Searching for "qualitative"

qualitative research in online environment

https://www.facebook.com/groups/onlinelearningcollective/permalink/557378281559541/

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.

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more on qualitative research in this IMS blog
http://blog.stcloudstate.edu/ims?s=qualitative+research

qualitative method research

Cohort 7

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Qualitative Method Research

quote

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 https://www.academia.edu/5366978/The_iPad_in_Education_uses_benefits_and_challenges._A_survey_of_6057_students_and_302_teachers_in_Quebec_Canada

unquote

 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
http://www.qualitative-research.net/index.php/fqs/article/view/75/153

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.
http://login.libproxy.stcloudstate.edu/login?qurl=http%3a%2f%2fsearch.ebscohost.com%2flogin.aspx%3fdirect%3dtrue%26db%3dkeh%26AN%3d89171709%26site%3dehost-live%26scope%3dsite
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, https://www.abbyy.com/en-us/finereader/
OmniPage,  http://www.nuance.com/for-individuals/by-product/omnipage/index.htm
Readirus http://www.irislink.com/EN-US/c1462/Readiris-16-for-Windows—OCR-Software.aspx
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.
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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): http://rqda.r-forge.r-project.org/ (see on youtube the tutorials of Metin Caliskan); one active developper.
GATE (the large one): http://gate.ac.uk/ | https://gate.ac.uk/download/

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.

quick and easy:

intermediate:

advanced:

http://tidytextmining.com/

Introduction to GATE Developer  https://youtu.be/o5uhMF15vsA


 

use of RapidMiner:

https://rapidminer.com/pricing/

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

– QDA Miner: http://provalisresearch.com/products/qualitative-data-analysis-software/

There is also a free version called QDA Miner Lite with limited functionalities: http://provalisresearch.com/products/qualitative-data-analysis-software/freeware/

– MAXQDA

–  NVivo

– SPSS Text Analytics

– Kwalitan

– Transana (include video transcribing capability)

– XSight

Nud*ist https://www.qsrinternational.com/

(Cited from: https://www.researchgate.net/post/Are_there_any_open-source_alternatives_to_Nvivo [accessed Apr 1, 2017].

– OdinText

IBM Watson Conversation
IBM Watson Text to Speech
Google Translate API
MeTA
LingPipe
NLP4J
Timbl
Colibri Core
CRF++
Frog
Ucto
– CRFsuite

– FoLiA
PyNLPl
openNLP
NLP Compromise
MALLET
Cited from: https://www.g2crowd.com/products/nvivo/competitors/alternatives [accessed April 1, 2017
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http://www.socresonline.org.uk/3/3/4.html
Christine A. Barry (1998) ‘Choosing Qualitative Data Analysis Software: Atlas/ti and Nudist Compared’
Sociological Research Online, vol. 3, no. 3, <http://www.socresonline.org.uk/3/3/4.html&gt;

Pros and Cons of Computer Assisted Qualitative Data Analysis Software

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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
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literature on quantitative research:
Borgman, C. L. (2015). Big Data, Little Data, No Data: Scholarship in the Networked World. MIT Press. https://mplus.mnpals.net/vufind/Record/ebr4_1006438
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

Emerging Trends and Impacts of the Internet of Things in Libraries

Emerging Trends and Impacts of the Internet of Things in Libraries

https://www.igi-global.com/gateway/book/244559

Chapters:

Holland, B. (2020). Emerging Technology and Today’s Libraries. In Holland, B. (Eds.), Emerging Trends and Impacts of the Internet of Things in Libraries (pp. 1-33). IGI Global. http://doi:10.4018/978-1-7998-4742-7.ch001

The purpose of this chapter is to examine emerging technology and today’s libraries. New technology stands out first and foremost given that they will end up revolutionizing every industry in an age where digital transformation plays a major role. Major trends will define technological disruption. The next-gen of communication, core computing, and integration technologies will adopt new architectures. Major technological, economic, and environmental changes have generated interest in smart cities. Sensing technologies have made IoT possible, but also provide the data required for AI algorithms and models, often in real-time, to make intelligent business and operational decisions. Smart cities consume different types of electronic internet of things (IoT) sensors to collect data and then use these data to manage assets and resources efficiently. This includes data collected from citizens, devices, and assets that are processed and analyzed to monitor and manage, schools, libraries, hospitals, and other community services.

