Why is calculating survey response rate so important and how is it determined?
The calculation of Response Rate (RR) is often inconsistent across research studies because each study may use its own definition. There are so many ways of calculating response rates that comparison across survey research studies can result in confusion and misinterpretations. In order to make practical comparisons across different research studies, it is important to standardize the calculation of response rate.
Consider adopting the calculations as defined by the Standard Definitions: Final Dispositions of Case Codes and of Outcome Rates for Surveys (American Association for Public Opinion Research, Revised 2011) and the various other response calculations, as shown below. Abbreviated terms include: I = Complete survey, P = Partial survey, R = Refusal and break-off, NC = Non-contact, O = Other, UH = Unknown if household/occupied housing unit, UO = Unknown, other, e = Estimated proportion of cases of unknown eligibility that are eligible. Continue reading →
Create higher response rates with proper online survey length
A streamlined online survey is more efficient and can yield a higher response rate. The shorter the online survey, the more likely respondents will complete it. The length of your online survey depends on many factors. Ideally, the length of your online survey is based on the number of relevant questions asked and the optimal length that will convince someone to respond. Beware that using extra questions in your survey may have a negative effect on your response rate, so only develop questions that are pertinent to your survey research objectives. Continue reading →
Often times Snap Research Services, Snap Survey’s survey research team, is asked the question: How many responses do I need to collect from a random sample of respondents in order to receive accurate results?
You ask, we respond! When surveying a random sample of respondents, there are two key measures – Confidence Level and Margin of Error. Each of these key measures works together. For example, if you have a 95% confidence level and a 5% margin of error, and a question answered by 50% of your sample, then 19 out of 20 times, the true answer – the answer you would find if every single person in the target population answered this question – can be assumed to be in the range of 45% to 55%.
The following table explains the number of survey responses you need, depending on the number of people in your target population. Continue reading →
The overall success of your survey depends greatly on a good quality response rate. The higher the response rate, the more representative the survey will be of the total population. Ideally, a higher than anticipated response rate will bring more assurance and reliability to the survey results. A higher response rate also allows more robust statistical calculations to be performed. In contrast, a response rate that falls short of the anticipated response rate may bring into question the dependability and representativeness of the survey data. Receiving a low response rate from your survey will skew the results due to response bias, as certain types of people are more likely to respond to surveys than others, so certain views may triumph.
By following these simple guidelines, you can considerably increase the number of respondents who complete your survey. Here are some actions you can take to maximize response rates. Continue reading →