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But How Do I *Do* Qualitative Research? Bridging the Gap between Qualitative Researchers and Methods Resources--PART 2

Mandy kicked off our 4-part blog series last week with an inaugural post that provided background and context for this project, which centers around a specific challenge faced by many qualitative researchers: lack of qualitative methods training.  This post offers some concrete ideas to data-support professionals on how to leverage library collections and other information sources that direct researchers to secondary and tertiary sources on qualitative methods to address this issue.

Possibly the most basic yet high-impact way to bridge the gap between researchers and qualitative methods is to create a LibGuide—or any other online research guide or pathfinder—dedicated to qualitative research resources.  It can be embedded within an existing resource, such as a library’s Data Services page or a course-specific guide, or it can exist as an independent guide unto itself.

Now, I know what you’re thinking: when faced with an instructional or research need, a librarian’s knee-jerk reaction is often, “Let’s make a LibGuide!”  While research guides aren’t necessarily appropriate for every topic or service we provide as information and data specialists (i.e., LibGuides aren’t a panacea, per se), they can be a useful didactic medium for culling and delivering information and resources specific to qualitative research methods.   

Online research guides, like LibGuides, allow researchers to benefit from the guidance and expertise of a data-support professional in an autonomous, self-paced manner.  This type of learning object is well-suited for researchers who fit the profile Mandy described in her introductory post for this series: those who aren’t getting formal qualitative methods training and need to get up to speed quickly on their own, and/or researchers with varying degrees of qualitative methods knowledge and experience who would like a set of materials, sources, and resources to which they can refer back periodically.  The portability of a LibGuide also makes it convenient for use by data-support professionals in mediated settings, such as consultations or instruction sessions.  And at institutions where demand for research methods and tools training outstrips an individual or staff’s capacity to provide one-on-one or even course-embedded support, a LibGuide can solve the scalability problem for a large and diverse set of needs (although, obviously, not all of them).  Lastly, LibGuides aren’t just for researchers; they also serve to assist colleagues and fellow information and data specialists in providing reference and research services at an institution (and beyond) and are an effective tool for cross-training.  This last point is relevant especially for fairly specialized, niche areas of expertise, like qualitative research methods.

 A number of qualitative research guides already exist and are worth checking out (e.g., Duke University Libraries’ guide on Qualitative Research by Linda Daniel, UCSF Library’s Qualitative Research Guide by Evans Whitaker).  Below are some suggestions for content to include—with a focus on resources related to learning about qualitative methods—if one were to consider creating anew or building on an existing qualitative research guide.

Select bibliography of relevant literature

A centralized bibliography, or simply citations/links dispersed throughout a guide, can be useful in directing users to relevant secondary and tertiary sources to learn more about qualitative research methods.  Citations to the following literature types may be appropriate to include:  

As a companion, it may be useful to link to relevant catalogs and social-sciences databases to search for additional literature and research.  However, not all catalogs and bibliographic databases index research methods, and even those that do often don’t index qualitative methods with appropriate granularity.  Thus, it may be necessary to provide tips on how best to search for studies that employ qualitative methods (e.g., NYU Libraries’ guide on Locating Qualitative Research by Susan Jacobs).  This way, a guide delivers select sources but also teaches users how to find additional methods-related sources themselves.

Links to subscription-based resources

There are a number of specialized library databases designed for quick reference and/or in-depth, self-guided learning, and these serve as rich fonts of information about qualitative methods.  Three possible resources that fit this bill are:   

  • Credo Reference
    While not specific to research methods, Credo is a multidisciplinary, searchable collection of digitized reference works (e.g., dictionaries, encyclopedias, and handbooks) that provide helpful background information on a topic but also act as springboards to connect users to further readings and cross-references.  Credo can serve as a good starting point for researchers looking to learn more about qualitative methods in general or about a particular methodology.   
  • Sage Research Methods Online (SRM)
    SRM is an online multimedia collection devoted to research methods (qualitative and quantitative), with a special emphasis on research skills training.  It includes e-versions of SAGE’s Little Blue Books, an instructional series on qualitative research methods, as well as many other searchable, full-text interdisciplinary SAGE reference and journal sources that allow users to deep-dive into a particular method at each stage of the research lifecycle.  SRM also provides resources for teaching qualitative methods, including case studies, sample datasets, and instructional videos.

