Research terminology can often be confusing or misinterpreted, particularly because market research is a complex field with nuanced language. Understanding these terms correctly is crucial for effective research and accurate results. Let’s clarify two frequently misunderstood terms: statistical significance and response rates.
Statistical Significance
“Statistical significance” is a term that’s frequently misused. In essence, it’s about determining whether the data you’re observing is due to actual effects or merely random variation from sampling. To fully understand statistical significance, data sets must be compared to see if differences are caused by chance or intentional factors.
For instance, in healthcare research, you might test whether the percentage of patients rating your care as “excellent” is significantly different from a previous year or from a baseline average. Here, statistical significance helps you assess if the observed changes are likely real or if they could have occurred by chance.
A common misuse occurs when people ask, “How many completions do we need to be statistically significant?” This question pertains more to the concept of validity or representation than significance itself. Essentially, they are asking how confident they can be about their data accurately representing the entire population. In theory, you could get a statistically significant result with just one response, but with a very high margin of error. This is an extreme example, but in the words of a former PRC statistical analyst, “Error is like dust. It always exists, it just matters how much you can live with.”
The real challenge is balancing sample size, error rates, and budget. Increasing the number of surveys reduces the error rate, making your data more representative of the whole population. However, beyond a certain point, the benefits can diminish, especially when considering the growing budget required for increased survey administration. Larger samples are particularly useful if you need to analyze smaller subgroups within your data; be sure to find the happy medium of your research.
Response Rates
Response rates are another critical aspect of research, but they are not as straightforward as one might think. Generally, a higher response rate suggests a more representative sample, but this is not always the case.
The key issue with response rates is non-response bias, which occurs when certain groups are underrepresented because they are less likely to participate. For example, if you’re researching the prevalence of depression, people with depression might be less inclined to take part in the survey, leading to underrepresentation.
In patient experience research, higher response rates help reduce non-response bias by ensuring a more balanced representation of experiences. Much like in politics, the extremes generally have the loudest voices, and by improving participation rates, you will include more of the “silently satisfied,” leading to a better representation of the population. This is why phone surveys, which actively reach out to participants, often achieve higher response rates than online or paper surveys.
Conversely, in consumer research, especially when surveys don’t have any sponsorship or brand awareness associated, lower response rates are more common. However, non-response bias is less of a concern here because the survey’s anonymity prevents skewed results due to participants’ awareness of the survey’s sponsor or other influencing factors.
According to Pew Research, response rates in telephone surveys have declined over the years due to changes in communication technology and increased call screening. Despite this decline, the accuracy of research results does not always correlate with response rates. For blind surveys, non-response bias is less of an issue if the sample is representative and weighted properly. Pew even goes so far as to say that response rates are generally an unreliable metric of accuracy for this type of research.
In summary, while response rates are crucial for some types of research, especially where participant bias might be a concern, they are less critical in blind consumer studies. The goal of any research is to achieve accurate and representative results, balancing sample size, response rates, and error rates as needed.
By understanding these terms and their proper application, you can better navigate the complexities of research and make more informed decisions based on your findings.
To learn more about how PRC can help you achieve the most representative information to make data driven decisions with your healthcare organization, reach out to [email protected] or visit us as www.prcexcellence.com.