ANALYZING DATA WITH TRANSANA PROFESSIONAL: AN OVERVIEW

Introduction

Transana offers multiple ways to explore and understand your video, audio, text, and image data. There are many different methodologies that fall under the umbrella of qualitative analysis, and a rich variety of theoretical orientations that inform those methodologies. Transana provides a set of tools useful to researchers with widely varying methodological approaches and theoretical orientations.

Transana’s tools have significant overlap and redundancy, so individual researchers have to determine how to best use those tools to accomplish their specific research goals given their theoretical orientation and preferred methodologies. Researchers are unlikely to use every tool Transana offers on any one project. You will want to use the elements of Transana that work best for your unique data set and methodological approach.

Media Data – Transcription IS Analysis

We have already discussed one form of analysis that Transana supports: transcription. A Transcript is an abstracted representation of recorded events designed by a researcher to make sense out of media data. Transcripts can take many different forms, and can serve as powerful analytic tools. All Transcripts are the product of a number of important analytic choices, starting with the basic question of creation. Should you transcribe or work without transcripts? What information should you transcribe, with what level of detail? How frequently do you insert time codes? What information is not reflected in the transcript? All transcripts present a particular view of the data, and that view influences how you see the data.

It is important to acknowledge that transcription is not required, and involves trade-offs. On the one hand, transcription can be time-consuming and expensive. It can feel like one is delaying getting to analysis while transcription is being done. On the other hand, transcription is a great way to get to know your media data, and a good transcript can serve as a map to the contents of a media file. When a media file has no transcript, it can be time-consuming and frustrating to look for particular incidents that the researcher did not initially recognize as important but remembers from earlier viewings. The more in-depth you intend to go with analysis of your media files, and the more viewings of these files you anticipate doing, the more important a Transcript can be.

Transana’s linkage between a Transcript and a media file is an important analytic tool. Reading a Transcript gives you an impression of what was said. But there is no substitute for hearing what is said and how it is said, seeing the speaker’s body language and facial expression, and seeing how others react. These elements provide considerable additional information, which is sometimes crucial to understanding the meaning of an entire interaction and how words should be interpreted in the context of gestures, expressions and the larger environment in which the words are spoken.

Researchers can use different Transcripts to encapsulate different analytic lenses or layers through which they view their media files. Complex analysis can be embedded within Transcripts, and Transcripts can be read analytically. See Working with Multiple Transcripts in the Transana Manual for more on this topic.

Identifying Meaningful Segments

One of the ways we come to understand large amounts of data is to look at the smaller segments that make up the whole.  Thus, one of the central analytic activities one does in Transana is to select meaningful segments from larger text or media files and to label what is meaningful about those segments.

In Transana, there are several ways to identify portions of larger source data files as analytically important and several ways to indicate what about the selection is meaningful. Researcher-identified segments of media files are called Clips, segments of source text files are called Quotes, and still images are analyzed as Snapshots. Quotes, Clips, and Snapshots are the basic units for most analytic work that is done within Transana. Quotes, Clips, and Snapshots can then be assigned analytic meaning through the processes of Coding and Categorization.

Working With Your Quotes, Clips and Snapshots

There are several ways to create Quotes and Clips in Transana.  The differences can be subtle at first, but you will figure out what works best for you with a little practice.

Quick Quotes and Quick Clips are data items that a researcher creates through the assignment of a Keyword, or code, to a portion of text or a segment of a media file. The researcher does not give the Quick Quote or Quick Clip a name, and Transana automatically places it in the “Quick Quotes and Clips” Collection, expediting the creation process. The Keyword selected during the creation of the analytic item is what signifies the analytic meaning of that item.  Quick Quotes and Clips are Keyword-oriented data items.

Standard Quotes, Standard Clips, and Snapshots are data items that a researcher creates, names, and places in a named Collection. Naming the item is an analytic act where one identifies something important about the item, usually the single most important analytic feature or why one has chosen to select this particular piece of data. In addition, the Collection in which an item is placed says something about what is analytically significant about that item. Standard Quotes, Standard Clips, or Snapshots may or may not have Keywords assigned to them as coding.  Standard Quotes, Standard Clips, and Snapshots are Collection-oriented data items.

