Bibliographical data analysis with Zotero and nVivo
Bibliographic Analysis for Graduate Students, EDAD 518, Fri/Sat, May 15/16, 2020
This session will not be about qualitative research (QR) only, but rather about a modern 21st century approach toward the analysis of your literature review in Chapter 2.
However, the computational approach toward qualitative research is not much different than computational approach for your quantitative research; you need to be versed in each of them, thus familiarity with nVivo for qualitative research and with SPSS for quantitative research should be pursued by any doctoral student.
Please use this link to install nVivo on your computer. Even if we were not in a quarantine and you would have been able to use the licensed nVivo software on campus, for convenience (working on your dissertation from home), most probably, you would have used the shareware. Shareware is fully functional on your computer for 14 days, so calculate the time you will be using it and mind the date of installation and your consequent work.
For the purpose of this workshop, please install nVivo on your computer early morning on Saturday, May 16, so we can work together on nVivo during the day and you can continue using the software for the next two weeks.
Please familiarize yourself with the two articles assigned in the EDAD 815 D2L course content “Practice Research Articles“ :
Brosky, D. (2011). Micropolitics in the School: Teacher Leaders’ Use of Political Skill and Influence Tactics. International Journal of Educational Leadership Preparation, 6(1). https://eric.ed.gov/?id=EJ972880
whereas the snapshots are replaced with snapshots from nVivol, version 12, which we will be using in our course and for our dissertations.
Concept of bibliographic data
Bibliographic Data is an organized collection of references to publish in literature that includes journals, magazine articles, newspaper articles, conference proceedings, reports, government and legal publications. The bibliographical data is important for writing the literature review of a research. This data is usually saved and organized in databases like Mendeley or Endnote. Nvivo provides the option to import bibliographical data from these databases directly. One can import End Note library or Mendeley library into Nvivo. Similar to interview transcripts, one can represent and analyze bibliographical data using Nvivo. To start with bibliographical data representation, this article previews the processing of literature review in Nvivo.
Importing bibliographical data
Bibliographic Data is imported using Mendeley, Endnote and other such databases or applications that are supported with Nvivo. Bibliographical data here refers to material in the form of articles, journals or conference proceedings. Common factors among all of these data are the author’s name and year of publication. Therefore, Nvivo helps to import and arrange these data with their titles as author’s name and year of publication. The process of importing bibliographical data is presented in the figures below.
select the appropriate data from external folder
Coding strategies for literature review
Coding is a process of identifying important parts or patterns in the sources and organizing them in theme node. Sources in case of literature review include material in the form of PDF. That means literature review in Nvivo requires grouping of information from PDF files in the forms of theme nodes. Nodes directly do not create content for literature review, they present ideas simply to help in framing a literature review. Nodes can be created on the basis of theme of the study, results of the study, major findings of the study or any other important information of the study. After creating nodes, code the information of each of the articles into its respective codes.
Nvivo allows coding the articles for preparing a literature review. Articles have tremendous amount of text and information in the forms of graphs, more importantly, articles are in the format of PDF. Since Nvivo does not allow editing PDF files, apply manual coding in case of literature review. There are two strategies of coding articles in Nvivo.
Code the text of PDF files into a new Node.
Code the text of PDF file into an existing Node. The procedure of manual coding in literature review is similar to interview transcripts.
The Case Nodes of articles are created as per the author name or year of the publication.
For example: Create a case node with the name of that author and attach all articles in case of multiple articles of same Author in a row with different information. For instance in figure below, five articles of same author’s name, i.e., Mr. Toppings have been selected together to group in a case Node. Prepare case nodes like this then effortlessly search information based on different author’s opinion for writing empirical review in the literature.
Nvivo questions for literature review
Apart from the coding on themes, evidences, authors or opinions in different articles, run different queries based on the aim of the study. Nvivo contains different types of search tools that helps to find information in and across different articles. With the purpose of literature review, this article presents a brief overview of word frequency search, text search, and coding query in Nvivo.
Word frequency in Nvivo allows searching for different words in the articles. In case of literature review, use word frequency to search for a word. This will help to find what different author has stated about the word in the article. Run word frequency on all types of sources and limit the number of words which are not useful to write the literature.
For example, run the command of word frequency with the limit of 100 most frequent words . This will help in assessing if any of these words remotely provide any new information for the literature (figure below).
Text search is more elaborative tool then word frequency search in Nvivo. It allows Nvivo to search for a particular phrase or expression in the articles. Also, Nvivo gives the opportunity to make a node out of text search if a particular word, phrase or expression is found useful for literature.
For example: conduct a text search query to find a word “Scaffolding” in the articles. In this case Nvivo will provide all the words, phrases and expression slightly related to this word across all the articles (Figure 8 & 9). The difference between test search and word frequency lies in generating texts, sentences and phrases in the latter related to the queried word.
Apart from text search and word frequency search Nvivo also provides the option of coding query. Coding query helps in literature review to know the intersection between two Nodes. As mentioned previously, nodes contains the information from the articles. Furthermore it is also possible that two nodes contain similar set of information. Therefore, coding query helps to condense this information in the form of two way table which represents the intersection between selected nodes.
