Surveys are conducted to gain meaningful insights from data. These insights are used to make better business decisions. The drivers could be many – reducing the cost, improving the ROI, improving customer satisfaction, or getting new ideas. Accuracy in survey research directly affects the correctness of the decisions and can have a massive impact on the business.
Survey accuracy can mean different things to different people. Hence it is important to have common standards for accuracy in market research. Also, there can be a number of parameters to measure accuracy. For example, if you are comparing trends over a few time intervals and see an unusual spike, it can be due to incorrect options unless there is a just explanation. This can lead to inaccurate data and affect the outcomes.
What is accuracy in survey research?
Survey accuracy is the extent to which the output from the responses aids in achieving survey goals. It represents the attribute being measured in the sample audience. If the results are inaccurate, they create a wrong picture of the audience’s choice, feedback, or experience.
Below are some of the common reasons behind inaccurate survey results.
- Sample audience distribution: The sample is not evenly distributed, and hence it will have a deviation towards a specific choice. For example, in an uneven distribution of audience consisting of 80% males, 10% females, and the rest 10% who do not prefer to reveal their gender, it is very likely that the sample audience would choose ‘yes’ to the question ‘Do you get a hair cut at least thrice in six months?
- Response bias: At times, if the respondents are not answering the questionnaire honestly, they would select the first choice (usually). This leads to a response bias and distorts the accuracy of the research study.
- Response distribution: The survey results are likely inaccurate if the creator has offered the wrong or insufficient choices.
- Data quality: If the tool controls the survey accuracy, you can get duplicate responses, incomplete surveys or answers with boorish language.
Survey accuracy standards
Survey accuracy depends on various factors such as target audience, time and frequency of survey, response rate, tools to collect responses, method of data collection, data analysis, and global events.
Let’s look at some of the examples of survey accuracy standards and how to reduce survey errors.
01. Survey Design
Organizations must design a survey that uses commonly accepted statistical methods. Define the target population, design the sampling plan, specify the data collection methods, create a schedule, and estimate costs. Any use of nonprobability sampling methods must be able to measure estimation error. The size and selection of the sample should be according to the level of detail needed.
- Ensure the sample audience design will give the data as needed to meet the objectives of the survey.
- If a nonprobabilistic sampling method is used, document what alternatives were considered and why a specific design was selected, a probability and estimate of the bias in the sample selection bias, and the method used to measure estimation error.
- If possible, compare the accuracy of survey estimates with that of other information sources. In the case of probability sampling, estimate the sampling error, whereas in the case of nonprobability sampling, calculate the estimation error.
- If possible, consider measurement errors due to errors in survey software, interviewers, mode of interview, coverage error, nonresponse error, and processing error.
02. Survey Response Rate
Market researchers must design the questionnaire to achieve a good response rate and make sure that the results reflect the target population. Only then, they can be trusted to make informed decisions.
- Set response rate goals for various phases of the survey. You can also set targets based on demographic variables and iterations of the survey.
- Identify approximate response rates at each data collection stage, depending on the content and type of survey.
- If the response rate is below 80%, identify the reasons behind respondent behavior.
03. Test survey systems
Ensure all components of the end-to-end survey system work without failure at peak load. Run a test check to make sure that there is no hiccups in the system and that the respondents have a great survey experience.
- Apply different testing methods like unit testing, integration testing, and usability testing to make sure the outputs match the benchmark quality. For instance, integrate your survey tool with a third-party app and see if data flows smoothly without any application errors.
- Test the survey software with trial responses. Ensure all the reports are generated and confirm the accuracy of the reports.
Why is accuracy important in surveying?
Directly impacts the business: Market researchers and businesses base their decisions on the outcomes of the research study. Hence it is extremely important to have accurate survey data. There must be a confidence level of at least 80% to trust the results. A deviation error of +/- 10% is acceptable, however, anything significantly out of these bounds could affect the profitability of the business.
Waste of resources: Inaccurate data can lead to poor resource utilization and, in the worst cases, a total waste of resources and huge losses.
Cuts downtime: When the researchers do not feel confident about the results, they need to repeat the exercise. Accurate surveys eliminate the need to conduct the research again.
Conclusion
Survey accuracy is extremely important in research and needs the expertise of experienced professionals. If you are looking for a technology partner who can guide you through the process, send an email to us at [email protected] or chat with us.
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