Posts Tagged ‘qualitative research’

qualitative research in online environment

A Facebook group thread:

Qualitative researchers: Does anyone have any general pointers on conducting qualitative work in this environment other than doing interviews or focus groups over Zoom? Example: I (normally) do a lot of participant observation work. Where and how will I do this or do it as well as I have done it?

At this moment, my focus is all on teaching. But if this situation becomes more prolonged, I need to figure out how to keep the research going too.

more on qualitative research in this IMS blog

NVivo workshop

Intro to NVivo – January 31
10:00 a.m. – 12:30 p.m.
440 Blegen Hall

NVivo is a qualitative data management, coding and markup tool, that facilitates powerful querying and exploration of source materials for both mixed methods and qualitative analysis. It integrates well with tools that assist in data collection and can handle a wide variety of source materials. This workshop introduces the basic functions of NVivo, with no prior experience necessary. The session is held in a computer lab with the software already installed. Register.

more on qualitative research in this IMS blog

embedded librarian

Bedi, S., & Walde, C. (2017). Transforming Roles: Canadian Academic Librarians Embedded in Faculty Research Projects. College & Research Libraries, 78(3), undefined-undefined.
As collections become increasingly patron-driven, and libraries share evolving service models, traditional duties such as cataloguing, reference, and collection development are not necessarily core duties of all academic librarians.1
Unlike our American colleagues, many Canadian academic librarians are not required to do research for tenure and promotion; however, there is an expectation among many that they do research, not only for professional development, but to contribute to the profession.
using qualitative inquiry methods to capture the experiences and learning of Canadian academic librarians embedded in collaborative research projects with faculty members.
The term or label “embedded librarian” has been around for some time now and is often used to define librarians who work “outside” the traditional walls of the library. Shumaker,14 who dates the use of the term to the 1970s, defines embedded librarianship as “a distinctive innovation that moves the librarians out of libraries [and] emphasizes the importance of forming a strong working relationship between the librarian and a group or team of people who need the librarian’s information expertise.”15
This model of embedded librarianship has been active on campuses and is most prevalent within professional disciplines like medicine and law. In these models, the embedded librarian facilitates student learning, extending the traditional librarian role of information-literacy instruction to becoming an active participant in the planning, development, and delivery of course-specific or discipline-specific curriculum. The key feature of embedded librarianship is the collaboration that exists between the librarian and the faculty member(s).17
However, with the emergence of the librarian as researcher… More often than not, librarians have had more of a role in the literature-search process with faculty research projects as well as advising on appropriate places for publication.
guiding research question became “In what ways have Canadian academic librarians become embedded in faculty research projects, and how have their roles been transformed by their experience as researchers?”
Rubin and Rubin20 support this claim, noting that qualitative inquiry is a way to learn about the thoughts and feelings of others. Creswell confirms this, stating:
Qualitative research is best suited to address a research problem in which you do not know the variable and need to explore. The literature might yield little information about the phenomenon of study, and you need to learn more from participants through exploration. [Thus] a central phenomenon is the key concept, idea, or process studied in qualitative research.21
eight participants
As Janke and Rush point out, librarians are no longer peripheral in academic research but are now full members of investigative teams.30 But, as our research findings have highlighted, they are making this transition as a result of prior relationships with faculty brought about through traditional liaison work involving collection development, acquisitions, and information-literacy instruction. As our data demonstrates, the extent to which our participants were engaged within all aspects of the research process supports our starting belief that librarians have a vital and important contribution to make in redefining the role of the librarian in higher education.
Carlson, J., & Kneale, R. (2017). Embedded librarianship in the research context: Navigating new waters. College & Research Libraries News, 72(3), 167–170.
Embedded librarianship takes a librarian out of the context of the traditional
library and places him or her in an “on-site” setting or situation that enables close coordination and collaboration with researchers or teaching faculty
Summey, T. P., & Kane, C. A. (2017). Going Where They Are: Intentionally Embedding Librarians in Courses and Measuring the Impact on Student Learning. Journal of Library and Information Services in Distance Learning, 11(1–2), 158–174.
Wu, L., & Thornton, J. (2017). Experience, Challenges, and Opportunities of Being Fully Embedded in a User Group. Medical Reference Services Quarterly, 36(2), 138–149.

more on embedded librarian in this IMS blog

intro to stat modeling

Introduction to Statistical Modelling (bibliography)

These are the books available at the SCSU library with their call #s:

Graybill, F. A. (1961). An introduction to linear statistical models. New York: McGraw-Hill. HA29 .G75

