In the realm of social services, effective program evaluation is critical to understanding what works, what doesn’t, and how to improve services for those who need them most. Whether it’s tracking the success of a youth outreach initiative or measuring the impact of mental health support programs, data plays a central role in driving decisions and policy changes. However, as with all data-driven processes, the reliability of that data is paramount—and this is where a subtle, yet significant, challenge comes into play: Common Method Bias (CMB).

You might not have heard of CMB, but it’s a factor that can distort the results of social service evaluations, leading to flawed insights. In essence, CMB occurs when the way data is collected introduces systematic error, which in turn skews the relationships between variables. For example, if all the data in a program evaluation is gathered from the same source—such as self-reported surveys filled out by participants—there’s a risk that the method of data collection influences the findings.

This blog unpacks the implications of common method bias, why it concerns social service professionals, and, most importantly, how to mitigate its effects to ensure your programs are assessed fairly and accurately.

The Problem: Why Common Method Bias is Bad for Social Services

Common method bias can distort the very foundation of program evaluation. For social service administrators, the consequences can be far-reaching. Imagine you’re running a program designed to support individuals with substance use disorders, and you’re using surveys to measure both the success of the program and participants’ satisfaction. Suppose both sets of data come from the same individuals at the same time. In that case, the participants’ current mood or their perception of the survey’s purpose might influence both their satisfaction ratings and their reported progress. This creates a false sense of correlation—are the participants really doing better, or are they simply reporting more positively due to external factors like social desirability or a desire to be consistent in their answers?

CMB can lead to two primary problems:

1. Inflated Correlations: This happens when relationships between variables appear stronger than they really are. For example, you might conclude that high satisfaction is directly tied to better outcomes when, in reality, this connection could be exaggerated by method bias.
2. Deflated Correlations: Sometimes, the bias works in reverse, and meaningful relationships between variables are obscured. For instance, a well-designed program that genuinely improves mental health might not show significant results because the method of data collection clouds the real impact.

In both cases, CMB undermines the accuracy of your evaluation, which can mislead decision-makers, funders, and even the staff working on the ground.

The Complexity: Why is Common Method Bias So Hard to Fix?

The complexity of common method bias lies in its multiple sources. There’s no single cause of CMB; rather, it arises from various factors such as the way questions are framed, the timing of data collection, and even the respondents’ moods when they complete surveys. As noted in the research, these biases are widespread across fields, meaning that no program or study is immune.

One significant source of CMB is same-source bias, where data for both independent and dependent variables come from the same respondents. This is especially problematic in social services, where evaluations often rely on participant feedback. Another source is item priming, where the order of questions influences how respondents think about subsequent questions. Both factors can distort the data, making it difficult to disentangle real program impacts from biases introduced by the data collection method itself.

Moreover, fixing CMB isn’t as simple as changing the wording of survey questions or collecting data at different times. As research shows, many remedies can inadvertently introduce new biases. For example, shortening a survey to reduce respondent fatigue might lead to incomplete data, while increasing the number of questions might cause respondents to be satisfied—answering questions without fully considering their responses.

The Solutions: Practical Steps for Social Service Professionals

So, how can you, as a social service professional, tackle this complex issue? Here are several actionable strategies you can employ to minimize common method bias in your program evaluations:

1. Use Multiple Data Sources: One of the most effective ways to reduce CMB is to collect data from different sources. For instance, instead of relying solely on participant self-reports, consider incorporating objective measures such as service utilization records or third-party assessments. This triangulation of data helps ensure that your findings reflect true program impact rather than biased responses.
2. Time Separation of Data Collection: If it’s not possible to gather data from multiple sources, consider separating the data collection for independent and dependent variables. For example, you might collect satisfaction surveys at the midpoint of the program and outcome data at the end, which reduces the likelihood that respondents will try to maintain consistency in their answers.
3. Anonymize Surveys: Anonymity can help reduce the influence of social desirability bias, where respondents might give answers they think are expected of them. When participants feel that their responses are truly anonymous, they are more likely to provide honest feedback, resulting in more reliable data.
4. Randomize Question Order: To combat item priming, consider randomizing the order of your surveys. This prevents respondents from being influenced by earlier questions when answering subsequent ones.
5. Combine Self-Reports with Behavioral Data: Self-reported data is inherently susceptible to CMB, but combining it with behavioral data can offer a clearer picture. For instance, if you’re evaluating a program that promotes physical activity, you might ask participants to report their exercise habits while also tracking their use of gym facilities.

Join the Conversation

What challenges have you faced in ensuring your program evaluations reflect true outcomes rather than biases in data collection? How might you implement some of the strategies outlined here to reduce bias in your evaluations? Share your experiences and ideas below.