For example, in below figure, researcher have search the intersection between three nodes namely, academics, psychological and social on the basis of three attributes namely qantitative, qualitative and mixed research. This coding theory is performed to know which of the selected themes nodes have all types of attributes. Like, Coding Matrix in figure below shows that academic have all three types of attributes that is research (quantitative, qualitative and mixed). Where psychological has only two types of attributes research (quantitative and mixed).
In this way, Coding query helps researchers to generate intersection between two or more theme nodes. This also simplifies the pattern of qualitative data to write literature.
Please do not hesitate to contact me with questions, suggestions before, during or after our workshop and about ANY questions and suggestions you may have about your Chapter 2 and, particularly about your literature review:
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.
Ungerer, L. M. (2016). Digital Curation as a Core Competency in Current Learning and Literacy: A Higher Education Perspective. The International Review of Research in Open and Distributed Learning, 17(5). https://doi.org/10.19173/irrodl.v17i5.2566
Dunaway (2011) suggests that learning landscapes in a digital age are networked, social, and technological. Since people commonly create and share information by collecting, filtering, and customizing digital content, educators should provide students opportunities to master these skills (Mills, 2013). In enhancing critical thinking, we have to investigate pedagogical models that consider students’ digital realities (Mihailidis & Cohen, 2013). November (as cited in Sharma & Deschaine, 2016), however warns that although the Web fulfils a pivotal role in societal media, students often are not guided on how to critically deal with the information that they access on the Web. Sharma and Deschaine (2016) further point out the potential for personalizing teaching and incorporating authentic material when educators themselves digitally curate resources by means of Web 2.0 tools.
p. 24. Communities of practice. Lave and Wenger’s (as cited in Weller, 2011) concept of situated learning and Wenger’s (as cited in Weller, 2011) idea of communities of practice highlight the importance of apprenticeship and the social role in learning.
criteria to publish a paper
Originality: Does the paper contain new and significant information adequate to justify publication?
Relationship to Literature: Does the paper demonstrate an adequate understanding of the relevant literature in the field and cite an appropriate range of literature sources? Is any significant work ignored?
Methodology: Is the paper’s argument built on an appropriate base of theory, concepts, or other ideas? Has the research or equivalent intellectual work on which the paper is based been well designed? Are the methods employed appropriate?
Results: Are results presented clearly and analyzed appropriately? Do the conclusions adequately tie together the other elements of the paper?
Implications for research, practice and/or society: Does the paper identify clearly any implications for research, practice and/or society? Does the paper bridge the gap between theory and practice? How can the research be used in practice (economic and commercial impact), in teaching, to influence public policy, in research (contributing to the body of knowledge)? What is the impact upon society (influencing public attitudes, affecting quality of life)? Are these implications consistent with the findings and conclusions of the paper?
Quality of Communication: Does the paper clearly express its case, measured against the technical language of the field and the expected knowledge of the journal’s readership? Has attention been paid to the clarity of expression and readability, such as sentence structure, jargon use, acronyms, etc.
Stanton, K. V., & Liew, C. L. (2011). Open Access Theses in Institutional Repositories: An Exploratory Study of the Perceptions of Doctoral Students. Information Research: An International Electronic Journal, 16(4),
We examine doctoral students’ awareness of and attitudes to open access forms of publication. Levels of awareness of open access and the concept of institutional repositories, publishing behaviour and perceptions of benefits and risks of open access publishing were explored. Method: Qualitative and quantitative data were collected through interviews with eight doctoral students enrolled in a range of disciplines in a New Zealand university and a self-completion Web survey of 251 students. Analysis: Interview data were analysed thematically, then evaluated against a theoretical framework. The interview data were then used to inform the design of the survey tool. Survey responses were analysed as a single set, then by disciple using SurveyMonkey’s online toolkit and Excel. Results: While awareness of open access and repository archiving is still low, the majority of interview and survey respondents were found to be supportive of the concept of open access. The perceived benefits of enhanced exposure and potential for sharing outweigh the perceived risks. The majority of respondents were supportive of an existing mandatory thesis submission policy. Conclusions: Low levels of awareness of the university repository remains an issue, and could be addressed by further investigating the effectiveness of different communication channels for promotion.
the researchers use the qualitative approach: by interviewing participants and analyzing their responses thematically, they build the survey.
Then then administer the survey (the quantitative approach)
How do you intend to use a mixed method? Please share
Metaphors: A Problem Statement is like… metaphor — a novel or poetic linguistic expression where one or more words for a concept are used outside normal conventional meaning to express a similar concept. Aristotle l The DNA of the research l A snapshot of the research l The foundation of the research l The Heart of the research l A “taste” of the research l A blueprint for the study
digital object identifier (DOI) is a unique alphanumeric string assigned by a registration agency (the International DOI Foundation) to identify content and provide a persistent link to its location on the Internet. The publisher assigns a DOI when your article is published and made available electronically.
Why do we need it?
