Synthetic qualitative data for research: a complete guide
FEB 2024
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Synthetic qualitative data is a powerful tool in research, offering a solution when real-world data is unavailable, prohibitively expensive, or too time-consuming for analysis.
It's artificially generated using algorithms or simulations, and is designed to reflect the complexity and characteristics of real-world data. This type of data is typically generated using Large Language Models (LLMs), without requiring complex data collection.
However, generating synthetic data requires a strong understanding of data modeling and a clear understanding of the real data and its environment.
By following best practices, you can effectively start generating and using synthetic data within your analysis and decision-making.
For a comprehensive guide on synthetic qualitative data for research, check out our full guide here.
Try MindPort for better insights, faster ⬇︎
MindPort empowers researchers, data teams, and agencies to effortlessly generate synthetic data with unmatched efficiency. Whether you want us to run an end-to-end research project, or just generate the data for your in-house team, we use our proprietary data-driven platform to scope, synthesise, collect, analyze and share data with your team.
Using GPT can only get you so far. Inside MindPort, our data team can organize your projects, scope your requirements, generate a synthetic sample and track individual synthetic participants. We're able to generate validated data in a fully bespoke workflow. This means your teams get the data and insights they need, in one centralized location.
Take your insights and analytics a step further with MindPort's synthetic data and insights:
Even before generating synthetic data, we can tailor over 180 unique categories of variable, totalling over 700 individual parameters per participant, based on your data requirements.
We can scale up from one to one thousand participants, enabling us to generate entirely unique three-dimensional personas that reflect the complexities, nuances, and fidelity of your real-world data.
We take this further by using 3rd party verification and our review framework to benchmark generated data against input data, carefully checking for biases, errors, and miscategorisations.
We're then able to package your synthesized data and insights within a team workspace, incorporating quotes for a visually compelling presentation.
Notably transforms your data analysis process, making synthetic data synthesis a seamless and insightful journey from start to finish.
Interested? Contact us and we'll send you a synthetic sample for free.
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