Makori, E. O. (2020). Blockchain Applications and Trends That Promote Information Management. In Holland, B. (Eds.), Emerging Trends and Impacts of the Internet of Things in Libraries (pp. 34-51). IGI Global. http://doi:10.4018/978-1-7998-4742-7.ch002
Blockchain revolutionary paradigm is the new and emerging digital innovation that organizations have no choice but to embrace and implement in order to sustain and manage service delivery to the customers. From disruptive to sustaining perspective, blockchain practices have transformed the information management environment with innovative products and services. Blockchain-based applications and innovations provide information management professionals and practitioners with robust and secure opportunities to transform corporate affairs and social responsibilities of organizations through accountability, integrity, and transparency; information governance; data and information security; as well as digital internet of things.
Hahn, J. (2020). Student Engagement and Smart Spaces: Library Browsing and Internet of Things Technology. In Holland, B. (Eds.), Emerging Trends and Impacts of the Internet of Things in Libraries (pp. 52-70). IGI Global. http://doi:10.4018/978-1-7998-4742-7.ch003
The purpose of this chapter is to provide evidence-based findings on student engagement within smart library spaces. The focus of smart libraries includes spaces that are enhanced with the internet of things (IoT) infrastructure and library collection maps accessed through a library-designed mobile application. The analysis herein explored IoT-based browsing within an undergraduate library collection. The open stacks and mobile infrastructure provided several years (2016-2019) of user-generated smart building data on browsing and selecting items in open stacks. The methods of analysis used in this chapter include transactional analysis and data visualization of IoT infrastructure logs. By analyzing server logs from the computing infrastructure that powers the IoT services, it is possible to infer in greater detail than heretofore possible the specifics of the way library collections are a target of undergraduate student engagement.
Treskon, M. (2020). Providing an Environment for Authentic Learning Experiences. In Holland, B. (Eds.), Emerging Trends and Impacts of the Internet of Things in Libraries (pp. 71-86). IGI Global. http://doi:10.4018/978-1-7998-4742-7.ch004
The Loyola Notre Dame Library provides authentic learning environments for undergraduate students by serving as “client” for senior capstone projects. Through the creative application of IoT technologies such as Arduinos and Raspberry Pis in a library setting, the students gain valuable experience working through software design methodology and create software in response to a real-world challenge. Although these proof-of-concept projects could be implemented, the library is primarily interested in furthering the research, teaching, and learning missions of the two universities it supports. Whether the library gets a product that is worth implementing is not a requirement; it is a “bonus.”
Rashid, M., Nazeer, I., Gupta, S. K., & Khanam, Z. (2020). Internet of Things: Architecture, Challenges, and Future Directions. In Holland, B. (Ed.), Emerging Trends and Impacts of the Internet of Things in Libraries (pp. 87-104). IGI Global. http://doi:10.4018/978-1-7998-4742-7.ch005
The internet of things (IoT) is a computing paradigm that has changed our daily livelihood and functioning. IoT focuses on the interconnection of all the sensor-based devices like smart meters, coffee machines, cell phones, etc., enabling these devices to exchange data with each other during human interactions. With easy connectivity among humans and devices, speed of data generation is getting multi-fold, increasing exponentially in volume, and is getting more complex in nature. In this chapter, the authors will outline the architecture of IoT for handling various issues and challenges in real-world problems and will cover various areas where usage of IoT is done in real applications. The authors believe that this chapter will act as a guide for researchers in IoT to create a technical revolution for future generations.
Martin, L. (2020). Cloud Computing, Smart Technology, and Library Automation. In Holland, B. (Eds.), Emerging Trends and Impacts of the Internet of Things in Libraries (pp. 105-123). IGI Global. http://doi:10.4018/978-1-7998-4742-7.ch006
As technology continues to change, the landscape of the work of librarians and libraries continue to adapt and adopt innovations that support their services. Technology also continues to be an essential tool for dissemination, retrieving, storing, and accessing the resources and information. Cloud computing is an essential component employed to carry out these tasks. The concept of cloud computing has long been a tool utilized in libraries. Many libraries use OCLC to catalog and manage resources and share resources, WorldCat, and other library applications that are cloud-based services. Cloud computing services are used in the library automation process. Using cloud-based services can streamline library services, minimize cost, and the need to have designated space for servers, software, or other hardware to perform library operations. Cloud computing systems with the library consolidate, unify, and optimize library operations such as acquisitions, cataloging, circulation, discovery, and retrieval of information.
Owusu-Ansah, S. (2020). Developing a Digital Engagement Strategy for Ghanaian University Libraries: An Exploratory Study. In Holland, B. (Eds.), Emerging Trends and Impacts of the Internet of Things in Libraries (pp. 124-139). IGI Global. http://doi:10.4018/978-1-7998-4742-7.ch007
