I have wine
my name is Gonzales
and Mrs. Chandler sorry because everyone Safari
today my guests hara-hara-hara
I am you say how about you
And goofy and carefully deduce yourself
Hi I’m gonna terrorize some facilities
and with the famous Fiona solid water
so here are she here must I learn
drops a lot of clothes and still flows
We get a fair number of requests to incorporate automated transcripts as a feature of Transana. We understand that transcription can be a time-consuming and potentially expensive part of doing qualitative research with audio or video data. Many people want to skip what seems like a tedious typing task and jump right to coding and analysis!
There are many instances when collecting data where there will be more going on at one time than we could possibly observe. A classroom can have one or more teachers providing a variety of learning activities simultaneously to a significant number of students. An expert can perform complex tasks with incredible subtlety and finesse. A “simple” interaction between two people may occur on multiple levels, verbal and non-verbal, which can be challenging to track and note all at once. A reseacher may want to see both the facial expression of the speaker in a focus group and the visceral reaction of all other members of the group at the same time.
When we know there will be more going on than we can observe and preserve at once, one solution is to create a plan to collect video as a representation of the reality we will observe. While video certainly has limitations, (for example, where you point the camera matters a lot,) it also has the advantages of permanence, allowing, for example, repeated viewing and easy sharing with colleagues for consultation. There are times when recording data with more than one camera at a time can provide exponentially more information that we can explore and interpret afterwards.
For many qualitative researchers, looking at the specific word choices used by your research participants offers an important first step in exploring your data. You listen to or read your data over and over; you become immersed in it, trying to identify themes and connections. But it can also be very beneficial to simply create a list of the words they are using, count how often certain terms are being used, and examine the contexts in which those words appear. This seemingly simple exercise can allow the researcher to note themes worthy of further exploration, by allowing the language of your data to guide your interpretation.
However, sorting and counting individual words by hand or with a spreadsheet or word file can require hours of tedious work. In Transana, the Word Frequency Report allows the researcher to take text or transcribed data, and easily generate a list of words used in your Documents, Transcripts, Quotes, or Clips, along with the number of times each word is used.
The following examples represent data from questions asked in the U.S. presidential debates in 2008, 2012, and 2016.
In addition to being organized in a list, you can generate a Word Cloud to present your Word Frequency Report data in a graphical format, with the frequency of each word represented by its relative size.
Video data is inherently different from most other forms of data. Video can capture tone, accent, inflection, pauses, facial expression, body language, and other observable, potentially interpretable and analyzable aspects of human behavior. It allows us to come closer to capturing the reality we observe for detailed study and multi-layered analysis than other forms of data collection.
The qualities that make video data so significant as data also make it very compelling to share with others when presenting our research. Video clips make direct, illustrative qualitative quotes during presentations at conferences, as well as on web sites and in other media-based venues for disseminating research results and findings, such as documentary films or television shows. The more visual the medium we use to share our research findings, the more effective video is for providing evidence of our trustworthiness and the validity of our conclusions.
Video data captures research participants in a way that makes the anonymization of data nearly impossible. Faces and voices are usually readily recognizable. This places an ethical burden on the researcher who collects video data and wants to use that data beyond analysis for their immediate research project. Fortunately, there are a couple of relatively easy steps a researcher can take to simultaneously protect the privacy and dignity of research participants and maximize the usability of the video data that they collect.
A recent support question:
I am a PhD student looking for an alternative to NVivo or Atlas.ti.
In an upcoming project I will analyze a huge amount of
(approximately 500 clips) of youtube videos and was wondering,
which version of Transana I need – also taking into consideration
that I barely have funding for technical devices (e.g. software…) at
Your main choice is between the Basic version (US$150, US$75 for current students) and the Professional version (US$350). I recognize the Professional is significantly more expensive, but I believe it provides excellent value for that additional money.
Both the Basic and Professional versions of Transana will allow you to organize and analyze your data set of 500 or so YouTube videos. They both offer the ability to make analytic selections from your video files, and to categorize and code these selections. That’s the main power of your analysis, and Transana will allow you to do that better than Nvivo or Atlas ti would. Given your data set, Transana is the right choice.
But let me talk a little about what you can do in the Professional version that you can’t do in the Basic version to help you make your decision about which Transana version to buy.