Descriptors Overview and Workspace Orientation

Descriptors Overview

This guide introduces Descriptors and how they can help maximize your analysis. Topics include: 


Descriptors Definition

Descriptors are any categorical, demographic, or quantitative data associated with a piece of media in your project. Often, descriptors are derived from what variables or demographics you are interested in about each unit of analysis or participant in a project. In other words, a descriptor is a piece of information that describes components of interest in your data source at a particular level of analysis (e.g., participant demographics, research site, time, Likert scale responses, etc.).



Utility and Purpose

Descriptors are a powerful tool that allow you to do relational or comparative analysis between descriptor categories, as well as visualize your coding in relation to your descriptors. For example, descriptors allow you to quickly analyze if participant experiences differed by race, gender, age, location, or any other descriptor in your study. If you have quantitative data attached to your media (e.g., Likert scale survey responses), descriptors allow you to see the qualitative data in relation to the quantitative data. 

Descriptors are also a powerful tool for project management. For example, if you want to assign specific team members to code designated pieces of media, you can attach a descriptor to do so. 

Descriptors Workspace Orientation

Below are descriptions of each area in the descriptors workspace, along with an example from a qualitative study.


Descriptor Sets: Container(s) for organizing your Descriptor Fields

Descriptor Field: Descriptor categories

Field Options/Values: The list of variables or values associated with each descriptor field category. Descriptor fields can be one of four types:

  • Text (or string)—a set of alphanumeric characters
  • Number
  • Date/Time
  • Option List (or categorical)—a custom list of values defined by you

Option List descriptor fields have a special role in Dedoose. As you will learn in the sections on data analysis, one of Dedoose’s most powerful analytic and presentation features is the charting engine. These visuals help you discover and explore patterns in your data and then allow you to drill beneath the pattern to explore the underlying meanings in the qualitative data. Thus, while the Dedoose auto-grouper will create classes for charting based on of number and date/time, controlling these groupings based on your understanding of your data gives you maximum control over the nature of the distinctions between groups represented in the visuals.

Descriptors in Set: Current Descriptor profiles in your project


Qualitative Example: Descriptors Workspace

The example below shows each descriptor area for a qualitative study about college athlete experiences. If you are interested in how this study used Dedoose, you can refer to the open-access dissertation here.


Descriptor Sets: The default set that Dedoose provides; no additional sets were needed

Descriptor Field: Gender, Race, Career Status, Sport, Divisional Classification

Field Values: Gender – Woman, Man; Race – white, Black, Latino, Multiracial (white and Latina); Career Status – Former athlete, Current athlete; Sport – women’s basketball, men’s basketball, women’s soccer, men’s soccer, track and field, swimming, softball; Divisional Classification – DI, DI no football

Descriptors in Set: Each row refers to a participant’s profile and their associated descriptor information


Other guides in Learning Center demonstrate how to: