In the world of qualitative and mixed methods research, effectively managing quantitative data can sometimes feel like a challenge. During a recent webinar the founder of Dedoose, Dr. Eli Lieber, shared valuable insights on integrating quantitative descriptors within the Dedoose platform.  These strategies and features offer researchers powerful ways to identify and explore patterns and relationships in their data. Below is an overview of key points from Dr. Lieber’s webinar, which can also be viewed at the bottom of the page.

At the heart of mixed methods research is what Dr. Lieber calls the ‘web of connections’—the intricate and methodologically strategic relationships between your qualitative media (interviews, documents, images), your participants' characteristics (demographic data), and the coding system you develop. Dedoose is designed to facilitate and capitalize on these connections, allowing researchers to move fluidly between quantitative patterns and rich qualitative insights.

One beautiful mixed methods aspect of this approach is that quantitative descriptors serve as gateways to deeper qualitative exploration. When you notice an interesting pattern in your quantitative data, you can immediately drill down to explore the rich and contextualized qualitative content that sits beneath the surface.

‍

While Dedoose isn't primarily a quantitative analysis tool, it offers several powerful features for working with numerical data outlined below.

Distribution Analysis and Segmentation

One of the platform's quantitative oriented features is its ability to display distributions of quantitative variables and segment them in meaningful ways. For example, in the webinar demonstration using Technology Readiness Index (TRI) scores, Dr. Lieber showed how researchers can:

• View the overall distribution of continuous variables

• Dynamically adjust the number of segments (creating groups like high/medium/low or more granular divisions)

• Click on any segment to immediately access all qualitative excerpts from participants in that segment

This segmentation approach allows researchers to transform continuous quantitative data into categorical descriptors that can be used throughout the platform's analysis features.

Statistical Analysis

Dedoose includes basic statistical functions that mixed methods researchers frequently need:

• T-tests for comparing two groups

• Analysis of Variance (ANOVA) for comparing multiple groups

• Correlation scatter plots for exploring the relationship between two quantitative variables and then drilling into the underlying qualitative content

The correlation scatter plot feature is particularly powerful, as it allows researchers to select any region of the plot to immediately view all qualitative excerpts from participants in that region. This creates a natural bridge between quantitative patterns and qualitative insights.

Two specialized features highlight the platform's innovative approach to mixed methods analysis:

Code Weights

The Code Weight system allows researchers to add dimensional information to coded qualitative content. For example, if participants discuss the difficulty of learning software, researchers can not only code these mentions but also rate them on a scale (e.g., from minimal to severe difficulty).

Dr. Lieber explains that this approach offers advantages over self-reported quantitative scales because it:

• Quickly facilitates comparison across all participants

• Is grounded in the actual qualitative data, so fundamentally developed for the particular study  

• Allows for researcher-imposed dimensions based on a holistic view of all responses

Code weights essentially allow you to develop quantitative dimensions built upon qualitative content, building natural bridges between the two data types.

Dynamic Descriptors

For longitudinal studies, dynamic descriptors track changes over time. Unlike static descriptors (demographic information that remains constant), dynamic descriptors can capture how participants' responses evolve across multiple data collection points.

In the example shared during the webinar, dynamic descriptors helped track changes in home reading practices before and after an early literacy intervention, allowing researchers to visualize improvements over time while maintaining access to the qualitative context explaining those changes.

The Power of Visualizing and Filtering

Perhaps the most practical and ubiquitous features for day-to-day analysis is Dedoose's robust filtering system. Using the dynamic data visualizations in Dedoose—including tables, graphs, and plots—researchers can drill into their data using filters based on:

• Quantitative descriptor values

• Segments of distributions

• Codes applied to excerpts

• Multiple criteria simultaneously

These filters allow researchers to focus on precisely the subset of data most relevant to their current analysis question, creating pathways to uncover patterns that might otherwise remain hidden.

‍

The integration of quantitative descriptors within  Dedoose represents a significant advancement for mixed methods researchers. Rather than keeping quantitative and qualitative data in separate silos, this approach creates multiple points of connection between them, allowing researchers to leverage the strengths of both.

As mixed methods research continues to gain prominence across disciplines, tools that facilitate this integration become increasingly valuable. By maintaining connections between quantitative patterns and contextual qualitative insights, researchers can develop more nuanced understandings of complex phenomena from more comprehensive perspectives.

Whether you're a seasoned mixed methods researcher or just beginning to explore this methodology, considering how quantitative descriptors can enhance your qualitative and mixed methods analysis may open new avenues for discovery in your research journey.

‍