This study represents a framework that digital libraries can leverage to increase usage and visibility. The adopted qualitative research aims to examine a digital engagement strategy for the libraries in the University of Ghana (UG). Data is collected from participants (digital librarians) who are key stakeholders of digital library service provision in the University of Ghana Library System (UGLS). The chapter reveals that digital library services included rare collections, e-journal, e-databases, e-books, microfilms, e-theses, e-newspapers, and e-past questions. Additionally, the research revealed that the digital library service patronage could be enhanced through outreach programmes, open access, exhibitions, social media, and conferences. Digital librarians recommend that to optimize digital library services, literacy programmes/instructions, social media platforms, IT equipment, software, and website must be deployed. In conclusion, a DES helps UGLS foster new relationships, connect with new audiences, and establish new or improved brand identity.
Nambobi, M., Ssemwogerere, R., & Ramadhan, B. K. (2020). Implementation of Autonomous Library Assistants Using RFID Technology. In Holland, B. (Ed.), Emerging Trends and Impacts of the Internet of Things in Libraries (pp. 140-150). IGI Global. http://doi:10.4018/978-1-7998-4742-7.ch008
This is an interesting time to innovate around disruptive technologies like the internet of things (IoT), machine learning, blockchain. Autonomous assistants (IoT) are the electro-mechanical system that performs any prescribed task automatically with no human intervention through self-learning and adaptation to changing environments. This means that by acknowledging autonomy, the system has to perceive environments, actuate a movement, and perform tasks with a high degree of autonomy. This means the ability to make their own decisions in a given set of the environment. It is important to note that autonomous IoT using radio frequency identification (RFID) technology is used in educational sectors to boost the research the arena, improve customer service, ease book identification and traceability of items in the library. This chapter discusses the role, importance, the critical tools, applicability, and challenges of autonomous IoT in the library using RFID technology.
Priya, A., & Sahana, S. K. (2020). Processor Scheduling in High-Performance Computing (HPC) Environment. In Holland, B. (Ed.), Emerging Trends and Impacts of the Internet of Things in Libraries (pp. 151-179). IGI Global. http://doi:10.4018/978-1-7998-4742-7.ch009
Processor scheduling is one of the thrust areas in the field of computer science. The future technologies use a huge amount of processing for execution of their tasks like huge games, programming software, and in the field of quantum computing. In real-time, many complex problems are solved by GPU programming. The primary concern of scheduling is to reduce the time complexity and manpower. Several traditional techniques exit for processor scheduling. The performance of traditional techniques is reduced when it comes to the huge processing of tasks. Most scheduling problems are NP-hard in nature. Many of the complex problems are recently solved by GPU programming. GPU scheduling is another complex issue as it runs thousands of threads in parallel and needs to be scheduled efficiently. For such large-scale scheduling problems, the performance of state-of-the-art algorithms is very poor. It is observed that evolutionary and genetic-based algorithms exhibit better performance for large-scale combinatorial and internet of things (IoT) problems.
Kirsch, B. (2020). Virtual Reality in Libraries. In Holland, B. (Eds.), Emerging Trends and Impacts of the Internet of Things in Libraries (pp. 180-193). IGI Global. http://doi:10.4018/978-1-7998-4742-7.ch010
Librarians are beginning to offer virtual reality (VR) services in libraries. This chapter reviews how libraries are currently using virtual reality for both consumption and creation purposes. Virtual reality tools will be compared and contrasted, and recommendations will be given for purchasing and circulating headsets and VR equipment. Google Tour Creator and a smartphone or 360-degree camera can be used to create a virtual tour of the library and other virtual reality content. These new library services will be discussed along with practical advice and best practices for incorporating virtual reality into the library for instructional and entertainment purposes.
Heffernan, K. L., & Chartier, S. (2020). Augmented Reality Gamifies the Library: A Ride Through the Technological Frontier. In Holland, B. (Ed.), Emerging Trends and Impacts of the Internet of Things in Libraries (pp. 194-210). IGI Global. http://doi:10.4018/978-1-7998-4742-7.ch011
Two librarians at a University in New Hampshire attempted to integrate gamification and mobile technologies into the exploration of, and orientation to, the library’s services and resources. From augmented reality to virtual escape rooms and finally an in-house app created by undergraduate, campus-based, game design students, the library team learned much about the triumphs and challenges that come with attempting to utilize new technologies to reach users in the 21st century. This chapter is a narrative describing years of various attempts, innovation, and iteration, which have led to the library team being on the verge of introducing an app that could revolutionize campus discovery and engagement.
Miltenoff, P. (2020). Video 360 and Augmented Reality: Visualization to Help Educators Enter the Era of eXtended Reality. In Holland, B. (Eds.), Emerging Trends and Impacts of the Internet of Things in Libraries (pp. 211-225). IGI Global. http://doi:10.4018/978-1-7998-4742-7.ch012
The advent of all types of eXtended Reality (XR)—VR, AR, MR—raises serious questions, both technological and pedagogical. The setup of campus services around XR is only the prelude to the more complex and expensive project of creating learning content using XR. In 2018, the authors started a limited proof-of-concept augmented reality (AR) project for a library tour. Building on their previous research and experience creating a virtual reality (VR) library tour, they sought a scalable introduction of XR services and content for the campus community. The AR library tour aimed to start us toward a matrix for similar services for the entire campus. They also explored the attitudes of students, faculty, and staff toward this new technology and its incorporation in education, as well as its potential and limitations toward the creation of a “smart” library.

