Why Common Method Bias Can Ruin Good Survey Data

Have you ever experienced this? You create an attractive survey,well-written questions, and seamless logic. The responses are flowing in their hundreds, and yet you still come to an incorrect conclusion. The experience you just had is called the Common Method Bias (CMB).

On the surface, your survey looks like it has good data; the correlations are high, the trends are clear, and the story is consistent. However, beneath his attractive surface, your results are skewed. In such instance you may be quick to assume it’s a result of respondent behaviour or their interest in your topic. That’s not the case. It’s your methodology; common method bias occurs when multiple variables are measured with the same method, from the same source, at the same time. In other words, the very way you’ve designed or crafted your survey has created relationships between variables.

Businesses use surveys to measure customer satisfaction, employee engagement, leader effectiveness, new product interest, & Guide business decisions. While Researchers use surveys to test theories, publish research, and contribute to knowledge. But what happens if common method bias enters into this process? It would mean inflated correlations, create false relationships, mislead decision-makers, undermine academic credibility, and encourage expensive strategic errors.

The worst part is, the data will look even better when it’s biased. In the next sections, we will discuss what common method bias really is, why it happens, and most importantly, how we can prevent it and show you why good data isn’t about the numbers, but the truth.

What Is Common Method Bias

Common Method Bias (CMB) is a problem in the world of research that happens when the process of data collection, as opposed to the data itself, influences the outcomes of the data collected. In other words, the method creates the pattern, not the truth. In other words, Common Method Bias is when the method of data collection affects the data more than the relationships between the variables.

For example, we may collect data on employees’ job satisfaction, performance, and stress levels. We gather all data from the same source and collect it at the same time, using the same rating scale. The results may show high correlations. These correlations may not reflect reality but appear because we used one method.

Why Common Method Bias Happens in Online Surveys

In the digital age, it’s easy to gather large amounts of data through online surveys. The problem is, the more data we gather, the higher the chances of common method bias. When respondents answer all the questions at once, using the same scales and question types, subtle psychological influences will start to affect the data before we even realize it.

Good survey data isn’t about asking good questions. Good survey data is about good design thinking. Understanding common method bias isn’t about being overly paranoid about the research process, its about ensuring the integrity of the research process. When the research process is flawed, the conclusions we draw from the research will also be flawed.

Single-source data collection:

One of the major reasons for this Bias in online survey research is the single-source data collection method used in most online survey research. When the respondents provide the data for the independent and dependent variables, their personal characteristics, such as optimism or negativity, can influence the data they provide for the survey.

For example, if the respondents want to portray themselves in a positive light, they can provide high ratings for the independent and dependent variables, thus inflating the correlation between the two variables.

Similarity Of Research Methodology:

Another reason for Common Method Bias in online survey research is the similar method of data collection used in the survey. In most online survey research, Likert scales are the most frequently used method of data collection, ranging from “strongly disagree” to “strongly agree.”

When the same scales are used for more than one variable in the survey, the respondents can end up with biases such as agreement bias, where the respondents tend to agree with the statements in the survey, or straight-lining, where the respondents tend to choose the same option in the Likert scale for more than one question in the survey.

Timing:

Another factor is the timing of the survey. The survey is conducted in one go using the online survey method. The mood or the environment of the respondents can influence the data in the online survey method. The data is collected in one go, and hence the relationships between the variables can beaffected by the mood of the respondents.

Survey Anonymity

Furthermore, the anonymity of the respondents in the online survey method, although beneficial in many ways, is not sufficient to overcome the Common Method Bias problem. The respondents can give the answers in the way they expect the respondents would answer in the online survey method, particularly in the areas of performance, ethics, income, etc.

In online surveys, researchers cannot supervise respondents. Respondents may interpret questions however they choose. This can result in Common Method Bias, more likely to appear in the data when:

• The source of the data is the same
• The same method of data collection is used (for example, Likert scales of 1-5 for everything)
• The data is collected in the same survey
• The data is influenced by social desirability (that is, people tend to present themselves in the best possible light)
• The data is influenced by mood (that is, people in a bad mood will tend to answer everything negatively)

In other words, the “method” is the hidden factor that is influencing the results. E,g, Suppose we have a student survey where we measure the following variables:

• Variable A: Self-confidence
• Variable B: Academic performance

We find that self-confidence is highly related to academic performance because the self-confident student puts high numbers on both scales, even though the student’s grade is average. Perhaps the student is self-confident because of things other than their grade. In other words, the self-confidence could simply be due to the student’s response style, not because self-confidence is related to grade in any meaningful way. This is Common Method Bias in action.

How Common Method Bias Affects Survey Results and Business Decisions

Increase correlations:

It affects survey results in such a way that the relationship between survey questions is distorted. This may cause decision-makers and survey researchers to make erroneous decisions and conclusions. Common Method Bias arises when two or more survey questions use the same measurement method or when the same respondents answer identical questions at the same time.

This bias can occur in survey results and organizational decisions. It happens when respondents answer related questions using the same scale in one survey. In such cases, CMB may cause the survey results to show that one variable is more influential on another variable when, in fact, this is not true.