2010 Changes to APA for Electronic Materials Digital object identifier (DOI). DOI available. If a DOI is available you no longer include a URL. Example: Author, A. A. (date). Title of article. Title of Journal, volume(number), page numbers. doi: xx.xxxxxxx
Accodring to Sugimoto et al (2016), the Use of social media platforms for by researchers is high — ranging from 75 to 80% in large -scale surveys (Rowlands et al., 2011; Tenopir et al., 2013; Van Eperen & Marincola, 2011) .
There is one more reason, and, as much as you want to dwell on the fact that you are practitioners and research is not the most important part of your job, to a great degree, you may be judged also by the scientific output of your office and/or institution.
In that sense, both social media and altimetrics might suddenly become extremely important to understand and apply.
Shortly altmetrics (alternative metrics) measure the impact your scientific output has on the community. Your teachers and you present, publish and create work, which might not be presented and published, but may be widely reflected through, e.g. social media, and thus, having impact on the community.
How such impact is measured, if measured at all, can greatly influence the money flow to your institution
Thelwall, M., & Wilson, P. (2016). Mendeley readership altmetrics for medical articles: An analysis of 45 fields. Journal of the Association for Information Science and Technology, 67(8), 1962–1972. https://doi.org/10.1002/asi.23501
Todd Tetzlaff is using Mendeley and he might be the only one to benefit … 🙂
Here is some food for thought from the article above:
Doctoral students and junior researchers are the largest reader group in Mendeley ( Haustein & Larivière, 2014; Jeng et al., 2015; Zahedi, Costas, & Wouters, 2014a) .
Studies have also provided evidence of high rate s of blogging among certain subpopulations: for example, approximately one -third of German university staff (Pscheida et al., 2013) and one fifth of UK doctoral students use blogs (Carpenter et al., 2012) .
Social data sharing platforms provide an infrastructure to share various types of scholarly objects —including datasets, software code, figures, presentation slides and videos —and for users to interact with these objects (e.g., comment on, favorite, like , and reuse ). Platforms such as Figshare and SlideShare disseminate scholars’ various types of research outputs such as datasets, figures, infographics, documents, videos, posters , or presentation slides (Enis, 2013) and displays views, likes, and shares by other users (Mas -Bleda et al., 2014) .
Frequently mentioned social platforms in scholarly communication research include research -specific tools such as Mendeley, Zotero, CiteULike, BibSonomy, and Connotea (now defunct) as well as general tools such as Delicious and Digg (Hammond, Hannay, Lund, & Scott, 2005; Hull, Pettifer, & Kell, 2008; Priem & Hemminger, 2010; Reher & Haustein, 2010) .
“The focus group interviews were analysed based on the principles of interpretative phenomenology”
if you are not podcast fans, I understand. The link above is a pain in the behind to make work, if you are not familiar with using podcast.
Here is an easier way to find it:
1. open your cell phone and go find the podcast icon, which is pre-installed, but you might have not ever used it [yet].
2. In the app, use the search option and type “stuff you should know”
3. the podcast will pop up. scroll and find “How the scientific method works,” and/or search for it if you can.
Once you can play it on the phone, you have to find time to listen to it.
I listen to podcast when i have to do unpleasant chores such as: 1. walking to work 2. washing the dishes 3. flying long hours (very rarely). 4. Driving in the car.
There are bunch of other situations, when you may be strapped and instead of filling disgruntled and stressed, you can deliver the mental [junk] food for your brain.
Earbuds help me: 1. forget the unpleasant task, 2. Utilize time 3. Learn cool stuff
Here are podcasts, I am subscribed for, besides “stuff you should know”:
TED Radio Hour
TED Talks Education
NPR Fresh Air
and bunch others, which, if i don’t go a listen for an year, i go and erase and if i peruse through the top chart and something picks my interest, I try.
If I did not manage to convince to podcast, totally fine; do not feel obligated.
However, this podcast, you can listen to on your computer, if you don’t want to download on your phone.
It is one hour show by two geeks, who are trying to make funny (and they do) a dry matter such as quantitative vs qualitative, which you want to internalize:
1. Sometimes at minute 12, they talk about inductive versus deductive to introduce you to qualitative versus quantitative. It is good to listen to their musings, since your dissertation is going through inductive and deductive process, and understanding it, can help you control better your dissertation writing.
2. Scientific method. Hypothesis etc (around min 17).
While this is not a Ph.D., but Ed.D. and we do not delve into the philosophy of science and dissertation etc. the more you know about this process, the better control you have over your dissertation.
3. Methods and how you prove (Chapter 3) is discussed around min 35
4. dependent and independent variables and how do you do your research in general (min ~45)
Shortly, listen and please do share your thoughts below. You do not have to be kind to this source offering. Actually, be as critical as possible, so you can help me decide, if I should offer it to the next cohort and thank you in advance for your feedback.
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)
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.
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).
text mining: https://en.wikipedia.org/wiki/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. https://ischool.syr.edu/infospace/2013/04/23/what-is-text-mining/
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.
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. https://doi.org/10.1111/dsji.12125
literature on quantitative research:
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. https://doi.org/10.1016/j.ins.2014.01.015