Response Bias vs. Nonresponse Bias: What's the Difference?

December 01, 2023
Table of Contents
Table of Contents

If you’re in the world of research and data, response bias vs. nonresponse bias is a critical distinction because they affect the outcomes of your research in different ways. Becoming aware of these biases is the first step toward preventing them from creeping into your research. 

As you read this post, you'll discover both types of biases, the problems they present, and how to prevent them from occurring. If you’re ready to enhance the reliability of your data, let’s begin!

What is response bias in statistics?

Response bias happens when people don't give true answers in a survey. This can be because of how questions are asked or how they want to appear in their response. This is a very common type of bias that skews survey results. 

Types of response bias in research

There are several types of response bias that can affect research:

  • Acquiescence bias: Respondents agree with all questions or statements, no matter their content
  • Social desirability bias: Respondents answer in a manner they believe is more socially acceptable rather than being truthful
  • Extreme responding: Respondents consistently choose the most extreme options on a scale
  • Moderacy bias: Opposite of extreme responding, where respondents only choose moderate or middle-range options
  • Question-order bias: Respondents’ answers to later questions are influenced by question-order
  • Confirmation bias: Respondents answer questions in a way that confirms their preexisting beliefs or opinions
  • Leading question bias: Question wording prompts a respondent to answer in a specific way

Why is response bias a problem for researchers?

Response bias is a problem for researchers because it leads to inaccurate and unreliable data. When survey respondents don't give honest answers, the results don't truly reflect their opinions, behaviors, or experiences. 

This misrepresentation causes researchers to draw incorrect conclusions based on skewed data. This affects the validity and reliability of the study, making the research less helpful in understanding the true nature of what's being studied.

Having systems for verifying and fact-checking responses at the beginning of the data analysis stage is one way of dealing with this type of bias. Tools like Form Publisher can come in handy when setting up approval workflows. 

What is nonresponse bias in statistics?

Nonresponse bias happens when the participants in a survey or research study differ significantly from those who participate. This difference can skew the results because the non-respondent's opinions, behaviors, or characteristics aren’t represented in the data.

Types of nonresponse bias in research

Nonresponse bias in research can manifest in various forms. Some of the different types include:

  • Item nonresponse bias: Respondents participate in a survey but skip certain questions 
  • Unit nonresponse bias: Entire groups of people don’t participate in a survey
  • Demographic bias: Certain demographics are underrepresented or overrepresented due to response rates
  • Mode of response bias: Response method (physical, online, mobile, etc.) influences who responds 

These are some of the nonresponse biases that can impact the accuracy and representativeness of research findings. Another thing that affects research findings is undercoverage; however, it’s different from nonresponse bias.

Nonresponse bias vs. undercoverage

Nonresponse bias arises when individuals who don’t participate in a survey differ significantly from those who do. Undercoverage bias, on the other hand, occurs when some segments of the population aren’t properly represented in the sample. 

Both affect survey results, but undercoverage results from a faulty sampling method, whereas nonresponse can happen in a survey even with a sound sampling technique.

Why is nonresponse bias a problem for researchers?

Nonresponse bias is a problem for researchers because it causes misrepresentation. If the people who choose not to respond to a survey differ in important ways from those who do respond, the findings may not accurately reflect the overall population. Due to this, the survey results won’t be practically applicable. 

Response vs. nonresponse bias: how do they compare?

Response bias involves the inaccuracy of submitted responses

This is a crucial aspect to highlight: response bias distorts the survey results as a result of inaccurate responses. The response rate is adequate, and the sampling might be sound, too. However, the results may turn out to be inaccurate as the participants who were a part of the study didn’t give the correct responses. 

Nonresponse bias involves a lack of response submission

Nonresponse bias, on the other hand, happens when certain segments of the target population don’t participate in the study. The absence of responses from these groups creates a gap in the collected data. This can occur for many reasons, but it is typically on the researcher to remedy certain processes.

Nonresponse bias leads to results that may not accurately apply to the broader population. This impacts the research findings' validity because certain views, behaviors, or characteristics remain unaccounted for. 

Anonymity can help reduce both kinds of bias

A small amount of bias is unavoidable in a study, as people change their behavior when observed—this is called the Hawthorne effect

However, anonymity can prevent some biases from creeping into your study. When they know their answers are anonymous, respondents are more likely to provide honest, accurate responses. Anonymity removes concerns about judgment or negative results based on their answers.

Ensuring anonymity is an easy way to deal with biases. If you’re using Google Forms for your data collection, even though there’s no native setting for making a form anonymous, you can still make anonymous Google Forms for your surveys. 

Response bias invalidates responses, nonresponse bias can invalidate research

This is a crucial difference. In response bias, only the responses are considered. All the other elements, like research design and sampling, may be sound. This means that the research is still usable after removing the inaccurate responses if identified. 

However, in nonresponse bias, the entire study is invalidated. The problem lies in the early stages, like sampling, and it can be difficult to salvage the research here. For this reason, nonresponse bias can be a more severe form of bias for researchers. 

Avoid research bias by setting up a survey approval workflow

Avoiding research bias is quite difficult, but it’s made easier when you know what to look for. Why not have tools and processes in place to help you catch bias, though?

Setting up an approval workflow is an easy and effective safeguard against biases. This step-by-step process ensures each survey response is rigorously reviewed before becoming part of your study or research.

With Form Publisher, you can easily set up an approval workflow to guard your survey against potential pitfalls like response and nonresponse biases. Act today for data you can trust tomorrow. Explore Form Publisher.

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