  • Oxford Bibliographies Online (OBO)
    OBO offers access to online, peer-reviewed, annotated bibliographies that are organized by discipline and are written by experts in their fields.  One can find entire bibliographies or portions of a bibliography dedicated to qualitative methods in a given field of social science (e.g., sociology, political science) or to a particular qualitative methodology (e.g., anthropology, education).  This makes OBO a good source of information for discipline-specific qualitative research methods.  

In addition to researchers, these resources can be indispensible to data-support professionals who are asked to consult on research projects using qualitative methods outside their own specializations, or who are asked to consult with researchers who sit in (or between) disciplines with which they are less familiar or comfortable.

List of professional development and ongoing learning opportunities

Researchers who are new to a qualitative research method may want to learn more about it beyond (or in lieu of) the classroom.  Opportunities to do so are plentiful and varied, and may take the following forms:

I hope you have enjoyed my suggestions in this post!  They are by no means exhaustive.  I would love to here what you think, what you would add, or what you’re already using on your guides to address the qualitative methods gap discussed in this series of blog posts.  

We welcome comments here, emails to the IASSIST listserv, the QSSHDIG google group, or directly to the authors, and/or comments in this “Blog Conversations” doc embedded in the QSSHDIG website. Also, there's a section at the bottom of the "Blog Conversations" doc for suggesting future QSSDHIG posts - please do!

Stayed tuned for Part 3 of our blog series next week when Mandy Swygart-Hobaugh will share ideas for developing and providing training resources in collaboration with faculty and academic departments that are mindful of the qualitative methods gap.

But How Do I *Do* Qualitative Research? Bridging the Gap between Qualitative Researchers and Methods Resources

The IASSIST Qualitative Social Science & Humanities Data Interest Group (QSSHDIG) was created in October 2016, its central purpose: to foster conversations regarding the needs of researchers who generate qualitative data, and what types of services librarians and other information professionals can develop to support these researchers in managing their data/source materials throughout the research lifecycle.

This four-post blog series engages in one particular conversation: challenges researchers face in terms of a lack of qualitative methods training, and strategies for how data-support professionals can address these challenges. The following QSSHDIG members are the series authors:

  • Jill Conte, Social Sciences Librarian at New York University
  • Liz Cooper, Social Sciences Librarian at the University of New Mexico
  • Mandy Swygart-Hobaugh, Social Sciences Librarian at Georgia State University

To foster conversation, we welcome comments here, emails to the IASSIST listserv, the QSSHDIG google group, or directly to the authors, and/or comments in this “Blog Conversations” doc embedded in the QSSHDIG website. Also, there's a section at the bottom of the "Blog Conversations" doc for suggesting future QSSDHIG posts - please do!

Why have this conversation?

Many social science researchers (students and faculty alike) are increasingly conducting qualitative research while lacking formal training in qualitative methods. This may be due to various factors, including but not limited to the following:

  • their particular discipline does not widely embrace qualitative research,
  • their discipline just recently began emphasizing mixed methods (using qualitative and quantitative methods) when previously it was predominantly quantitative-based,
  • they are in an interdisciplinary academic program without a strong research methods training component.

Those of us who offer training sessions on qualitative data analysis software (such as NVivo, Atlas.ti, Quirkos, or Dedoose) often experience researchers coming to these sessions without the methodological background to *do* qualitative research or understand what the software can/cannot do for them - sometimes hoping that the software will have the “magic button” to solve their lack of training. Similarly, as social science liaison librarians we often witness this qualitative methods gap during our research consultations. Although this dilemma of lack of methods training is not unique to qualitative research (i.e., researchers lacking quantitative research training are known to attend statistical software training sessions), when compared to their quantitative counterparts, qualitative researchers often have less resources for support and for building necessary skills.

The three posts in the remainder of this blog series will offer concrete strategies for how data-support professionals can act as bridges between social science researchers and the resources they need to strengthen their qualitative research and methodologies skills:

  • Jill Conte’s post will offer suggestions for connecting researchers with secondary and tertiary sources for qualitative research training. [to be posted Monday, July 24] 
  • Mandy Swygart-Hobaugh’s post will share ideas for developing and providing training resources in collaboration with faculty and academic departments that are mindful of this methods gap. [to be posted Monday, July 31] 
  • Liz Cooper’s post will address how librarians and other data-support professionals can help build community at their institutions around qualitative research. [to be posted Monday, August 7] 

IASSIST 2016 Program At-A-Glance, Part 2: Data infrastructure, data processing and research data management

 

Here's another list of highlights from IASSIST2016 which is focusing on the data revolution. For previous highlights, see here.

