By Angus Mungal
November 24, 2009
Each fall, as a new semester begins, undergraduates, graduates, and professors alike face the same problem—managing copious amounts of readings, notes, and other documents. There are documents for courses, for our individual assignments, and for research questions, websites, and even transcriptions of interviews or other data. Not only are we required to read these documents; we are also responsible for making sense of this information through analysis and discussion of relevant themes and ideas.
How will you manage all of this information? Will you find yourself attempting to decipher scribbled notes in margins in long-forgotten or misplaced documents? Perhaps you made extensive notes, in a notebook now hidden somewhere in your home beneath a pile of books, for a class you took two semesters ago. You may have a collection of phrases or quotes in a Microsoft Word document that, without their original context, have lost their meaning. If you would like to change the way you organize, manage, and analyze material of this sort, you might consider doing what I have done—using Atlas.ti, a qualitative statistics software package.
While some scholars approach statistics with a degree of trepidation, others embrace the organizational potential of qualitative, quantitative, and (more recently) mixed-method research. And while many students of statistics are familiar with software for quantitative statistical analyses, such as PASW, SPSS, STATA, and GIS, fewer are aware of the usefulness of qualitative data analysis (QDA) software like NVIVO, NUD*IST, and the focus of this article—Atlas.ti.
Atlas.ti (www.atlasti.com) was developed by Thomas Muhr in Berlin and released commercially in 1993. It is a qualitative data analysis software program that enables you to manage, code, analyze, and output data in a variety of convenient methods, making your data more understandable. Atlas.ti can help you "explore the hidden phenomenon within your data" by permitting you to collect large bodies of data, including interview transcriptions, PDFs, Microsoft Word documents, html, pictures, and even audio and video recordings.1 The data can then be coded and analyzed for themes and other information.
I have been using Atlas.ti for almost two years, and I am still finding new ways to apply the software. When I first began learning Atlas.ti, under the guidance of Frank LoPresti at the Data Service Studio, I purposely decided to experiment with the program to see what I could learn without the manual, and also to see what some of the pitfalls would be for someone who might be using the software for the very first time. After I pinpointed these, I set out to create a method for teaching others how to overcome a beginner's confusion.
For students and professors, Atlas.ti can be a useful aid for simply managing similar-themed documents or for more complex tasks, such as mining for information. Using QDA software like Atlas.ti can help one find deeper meaning and connections within the data—be it documents, audio/video, or interview transcripts—by mining for themes, similarities, and differences.
Atlas.ti can be used in many disciplines, from the social sciences to medicine, from psychology, literature, quality control, engineering, criminology, and text linguistics to history, geography, theology, and law. One of Atlas.ti's benefits is that it lends credibility to the research process by making it more transparent and replicable.2
Many who are new to Atlas.ti are nervous about the learning curve. Atlas.ti does not automatically do the work for you, and organizing your documents to input into Atlas.ti requires an initial investment of time. I believe, however, that those who have the time and inclination to devote to these initial steps will find that their efforts are rewarded. Additionally, there are some limitations; most notably, Atlas.ti works only on Windows, and must be installed on the computer on which the material that you wish to work with is stored.
Atlas.ti allows you to create projects called Hermeneutic Units (or HUs). An HU includes the primary documents—e.g., quotations, codes, and memos. Within these HUs, you can collect and organize documents associated with a particular course or topic. For example, you could create separate HUs for your documents on public policy, historical primary documents, documents on immigration, law articles, or teacher education documents, or even for analyzing maps or photos. As your readings grow, you can add more documents to your HUs.
Atlas.ti has a number of useful features that make gathering important information easy. You can:
Imagine being able to produce one document that lists a specified subset or all of the codes and phrases you've created for the articles you've read and analyzed!
At the Data Service Studio, I have worked with a variety of schools, individuals, and researchers who are interested in or are currently using Atlas.ti. The software works particularly well for organizing a combination of class notes, research, and field study notes—any area in which "soft data" is employed. It has been popular among the students, staff, and faculty of such diverse schools as the NYU School of Medicine, the College of Nursing, and the Silver School of Social Work. In addition, I have worked with individuals and groups at NYU Wagner, Stern School of Business, the College of Arts and Science, and the Steinhardt School of Culture, Education, and Human Development, all of whom are sharing information and data via Atlas.ti.
This has been only a brief glimpse into capabilities of Atlas.ti. If you would like to explore its potential, the Data Service Studio staff can assist you with Altas.ti, as well as many of the other software packages mentioned in this article. To arrange a one-on-one or class tutorial in Atlas.ti, please email the Data Service Studio at email@example.com.
(For an overview of another tool that might help you organize your digital scholarly notes and materials, see A.nnotate & Pliny: Learning, Study & Research Tools for the Digital Age, also in this issue of Connect.)
Angus Mungal is a Ph.D. candidate in the Department of Administration, Leadership, and Technology (Steinhardt), and works as an aide in the NYU Data Service Studio.
This Article is in the following Topics:
Connect - Information Technology at NYU