bibliographical data analysis nVivo

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:

http://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:

http://blog.stcloudstate.edu/ims?s=qualitative+research

Workshop on computational practices for QR:

http://blog.stcloudstate.edu/ims/2017/04/01/qualitative-method-research/

Here is a library instruction session for your course
http://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:
http://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

http://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

http://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.

import Zotero data in nVivo

 

 

 

 

select the appropriate data from external folder

select the appropriate data from external folder

step 1 create record in nVIvo

 

step 2 create record in nVIvo

step 3 create record in nVIvo

 

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.

  1. Code the text of PDF files into a new Node.
  2. Code the text of PDF file into an existing Node. The procedure of manual coding in literature review is similar to interview transcripts.

Add Node to Cases

 

 

 

 

 

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).

Query Text Frequency

andword frequency search

and

word frequency query saved

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.

Query Text Search

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
http://blog.stcloudstate.edu/ims?s=nvivo

more on Zotero in this IMS blog
http://blog.stcloudstate.edu/ims?s=zotero

Remote UX Work: Guidelines and Resources

https://www.nngroup.com/articles/remote-ux/

capture qualitative insights from video recordings and think-aloud narration from users:  https://lookback.io/  https://app.dscout.com/sign_in  https://userbrain.net/

capture quantitative metrics such as time spent and success rate:   https://konceptapp.com/

Many platforms have both qualitative and quantitative capabilities, such as UserZoom and UserTesting

Tips for Remote Facilitating and Presenting:

  • turn on your camera
  • Enable connection
  • Create ground rules
  • Assign homework
  • Adapt the structure

Tools for Remote Facilitating and Presenting

  • Presenting UX work: Zoom, GoToMeeting, and Google Hangouts Meet
  • Generative workshop activities: Google Draw, Microsoft Visio, Sketch, MURAL, and Miro
  • Evaluative workshop activities: MURAL or Miro. Alternatively, use survey tools such as SurveyMonkey or CrowdSignal, or live polling apps such as Poll Everywhere that you can insert directly into your slides.

Remote Collaboration and Brainstorming

  • Consider both synchronous and asynchronous methods
  • Enable mutual participation
  • Respect schedules
  • Keep tools simple

White boards: https://miro.com/ and https://mural.co/

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.

