics (4th ed.). UK: Sage Publications Ltd. VitalBook file.

Quantitative Vs Qualitative Analysis

Field (2013) states that both quantitative and qualitative methods are complimentary at best not competing approaches to solving the world’s problems. Although these methods are quite different from each other. Creswell (2014) explain how these two, quantitative and qualitative methods, can be combined to study a phenomenon through what is called a “Mixed Method” Approach, which is out of scope for this discussion.  Simply put, quantitative methods are utilized when the research contains variables that are numerical, and qualitative methods are utilized when the research contains variables that are based on language (Field, 2013).  Thus, each methods goals and procedures are quite different

Goals and procedures

Quantitative methods derive from positivist, numerically driven, and epistemological (Joyner, 2012).   Quantitative methods use closed-ended questions, i.e. hypothesis, and collect their data numerically through instruments (Creswell, 2014). In quantitative research, there is an emphasis on experiments, measurement, and a search of relationships via fitting data to a statistical model and through observing a collection of data graphically to identify trends via deduction (Field, 2013; Joyner, 2012). According to Creswell (2014), quantitative researchers build protections against biases and control for alternative explanations through experiments which are generalizable and replicable. Quantitative studies could be experimental, quasi-experimental, causal-comparative, correlational, descriptive, and evaluation (Joyner, 2012).  According to Edmondson and McManus (2007), quantitative methodologies fit best when the underlying research theory is mature.  The maturity of the theory should tend to drive researchers towards one method over the other, along the spectrum quantitative, mixed, or qualitative methodologies (Creswell, 2014; Edmondson & McManus, 2007).

Comparatively, Edmondson and McManus (2007) stated, qualitative methodologies fit best when the underlying research theory is nascent. Quantitative methods derive from phenomenological view, the perceptions of people (Joyner, 2012).  Qualitative methods use open-ended questions, i.e. interview questions and collect their data through observations of a situation (Creswell, 2014).  Qualitative research focuses on meaning and understanding of a situation where the researcher searches for meaning through interpretation of the data via induction (Creswell, 2014; Joyner, 2012).  Qualitative research could be case studies, ethnographic, action, philosophical, historical, legal, educational, etc. (Joyner, 2012).

Commonalities and differences

The commonalities that exist between these two methods is that each method has a question to answer, an identified area of interest (Creswell, 2014; Edmonson & McManus, 2007; Field, 2013; Joyner 2012).  Each method requires a survey of the current literature to help develop the research question (Creswell, 2014; Edmondson & McManus, 2007). Finally, there is a need to design a study to collect and analyze data to help answer that research question (Creswell, 2014; Edmonson & McManus, 2007; Field, 2013; Joyner 2012).  Therefore, the similarities between these two methods exist on why research is conducted and at a high level the what and the how research is conducted.  They differ in the particulars of the what and the how research is conduction.

The research question(s) can either become a centralized question with(out) sub-questions, but in quantitative research is driven by a series of statistically testable theoretical-hypothesis (Creswell, 2014; Edmonson & McManus, 2007). For quantitative methods data analysis, statistical tests are done to seek relationships, with hopes of testing a theory-driven hypothesis and providing a precise model, via a collection of numerical measures and established constructs (Edmonson & McManus, 2007). Given the need to statistically accept or reject theoretical-hypothesis, the sample size for a quantitative methods tend to be greater than those of qualitative methods (Creswell, 2014).  Qualitative research is driven by exploration and observations to test their hypothesis (Creswell, 2014; Edmonson & McManus, 2007). For qualitative methods data analysis, there should be an iterative and explorative content analysis, with hopes to build a new construct (Edmonson & McManus, 2007).  These are some of many other differences that exist between these two methods.

When are the advantages of quantitative methods maximized

Based off of Edmondson and McManus (2007), the best time to use quantitative methods is when the underlying theory of the research subject is mature.  Maturity consists of extensive literature that could be reviewed, the existence of theoretical constructs, and extensively tested measures (Edmondson & McManus, 2007).  Thus, the application of quantitative methods will help build effectively on prior work which will help fill in the gap of knowledge on a particular topic, whereas qualitative methods and mixed methods would fail to do so. Applying quantitative methods to a mature theory is reinventing the wheel, and applying mixed methods to it, will uneven the status of the evidence (Edmondson & McManus, 2007).

References:

  • Creswell, J. W. (2014) Research design: Qualitative, quantitative and mixed method approaches (4th ed.). California, SAGE Publications, Inc. VitalBook file.
  • Edmondson, A. C., & McManus, S. E. (2007). Methodological fit in management field research. Academy of Management Review, 32(4), 1155–1179. http://doi.org/10.5465/AMR.2007.26586086
  • Field, A. (2013) Discovering Statistics Using IBM SPSS Statistics (4th ed.). UK: Sage Publications Ltd. VitalBook file.
  • Joyner, R. L. (2012) Writing the Winning Thesis or Dissertation: A Step-by-Step Guide (3rd ed.). Corwin. VitalBook file.