Data Analysis of Qualitative data

Each of the methods has at its core a thematic analysis of data, which is methodically and categorically linking data, phrases, sentences, paragraphs, etc. into a particular theme.  Coring up these themes by their thematic properties helps in understanding the data and developing meaningful themes aiding in building a conclusion to the central question.

Ethnographic Content Analysis (Herron, 2015):  Thick descriptions (collection of field notes that describe and recorded learning and a collection of perceptions of the researcher) help in the creation of cultural themes (themes related to behaviors on an underlying action) from which information was interpreted.

Phenomenological data analysis (Kerns, 2014): Connections among different classes of data through a thematic analysis were used for which results could be derived from.

Case study analysis (Hartsock, 2014): Through the organization of data within a specific case design and treating each distinct data set as a case study, one could derive some general themes within each individual case.  Once, all these general themes are identified, we should look for some cross-case themes.

Grounded Theory Data Analysis (Falciani-White, 2013): Code data through comparing incidents/data to a category (by breaking down, analyzing, comparing, labeling and categorizing data into meaningful units of data), and integrating categories by their properties, in order to help you identify a few themes in order to drive a theory in a systematic manner.


Interviewing strategy and qualitative sampling

As an interviewing strategy, open-ended questions leave the responses open to participant experience and categories and don’t close down the discussion or allow the participant to answer the question in one word (Snow et al, 2005).  Though in the past it was rejected because it did not involve a precise measurement, sometimes data that may not be easily measurable or counted, have value because of its intrinsic complexity and showcase of the “conditional nature of reality” (Rubin, 2012).  A whole field of text-analytics is aiming to prove that this data, considered as unstructured data, is an important part of knowledge discovery and knowledge sharing. Thus, Rubin (2012) says that open-ended questions grant the participant the chance to respond to the question in any way they choose, as elaborated on a response, allow participants to raise issues that are important to them, or even raise new issues not thought of by the interviewer.  Creswell (2013), further states that the more open the questions the better because it will allow the interviewer to listen to what people say and how they say, which can allow the participants to share their own views.  Usually, there are a few open-ended questions.  Finally, open-ended questions are used primarily in qualitative studies, but a mixture of both close-ended and open-ended questions could be asked in mixed methods studies.

One thing is to have the right questions as part of your interviewing strategies, it is another thing to have the right qualitative sampling plan.

Sampling Plans {purposeful/judgmental sampling, maximum variation sampling, sampling extreme or deviant cases, theoretical sampling, snowball/chain-referral sampling, cluster sampling, single-stage sampling, random sampling} (Creswell, 2013, Rubin, 2012, & Lofland et al, 2005). Here are just three of the many sampling plans listed in the sampling plan space.

  • Purposeful/judgmental sampling: In order to learn about a selective character, group, or category or their variations, you group the population into different characters, groups, or categories to collect data from with the participants now representing those divisions. (Creswell, 2013 & Lofland et al, 2005)
  • Maximum variation sampling: Allows for an analysis of error and bias in a phenomenon, through sampling and discovering the widest range of diversity in the phenomena of interest. (Lofland et al, 2005)
  • Snowball/chain-referral sampling: Asking your initial set of contacts with characteristics X, if they can refer to you their network that has the same characteristics X that you are studying. This is a means to enlarge your sample size and break down barriers to the entrance of your future participant. (Lofland et al, 2005). Depending on the characteristic X, like domestic violence, sexual assault, etc., this technique may run into IRB issues (Rubin, 2012).  Rubin (2012), stated that the way to avoid IRB issues if you have the current participants contact the future participants on your behalf to participate in the interview process, but this can drastically reduce the number maximum number of participants you could have gotten.