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more on qualitative research in this IMS blog
http://blog.stcloudstate.edu/ims?s=qualitative

Data driven design

Valuing data over design instinct puts metrics over users

Benek Lisefski August 13, 2019

https://modus.medium.com/data-driven-design-is-killing-our-instincts-d448d141653d

Overreliance on data to drive design decisions can be just as harmful as ignoring it. Data only tells one kind of story. But your project goals are often more complex than that. Goals can’t always be objectively measured.

Data-driven design is about using information gleaned from both quantitative and qualitative sources to inform how you make decisions for a set of users. Some common tools used to collect data include user surveys, A/B testing, site usage and analytics, consumer research, support logs, and discovery calls. 

Designers justified their value through their innate talent for creative ideas and artistic execution. Those whose instincts reliably produced success became rock stars.

In today’s data-driven world, that instinct is less necessary and holds less power. But make no mistake, there’s still a place for it.

Data is good at measuring things that are easy to measure. Some goals are less tangible, but that doesn’t make them less important.

Data has become an authoritarian who has fired the other advisors who may have tempered his ill will. A designer’s instinct would ask, “Do people actually enjoy using this?” or “How do these tactics reflect on our reputation and brand?”

Digital interface design is going through a bland period of sameness.

Data is only as good as the questions you ask

When to use data vs. when to use instinct

Deciding between two or three options? This is where data shines. Nothing is more decisive than an A/B test to compare potential solutions and see which one actually performs better. Make sure you’re measuring long-term value metrics and not just views and clicks.

Sweating product quality and aesthetics? Turn to your instinct. The overall feeling of quality is a collection of hundreds of micro-decisions, maintained consistency, and execution with accuracy. Each one of those decisions isn’t worth validating on its own. Your users aren’t design experts, so their feedback will be too subjective and variable. Trust your design senses when finessing the details.

Unsure about user behavior? Use data rather than asking for opinions. When asked what they’ll do, customers will do what they think you want them to. Instead, trust what they actually do when they think nobody’s looking.

Building brand and reputation? Data can’t easily measure this. But we all know trustworthiness is as important as clicks (and sometimes they’re opposing goals). When building long-term reputation, trust your instinct to guide you to what’s appealing, even if it sometimes contradicts short-term data trends. You have to play the long game here.

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more on big data in this IMS blog
http://blog.stcloudstate.edu/ims?s=big+data

embedded librarian

Bedi, S., & Walde, C. (2017). Transforming Roles: Canadian Academic Librarians Embedded in Faculty Research Projects. College & Research Libraries, 78(3), undefined-undefined. https://doi.org/10.5860/crl.78.3.314
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.
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Carlson, J., & Kneale, R. (2017). Embedded librarianship in the research context: Navigating new waters. College & Research Libraries News, 72(3), 167–170. https://doi.org/10.5860/crln.72.3.8530
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
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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.

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more on embedded librarian in this IMS blog
http://blog.stcloudstate.edu/ims?s=embedded

social media for research

Using Social Media for Research – November 16
12:00 – 1:00 p.m.
1314 Social Sciences

Professor Lee-Ann Kastman Breuch (Writing Studies) and Michael Beckstrand (Mixed-Methods Research Associate, LATIS) will discuss how to retrieve, prepare, and analyze social media data for research projects. Using two case studies, Lee-Ann will share examples of a grounded theory analysis of blog, Twitter, and Facebook data.  Michael will speak about the technical aspects of retrieving and managing social media data. Pizza will be provided. Learn more and register here.
This event is part of the 2018-19 Research Development Friday Roundtable Series organized by the CLA Research Development Team.

Social media and Data Visualization

Workshop materials

Number of participants: 10
Prerequisites: None
Duration: 2 days

Technologies
Software

Online

Agenda

All workshop sessions will take place 9:00 a.m. – noon, with lab time and office hours 1:30 -3:30 p.m.

Tuesday, August 22

  • Introduction to web-scraping
  • Introduction to APIs
  • Facepager
  • Activities
  • Work & get help on your own projects

Wed, August 23

  • Recap
  • Introduction to OpenRefine
  • Cleaning social media data with OpenRefine
  • Analyzing/Visualizing the social media data
    • Atlas.TI
    • Voyant
    • Gephi

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more on social media for research in this IMS blog
http://blog.stcloudstate.edu/ims?s=social+media+research

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