Dobson, A. J. (1983). Introduction to statistical modelling. London ; New York: Chapman and Hall. QA276 .D59 1983

Janke, S. J., & Tinsley, F. (2005). Introduction to linear models and statistical inference. Hoboken, NJ: Wiley. QA279 .J36 2005

resources from the Internet:

visuals (quick reference to terms and issues)

consider this short video:

more on quantitative and qualitative research in this IMS blog

qualitative method research

Cohort 7

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Qualitative Method Research


Data treatment and analysis

Because the questionnaire data comprised both Likert scales and open questions, they were analyzed quantitatively and qualitatively. Textual data (open responses) were qualitatively analyzed by coding: each segment (e.g. a group of words) was assigned to a semantic reference category, as systematically and rigorously as possible. For example, “Using an iPad in class really motivates me to learn” was assigned to the category “positive impact on motivation.” The qualitative analysis was performed using an adapted version of the approaches developed by L’Écuyer (1990) and Huberman and Miles (1991, 1994). Thus, we adopted a content analysis approach using QDAMiner software, which is widely used in qualitative research (see Fielding, 2012; Karsenti, Komis, Depover, & Collin, 2011). For the quantitative analysis, we used SPSS 22.0 software to conduct descriptive and inferential statistics. We also conducted inferential statistics to further explore the iPad’s role in teaching and learning, along with its motivational effect. The results will be presented in a subsequent report (Fievez, & Karsenti, 2013)

Fievez, A., & Karsenti, T. (2013). The iPad in Education: uses, benefits and challenges. A survey of 6057 students and 302 teachers in Quebec, Canada (p. 51). Canada Research Chair in Technologies in Education. Retrieved from


 The 20th century notion of conducting a qualitative research by an oral interview and then processing manually your results had triggered in the second half of the 20th century [sometimes] condescending attitudes by researchers from the exact sciences.
The reason was the advent of computing power in the second half of the 20th century, which allowed exact sciences to claim “scientific” and “data-based” results.
One of the statistical package, SPSS, is today widely known and considered a magnificent tools to bring solid statistically-based argumentation, which further perpetuates the superiority of quantitative over qualitative method.
At the same time, qualitative researchers continue to lag behind, mostly due to the inertia of their approach to qualitative analysis. Qualitative analysis continues to be processed in the olden ways. While there is nothing wrong with the “olden” ways, harnessing computational power can streamline the “olden ways” process and even present options, which the “human eye” sometimes misses.
Below are some suggestions, you may consider, when you embark on the path of qualitative research.
The Use of Qualitative Content Analysis in Case Study Research
Florian Kohlbacher

excellent guide to the structure of a qualitative research

Palys, T., & Atchison, C. (2012). Qualitative Research in the Digital Era: Obstacles and Opportunities. International Journal Of Qualitative Methods, 11(4), 352-367.
Palys and Atchison (2012) present a compelling case to bring your qualitative research to the level of the quantitative research by using modern tools for qualitative analysis.
1. The authors correctly promote NVivo as the “jaguar’ of the qualitative research method tools. Be aware, however, about the existence of other “Geo Metro” tools, which, for your research, might achieve the same result (see bottom of this blog entry).
2. The authors promote a new type of approach to Chapter 2 doctoral dissertation and namely OCR-ing PDF articles (most of your literature as of 2017 is mostly either in PDF or electronic textual format) through applications such as
Abbyy Fine Reader,
The text from the articles is processed either through NVIVO or related programs (see bottom of this blog entry). As the authors propose: ” This is immediately useful for literature review and proposal writing, and continues through the research design, data gathering, and analysis stages— where NVivo’s flexibility for many different sources of data (including audio, video, graphic, and text) are well known—of writing for publication” (p. 353).
In other words, you can try to wrap your head around huge amount of textual information, but you can also approach the task by a parallel process of processing the same text with a tool.
Here are some suggestions for Computer Assisted / Aided Qualitative Data Analysis Software (CAQDAS) for a small and a large community applications):

