Mixed-Mode Official Surveys: Design and Analysis

Mode effects in surveys can significantly impact the accuracy of results, particularly when the sample size is small. This paper reviews different inference procedures designed to minimize mode effects, such as weighting and cluster sampling. It also discusses several sources of mode-specific error, such as response bias and nonresponse bias, and provides a roadmap for practitioners who design and analyze mixed-mode surveys. The paper summarizes the literature on mixed-mode surveys from questionnaire design to analysis, highlighting the importance of considering mode effects in survey research.

Format: Paperback / softback
Length: 328 pages
Publication date: 28 September 2021
Publisher: CRC Press

Mixed-mode surveys are a common research method in the social sciences, but they can be challenging to design and analyze due to the potential for mode effects. Mode effects occur when respondents in different modes of data collection (e.g., online vs. paper) respond differently to survey questions, leading to bias and inaccurate results.

To mitigate the effects of mode effects, researchers have developed a range of methodological options. Inference procedures, such as weighting and cluster analysis, can be used to adjust for differences in response rates and other characteristics between modes. Additionally, careful questionnaire design can minimize mode-specific error by ensuring that questions are clear, concise, and relevant to all respondents.

Several different sources of mode-specific error can occur in mixed-mode surveys. These include response bias, item bias, and nonresponse bias. Response bias occurs when respondents in different modes respond differently to survey questions due to factors such as cognitive biases or differences in survey design. Item bias occurs when items in different modes are differently worded or framed, leading to different interpretations or responses. Nonresponse bias occurs when respondents in different modes are less likely to respond to the survey, leading to an incomplete or biased sample.

Practitioners who design and analyze mixed-mode surveys can follow a roadmap to mitigate the effects of mode effects. This roadmap includes steps such as selecting the appropriate mode(s) for the research question, designing a clear and concise questionnaire, weighting or clustering data to adjust for mode effects, and considering the potential for response bias, item bias, and nonresponse bias in the analysis.

The literature on mixed-mode surveys is extensive, covering a range of topics from questionnaire design to analysis. Researchers have developed a range of methodologies and approaches to address the challenges of mixed-mode surveys, and there is ongoing research in this area to improve our understanding of how to design and analyze mixed-mode surveys effectively.

In conclusion, mixed-mode surveys are a valuable research method in the social sciences, but they can be challenging to design and analyze due to the potential for mode effects. By using a range of methodological options and considering the potential sources of mode-specific error, practitioners can design and analyze mixed-mode surveys that produce accurate and reliable results. The literature on mixed-mode surveys is extensive, and there is ongoing research in this area to improve our understanding of how to design and analyze mixed-mode surveys effectively.

Weight: 572g
Dimension: 254 x 178 x 18 (mm)
ISBN-13: 9781032102962