Create a spurious relationship

It also causes decision-makers and survey researchers to make erroneous decisions and conclusions about performance and evaluation in organizations. This may cause survey respondents to report on two or more survey questions concerning their performance. Plus, their level of engagement in the same organization and within the same survey instrument.

In such cases, the survey results show that one variable is more influential on another variable when, in fact, this is not true.

Damage the validity of the study

For example, in marketing, a company might believe that improving its image can lead to more sales, given a high level of correlation between image and sales, as determined by survey results. However, this might be Common Method Bias, as both variables are subject to survey results, which might not be entirely accurate.

Similarly, in human resources, a company might invest in training its employees, given a high level of correlation between attitude and productivity, as determined by survey results. Again, this might be Common Method Bias, as both variables are subject to survey results, which might not be entirely accurate. 

Common Method Bias can impact survey results by introducing artificial relationships between variables. Relying on this biased data, businesses risk making strategic decisions based on flawed measurements instead of real results.

Common Signs Your Survey Data May Be Biased

Common signs of biased results in the survey data can be identified as certain patterns in the results that look too strong or too consistent. 

• Extremely high correlations between variables can be an indication of biased results in the survey data.
  If different variables show high predictive power for one another, it may be an indication. It could be that the respondents have answered the questions in a certain pattern, without being careful with the distinctions between the questions.
• Low variability in the results can also be an indication of biased results in the survey data.
  If the respondents have answered the questions with the same option, such as “agree” or “strongly agree,” throughout the survey, it may be an indication of acquiescence, social desirability, or disengagement. Another indication of biased results in the survey data is the presence of straight-lining, whereby the respondents have answered the same questions in the same way in the questions in the matrix.
• Extremely high internal consistencies between different constructs.
  This may indicate biased survey results. It suggests the same response set influenced outcomes. It did not reflect the different constructs measured. A single factor explaining most variance also signals bias. This shows that the same method effect influenced the results.

Best Practices for Preventing Common Method Bias During Survey Design

Preventing Common Method Bias starts in the survey design phase, not after data collection. The objective is to minimize the probability that the method used will overstate relationships between variables.

 Collecting data from other sources:

A good method of preventing Common Method Bias is to ensure that the survey uses different sources of measurement for the same variable. In this approach, researchers collect independent variables from respondents. They obtain dependent variables, such as performance, from supervisors or organizational records.

Using other methods of measurement:

Another way is to separate the measurement of the variable over time rather than administering the entire survey in one session. In the approach, researchers may separate the measurement of predictor and outcome variables and have the respondents answer the questions over two separate occasions instead of one. Vary the format and scale types used in the survey and use different types of scales and questions to measure different variables, rather than using the same scale and format for all questions.

Survey Design:

Designing questions to be clear, specific, and unambiguous is crucial to prevent consistent misinterpretation across the survey. This also means avoiding double-barreled questions and ensuring that questions are not too similar in terms of construct measurement.

Making the data collection anonymous

Ensuring that the survey is anonymous and that there are no right or wrong answers may also help to reduce CMB. When respondents focus on their true experiences, they provide accurate answers instead of what they believe is expected of them.

Randomizing questions

This also helps to reduce CMBs in surveys conducted online. This is because randomizing questions may help to reduce response patterns and the effect of previous questions on later questions.

Carrying out statistical tests, such as Harman’s Single-Factor Test:

Finally, procedural and statistical tests may also help to reduce Common Method Bias. This is because researchers may choose to use marker variables that are not related to the main construct of the study, or may choose to perform statistical tests such as Harman’s single-factor test or confirmatory factor analysis to determine whether or not the Common Method Factor is present.

You can prevent it by carefully designing the survey. Introduce variation in sources, timing, and question structure. By doing this, researchers are more likely to ensure that their findings are more credible and valid.

What to Do If You Suspect Common Method Bias After Data Collection

In the event you realize you have encountered CMB at the time of data collection, there are several measures you can take to limit the bias. For instance, you can use:

Statistical Measures:

Such as Harman’s single-factor test, confirmatory factor analysis, or the use of a marker variable to evaluate the presence of bias. If you realize you have encountered bias, you can use several measures to limit the bias.

Partial correlation:

For example, you can use partial correlation to separate the true correlations from the bias-induced correlations.

Evaluation:

You can evaluate the data for signs of response patterns gotten. These include straight lining, extreme uniformity, or extremely high correlations between different variables. Finally, it is important to make the audience aware of the possibility and the possible impact it could have on the study. This helps to maintain credibility.

To conclude,

Common Method Bias is a subtle phenomenon that can affect the outcome of your study. Eventually, leading to business or research decisions that can have negative outcomes, like misleading business or research decisions. It occurs when occur in survey research when the same source, time, or method is used to gather the data.

However, it is possible to take measures to prevent the bias and limit it at the time of data collection. Such as the use of multiple sources, varied methods, and separating the tools for measurement. Understanding this bias and steps to preventing it is important to ensure the validity, accuracy, and representational quality of the results obtained in survey research.