NVivo 10: First Steps

Pretty regularly, I am asked to do NVivo trainings.  Remember, that NVivo is a qualitative analysis software package.  Often, requests are by faculty who are thinking about adding a qualitative component to their research or maybe they are thinking about doing a qualitative research project.  Sometimes faculty requests are rather specific (they might need some specific help with a type of coding or such), but for the most part, a lot of people just need help getting off the ground with NVivo.

The Basics:

QSR International offers a free two week trial of NVivo.  A license for an Individual Starter Pack will set you back $700, while a 2-year license for the Pro license is $710.  There is a fundamentals course that can be added on for $100.

This is all well and good.  But the best part is that QSR International has a Youtube Channel with 21 “how-to” videos to get up and running.  The hits:

Importing PDFs in NVivo 10 in 1 minute:

Creating codes (read: nodes) in NVivo 10 in 2 minutes:

Creating a word frequency query in NVivo 10 in 4 minutes:

Actually pretty great.

Podcasts for Social Scientists and Historians

We’re in the run up to travel season, so I thought I would assemble a list of some great podcasts.  Some of these you have heard, some you haven’t.

FiveThirtyEight: FiveThirtyEight is the brain child of stats wunderkid Nate Silver (yeah, THAT Nate Silver).  Their offerings run the gamut from podcasts about sports (How Many Times did the Mets Blow It?) to sciencey-science (Big Data is Saving this Little Bird) and modern work places (When Your Boss is Big Brother).  There is lots of good stuff here, definitely worth a visit for bookmarking on your mobile device, for when you are on that God awful stretch of I-65/I-57/I-55 between Chicago and ANYWHERE.

BackStory with the American History Guys: BackStory is a program of the Virginia Foundation for the Humanities.  I have heard their show on WBEZ.  But in the event that you are not able to access radio waves, maybe you ought to just subscribe to their podcast.  Their topics are mostly history oriented, with podcasts on A History of Farming in America to Rare History Well Done: Meat in America.  These are good for those early morning walks when you’re trying to maintain your sanity while staying in a too-full house with family that you adore, but also that drives you crazy.

Freakonomics: Freakonomics Radio is the podcasting branch of the Freakonomics Franchise (developed by Stephen Dubner and Steven Levitt).  The podcasts offer also offer a wide variety of topics from How to Create Suspense to Whether Kids Should Pay Back Their Parents for Raising Them, the latter of which you might actually be interested in, depending on the degree to which your offspring are bugging the crap out of you on your second delayed flight coming back to Chicago after Thanksgiving. AmIRite?  CanIGetanAmen?

Learning Tools: Learn R with Data Camp

I recently discovered DataCamp, an online tool for learning R.  Courses are organized to get users familiar with R before moving to working with data in R (cleaning, manipulation, and analysis)3281139507_31ca7ceedd_o.  The following courses are estimated to require about 100 hours to complete.  The modules are web-based, so downloading R or working with R from the beginning is not necessary.

DataCamp is a subscription based enterprise: $25 a month to become proficient in a pretty high powered tool like R is a steal, if you ask me.  I think it might be useful for enterprising graduate students wanting to add another skill to their toolkit.

Step 1: Get to Know R
-Introduction to R
-Introduction to R (beta)
-Intermediate R

Step 2: Manipulate Your Data
-Data Manipulation with dplyr
-Data Analysis in R, the data.table Way

Step 3: Data Visualization with R
-Data Visualization in R with ggvis
-Reporting with R Markdown

Step 4: Statistics with R
-Introduction to Statistics with R
-Introduction to Machine Learning

Step 5: Big Data with R
-Big Data Analysis with Revolution R Enterprise

Step 6: Electives
-R for SAS, SPSS, STATA users
-How to work with Quandl in R
-Kaggle R Tutorial on Machine Learning

Visualizing ‘The Star’

While Mozart is popularly believed to have originated the lullaby, “Twinkle, Twinkle, Little Star,” the words to the famous cradle song were written by Jane Taylor, an English poet and novelist. It was first published in 1806 in “Rhymes for the Nursery,” written and complied by Jane and her sister Ann Taylor. Like other lullabies, it came to be paired with the melody of a popular French children’s song, “Ah! vous dirais-je, Maman,” a tune popularized further by Mozart’s twelve different tune variations.
The infograph below creates a contemporary visualization of this classic lullaby.

Click through to see the enlarged image.


Techniques Used
The above visualization includes 3 types of analysis techniques:

Text Analysis: The word trend graph shows the relative frequencies of the words used most. The word cloud displays all the words of the lullaby in the form of a cloud with the size of the text proportional to the word frequency. The text network shows the most influential words in the lullaby responsible for the theme shifts and other themes associated with these influential words using a non-linear network diagram.

Statistical Analysis: The statistical analysis ranges from basic counts such as total characters and words, number of lines and syllables, and average words per line or sentence to more complex indices and densities.

Quantitative Analysis: Two chart types were used to visualize quantitative data — a trend chart showing word counts of the most frequently used words and a bubble chart showing the word count of all words in the lullaby.

Implementing visualization techniques in faculty research
The image above shows different visualization techniques used in analyzing text such as books, articles or even candidate stump speeches. These might be used to effectively convey data or research conclusions to different types of audiences in various disciplines or industries.

Ask us how to visualize your research
If you want help visualizing your own research findings or wonder if your research lends itself to similar techniques including data acquisition and preprocessing of both quantitative and qualitative data, contact Nandhini Gulasingam at mgulasin@depaul.edu.