Advantages

• Anonymity: Anonymous surveys can increase comfort in sharing personal or sensitive information. Respondents may feel more at ease providing honest answers when their identities are not disclosed, leading to more accurate data. [3]
• Broad Reach: Surveys / opinion polls allow researchers to reach large, diverse populations. For instance, online surveys can be distributed globally, allowing for data collection from a wide audience. [4]
• Cost-Effective: Surveys are accessible across industries for large-scale data collection. Online surveys, in particular, are less expensive compared to other methods, such as face-to-face or telephone interviews. [5]
• Quantifiable Data: Surveys allow for measurable insights into the target population. By using structured questions, researchers can collect data that is easy to analyze statistically, facilitating the identification of patterns and trends. When designed and applied correctly, surveys can increase consistency and reduce researcher bias. By administering the same set of questions to all respondents, surveys can bring uniformity in opinion collection and analysis.

Limitations

1. Ineffective or Misleading Questions: Survey questions are sometimes written in vague, overly broad, or ambiguous ways that can lead to misinterpretation or inaccurate responses. Closed-ended questions, such as yes/no or rating scale questions, may lack depth. While these types of questions facilitate quantitative analysis, they often fail to capture the full complexity of respondents’ thoughts and feelings. [6]
   • Example: A closed-ended question like “Do you support increased sales taxes to improve education and healthcare? YES or NO” can be misleading. It combines two issues—education and healthcare—forcing respondents to address both at once, even if they feel differently about each. It also presents “increased taxes” as the only solution, ignoring other funding options. Imagine if 70% of respondents to this question answered “NO,” a news headline claiming “Poll Shows 70% of Voters Don’t Want to Improve Education & Healthcare” would also be misleading. Many may have chosen “NO” not because they oppose improvements, but because they doubt the effectiveness using higher sales taxes to fund them.
2. Low Response Rates: Surveys / opinion polls can lead to unrepresentative samples. A low response rate may result in a sample that does not accurately reflect the target population, potentially skewing the results. [7]
   • Example: In the 2016 U.S. presidential election, many opinion polls predicted a victory for Hillary Clinton. However, these polls often had low response rates, leading to unrepresentative samples that underestimated support for Donald Trump. This discrepancy highlighted how low participation can skew survey results. [8]
3. Sampling Challenges: Surveys risk sampling bias if the sample lacks diversity. If certain groups are underrepresented, the findings may not be generalizable to the entire population.
   • Example: A 1936 Literary Digest poll for U.S. presidential election predicted Alf Landon’s victory over Franklin D. Roosevelt by sampling its readers, car owners, and telephone users—groups not representative of the general population during the Great Depression. This sampling bias led to an incorrect prediction. [9]
4. Response Bias: Surveys may not always provide truthful answers. Respondents might give socially desirable responses or may not recall information accurately, leading to biased data. [10] Surveys may also lead to other self-reporting inaccuracies due to memory lapses, misinterpreting questions, or self-censorship. [11]
   • Example: Surveys on sensitive topics, such as dietary habits or illicit drug use, often face response bias, with respondents under-reporting due to social desirability bias. This can result in data that grossly misrepresents actual behaviors.