This article presents the most common types of errors in surveying respondents and concise guidelines on how to avoid them.
Eliminating such errors is easy but it requires some experience in data and analytics. Luckily, SurveyPlanet is here to help you become a more seasoned survey writer and thus improve the quality of your research.
Knowing what kind of measurement errors to look for makes it easier to maintain proper methodology, which will help reduce survey errors in research and ensure the gathering of accurate, reliable, and valid data.
These tips will ensure that questionnaires are well-written and designed to collect reliable data from respondents. Continue reading to better understand common types of errors in surveying respondents that can harm data quality.
Why conduct surveys
The purpose of a survey is to get accurate results that quickly provide an understanding of a large population, evaluating its opinions regarding a particular topic. Surveys are a savvy method to analyze a target audience before launching a new product. They can also deliver a better understanding of employees and ways to improve workplace satisfaction.
Good surveys help you gain insight, collect feedback, and are regularly implemented to meet dozens of everyday life scenarios.
Such research furnishes accurate insights that can help start—or maintain—a business by assessing the opinions of participants and turning them into quantitative data. This is a process that will ensure accurate inferences while minimizing errors.
Understanding and dealing with survey research errors
Any measurement can contain errors. Rigorous techniques allow them to be kept to an acceptable level that will not affect the accuracy of results. To better understand errors that might crop up, survey technicians must determine the potential magnitude of such errors.
An essential part of the process is controlling or eliminating errors while collecting and evaluating data. This requires taking the first step, which is looking for errors and realizing their effect on outcomes. Basically, more than one ball has to be kept in the air simultaneously.
How challenging is it to notice them? You’d be surprised. But with the right approach, the trustworthiness of survey data can be maintained by catching errors as they occur.
The main sources of surveying errors
A key to successful research is knowing which errors to expect and what to do with survey responses that might be tainted. To interpret collected data correctly, all such results that can ruin your research must be eliminated.
Knowing the potential sources of the most common errors in surveying respondents will help you recognize them before pursuing a deeper dive into analyzing the data. These come in two main categories: sampling and non-sampling errors.
Four types of survey errors to understand
There are at least four types of survey errors examples to know about:
- Sampling error: The sample selected for the survey is not representative of the population being studied, which can happen when the sample is too small or the sampling method is biased.
- Non-response error: Some members of the sample do not respond to the survey, which can lead to bias if the non-responders are different from the responders in important ways.
- Measurement error : Survey questions or methods used to collect data are flawed or ambiguous, leading to inaccurate or incomplete responses.
- Processing error: Mistakes during the data processing stage, either when data is entered or it is analyzed, which can happen due to human error or technical issues that lead to incorrect or inconsistent results.
Sampling error in survey research: How to reduce it
Targeting the wrong respondents will obviously produce incorrect survey outcomes. Quality sampling simply means selecting the right group of people who genuinely represent the appropriate audience of interest.
If the research involves everyone you want to learn about, then preventing sampling errors shouldn’t be a major headache—it’s pretty easy to create a survey that reaches the entire target audience.
In other cases, some amount of sampling error is inevitable. You should always follow three essential principles while choosing a sample from which to collect data: diversity, consistency, and transparency.
Types of non-sampling errors in survey research
These errors can crop up in all aspects of the research process and indicate that the survey design needs to be improved. Such errors include poor survey design, lousy survey questions, response bias, missing data, etc.
Although it may be already known what specific information is being sought from respondents, writing survey questions can be challenging. It’s easy to make mistakes.
While a quality survey encourages respondents to answer truthfully, inaccurate questions leave them confused. The most common errors are asking leading or double-barreled questions. Unfortunately, these are not the only ways to cause misunderstanding or lead respondents to answer inaccurately.
How to prevent non-sampling errors in survey research
First, know your target audience.
Don’t rush into writing questions before dedicating time to understand who is being communicated with and how they think. Don’t forget that one goal is making the process enjoyable for participants, which makes them more likely to share honest opinions on a chosen topic.
Consider the potential impact of response bias, which creates a tendency for respondents to give false answers. These biases are usually a consequence of surveys that involve respondents self-reporting:
- “Yea-saying” bias (also known as the “friendliness effect”) is a phenomenon in which respondents tend to agree with whatever is said to them. “Nay saying” bias is the exact opposite—always disagreeing regardless of the particulars of the questions.
- Extreme responding is the tendency of respondents to falsely answer in the extreme—even if that’s not their honest opinion.
- Social desirability bias occurs when participants want to hide their socially undesirable traits (such as drinking alcohol frequently) by answering dishonestly because they are afraid of being judged.
- Order effects bias is a term describing respondents answering untruthfully to questions due to the order in which the questions appear. Survey questions should follow a logical flow.
Types of errors in surveying measurement and response bias
Measurement error refers to the inaccuracy or imprecision of a survey’s process. It can occur due to a variety of reasons, such as poorly worded survey questions, the use of ambiguous terms, or issues with survey design or administration. Measurement errors can lead to inaccurate or incomplete data, which can affect the reliability and validity of the results.
One common source of measurement error is response bias. It occurs when respondents provide answers that are not truthful or accurate, which can happen if kkrespondents feel pressure to provide socially desirable responses, they don’t understand the question, or they don’t remember the information accurately.
To minimize measurement error, survey designers should take steps to ensure that questions are clear and unambiguous, instructions are easy to understand, and response options are appropriate for the question being asked. They should also pre-test the survey with a small sample of respondents to identify any potential issues or sources of error. Additionally, survey designers can use statistical methods to adjust for measurement error and improve the accuracy of the survey results.
How to avoid response bias?
In order to avoid any type of response bias, make sure your question-and-answer options are not misleading. Ask neutrally worded questions and keep them short and easy to understand.
Using an anonymous questionnaire can help prevent respondents from misrepresenting themselves, so this option should be considered for topics that don’t require that participants’ identities be revealed. More reliable research results will be returned.
Also, stick to the previously defined purpose of your research. The specific topic of research was chosen for a reason. Don’t fall into the trap of leaning in the wrong direction when the creative flow kicks in. This is a pretty common mistake.
Make sure the research has only one purpose. If more varied conclusions are required, then create more than one survey (and hey, making them is fun). When a survey is created, the goal is to collect feedback from respondents that truly represents the target audience and their opinion about specific topics.
The reliability of the captured data depends on several factors:
- Response bias
- Research skills of the author
- Potential question or sampling errors
- Certain environmental factors
Processing errors and research outcomes
Research outcomes are never exact. They will always contain a certain measure of variance regardless of how carefully questionnaire procedures were followed.
Keeping this in mind when creating a questionnaire allows for the creation of a quality data collection design and will reduce (or completely avoid) the consequences of errors and mistakes.
Processing errors are mistakes that occur during the data processing stage of a survey. They can happen when data is being entered into a computer database, analyzed, or reported. Processing errors can arise due to a variety of reasons, such as human error, technical issues, or software glitches. Processing survey errors examples include:
- Data entry errors: Entries are made into a database incorrectly or data is missed during the entry process.
- Data coding errors: The coding used to classify responses is incorrect or inconsistent.
- Analysis errors: Data is analyzed using incorrect statistical methods or the analysis is based on incomplete or inaccurate data.
Now that you know how to recognize the most common errors in surveying respondents, browse our survey examples, where you will find questions, apply gained knowledge, and quickly become a skilled survey technician. Sign up for an account with SurveyPlanet and start your research journey today!