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When presented with a research question, it’s natural to ask whether data already exists that could provide an answer. Simply repurposing existing data may leave blind spots. However, a dedicated research study may not be able to answer all aspects of the question due to scarcity of time and resources. Leveraging existing data with primary data collected from a dedicated research study can provide a more holistic answer to the research question and yield much more powerful and nuanced insights.
Research using a single method is quicker and more cost effective than using multiple methods. Unfortunately, a single-method design can be akin to a single blind man attempting to identify an elephant: it can be unable to capture the full picture and limits the insights that can be derived. KJT Group often recommends mixed methods and multi-methods research to obtain data via multiple sources to provide a more reliable insight narrative. Read the full post to learn more!
Presentation Presented at Quirks Chicago, April 2, 2019
Market research often takes a hedonic approach to understanding and predicting consumer behavior: the more people like something, the more likely they are to purchase it. Research methods that follow this assumption are simple: to predict the likelihood of purchase, measure how much they like it. Some market research recognizes that real-world barriers exist so incorporate questions to discover these. Despite the appeal of such a simple approach, it does not capture the complexities of the attitude-behavior link that best predict specific behaviors.
In the healthcare industry, we’re privileged to work alongside some of the brightest and most driven individuals in the world. The top voices in a medical field function as a trusted source of information to their peers as well as to those bringing new medical interventions to market.
When it comes to capturing honest, representative information from respondents about their attitudes, their behaviors, and their feelings, even the smallest of things can risk skewing the results.