What are effective data collection methods for low literacy participants?

Sometimes simplification looks like adjusting the language and/or phrasing to be something your target community is familiar with such as asking if they have a discounted bus pass vs asking if they are part of a fee assistance program.

Data Collection methods can include surveys, interviews, experiments, observations, focus groups, secondary data analysis, or mixed-method approaches combining quantitative and qualitative techniques. The choice depends on the factors mentioned and the context of the research or data collection endeavor. Combining methods might offer a more comprehensive understanding of the phenomenon being studied, providing both statistical rigor and in-depth insights into participants' experiences or perspectives. Overall, aligning the data collection method with the research objectives and the context ensures the collected data's relevance, reliability, and usability for analysis and decision-making.

Integration and Hybrid Approaches: Combining visual and verbal methods can harness the strengths of both, providing a comprehensive understanding while accommodating diverse participant needs. For instance, using visual aids during interviews or incorporating storytelling with visual prompts can enhance data richness and participant engagement. When working with low literacy populations or diverse communities, employing a mix of visual and verbal methods ensures inclusivity, enhances participant involvement, and yields more comprehensive and meaningful data.

In my experience working with a community outreach program, defining data needs involved understanding the stakeholders' requirements and aligning them with the program's objectives. We used a combination of visual aids (such as drawings depicting scenarios) during group discussions and encouraged participants to express their thoughts verbally. This approach not only accommodated different literacy levels but also allowed for deeper insights as participants freely engaged through both visual prompts and spoken narratives. By offering multiple-choice formats instead of open-ended queries, we ensured easier comprehension, improving response rates and accuracy among participants with varying literacy levels.

Involving Participants: Co-designing Data Collection Methods: Engaging participants in designing data collection tools ensures that they're understandable and culturally sensitive. This involvement empowers participants by allowing them to express preferences and needs, increasing their investment in the process. Involving Facilitators: Guidance and Explanation: Facilitators play a crucial role in explaining the purpose and process of data collection to participants. By involving both participants and facilitators, you create a collaborative and inclusive environment that respects the participants' perspectives, ensures ethical practices, and results in more accurate, relevant, and meaningful data.

Factors to Consider: Literacy Levels and Languages: Recognize linguistic diversity and literacy variations among participants. Utilize simple language, local dialects, or visual aids to enhance comprehension. Education and Skills: Acknowledge varying levels of formal education and skills among participants. Opt for methods that don't heavily rely on reading or writing skills. Strategies for Adaptation: Tailored Approaches: Customize data collection methods and tools to suit the specific context and preferences of the participants. By considering these diverse factors and employing adaptive strategies, data collection processes can become more inclusive, respectful, and responsive to the needs and contexts of low literacy participants.

It’s important for people to know how their information and/or input is being used and what the impact of their participation is. If you don’t intend to inform any decision or action with the information you are collecting, you may want to reconsider asking for information.