On Implicit Bias

In Chapter Four of Religion: The Social Context, McGuire writes on the topic of measuring religiosity. She lists a set of beliefs that could qualify someone as “highly religious.” However, she points out how this measuring system can unfairly discriminate against religious minorities. This brought me to question how one can accurately measure religiosity, without letting cultural or implicit bias disrupt the data. McGuire mentions that, “a person can be highly religious in one dimension (e.g., go to church regularly, pray often) and yet not know church teachings (intellectual dimension) or have had any religious experiences.” Is it possible to avoid this problem when measuring religiosity? It seems to me that, especially in the historically Protestant United States, it may be impossible for data to give an accurate representation of one’s religious fervor. One would need to dive into the data and really pull it apart to get a more accurate measurement, but even then there may be too many factors that affect the data, once again making it impossible to accurately measure such a boundless concept. The only other option that I can think of is to cut out  the numerical measurement aspect of data and switch to an entirely interview-based system of assessment. However, this bring about new problems, such as the implicit bias of the interviewer/s, as well as variables such as cost, plausibility, etc. How can a sociologist get an accurate picture of the religiosity of a congregation if they are limited by very the human problem of bias and the immeasurability of fluid, feeling-based concepts? This is a question I hope to answer as I continue my studies in this course.