While Quick Quotes and Clips and Standard Quotes and Clips originate in different ways, their use tends to converge later in analysis. Quick Quotes and Clips can be optionally re-named and moved to named Collections. Standard Quotes and Clips, as well as Snapshots, can be assigned Keywords to code them beyond Collection membership. If you are unsure which approach is more appropriate for your analytic methods and style, experiment with both to see what works better for you, your methodology, and your data.

In addition, it is possible to make Clips without Transcripts in Transana.  There may be times when you choose not to transcribe your media files or when you want to capture something about your media data that isn’t reflected in your transcript.

Two Organizational Methods: Categorization and Coding

There are two ways that researchers can signify analytic meaning within Transana.

Coding with Keywords

Keywords are analytic tags that one can assign to Quotes, Clips, and Snapshots in Transana to indicate analytic significance. The act of associating a Keyword with a data item is often referred to as Coding.

One advantage of Keywords is that you can assign multiple keywords to a data item to describe that data item on multiple dimensions, while a Collection usually only indicates the single most important aspect of a data time.

Coding can be accomplished very quickly in Transana.  Coding can be a bit more abstract than Categorization as well.  One implication of this is that in some circumstances, this allows a researcher to defer some of their analytic thinking, requiring them to do more work to make sense of coded data items and to figure out their relationships to one another at a later stage in the analytic process.

One side effect of this is that all data items are independent of one another in the Coding model. If you want to find out what data items are similar in some way to each other, you need to create a report or do a search to gather data items with similar coding together.

Categorization with Collections

Collections can hold analytic data as represented in Quotes, Clips, and Snapshots in Transana. Items with similar analytic meaning can be grouped into the same Collection, and can be sorted into sub-collections that represent subcategories of the analytic concept the Collection represents. Old-school manual qualitative methods often involved making literal piles of slips of paper, and you can think of Collections as the electronic equivalent. This process of grouping selections together is sometimes called Categorization.

One of the implications of this is that data items can be considered in relation to one another. First, these data items are grouped together in a Collection that describes what they have in common. Further, data items in a Collection can be placed in a specific order, which can be important to illustrate the development of a narrative or change over time.  If order of items is important in your analysis, you will want to use Categorization in the analysis of your data.

One common analytic task when using Categorization is the manipulation of data items within Collections. One way to think about this is that a Collection represents a theoretical construct, and an item contained in that Collection is essentially a piece of evidence that supports (or that contradicts) that theory.  (This may not be where you start out in most qualitative analyses, but that’s where you want to end up when your analysis is complete!)

In qualitative analysis, Collections often evolve as the researcher’s understanding of the data changes. A Collection might start out as a broad construct, such as teacher questions. As the researcher understands that construct better, he or she might create sub-collections to represent different subcategories, or different types of questions that have been observed in the larger grouping. The researcher would then sort Clips into these different nested collections to indicate the more nuanced understanding emerging from the data. Dealing with the items that don’t fit into this new understanding may lead to further insights.

These two systems have different advantages and disadvantages. Some theoretical approaches favor one or the other. Many researchers use both, sometimes at different phases of the analytic process or to address different analytic questions or needs. Supporting both makes Transana more flexible, but may also seem a bit more complicated at first. Often, the same results can be achieved either way; rarely is there a “right” or “wrong” choice between the two.

Analytic Memos

Remember: at any point while working in Transana and at any stage during the analytic process, researchers can – and should – add Notes to your Collections and your Quotes, Clips, and Snapshots to document your analytic thinking, insights, and understandings. These analytic memos can be very useful in recording the process by which you reach conclusions, an aspect of research which deserves substantial attention when writing up the results of an analysis. The Notes Browser is very helpful for reviewing notes.

Making Sense of Categorized and Coded Data

Transana offers a wide variety of reports designed to help you make sense of the categorized and coded data you have created.  There are text-based reports and graphical reports; there are reports about word usage and frequency, about quotes, clips, snapshots, and coding; there are reports looking from source data out to Collections and looking from Collections back at data sources; reports can often be saved as a snapshot of current data, or as a reproducible template that can be applied later in analysis when more data has been added.  And Transana’s Search tools give you flexible ways of exploring your data in a variety of creative ways.