A well-designed plan is critical to high-quality research. Survey and other sample-based research have their own set of measurement advantages and limitations and require careful methodological consideration to obtain worthwhile results. While quick and easy survey tools and platforms abound, CSR can provide the research-based expertise to help maximize data quality.
Total survey error approach
Our survey methodologists take a “total survey error” approach to design. In the field of survey methods, Total Survey Error is a conceptual framework for understanding different sources of “error” (or deviation from the “true” statistic in the population) of scientific sample surveys. For example, one important source of potential error is coverage error. Consider a list of potential respondents to be sent a questionnaire: coverage error is the bias that could result from those left out of that list due to unknown or incorrect contact information, not having access to a particular mode of communication or not being part of a membership group, or other sampling frame problem. In the real world, such gaps in coverage are not an infrequent occurrence. Thus it is not always a straightforward or one-way application of a research design in the abstract to the operationalization of that design. Choices made about the mode of survey data collection or specific implementation protocols can have profound impact not only on the quality of the data but also the feasibility of the research question itself.
Cost of designs
Inherent in such considerations too is the monetary cost of designs. Whether the design is experimental or quasi-experimental, or whether it employs quantitative, qualitative, or mixed methods, or whether the project aim is simply to obtain feedback from a select group of people, we are equipped to help guide these cost-benefit trade-off decisions and design in a manner fit-for-purpose in the context of finite budgets.
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