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.
Qualitative Research
Here a short presentation on the basics:
https://blog.stcloudstate.edu/ims/2019/03/25/qualitative-analysis-basics/
Further, if you wish to expand your knowledge, on qualitative research (QR) in this IMS blog:
https://blog.stcloudstate.edu/ims?s=qualitative+research
Workshop on computational practices for QR:
https://blog.stcloudstate.edu/ims/2017/04/01/qualitative-method-research/
Here is a library instruction session for your course
https://blog.stcloudstate.edu/ims/2020/01/24/digital-literacy-edad-828/
Once you complete the overview of the resources above, please make sure you have Zotero working on your computer; we will be reviewing the Zotero features before we move to nVivo.
Here materials on Zotero collected in the IMS blog:
https://blog.stcloudstate.edu/ims?s=zotero
Of those materials, you might want to cover at least:
https://youtu.be/ktLPpGeP9ic
Familiarity with Zotero is a prerequisite for successful work with nVivo, so please if you are already working with Zotero, try to expand your knowledge using the materials above.
nVivo
https://blog.stcloudstate.edu/ims/2017/01/11/nvivo-shareware/
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
Tooms, A. K., Kretovics, M. A., & Smialek, C. A. (2007). Principals’ perceptions of politics. International Journal of Leadership in Education, 10(1), 89–100. https://doi.org/10.1080/13603120600950901
It is very important to be familiar with the articles when we start working with nVivo.
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How to use Zotero
https://blog.stcloudstate.edu/ims/2020/01/27/zotero-workshop/
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How to use nVivo for bibliographic analysis
The following guideline is based on this document:
Bibliographical data analysis using Nvivo
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
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).
and
and
Text search
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.
Coding query
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.
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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:
Plamen Miltenoff, Ph.D., MLIS
Professor | 320-308-3072 | pmiltenoff@stcloudstate.edu | http://web.stcloudstate.edu/pmiltenoff/faculty/ | schedule a meeting: https://doodle.com/digitalliteracy | Zoom, Google Hangouts, Skype, FaceTalk, Whatsapp, WeChat, Facebook Messenger are only some of the platforms I can desktopshare with you; if you have your preferable platform, I can meet you also at your preference.
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more on nVIvo in this IMS blog
https://blog.stcloudstate.edu/ims?s=nvivo
more on Zotero in this IMS blog
https://blog.stcloudstate.edu/ims?s=zotero
Sugimoto, C. R., Work, S., Larivière, V., & Haustein, S. (2016). Scholarly use of social media and altmetrics: a review of the literature. Retrieved from
https://arxiv.org/abs/1608.08112
One of the central issues associated with altmetrics (short for alternative metrics) is the identification of communities engaging with scholarly content on social media (Haustein, Bowman, & Costas, 2015; Neylon, 2014; Tsou, Bowman, Ghazinejad, & Sugimoto, 2015) . It is thus of central importance to understand the uses and users of social media in the context of scholarly communication.
most identify the following major categori es: social networking, social bookmarking, blogging, microblogging, wikis , and media and data sharing (Gu & Widén -Wulff, 2011; Rowlands, Nicholas, Russell, Canty, & Watkinson, 2011; Tenopir et al., 2013) . Some also conside r conferencing, collaborative authoring, scheduling and meeting tools (Rowlands et al., 2011) or RSS and online documents (Gu & Widén -Wulff, 2011; Tenopir et al., 2013) as social media. The landscape of social media, as well as that of altmetrics, is constantly changing and boundaries with othe r online platforms and traditional metrics are fuzzy. Many online platforms cannot be easily classified and more traditional metrics , such as downloads and mentions in policy documents , have been referred to as altmetrics due to data pr ovider policies.
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) .
but
less than 10% of scholars reported using Twitter (Rowlands et al., 2011) , while 46% used ResearchGate (Van Noorden, 2014) , and more than 55% use d YouTube (Tenopir et al., 2013) —it is necessary to discuss the use of various types of social media separately . Furthermore, there i s a distinction among types of us e, with studies showing higher uses of social media for dissemination, consumption, communication , and promotion (e.g., Arcila -Calderón, Piñuel -Raigada, & Calderín -Cruz, 2013; Van Noorden, 2014) , and fewer instances of use for creation (i.e., using social media to construct scholarship) (British Library et al., 2012; Carpenter, Wetheridge, Tanner, & Smith, 2012; Procter et al., 2010b; Tenopir et al., 2013) .
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) .
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) . GitHub provides for uploading and stor ing of software code, which allows users to modify and expand existing code (Dabbish, Stuart, Tsay, & Herbsleb, 2012) , which has been shown to lead to enhanced collaboratio n among developers (Thung, Bissyande, Lo, & Jiang, 2013) . As w ith other social data sharing platforms, usage statistics on the number of view and contributions to a project are provided (Kubilius, 2014) . The registry of research data repositories, re3data.org, ha s indexed more than 1,200 as of May 2015 2 . However, only a few of these repositories (i.e. , Figshare, SlideShare and Github) include social functionalities and have reached a certain level of participation from scholars (e.g., Begel, Bosch, & Storey, 2013; Kubilius, 2014) .
Video provide s yet another genre for social interaction and scholarly communication (Kousha, Thelwall, & Abdoli, 2012; Sugimoto & Thelwall, 2013) . Of the various video sharing platforms, YouTube, launched in 2005, is by far the most popular
A study of UK scholars reports that the majority o f respondents engaged with video for scholarly communication purposes (Tenopir et al., 2013) , yet only 20% have ever created in that genre. Among British PhD students, 17% had used videos and podcasts passively for research, while 8% had actively contributed (British Library et al., 2012) .
