Survey response rates by requestor’s characteristics

Response rates are crucial for the effectiveness of survey-based behavioral science and the validity of research conclusions. This study investigates the impact of the characteristics of individuals requesting participation on response rates in online surveys within the agricultural sector. Using social exchange theory and Coleman’s social capital theory as guiding frameworks, we examined whether the personal characteristics of the requestor influence response rates. Data were collected from a sample of 1,452 agricultural development personnel using four different request formats varying by the gender and position of the requestor. Following Dillman’s tailored design method, participants received a pre-email, a request email with a link to the survey, and four waves of follow-up emails. Response rates were analyzed based on the four treatment groups, the gender of the requestor, and the position of the requestor. The findings indicate no significant differences in response rates based on the requestor's gender or position. Analysis of variance (ANOVA) and independent t-tests revealed that neither the highest level of education, years of teaching experience, nor the wave of response significantly affected by the requestor's characteristics. These results suggest that the established trust and social capital within the agricultural community do not significantly influence survey participation. The study highlights the need for researchers to address declining response rates in survey research. It recommends building and maintaining community trust by providing clear, concise, and accessible research findings. Researchers should also consider more targeted sampling methods to reduce survey fatigue and improve response rates. The implications of these findings extend to the broader field of social science research, emphasizing that the gender and position of the requestor do not increase response rates or reduce selection bias. Future research should explore alternative methods to enhance survey participation and address the challenges of non-response bias in agricultural education research.