Don’t let the data speak for itself: Presenting mixed methods health data
Abstract
Mixed methods study designs are commonly used in public health research, with a growing recognition that the combined use of qualitative and quantitative methods can provide more robust information to address complex public health issues. Despite the rapid growth in these approaches, many researchers still seek to publish their qualitative and quantitative results separately. Even when reported together, these results are often not integrated.
To maximize the outcomes of public health research, it is essential to strengthen our ability to narrate how multiple sources of data can corroborate one another, generate new hypotheses, develop and refine study instruments, and offer insights into explaining results. Therefore, the goal of this workshop is to share approaches for communicating mixed methods data and to provide models for how joint data matrices and other data displays can enhance the dissemination of mixed methods research.
Participants will learn best practices for designing research with mixed methods dissemination in mind and identify different uses for joint data matrices. In particular, participants will learn how to develop visual displays that highlight key study findings and facilitate a conversation between the data from individual methods. Lastly, participants will discuss and differentiate between displays that seek to compare versus those that aim to integrate data findings. By the end of this workshop, participants will be able to discuss how data displays can effectively communicate their mixed methods research and create tables for data triangulation.
Keywords
Communication, Community Health, Data Visualization, Dissemination, Mixed methods, public scholarship
Conflict of Interest Form
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Don’t let the data speak for itself: Presenting mixed methods health data
Mixed methods study designs are commonly used in public health research, with a growing recognition that the combined use of qualitative and quantitative methods can provide more robust information to address complex public health issues. Despite the rapid growth in these approaches, many researchers still seek to publish their qualitative and quantitative results separately. Even when reported together, these results are often not integrated.
To maximize the outcomes of public health research, it is essential to strengthen our ability to narrate how multiple sources of data can corroborate one another, generate new hypotheses, develop and refine study instruments, and offer insights into explaining results. Therefore, the goal of this workshop is to share approaches for communicating mixed methods data and to provide models for how joint data matrices and other data displays can enhance the dissemination of mixed methods research.
Participants will learn best practices for designing research with mixed methods dissemination in mind and identify different uses for joint data matrices. In particular, participants will learn how to develop visual displays that highlight key study findings and facilitate a conversation between the data from individual methods. Lastly, participants will discuss and differentiate between displays that seek to compare versus those that aim to integrate data findings. By the end of this workshop, participants will be able to discuss how data displays can effectively communicate their mixed methods research and create tables for data triangulation.
