7 reasons you should read Qualitative Data Analysis with NVivo

Kath McNiff is a Technical Communicator at QSR. You can contact Kath on @KMcNiff. This post was originally published on the NVivo blog. You can read more by Kath and other NVivo bloggers by visiting http://blog.qsrinternational.com/

Somewhere on your computer there are articles to review and interviews to analyze. You also have survey results, a few videos and some social media conversations to contend with.

Where to begin?

Well, here’s one approach: Push a few buttons and bring everything into NVivo. Then dive head-first into your material and code the emerging themes. Become strangely addicted to coding and get caught up in a drag and drop frenzy. Then come up for air – only to be faced with 2000 random nodes and a supervisor/client demanding to know what it all means.

Or, you could do what successful NVivo users have been doing for the past six years – take a sip of your coffee and open Qualitative Data Analysis with NVivo.

This well-thumbed classic (published by SAGE) has been revised and updated by Pat Bazeley and co-author Kristi Jackson.

Here are 7 reasons why you should read it:

1. Pat and Kristi guide you through the research process and show you how NVivo can help at each stage. This means you learn to use NVivo and, at the same time, get an expert perspective on ‘doing qual’.
2. No matter what kind of source material you’re working with (text, audio, video, survey datasets or web pages)—this updated edition gives you sensible, actionable techniques for managing and analyzing the data.
3. The authors share practical coding strategies (gleaned from years of experience) and encourage you to develop good habits—keep a research journal, make models, track concepts with memos, don’t let your nodes go viral. Enjoy the ride.
4. The book is especially strong at helping you to think about (and setup) the ‘cases’ in your project—this might be the people you interviewed or the organizations you’re evaluating. Setting-up these cases and their attributes helps you to unleash the power of NVivo’s queries. How are different sorts of cases expressing an idea? Why does this group say one thing and this group another? What are the factors influencing these contrasts? Hey wait a minute, I just evolved my theme into a theory. Memo that.
5. If you’re doing a literature review in NVivo – chapter 8 is a gold mine (especially if you use NCapture to gather material from the web or if you use bibliographic software like EndNote.)
6. Each chapter outlines possible approaches, gives concrete examples and then provides step-by-step instructions (including screenshots) for getting things done. All in a friendly and approachable style.
7. This book makes a great companion piece to Pat’s other new text – Qualitative Data Analysis Practical Strategies. Read the ‘strategies’ book for a comprehensive look at the research process (in all its non-linear, challenging and exhilarating glory) and read this latest book to bring your project to life in NVivo. - See more at: http://blog.qsrinternational.com/qualitative-data-analysis-with-nvivo/#sthash.8odh8Olf.dpuf

7 Ways NVivo Helps Researchers Handle Social Media Data

Kathleen McNiff is a blogger with QSR International, the people that brought you NVivo. Get in touch with Kath on Twitter @KMcNiff.


Imagine you’re sitting on a qualitative goldmine—in-depth interviews, focus groups, intriguing survey results, nuanced observations and a comprehensive lit review.

 All the traditional boxes are ticked and yet there’s the nagging feeling that something is missing. 

Chances are, it’s social media—and that’s probably why you’re here.

 

The Challenges

There are so many impassioned and revealing conversations taking place online that it’s becoming harder (and more dangerous) to ignore them. 

But embracing social media is not straightforward and you may be grappling with questions such as:

 

• How do I build social media into my research design?
• What platforms are worth concentrating on?
• How should I collect the data?
• What tools and methods should I use to analyse it?

NVivo gives you a practical way to face these challenges.

 

NCapture the web

If you already work with NVivo, you’ll know that it’s a tool for organizing and analyzing qualitative data—but you may not realise that NVivo 10 for Windows comes with a raft of features to support your foray into the brave new world of social media.

 

It all starts with NCapture.

 

This small but powerful plugin sits quietly at the top of your browser (Internet Explorer or Chrome) and lets you capture web pages and social media—and then bring them into NVivo for analysis. It’s a bit like that helpful elephant from Evernote.

You can also capture YouTube videos and conversations from Facebook, Twitter or LinkedIn. This is a boon for researchers who want to facilitate ‘online focus groups’ using these social media platforms—as well as for those who want to get a well-rounded view of their topic by following the latest conversations.

 This brief video (with lovely music) shows you how to gather Twitter data using NCapture:
 

If you use NVivo 10 for Mac—stay tuned, because NCapture is coming soon.

Now, let’s focus on 7 ways NVivo helps you to make sense of your social media data.

 

#1: Gather tweets or posts in a dataset

 

You can search for tweets in your browser and then use NCapture to pull them into a PDF or dataset. The dataset it especially handy because you can filter or sort the content—and use tools to slice and dice the data in different ways.

 

You can do the same for discussions and posts from Facebook or LinkedIn—on these platforms, you can also use the biographical data from user profiles to compare attitudes (men vs women, young vs old—that kind of thing).