Blogs began in the mid -1990s and were considered ubiquitous by the mid- 200 0s (Gillmor, 2006; Hank, 2011; Lenhart & Fox, 2006; Rainie, 2005) . Scholarly blogs emerged during this time with their own neologisms (e.g., blogademia , blawgosphere , bloggership) and body of research (Hank, 2011) and were considered to change the exclusive structure of scholarly communication
Technorati, considered t o be on e of the largest ind ex of blogs, deleted their entire blog directory in 2014 3 . Individual blogs are also subject to abrupt cancellations and deletions, making questionable the degree to which blogging meets the permanence criteria of scholarly commu nication (Hank, 2011) .
ResearchBlogging.org (RB) — “an aggregator of blog posts referencing peer -reviewed research in a structured manner” (Shema, Bar -Ilan, & Thelwall, 2015, p. 3) — was launched in 2007 and has been a fairly stable structure in the scholarly blogging environment. RB both aggregates and —through the use of the RB icon — credentials scholarly blogs (Shema et al., 2015) . The informality of the genre (Mewburn & Thomson, 2013) and the ability to circumve nt traditional publishing barr iers has led advocates to claim that blogging can invert traditional academic power hierarchies (Walker, 2006) , allow ing people to construct scholarly identities outside of formal institutionalization (Ewins, 2005; Luzón, 2011; Potter, 2012) and democratize the scientific system (Gijón, 2013) . Another positive characteristic of blogs is their “inherently social” nature (Walker, 2006, p. 132) (see also Kjellberg, 2010; Luzón, 2011 ). Scholars have noted the potential for “communal scholarship” (Hendrick, 2012) made by linking and commenting, calling the platform “a new ‘third place’ for academic discourse” (Halavais, 2006, p. 117) . Commenting functionalities were seen as making possible the “shift from public understanding to public engagement with science” (Kouper, 2010, p. 1) .
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) .
Academics are not only producers, but also consumers of blogs: a 2007 survey of medical bloggers foundthat the large majority (86%) read blogs to find medical news (Kovic et al., 2008)
Mahrt and Puschmann (2014) , who defined science blogging as “the use of blogs for science communication” (p. 1). It has been similarly likened to a sp ace for public intellectualism (Kirkup, 2010; Walker, 2006) and as a form of activism to combat perceived biased or pseudoscience (Riesch & Mendel, 2014. Yet, there remains a tension between science bloggers and science journalists, with many science journals dismissing the value of science blogs (Colson, 2011)
.
while there has been anecdotal evidence of the use of blogs in promotion and tenure (e.g., (Podgor, 2006) the consensus seem s to suggest that most institutions do not value blogging as highly as publishing in traditional outlets, or consider blogging as a measure of service rather than research activity (Hendricks, 2010, para. 30) .
Microblogging developed out of a particular blogging practice, wherein bloggers would post small messages or single files on a blog post. Blogs that focused on such “microposts” were then termed “tumblelogs” and were described as “a quick and dirty stream of consciousness” kind of blogging (Kottke, 2005, para. 2)
most popular microblogs are Twitter (launched in 2006), tumblr (launched in 2007), FriendFeed (launched in 2007 and available in several languages), Plurk (launched in 2008 and popular in Taiwan), and Sina Weibo (launched in 2009 and popular in China).
users to follow other users, search tweets by keywords or hashtags, and link to other media or other tweets
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Conference chatter (backchanneling) is another widely studied area in the realm of scholarly microblogging. Twitter use at conferences is generally carried out by a minority of participants
Wikis are collaborative content management platforms enabled by web browsers and embedded markup languages.
Wikipedia has been advocated as a replacement for traditional publishing and peer review models (Xia o & Askin, 2012) and pleas have been made to encourage experts to contribute (Rush & Tracy, 2010) . Despite this, contribution rates remain low — likely hindered by the lack of explicit authorship in Wikipedia, a cornerstone of the traditional academic reward system (Black, 2008; Butler, 2008; Callaway, 2010; Whitworth & Friedman, 2009) . Citations to scholarly documents —another critical component in the reward system —are increasingly being found i n Wikiped ia entries (Bould et al., 2014; Park, 2011; Rousidis et al., 2013) , but are no t yet seen as valid impact indicators (Haustein, Peters, Bar -Ilan, et al., 2014) .
The altmetrics manifesto (Priem et al., 2010, para. 1) , altmetrics can serve as filters , which “reflect the broad, rapid impact of scholarship in this burgeoning ecosystem”.
There are also a host of platforms which are being used informally to discuss and rate scholarly material. Reddit, for example, is a general topic platform where users can submit, discuss and rate online content. Historically, mentions of scientific journals on Reddit have been rare (Thelwall, Haustein, et al., 2013) . However, several new subreddits —e.g., science subreddit 4 , Ask Me Anything sessions 5 –have recently been launched, focusing on the discussion of scientific information. Sites like Amazon (Kousha & Thelwall, 2015) and Goodreads (Zuccala, Verleysen, Cornacchia, & Engels, 2015) , which allow users to comment on and rate books, has also been mined as potential source for the compilation of impact indicators
libraries provide services to support researchers’ use of social media tools and metrics (Lapinski, Piwowar, & Priem, 2013; Rodgers & Barbrow, 2013; Roemer & Borchardt, 2013). One example is
Mendeley Institutional Edition,
https://www.elsevier.com/solutions/mendeley/Mendeley-Institutional-Edition, which mines Mendeley documents, annotations, and behavior and provides these data to libraries (Galligan & Dyas -Correia, 2013) . Libraries can use them for collection management, in a manner similar to other usage data, such as COUNTER statistics (Galligan & Dyas -Correia, 2013) .
Factors affecting social media use; age, academic rank and status, gender, discipline, country and language,
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h-index
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more on altmetrics in this IMS blog:
https://blog.stcloudstate.edu/ims?s=altmetrics
