Dedoose is an easy-to-use, collaborative, web based application that facilitates all types of research data management and analysis. Here's what you need to know about how to use it.
A descriptor set is a collection of information that describes the source of your data (e.g., research participants, families, schools, other settings, etc.) at a particular level of analysis.
Clicking on the ‘Add’ button in the ‘Descriptor Sets’ panel header will prompt you to provide a title for the new descriptor set to be added to the project or you can simply begin by using the default set in every project. Clicking the Cog Wheel icon to the right of the Descriptor Set name toggle editing mode, allowing you to rename or delete Descriptor Sets.
Using Multiple Descriptor Sets
The use of multiple descriptor sets is ideal for studies focused on questions that look across different levels of analysis. For example, if a study is comparing student outcomes across different school districts there might be three levels of data: 1) the district, 2) the schools within each district, and 3) the students within each school. Commonly, different sets of descriptor fields/variables would be collected to distinguish the cases at each level (i.e., district level fields might include average family annual income, square miles of capture area, percent rural versus urban neighborhoods; school level fields might include size of student population, student-teacher ratio, percent of children on free lunch program; and student level fields might include age, gender, grade level, size of family, language spoken at home, and standardized-test scores).
Further, imagine the study’s qualitative data come from interviews with parents about the home educational environment. Under these circumstances, each interview could be linked to three descriptors: the one specific to their child, one for their child’s school, and one for their school district. Dedoose allows exploration of qualitative data and coding activity across multiple descriptors. Thus, in a study like this, variations in the qualitative data and coding activity could be explored as a function of district, school, or student fields and combinations of fields across these levels.
Descriptor fields can take one of four types:
Tip: 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. This feature allows for the generation and interaction with dynamic ‘Live’ or ‘Hot’ charts of your excerpting, coding, and code weighting activity. 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 content. Thus, while the Dedoose auto-grouper will create classes for charting based on of number, date/time, and (occasionally) text fields, 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.
Accordingly, we strongly recommend as much use of option list descriptor fields as possible. Categorical data (e.g., sex, race, language spoken, age group, income group, education level …) are naturally represented by option list fields. However, other variables not thought to be categorical can also be represented to take advantage of the option list features. For example, responses to numerical scales can be grouped into discrete categories (e.g., high, medium, low; 1, 2, 3, 4, 5, 6, 7, 8; less than 1, 1-1.9, 2-2.9, 3-3.9, 4-4.9, 5 or greater; present/absent).
Tip: continuous variables created and analyzed in statistical software can be meaningfully converted to categorical forms in order to be represented in Dedoose as the recommended Option List type fields. An examination of a continuous variable’s distribution within the sample population will provide important insight into how many and by what criteria these data could be meaningfully represented in a categorical form. With this information, a simple recoding prepares these data for import to Dedoose as an option list descriptor field. In short, when you define an option list field, you are deciding how you wish to slice up the data as presented in the Dedoose data visualizations.
Manually Adding/Modifying Descriptor Fields
Clicking the 'Add’ icon in the ‘Set Fields’ panel header will allow you to define a new descriptor field in the currently selected set. Also, these data can be imported directly from an Excel file (see below)—eliminating the need to define the fields manually within Dedoose.
In this example, a new field, ‘Population Density,’ is being created with a particular description, type, and values - here, an ‘Option List’ type, in which a categorical variable is being defined with three possible values: Low, Moderate, and High.
Manually Adding/Modifying Descriptor Data
Clicking the ‘Add’ icon in the ‘Descriptors In Set’ panel header will allow you to create a new descriptor with the associated fields defined in the selected descriptor set. Similarly, clicking on a particular descriptor row will bring up that descriptor with the same view for modification. The example below, show the descriptor editing view as well as the documents to which this descriptor has been linked. Again, as with descriptor fields, these data can easily be imported from an existing Excel file.
Importing Descriptor Field Definitions and Data
As many researchers organize and manage their quantitative and categorical data in spreadsheet or statistical software, importing descriptor fields and data is a quick and easy way to get these data into a Dedoose project. There are approaches to carrying out this import. While the first allows less control over all details, note that it is often sufficient and effective.
Importing Descriptor Field Definitions
Full sets of descriptor field definitions can be imported directly to Dedoose from an Excel file, Open XML—Excel 2007/2010—format (.xlsx) or Excel format (.xls), thus avoiding the need to manually type or re-enter information that already may have been created elsewhere. To accomplish this task, the imported data file MUST be structured with the following format:
Example of Format and Column Headers for Importing Descriptor Field Definitions
NOTE that 'List' type fields include the specific values defined as 'valid' for the particular field/variable.
After creating the descriptor field definition file to import click the 'Import Fields' icon at the top of the ‘Set Fields’ panel in the Descriptor Workspace and then follow the prompts to:
Example of Descriptor Field Importing Review
Importing Descriptor Data
To import Descriptor data to Dedoose (after descriptor fields have been imported or defined in Dedoose) from an Excel file (.xls or .xlsx), you must create an Excel file with:
Example of Excel file format for Importing Descriptor Data
After you have created this file:
Example of Descriptor Data Importing Review
In this example, we see the Dedoose import review includes a number of errors. The report indicates the location of the error, the data point in question, and the nature of the error. For example, row 21/column 4 contained 'not a valid option' because 'Bilingual' is not an option in the field 'Reading Language.' Similarly, the titles 'name', 'dob', and 'child sex' do not match exactly to the descriptor fields that were imported and will therefore not be found. This is why the column 'Child Gender' has no data. While you can continue to import these data by clicking ‘Submit,’ errant data can lead to results that exclude these data. While missing data is less of a problem than invalid data, each user must decide how to proceed based on the information Dedoose provides in the error report. Invalid or missing data points will both be considered ‘missing’ by the Dedoose import engine.
Updating or Adding to Descriptor Fields and Data
When new cases, 'Descriptors,' are added to a study or new data, 'Fields,' are collected for existing participants these data can also be imported to a Dedoose project.
Importing new field definitions is accomplished exactly as importing an original set of fields. See the ‘Importing Descriptor Field Definitions’ section above for step-by-step instructions on how to prepare and import these definitions from an Excel file.
Importing new or updated descriptor data takes place in three steps:
Dynamic descriptor fields are a special type of field available in Dedoose. Dynamic descriptor fields are designed primarily to help you map and explore change over time when you collect qualitative data at multiple time points. The specific values for dynamic descriptor fields are set at the time a static descriptor is linked to a media item.
To illustrate when a dynamic descriptor might be used, imagine you are doing an 18-month study on elementary school student reading skills and will interview the students at three time points. Some of the descriptor data for each participant will be the same at all time-points, but some will change—for example, student gender, ethnicity, and family Socio-Economic Status Group (SES) might stay the same over the course of the project, but the time of measurement (e.g., Time 1, Time 2, Time 3) or classroom grade (e.g., 3rd, 4th, 5th) might change. If you make time of measurement or class grade dynamic fields, you will be prompted to set the value (e.g., baseline, time 1, time 2) when linking the qualitative document to a descriptor. Subsequently, your excerpting and coding activity for those qualitative documents will be linked to the specific time point. With data like these, Dedoose allows you to explore change over the three time-points and there are a set of graphs built into Dedoose to facilitate exposing patterns of change and drilling into the meaning of the patterns that emerge.
To define and apply dynamic descriptors:
Dynamic descriptor fields perform in identical ways as static fields in Dedoose analytics and filtering, so when put to use you are set to explore chronological or other 'dynamic' changes.