Home > Library > Research Bias > What is Recall Bias – Causes and Examples

What is Recall Bias – Causes and Examples

Published by at July 10th, 2023 , Revised On October 3, 2023

Research and statistical data depend on our memory. Our ability to remember past events correctly can alter someone’s research. Sometimes, we can’t recollect the information, and this systematic error is termed recall bias. It happens due to the lack of precision in remembering the past. Let’s understand this term comprehensively in this blog. 

What is Recall Bias?

One can define recall bias as, ‘’Recall bias is a systematic inaccuracy or bias in research investigations when individuals give erroneous accounts of previous experiences, exposures, or events.’’

It happens when the participant’s ability to recall and report their experiences and exposures accurately differs from what they went through or were exposed to. 

What are the Causes of Recall Bias?

Human memory is unreliable and subject to several restrictions. When it comes to incidents that happened in the distant past, people are more likely to forget specific details over time. Moreover, experiences that happen later affect memories.

The following are some causes of recall bias:

Memory Decay and Limitations

Recall bias occurs when reflecting on previous experiences; one cannot gather the required information due to memory decay, which will generate prejudice.

Selective Memory and Salience

People may recall or forget certain occurrences depending on personal prejudices, emotional relevance, or the experience’s overall salience. Disregarding or mistakenly recalling routine experiences is possible; however, significant and emotionally charged ones may be remembered more vividly.

Temporal Distortion and the Telescoping Effect

One major cause is the telescoping effect. It means that people incorrectly assign the chronology of events, either compressing them into a more recent timeframe or stretching them into a more distant past. Therfore, participants do not accurately remember the order and timing of events due to this temporal distortion.

Social Desirability Bias

Another cause of recall bias is the desirability bias. Many participants change their answers to fit social expectations or come off more favourably. They could overstate socially desirable behaviours or underreport socially stigmatised behaviours, introducing bias into the reported information.

How to Reduce Recall Bias?

It is important to overcome recall bias, but what are the ways to reduce it? What measures can one take to reduce recall bias in research? Let’s study a few points to reduce recall bias:

Use Standardised Questionnaires or Interview Techniques

These tests should ask simple, precise questions to reduce ambiguity and promote reliable memory.

Shorter Recall Periods

Reduce the time between the relevant event or exposure and the data collection by using shorter recall periods. Participants can recall current events or experiences more precisely than those that had a place in the distant past.

Objective Metrics

When possible, include objective metrics to support or validate participant self-reporting. This can include data from administrative records, medical files, or other sources that independently verify the study’s variables.

Use Prompts 

To aid in recall, give participants memory aids like calendars, diaries, or visual clues. These tools can aid in conjuring up certain memories and serve as a point of reference for participants to recollect previous experiences or events accurately.

Data Triangulation

Look for evidence from several sources to support participant memories. This can entail gathering information from several respondents, utilising various data collection techniques, or contrasting self-report information with information from outside sources.

Blinding and Impartial Interviewers

To reduce potential biases in probing or follow-up questions, blind the interviewers to the participant characteristics or study aims. Ascertain that interviewers receive impartiality training and uphold objectivity throughout the data collection process.

Conduct a Pilot Study 

Pretest the data collection tools and methods to find any problems with question clarity, participant understanding, or instructions. This can aid in improving the tools and guarantee that participants can answer the questions truthfully.

Statistical Methods

To account for recall bias in data analysis, consider employing statistical methods such as sensitivity analyses, stratification, or correction for relevant confounders. These methods can minimise the potential effects of recall bias on the study’s findings.

Is your research paper plagiarism-free?

Find out with our plagiarism checker today and save yourself from embarrassment. 💁‍

What are Some Examples of Recall Bias in Studies?

Recall bias plays a great role in research studies. It impacts the qualitative data and, eventually, the results of your research. Let’s study how recall bias affects different study methods with relevant examples.

Recall Bias in Research

Recall bias can significantly alter the validity and reliability of study results and impact research findings. It is particularly pertinent in case-control studies or surveys about historical behaviours, which rely on participants’ recollections of prior experiences.