Infrastructure

  • For those of you with an interest in technical infrastructure, the University of Applied Sciences HTW Chur will showcase an early protype MMRepo (1 June, 3F), whose function is to store qualitative and quantitative data into one big data repository.
  • The UK Data Service will present the following panel "The CESSDA Technical Framework - what is it and why is it needed?", which elaborates how the CESSDA Research Infrastructure should have modern data curation techniques rooted in sophisticated IT capabilities at its core, in order to better serve its community.

  • If you have been wondering about the various operational components and the associated technology counterparts involved with running a data science repository, then the presentation by ICPSR is for you. Participants in that panel will leave with an understanding of how the Archonnex Architecture at ICPSR is strengthening the data services offered to new researchers and much more.

Data processing

Be sure to check out the aforementioned infrastructure offerings if you’re interested in data processing, but also check out a half-day workshop on 31 May, “Text Processing with Regular Expressions,” presented by Harrison Dekker, UC Berkeley, that will help you learn regular expression syntax and how to use it in R, Python, and on the command line. The workshop will be example-driven.

Data visualisation

If you are comfortable working with quantitative data and are familiar with the R tool for statistical computing and want to learn how to create a variety of visualisations, then the workshop by the University of Minnesota on 31 May is for you. It will introduce the logic behind ggplot2 and give participants hands-on experience creating data visualizations with this package. This session will also introduce participants to related tools for creating interactive graphics from this syntax.

Programming

  • If you’re interesting in programming there’s a full-day Intro to Python for Data Wrangling workshop on 31 May, led by Tim Dennis, UC San Diego,  that will provide tools to use scientific notebooks in the cloud, write basic Python programs, integrate disparate csv files and more.

  • Also, the aforementioned Regular Expressions workshop also on 31 May will offer  in-workshop opportunities  to working with real data and perform representative data cleaning and validation operations in multiple languages.

Research data management

  • Get a behind-the-scenes look at data management and see how an organization such as the Odum Institute manages its archiving workflows, head to “Automating Archive Policy Enforcement using Dataverse and iRODS” on 31 May with presenters from the UNC Odom Institute, UNC Chapel Hill. ’Participants will see machine actionable rules in practice and be introduced to an environment where written policies can be expressed in ways an archive can automate their enforcement.

  • Another good half-day workshop, targeted to for people tasked with teaching good research data management practices to researchers is  “Teaching Research Data Management Skills Using Resources and Scenarios Based on Real Data,” 31 May, with presenters from ICPSR, the UK Data Archive and FORS. The organisers of this workshop will showcase recent examples of how they have developed teaching resources for hands-on-training, and will talk about successes and failures in this regard.

Tools

If you’re just looking to add more resources to your data revolution toolbox, whether it’s metadata, teaching, data management, open and restricted access, or documentation, here’s a quick list of highlights:

  • At Creating GeoBlacklight Metadata: Leveraging Open Source Tools to Facilitate Metadata Genesis (31 May), presenters from New York University will provide hands-on experience in creating GeoBlacklight geospatial metadata, including demos on how to capture, export, and store GeoBlacklight metadata.

  • DDI Tools Demo (1 June). The Data Documentation Initiative (DDI) is an international standard for describing statistical and social science data.

  • DDI tools: No Tools, No Standard (3 June), where participants will be introduced to the work of the DDI Developers Community and get an overview of tools available from the community.

Open-access

As mandates for better accessibility of data affects more researchers, dive into the Conversation with these IASSIST offerings:

Metadata

Don’s miss IASSIST 2016’s offerings on metadata, which is the data about the data that makes finding and working with data easier to do. There are many offerings, with a quick list of highlights below:

  • Creating GeoBlacklight Metadata: Leveraging Open Source Tools to Facilitate Metadata Genesis (Half-day workshop, 31 May), with presenters from New York University

  • At Posters and Snacks on 2 June, Building A Metadata Portfolio For Cessda, with presenters from the Finnish Social Science Data Archive; GESIS – Leibniz-Institute for the Social Sciences; and UK Data Service

Spread the word on Twitter using #IASSIST16. 


A story by Dory Knight-Ingram (
ICPSR)

Introducing the IASSIST Data Visualization Interest Group (DVIG!)

