Polling is a simple way to collect data. If simplicity “is the key to brilliance” (thank you, Bruce Lee), then pursuing strategies to collect the most accurate data is very important. The goal is always to collect more representative data for a conclusive outcome. Here are a few things you should consider while building a poll:
Understanding Your Target Group
Understanding what your target group looks like is the first step for data collection. Your sample needs to be controlled and representative of the population you are trying to survey. This is accomplished by researching your target group’s demographics. By understanding these target demographic features, you eliminate the noise, focusing on the specific characteristics, such as the ratio of females to males or your population’s age breakdown.
Once the demographics are thoroughly researched, there is enough information to get into the next step: pre-stratification. Pre-stratification breaks the population into different layers, selecting a certain number of respondents from each layer, verifying that your sample reflects the composition of the target group. The demographic questions are included at the beginning of the survey to help the interviewer, or the platform, identify if the respondent is in the correct target group.
Spending time on the front-end helps control the response number on the backend. For example, if there are more people in Maricopa than Pima in your target group, you would need to strategize your questions by asking for the current county in which they reside. It is essential to pre-stratify your poll so that your sample will be closer to your target.
After receiving the responses, do not rush into an analysis. First, check the quality of the data by cleaning out the speeders. Speeders are those who finish the survey at a remarkably high speed. It is possible that respondents may not read the question and answer choices thoroughly. For example, if a respondent spends a quarter of the time the average respondent spends on the survey, the answers may not be consistent. Cleaning out the speeders from your responses can help enhances the quality of your survey data.
Second, verify the data quality by looking at the respondent’s relative information, such as state and zip code. Suppose a respondent said they are currently living in Arizona but provided a zip code from California. In this case, you know that this respondent might not belong to the target group, or the respondent might not have been reading the question thoroughly.
It is crucial to make sure your poll is accurate and conclusive.
The final step before analysis is to apply post-stratification, or “weighting.” There are many variables to control to make the sample closer to the target population. However, sometimes not all variable restrictions were in place during pre-stratification – that’s where post-stratification comes into play. With post-stratification, data is plugged into statistical software, and weighting is applied to the dataset. By weighing the data, the sample is closer to what we defined in our methodology.
Here at OHPI, accurate data is business. OHPI provides analysts who use these strategies to carry out their data collections. With our level of expertise, we deliver results using these strategies, providing accuracy to benefit your company or organization.