In the digital world, the quality of online research data has been questioned time and again. Therefore, it is of significant importance for researchers to validate survey respondents in order to reduce errors and ensure data quality. As a result, a variety of methods and techniques have been instituted by companies who claim their process is better than others. However, the variation in the validation process used by different providers along with the cost associated with validation and other related complexities, makes it a daunting task for researchers to evaluate and determine if there is a real need for validation and the resulting difference in the survey data. For companies that do validate respondents, because the validation effort is essentially meant to improve data quality, it becomes critical for the buyers to make sure that any potential bias is not being introduced by eliminating key demographics or hard to reach populations. In this paper, we will look at sampling frames and data of valid vs. invalid respondents to determine the extent of variation along with evaluating data by providers to compare differences in the match rate and data shifts. Additionally, respondents who are unwilling to provide their identification information will be profiled and compared against respondents who shared their identification information to learn differences and find out if the validation process itself may produce data bias.