Qualitative Data

"Qualitative data" means many things to many people. In general, the term "qualitative data" covers information that is non-numerical in nature, usually collected through surveys, focus groups, interviews, written comments, etc. Depending on your needs, qualitative data can be analyzed quantitatively (such as with indicators that group and count responses by categories) or qualitative data might be read, interpreted, or summarized by a person. 

We've developed more specific in-app terms to promote a common understanding of the kinds of qualitative data that we encounter in international development and that DevResults supports.

  • Categorical data: Qualitative data grouped into categories for analysis
  • Ordinal data: Qualitative data grouped into categories with a meaningful order (e.g. strongly disagree, disagree, neutral, agree, strongly agree)
  • Free response: Open-ended text responses (e.g. survey participants responding to questions in their own words)
  • Narrative context: Comments that explain or contextualize quantitative data, categorical data, or indicator results
  • Qualitative indicator: An indicator that stores narrative, descriptive, or explanatory text that is defined by which activity generated the information, in which area or location, and during what reporting period
  • Narrative reporting: Questions posed to partners (or teams working on an activity) each reporting period to be answered in paragraph format alongside a set of indicator results
  • Discussion: Written conversations, questions, and comments about activities and reporting period data submissions
  • Documents: Files stored alongside activities or indicators
  • Photos and captions: Photos stored in DevResults with explanatory text for data quality assurance or additional context
  • Tags and sectors: User-defined fields that provide additional information about activities, indicators, organizations, locations, and photos
  • Descriptive dashboards: Text added to dashboards to provide additional context

Categorical data

Data collection: Categorical data refers to data that is grouped into "buckets" based on shared features. For example, when keeping records of beneficiary trainings, you may want to group attendees based on the training topic. DevResults allows you to define these categories as disaggregations, and then lets you categorize a single log, record, or survey response in a data table by selecting a category from a dropdown menu. 

Alternatively, numerical results could be reported directly for a category.

Data analysis: Our indicator analysis tools can calculate disaggregated indicator results automatically from data tables and enable you to visualize categorical data. 

Ordinal data

Data collection: Ordinal data is similar to categorical data in that it is also grouped into "buckets". The difference between the two is that the buckets in ordinal data have a meaningful order: from low to high, bad to good, small to large, etc. Examples of ordinal data include Likert scales, interval scales, beneficiary satisfaction survey responses, etc. Users can create a set of responses as a disaggregation in DevResults and assign responses ordinal values in a data table. When a category is selected from a dropdown in a linked log, record, or survey table, the value is populated automatically and is ready for quantitative analysis.

Data analysis: Our indicator analysis tools can calculate disaggregated indicator results automatically from data tables. 

Users can also configure data table indicator filters to count records only if they fulfill certain requirements (e.g. # participants who scored more than 5 in the post-training survey).

Free response

Data collection: DevResults data tables let you record any amount of text in response to survey questions, written tests, or descriptions in logs and records. 

Data analysis: Natural language processing and sentiment analysis are two forms of analysis performed on free response data. The option to automate these is not built into DevResults, but you can reach out to us about projects like this and we'd be glad to help. If you are using an external tool to perform natural language processing on your free response data, you can still log the results of that analysis in a data table and create categorical indicators that automatically calculate indicator results from them.

Narrative context

Data collection: Users can add comments and explanations for their indicator results with a high degree of precision in DevResults. Comments can be added per indicator, per reporting period, per activity, and per geographic place. All comments can be exported with a single click and are displayed alongside data when submitted for a reporting period. 

Data analysis: Narrative context often supplements quantitative or categorical data, and provides additional context to indicator results. If you need additional analysis performed on narrative context information, please reach out to us and we'd be happy to help.

Qualitative indicator

Data collection: Both numerical indicator results and qualitative indicator results are defined by: 

  1. Which (if any) activity generated the data
  2. In which area or location
  3. During which reporting period

Qualitative indicator results consist of narrative, descriptive, or explanatory text. In DevResults, defining a Yes/No type indicator creates the structure to record text per activity, location, and reporting period. The Yes/No format incidentally enables a percentage metric for the proportion of comments that were filled out, polling stations visited, facilities assessed, etc., for any topic for which you're reporting qualitative indicator results. 

Data analysis: A percent completion metric supplements narrative indicator results and provides an additional tool for review and approval. These metrics can be visualized as with any other numerical indicator data in DevResults. All qualitative indicator text can be exported and sorted by activity, place, and reporting period for development of highly textured understanding, insight, and through-line narratives. If you need additional analysis performed on qualitative indicator responses, please reach out to us and we'd be happy to help.

Narrative reporting

Data collection: Admin users can create a fixed set of questions for an activity to answer each reporting period alongside their indicator results. These responses can vary from bullet points to paragraph form

Data analysis: Narrative reporting often supplements quantitative or categorical data and provides additional context for an activity's work through a reporting period. If you need additional analysis performed on narrative reports, please reach out to us and we'd be happy to help.


Data collection: Every activity in DevResults comes with its own discussion page to allow questions, comments, and conversation, including threaded replies. Additional discussion pages exist for each reporting period for each activity. These pages keep a record of the submission process and support communication about reporting data. 

Data analysis: Discussion data supplements indicator results information. If you need additional analysis performed on discussion responses, please reach out to us and we'd be happy to help.


Data collection: Every activity in DevResults comes with its own document storage area and nested folders that let you keep things organized. Users can store documents of any file type that tell a story about quarterly or annual results. DevResults also comes with a global document area not associated with particular activities. 

For Dropbox users, our integration allows for a seamless experience between your desktop and DevResults documents.  

Photos and captions

Data collection: DevResults enables unlimited photo storage to capture hand-written logs, beneficiary events, or any other supporting evidence for progress toward objectives or deliverables achieved. Photos themselves are a kind of qualitative data, but we also enable text captions to explain and record the context for any photo and the coordinates if taken with a location-enabled device. 

Data analysis: Photos in DevResults can be tagged with user-defined fields. This helps users sort, filter, and group photos for additional context or analysis.

Tags and sectors

Data collection: DevResults enables the organization, sorting, and filtering of indicators, activities, organizations, photos, and locations with user-defined tags and sectors. These fields also help provide additional context to data in DevResults.

Descriptive dashboards

Data collection: When you design a dashboard in DevResults with maps, graphs, and numbers, you can also add unlimited "markdown" tiles with formatted, explanatory text, success stories, beneficiary highlights, or program summaries (including links). This helps you tell a story, or add additional context to, a group of indicators and / or activities. 

Didn't answer your question? Please email us at help@devresults.com.