Hello fellow IASSISTer’s

     With the upcoming 2013 Conference nearing, we thought it very fitting to introduce you all to the newly created IASSIST Data Visualization Interest Group. Formed over the winter and now spring of 2013, this group brings together over 46 IASSIST members from across the world (literally across-the-world! check out the map of our locations), who are all interested in data visualization.  We hope to share a range of skills and information around tools, best practice visualization, and discuss innovative representations of data, statistics, and information. Here is just a glimpse of our group’s tools exposure.

    As research becomes more interdisciplinary and data and information are more readily used and reused, core literacies surrounding the use and understandability of data are required. Data Visualization supports a means to make sense of data, through visual representation, and to communicate ideas and information effectively. And, it is quickly becoming a well-developed field not only in terms of the technology (in the development of tools for analyzing and visualizing data), but also as an established field of study and research discipline. As data and information professionals, we are required to stay abreast of the latest technologies, disciplines, methods and techniques, used for research in this data-intensive and changing research landscape. Data Visualization, with its many branches and techniques seeks to present data, information, and statistics in new ways, ways that our researchers are harnessing with the use of high-powered computers (and sometimes not so high-powered) to perform analysis of data.  From conventional ways to visualize and graph data – such as tables, histograms, pie charts, and bar and line graphs, to the often more complex network relationship models and diagrams, cluster and burst analysis, and text analysis charts; we see data visualization techniques at play more than ever. 

This group has set a core mission and charge to focus on promoting a greater understanding of data visualization – its creation, uses, and importance in research, across disciplines.  Particular areas of focus include, but are not limited to the following:

  • Enable opportunities for IASSIST members to learn and enhance their skills in this growing field;
  • Support a culture of best practice for data visualization techniques; creation, use, and curation;
  • Discussion of the relevant tools (programs, web tools, and software) for all kinds of data visualizations (spatial, temporal, categorical, multivariate, graphing, networks, animation, etc.);
  • Provide input and feedback on data visualization tools;
  • Capture examples of data visualization to emulate and avoid;
  • Explore opportunities for service development in libraries;
  • Be aware of and communicate to others the needs of researchers in this field;
  • Use of data visualization for allowing pre-analysis browsing of data content in repositories
  • Connect with communities of metadata developers and users (e.g., DDI Alliance) to gain better understanding of how metadata can enable better visualization, and how in turn visualization need might drive development of metadata standards.
  • And more!

Please join me in welcoming this new interest group, and we hope to share and learn from you all at the upcoming conference! We are always seeking input and to share ideas, please get in touch with us at iassist-dataviz@lists.carleton.edu (either myself or another member can add you to the group).

All the best, and Happy Easter!

Amber Leahey

In search of: Best practice for code repositories?

I was asked by a colleague about organized efforts within the economics community to develop or support repositories of code for research.  Her experience was with the astrophysics world which apparently has several and she was wondering what could be learned from another academic community.  So I asked a non-random sample of technical economists with whom I work, and then expanded the question to cover all of social sciences and posed the question to the IASSIST community. 

In a nutshell, the answer seems to be “nope, nothing organized across the profession” – even with the profession very broadly defined.  The general consensus for both the economics world and the more general social science community was that there was some chaos mixed with a little schizophrenia. I was told there are there are instances of such repositories, but they were described to me as “isolated attempts” such as this one by Volker Wieland:  http://www.macromodelbase.com/.  Some folks mentioned repositories that were package or language based such as R modules or SAS code from the SAS-L list or online at sascommunity.org.

Many people pointed out that there are more repositories being associated with journals so that authors can (or are required to) submit their data and code when submitting a paper for publication. Several responses touched on this issue of replication, which is the impetus for most journal requirements, including one that pointed out a “replication archive” at Yale (http://isps.yale.edu/research/data).  I was also pointed to an interested paper that questions whether such archives promote replicable research (http://www.pages.drexel.edu/~bdm25/cje.pdf) but that’s a discussion for another post.

By far, the most common reference I received was for the repositories associated with RePEc (Research Papers in Economics) which offers a broad range of services to the economic research community.  There you’ll find the IDEAS site (http://ideas.repec.org/) and the QM&RBC site with code for Dynamic General Equilibrium models (http://dge.repec.org/) both run by the St. Louis Fed.

I also heard from support folks who had tried to build a code repository for their departments and were disappointed by the lack of enthusiasm for the project. The general consensus is that economists would love to leverage other people’s code but don’t want to give away their proprietary models.  They should know there is no such thing as a free lunch! 

