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Sampling bias is something you can easily do in your questionnaire software surveys when you unwittingly use methods that favor certain outcomes. And, while even experienced professionals can make this mistake, there are several ways to avoid this critical mistake that can minimize the validity of your results.
What is Sampling Bias?
But, first, what exactly is sampling bias? In short, sampling bias is simply where errors occur in research studies and surveys when you don't accurately select their participants. You should be preferably selecting your survey software participants randomly. When you fail to do this, you'll increase your chances of severely affecting the validity of your findings and results since your sample won't reflect your population of interest accurately.
Ways to Avoid Sampling Bias in Your Online Survey Software
Here are three ways to avoid sampling bias:
1. Use Simple Random Sampling
Probably the most effective method researchers use to prevent sampling bias is through simple random sampling where samples are selected strictly by chance. By implementing this method, it provides you with equal odds that every member of the population is selected as a participant in your research, questionnaire or survey.
For instance, you could use a computer to randomly choose names from a master list and these chosen names could become study participants. Random selection could similarly be done on a graphic calculator through the use of the "Rand" command.
2. Use Stratified Random Sampling
This is another method you can use to prevent sampling bias. Using this method, you'll be able to examine the population you'll be working with in your research and come up with an accurately representative sample.
For instance, stratified random sampling can work if you have 1,000 people in a population and this form of sampling provides a more exact way of choosing your respondents and you require 10 individuals from the population in order to conduct the study. If 500 population members are men and 500 are women, your sample should reflect this accurately. In this example, it would mean your sample must be comprised of five men and five women.
3. Avoid Asking the Wrong Questions
You can't obtain the right answers if you're asking the wrong questions. Unfortunately, results from surveys are compromised easily by questions falling short of capturing the whole scope of the survey's issue.
For instance, say you created your survey to understand your participants' favorite flavor of ice cream. But, you ask the questions, "Do you like chocolate, vanilla or strawberry ice cream?" While there are many types of ice cream, you left them out of the question. So, now rather than measuring the most favored type of ice cream, your survey tool is measuring only the preference between these three flavors.
Because sampling bias can occur with even experienced professionals, irrespective of what method you choose to use for creating samples accurately, you need to double-check your work to ensure you're not making costly mistakes later on.
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