In the most simple sense, a survey error is a mistake that occurs when gathering and interpreting research data using a survey. It could be as little as forgetting to include a specific question in the form, sending out the questionnaire when it's yet to be vetted, or misinterpreting results.
Whatever the magnitude of these errors are, they can significantly affect the quality of data gathered and alter the course of your systematic investigation. Since surveys depend on quality data to make feasible predictions within the research context, any error takes the researcher a step farther from making accurate inferences.
For example, excluding a vital question from your survey research form can prevent you from accessing important information about your audience, leading to false conclusions. Also, listing leading or loaded questions in the questionnaire affects the validity of responses collected from survey participants.
In this article. we'll identify specific survey errors you might encounter and how to avoid them when conducting your research survey.
Survey errors are broadly categorized into two groups, namely: Sampling errors and non-sampling errors.
A sampling error is a result of choosing a subset of the larger population for research. Although the subgroup represents the research subjects, it might not account for all the characteristics of the population. This causes a margin of error in your research outcomes and conclusions.
Sampling errors are inevitable in surveys. While research populations have dominant characteristics that cut across the majority, minor differences might go unnoticed.
For instance, most of the students in an African school will be dark in complexion, while a handful might be light-skinned. If a researcher picks a subset of only dark-complexioned students, they might conclude that all students in the school are dark-skinned, which isn't the case.
Non-sampling errors happen when there's a problem with the design and/or structure of your survey. For example, if the questionnaire contains leading or loaded questions that skew responses, it becomes a non-sampling error.
In addition, the structure of your questionnaire can discourage respondents from participating in your data collection process. Question order bias is a typical example here. In this case, the survey design subtly nudges respondents towards a pattern of answers—you may find respondents choosing a specific answer-option or rating continuously.
More than the broad survey error categories, researchers also face different types of errors in survey research. Knowing these errors would help you avoid them and create a more valid research process.
A coverage error happens when some members of your research population are ignored in your data collection process due to one reason or the other. For example, if your product appeals to people aged 18–25 and 26–40, but the latter is unrepresented in your research, it leads to a coverage error.
Typically, coverage error results from undercoverage bias. This happens when a researcher excludes some of the target audience subsets due to convenience sampling, time and resources constraints, and other similar factors.
For example, if your research target is distributed across rural and urban areas, and you only collect survey responses from the urban sub-group, coverage errors would affect the results.
Measurement instrument errors happen when the survey design or the sequence of the data collection process affects the validity of results from the data collection process. Sometimes, an inexperienced interviewer or a lack of budget can lead to measurement errors, ruining an otherwise perfect data collection process. A common example is when a researcher asks bad survey questions that prevent respondents from providing objective answers in research.
These errors are systematic, and to discover them, you need to weigh the research outcomes against the valid results from an equivalent error-free process. When the researcher uncovers the error, they need to change the research structure as required and repeat the data collection process.
In some cases, a researcher might collect responses from all the sub-groups in the target population. Yet, these responses might not be evenly distributed across these subsets. This situation passes for non-response bias.
Here's what we mean
Suppose your research population is made up of two sub-groups: Male and Female. If 60% of men complete your survey compared to 35% of women, then this is a non-response error.
Another way to look at this is through the lens of Klofstad in the Encyclopedia of Social Measurement. Here, he defines non-response errors as when the individuals who complete the interview are somehow systematically different from those who were unable to be contacted and those who chose not to participate.
From our discussion on types and categories of survey errors, you should already know some of the sources of errors in survey research. Typically, survey errors happen when the researcher fails to ask the right questions, excludes some subgroups from the data collection process, or lacks the competence to interpret results accurately.
When survey questions are confusing or misleading, they affect the credibility of your research process. For example, suppose your questions are vague, difficult to understand, or ambiguous. In that case, it forces respondents to choose answers that do not align with their true experience or perception of the subject.
Sometimes, the researcher influences the entire systematic investigation to arrive at predetermined results. This is known as researcher bias. Researcher bias often stems from the influence of one's personal preferences, way of life, or experiences on the study.
Here's a simple illustration. Let's say an atheist researcher is conducting research on religion. Due to their belief, they might create a biased survey with questions that portray religion as irrelevant or unnecessary. Researcher bias also takes the form of selection bias when you exclude some members of the target audience on purpose.
Your survey is only as good as the quality of responses collected. After choosing the right design and listing appropriate survey questions, you need to make sure the survey is distributed to the right audience. Administering a questionnaire to the wrong population defeats the purpose of any systematic investigation.
Survey errors are easy to miss, and this is why every good researcher conducts a quality check before going ahead with data collection. Here are things you should definitely check before your next survey goes live.
To avoid measurement instrument errors, make sure you're asking the right questions and using the suitable survey method for data collection. For example, suppose you're conducting qualitative research. In that case, you should have open-ended questions that prompt respondents to share their deep insights into their experiences—you can't achieve this with close-ended questions.
Another thing you should pay attention to is the format of responses. For example, if you want your respondents to input numerical data, you should spell this out in your survey. With the field validation option on Formplus, you can automatically authenticate responses and show an error message for invalid inputs.
If respondents find it challenging to understand and fill your questionnaire, it could lead to fatigue and high survey dropout rates. So, make sure your entire data collection process is user-friendly, mobile-responsive, and easy to navigate.
One thing you can do is split your survey into multiple pages rather than listing all your questions on a page. With Formplus, you can break your survey into sections and place each section on a page. Alternatively, you can organize your survey as one-question-per-page.
Guerilla testing involves administering your survey to a small group representative of your target population. It allows you to identify gaps in your data collection process, collect feedback from respondents and improve the survey before it goes live. You can think of this as a variation of concept testing.
As part of your guerilla test, you should ask questions like:
You want to be sure that your survey can collect responses and store data in your preferred cloud storage. So, an intelligent thing to do is run dummy test responses through the survey to check its functionalities.
With Formplus, you can create accurate surveys and collect valid responses from your target audience. Our user-friendly survey layouts allow you to limit occurrences of measurement instrument errors. You can also add different form fields to help you gather data in various formats seamlessly.
Here's a simple step-by-step guide on creating error-free surveys on Formplus.
Step 1: Log into your Formplus account to access your dashboard. You can sign up for a free Formplus account here.
Step 2: On your Formplus dashboard, choose the "create new form" option to access the form builder. If you'd like to use any templates, click "templates" and follow the prompts.
Step 3: On the Formplus builder, you have access to multiple fields, including rating fields, scales, and advanced features for calculation. Drag and drop preferred fields from the inputs section into your work area.
Step 4: After adding the fields you'd like, click on the small pencil icon beside each one to edit the field. On the edit tab, you can add questions and answer options and also make the fields "hidden,” "required," and "read-only.”
Step 5: Click on the "save" icon to save all the changes made to your form. This also gives you automatic access to the builder's customization section.
Step 6: Use the form customization options to change the look and feel of your survey. You can add preferred background images, choose new fonts and add your organization's logo to the survey.
Step 7: Use the form share options to share the survey with respondents. You can copy the form link and share it as a QR code or via the form builder’s social media direct sharing buttons.
While survey errors are inevitable, you should strive to limit them to the barest minimum—and we've shared exactly how to do that in this article. More than aligning your survey process with specific goals and objectives, you need to ask the right questions, adopt a suitable survey structure and limit researcher bias—this is the way to an error-free systematic investigation.
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