 I did hear that project specific repositories were found to be useful but I think of those as collaboration tools rather than a dissemination platform.  That said, one economist did end his email to me with the following plea:  “lots of authors provide code on their websites, but there is no authoritative host. Will you start one please?”

/san/

Data Visualization tools & greater emphasis on teaching to be incorporated into R-Studio!

Reposted from the RStudio Blog:

Welcome Hadley, Winston, and Garrett!

RStudio’s mission from the beginning has been to create powerful tools that support the practices and techniques required for creating trustworthy, high quality analysis. For many years Hadley Wickham has been teaching and working on his own set of tools for R with many of the same core goals. We’ve been collaborating quite a bit with Hadley over the past couple of years and today we’re excited to announce that Hadley, Winston Chang, and Garrett Grolemund are joining RStudio so we can continue to work together much more closely.

You probably know Hadley from his work on ggplot2plyr, and many other packages. Garrett was a PhD student of Hadley’s at Rice, and you might also know him from the lubridate package, which makes dealing with dates and time easier; he’s also been working on new tools for visualisation and new ways of thinking about the process of data analysis. Winston has been working full-time on ggplot2 for the last couple of months, squashing many bugs and repaying a lot of the technical debt that’s accumulated over the years. Winston’s also writing an R Graphics Cookbook for O’Reilly that should be available in the near future.

What does this mean for RStudio? We’ll of course continue developing open-source software like the RStudio IDE, ggplot2, and plyr (among many other projects). One of Hadley’s core focuses at RStudio will also be expanding our mission to include education, which we plan to offer in a variety of formats ranging from in-person training to some innovative new online courses. We’ll also be working on hosted services (like RPubs) as well as some new products that address the challenges of deploying R within larger organizations.

We’re all excited to begin this next phase of work together and will have lots more details to announce later this fall!

IASSIST Quarterly (IQ) volume 34-2 now on the web

The new issue of the IASSIST Quarterly is now available on the web. This is the volume 34 (number 2, 2010).

 http://iassistdata.org/iq/issue/34/2

The layout has changed. We hope you’ll enjoy the new style presented. It seems to be a more modern format and more suited for the PDF presentation on the web. Walter Piovesan – our publication officer – had a biking accident. To show that nothing is so bad that it is not good for something Walter used his recovery time to redesign the IQ. Furthermore, Walter is the person in charge of the upcoming 2011 IASSIST conference, so he is a busy guy. And I’m happy to say that Walter should be fit for the conference.

This issue of the IQ features the following papers:

Rein Murakas and Andu Rämmer from the Estonian Social Science Data Archive (ESSDA) at the University of Tartu describe in their paper "Social Science Data Archiving and Needs of the Public Sector: the Case of Estonia" how the archive had a historical background in the empirical research of the Soviet Union.

From the historical background we move to web 2.0 in a paper  by Angela Hariche, Estelle Loiseau and Philippa Lysaght on "Wikiprogress and Wikigender: a way forward for online collaboration". The authors are working at the OECD and the paper's statement is that "collaborative platforms such as wikis along with advances in data visualisation are a way forward for the collection, analysis and dissemination of data across countries and societies”.

The third paper addresses an issue of central importance for most data archives. The question concerns balancing data confidentiality and the legitimate requirements of data users. This is a key problem of the Secure Data Service (SDS) at the UK Data Archive, University of Essex. The paper "Secure Data Service: an improved access to disclosive data" by Reza Afkhami, Melanie Wright, and Mus Ahmet shows how the SDS will allow researchers remote access to secure servers at the UK Data Archive.

The last article has the title "A user-driven and flexible procedure for data linking". The authors are Cees van der Eijk and Eliyahu V. Sapir from the Methods and Data Institute at the University of Nottingham. The data linking relates to research combining several different datasets. The implementation is developed for the PIREDEU project in comparative electoral research. The authors are combining traditional survey data with data from party manifestos and state-level data.

Articles for the IQ are always very welcome. They can be papers from IASSIST or other conferences, from local presentations or papers directly  written for the IQ.

Notice that chairing a conference session with the purpose of aggregating and integrating papers for a special issue IQ is much appreciated as the information reaches many more people than the session participants and will be readily available on the IASSIST website.

Authors are very welcome to take a look at the description for layout and sending papers to the IQ:

http://iassistdata.org/iq/instructions-authors

Authors can also contact me via e-mail: kbr @ sam.sdu.dk. Should you be interested in compiling a special issue for the IQ as guest editor or editors I will also be delighted to hear from you.

Karsten Boye Rasmussen, editor

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