– RQDA (the small one): (see on youtube the tutorials of Metin Caliskan); one active developper.
GATE (the large one): |

text mining:
Text mining, also referred to as text data mining, roughly equivalent to text analytics, is the process of deriving high-quality information from text. High-quality information is typically derived through the devising of patterns and trends through means such as statistical pattern learning. Text mining usually involves the process of structuring the input text (usually parsing, along with the addition of some derived linguistic features and the removal of others, and subsequent insertion into a database), deriving patterns within the structured data, and finally evaluation and interpretation of the output.
Qualitative data is descriptive data that cannot be measured in numbers and often includes qualities of appearance like color, texture, and textual description. Quantitative data is numerical, structured data that can be measured. However, there is often slippage between qualitative and quantitative categories. For example, a photograph might traditionally be considered “qualitative data” but when you break it down to the level of pixels, which can be measured.
word of caution, text mining doesn’t generate new facts and is not an end, in and of itself. The process is most useful when the data it generates can be further analyzed by a domain expert, who can bring additional knowledge for a more complete picture. Still, text mining creates new relationships and hypotheses for experts to explore further.

quick and easy:



Introduction to GATE Developer


use of RapidMiner:

– Coding Analysis Toolkit (CAT) from University of Pittsburgh and University of Massachusetts
– Raven’s Eye is an online natural language ANALYSIS tool based

– QDA Miner:

There is also a free version called QDA Miner Lite with limited functionalities:


–  NVivo

– SPSS Text Analytics

– Kwalitan

– Transana (include video transcribing capability)

– XSight


(Cited from: [accessed Apr 1, 2017].

– OdinText

IBM Watson Conversation
IBM Watson Text to Speech
Google Translate API
Colibri Core
– CRFsuite

– FoLiA
NLP Compromise
Cited from: [accessed April 1, 2017
Christine A. Barry (1998) ‘Choosing Qualitative Data Analysis Software: Atlas/ti and Nudist Compared’
Sociological Research Online, vol. 3, no. 3, <;

Pros and Cons of Computer Assisted Qualitative Data Analysis Software

more on quantitative research:

Asamoah, D. A., Sharda, R., Hassan Zadeh, A., & Kalgotra, P. (2017). Preparing a Data Scientist: A Pedagogic Experience in Designing a Big Data Analytics Course. Decision Sciences Journal of Innovative Education, 15(2), 161–190.
literature on quantitative research:
Borgman, C. L. (2015). Big Data, Little Data, No Data: Scholarship in the Networked World. MIT Press.
St. Cloud State University MC Main Collection – 2nd floor AZ195 .B66 2015
p. 161 Data scholarship in the Humanities
p. 166 When Are Data?
Philip Chen, C. L., & Zhang, C.-Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275(Supplement C), 314–347.

Nvivo shareware

Nvivo shareware

information courtesy

Sandrine Zerbib

Associate Professor of Sociology
St. Cloud State University
Department of Sociology and Anthropology
266 Stewart Hall
720 Fourth Avenue South
St Cloud, MN 56301-4498
Office: (320) 308-3046

more on Nvivo in this IMS blog:

school leadership and digital storytelling

Guajardo, M., Oliver, J. A., Rodríguez, G., Valadez, M. M., Cantú, Y., & Guajardo, F. (2011). Reframing the Praxis of School Leadership Preparation through Digital Storytelling. Journal of Research on Leadership Education, 6(5), 145–161.
p. 149-150. Digital storytelling applies techniques that cross disciplines, fields, and subject matter. Digital storytelling pioneer Dana Atchley used the varied techniques such as case study, personal experience, introspection, life story, interviews, artifacts, cultural texts, observations, historical interaction, visual texts, and others (Lambert, 2002, 2006). Atchley’s techniques are firmly rooted in research methodology and collectively describe routine and problematic moments and meanings in individuals’ lives (Denzin & Lincoln, 2000; Lambert, 2006). Qualitative researchers often refer to this process as a bricolage, or the creation or construction from a variety of things. This bricolage helps Downloaded from at SAINT CLOUD STATE UNIV on June 8, 2016 Guajardo et. al./REFRAMING THE PRAXIS OF SCHOOL LEADERSHIP 150 to clarify our ontologies and inform epistemologies. Ladson-Billings (2000) explained epistemologies as more than the traditional way of knowing. Instead, epistemologies are a system of knowing that has both internal logic and external validity. The assortments of experiences used to inform our way of knowing then become the deliberate choices between hegemony and liberation. This process allows individuals to move beyond a traditional epistemological stance, or what Stanley (2007) has called the master narrative. Shujaa (1997) has called it a worldview epistemology that looks at knowledge as a symbiotic interaction of how we view the world, the knowledge we possess, and the knowledge we are capable of passing on to others.
p. 156 digital storytelling has been found to help organizations understand themselves (Militello & Guajardo, 2011). When organizations delve into introspective practices through the use of digital media, small and large organizations alike invite the opportunity to learn from deep, digital reflection.