Researchers use various techniques to reduce recall bias, including using objective measurements whenever practical, prospective data collection rather than retrospective, standardised questionnaires, and awareness of potential biases during data processing and interpretation.

Recall Bias in Case-Control Studies

Recall bias occurs in case-control studies when cases (those with the desired outcome) remember past exposures or events more accurately or differently than controls (people without the desired outcome). 

The validity of study findings may be jeopardised by biased connections between exposure and outcome caused by this uneven memory. Utilising standardised and validated questionnaires, gathering data quickly following a diagnosis, and blinding interviewers to a participant’s case or control status are all ways to reduce recollection bias.

How to Reduce Recall Bias in Case-Control Study?

To reduce recall bias, researchers use a variety of techniques. Some of them are stated as follows:

  • Objective measurements, whenever practical, data collection that is prospective rather than retrospective, 
  • Standardised questionnaires
  • Awareness of potential biases during data processing and interpretation.

Recall bias in case-control studies arises when cases have a differential ability to recall past exposures compared to controls, potentially introducing bias in the association between exposure and outcome.

Example of Recall Bias in Cross-Sectional Studies

Participants are asked to recollect their alcohol use patterns and describe the severity of their current depressive symptoms in a cross-sectional study examining the relationship between alcohol intake and depressive symptoms. 

Due to the prominence of their mental health condition, people experiencing depressive symptoms may be more likely to recall and report their alcohol consumption accurately. On the other hand, those who aren’t experiencing depressive symptoms might be less interested in it, which could result in an underestimate of the link between alcohol and depressive symptoms. 

Example of Recall Bias in Epidemiologic Studies

For instance, Participants are asked to recall their daily caffeine use over the previous five years as part of a study examining the connection between caffeine consumption and the risk of heart disease. Due to improved health awareness, people with heart attack (cases) may be more motivated to recall and record their coffee intake accurately. 

On the other hand, those who have never had a heart attack (controls) might need to pay more attention to their previous caffeine usage, which could underestimate the link between caffeine and heart disease. This recollection bias may distort the true association between caffeine consumption and the risk of heart disease.

Recall Bias in Qualitative Research

Recall bias in qualitative research can occur when people remember just some experiences or recall them vividly, possibly overrepresenting extreme events and underrepresenting less significant or memorable occurrences.

Example of Recall Bias in Epidemiologic Studies

Let’s understand recall bias in epidemiologic studies with the following example: 

While investigating the association between pesticide exposure and the development of a specific disease. Participants are asked to recall their past pesticide exposure, which may have occurred many years ago. 

Individuals with the disease may be more likely to remember and report pesticide exposure due to their heightened awareness and desire to find a potential cause for their illness. In contrast, healthy individuals without the disease may not recall or report pesticide exposure accurately, leading to overestimating the association between pesticide exposure and the disease. 

Furthermore, recall bias in this example can introduce inaccuracies and distort the true relationship between the exposure and the disease outcome.

Hire An Expert Editor

  • Precision and Clarity
  • Zero Plagiarism
  • Authentic Sources

Frequently Asked Questions

Recall bias is a systematic inaccuracy or bias in research investigations when individuals give erroneous accounts of previous experiences, exposures, or events.

  • Memory decay and limitations
  • Selective memory and salience
  • Temporal distortion and the telescoping effect
  • Social desirability bias
  • Use Standardised Questionnaires.
  • Minimise Recall Period
  • Employ Objective Measures
  • Provide Memory Aids
  • Blind Interviewers
  • Conduct Pilot Testing
  • Triangulate Data Sources

Recall bias in research can distort associations, introduce misclassification, reduce precision, limit generalisability, hinder comparability, and have ethical implications, impacting the validity and reliability of study findings.

About Owen Ingram

Avatar for Owen IngramIngram is a dissertation specialist. He has a master's degree in data sciences. His research work aims to compare the various types of research methods used among academicians and researchers.