 

Sometimes you have to work around the limitations of a particular platform. For example, the number of tweets you can capture is determined by Twitter and can vary depending on the vagaries of Twitter traffic. To follow a particular topic over time, the best approach is to take captures at periodic intervals.

If you want to know more about the inner workings of Twitter—there is a fantastic post right here on NSMNSS blog.

#2: Visualize the most frequently used words

 

You can run a Word Frequency query to see which words contributors are using most often—this can help you get a handle on the themes in your social media data.

 

Visualizing the results in a word cloud may spark insights and reveal connections—they can also liven up a presentation, final paper or blog post.

 

#3: Map the location of tweets or posts

 

You can open a map to see where the action is—and then use this as a launching point for further investigation. For example, you could click on a pin to see the tweets or posts from a particular location.

 

 

#4: Chart users by the number of followers

Shares, likes and follows are the new social currency and they can help to inform your research. If you’re exploring Twitter users - you can create a chart to compare the numbers:

#5: Organize the content into themes

 

You know that qualitative goldmine I mentioned earlier? Well, you can bring the whole thing into NVivo 10 for Windows (including your newly NCaptured social media data) and use ‘coding’ to organize it into themes.

 

For example, whenever you see a reference to ‘education’—whether it be in an interview, article or social media conversation—you can select the content and code it at a ‘node’. Then you can open the node (which is a fancy word for container) and explore all the references to ‘education’ in one place.

 

Coding is a great way to wrangle the chaos of qualitative data—and it’s slightly addictive.

#6: Explore by username or hashtag

 

Do you want to gather tweets from a particular user or hashtag? If your tweets are in a dataset, then ‘auto coding’ is your answer. You can easily roll up the tweets to coding collections by username, by hashtag (what did everyone say about #NSMNSS?), or even by location.

 

Speaking of hashtags - why not start your own twitter chat to gather feedback about an issue or idea?

 

#7: Press the ‘Analyze This’ button

 

Can’t find it?

That’s because, as awesome as NVivo is, it won’t do the analysis for you.

 

Don’t fret because you’ll find plenty of tools for querying the data as well as ways of organizing your own analytical insights (including memos, annotations, models and framework matrices).

Explore the possibilities

Social media has opened a Pandora’s box of opportunities for qualitative research—but you needn’t be overwhelmed because NVivo provides a safe to place to put the box while you explore its contents.

 

Maybe you’re already using NVivo to analyze your social media data?
 

 

Share how you are using NCapture in a short blog post by emailing NSMNSS@natcen.ac.uk

 

 

Challenges and opportunities of Twitter as a corpus

In the run up to our next Knowledge Exchange Event we'll be posting a series of blogs on new social media and qualitative research methods. The first is by Amy Aisha Brown, a research student in the Faculty of Education and Language Studies at the Open University. 

I won’t deny it, I am another one of those researchers who has been wowed by the idea of using social media in research, but I’d argue that it hasn’t been without good reason. I am interested in the ideologies of the English language in Japan, and I am looking to find out how these ideologies pan out in everyday discussions. The hope is that a wide scale investigation will complement research in the area that takes a more ethnographic approach (see Philip Seargeant’s work). So, what really pulled me into the idea of using social media, and Twitter specifically, were the possibilities for accessing a large body of relevant, naturally occurring discourse on everyday topics.

A quick search for “英語” (Japanese for ‘English’, as in the language rather than the people or the muffins) brings up new tweets every few seconds. While this shows just how much potential data is out there, ways of getting hold of tweets and getting them into a format that I can work with for the corpus analysis element of my study, are not as easy to find.

NVivo 10 and the associated browser plugin NCapture are two of-the-shelf tools I have used so far. NCapture lets you use Twitter’s simple search feature to find relevant tweets, and once you import the search results into NVivo, they appear alongside their metadata as a searchable data set that is ready for qualitative coding. This has been a useful way of getting an initial idea about what I can expect to get from tweets, but NVivo is unlikely to be a long-term solution for collect and corpus analysis for two reasons:

      1. Collecting tweets 

• Using Twitter’s basic search function only gives access to a selection of the public tweets produced, a selection that is “optimized to serve relevant tweets to end-users” rather than a random sample or a sample based on any published definition. 
• This way of collecting tweets also only allows you to collect around 1500 at a time, making it difficult (or at least very time consuming) to collect most of the relevant tweets accessible through the search function. 

      2. Corpus tools 

• NVivo has lots of nice tools for visualizing text, such as word frequency lists and tag clouds but neither is it a tool built for corpus analysis nor one that is optimised for Japanese text. 
• NCapture does not capture tweets in a way that makes them easily processed by software other than NVivo. 

In many ways, these are not just the limitations of the NVivio/NCapture combo, they are the technical challenges of my research in general. It might be that I have to compromise on what I hope to achieve, but for the time being I am enjoying looking into other options. If you have any suggestions, I'd be happy to hear. Otherwise, I’ll be getting back to it …