Do not be deceived: God is not mocked, for whatever one sows, that will he also reap. For the one who sows to his own flesh will from the flesh reap corruption, but the one who sows to the Spirit will from the Spirit reap eternal life. Galatians 6:7-8

In a social work research class that I attended years ago; a professor suggested that sometimes it is ok to fudge the results of a survey because the respondents may not know their own biases. Yes, he really said this. Unfortunately, the example he used revolved around the issue of racism. He said that the people responding do not know their own levels, or the definition[s] of racism therefore, it is ok to manipulate the results to reflect what social workers tend to believe is a racist society. This is an example of bias in research, in my opinion. This social work professor is reflecting his own left leaning political views on his students while teaching them that is ok to manipulate survey results to reflect this bias. Left leaning politics are well known to dominate academic fields such as sociology (Wills, Brewster & Gerald, 2018), creating conflict between many religious/conservative students and professors (Wills, Brewster & Gerald, 2018).

In this case, the research revolved around an invalid assumption. Even if the professor didn’t suggest that research can be manipulated, assuming America represents a racist society can influence the design of his research. Introducing any conclusions into a research project that may contain a biased position is considered immoral (Simundic, 2013). Editors and publishers of scientific research  have a responsibility of detecting biases because of the effects it has on the results (Simundic, 2013). Today, many research projects in the field of sociology are started with a pre-assumption of a racist nation rooted in white supremacy. It has gotten so far out of hand that even the English language is now being viewed as a tool of oppression (Pac, 2012) and is being taught as such in some Universities, like The University of Washington Tacoma, for instance. This is an example of the consequences of making invalid assumptions when designing research questions. An individual’s biases can subconsciously affect the design.

Because of personal bias, scientific research is facing a crisis in credibility (Ioannidis, Stanley & Doucouliagos, 2017). Because good policy and practice are dependent upon the results of research (Ioannidis, Stanley & Doucouliagos, 2017), it is imperative that all types of bias be considered, and efforts made to control it.

One method of controlling bias is ensuring that the data collected is only read if there is an obvious change in the perceived relationship between the subject being studied and the variable (Simundic, 2013). It is difficult to not let personal beliefs get in the way. “Some researchers tend to believe so much in their original hypotheses that they tend to neglect the original findings and interpret them in favor of their beliefs” (Simundic, 2013). Even when it comes to academic journals it is likely there is bias influencing what is being published. According to Simundic (2013), journals are more likely to publish articles which represent “positive findings” opposed to negative ones. This most likely means they are publishing results which reflect the overall beliefs of the publication. It is unlikely as an example, that a social science journal focusing on social justice issues will publish anything that shows Americans to be anything other than “racially biased” because the issue of social justice involves the justification of redistributing wealth.

In 2017 an article entitled “Peer-reviewed science losing credibility as large amounts of research shown to be false” hit the alternative media. The author highlights how political views are shaping the data of research opposed to actual results. The main issue being discussed in the article is climate science; however, it can certainly be argued that my former professors’ political views were shaping his opinions and observations of social research dealing with racism. To make the claim that people do not know how racist they are asserts that that he alone is the one who defines such terms. That is most definitely an invalid assumption that is reaping consequences across the country.

Ioannidis, J., Stanley, T. D. & Doucouliagos, H. (2017) The power of bias in economic research. The economic journal: The journal of the British economic association. 127(605) pp. 236-265 Retrieved from https://watermark.silverchair.com/ejf236.pdf?token=AQECAHi208BE49Ooan9kkhW_

Pac, T. (2012) The English only movement in the U.S. and the world in the twenty first century. Perspectives on Global Development and Technology. 11(1), pp. 192-210 Retrieved from https://doi.org/10.1163/156914912x620833  

Simundic, A. (2013) Bias in research. Biochemia medica. 23(1) pp.12-15 Retrieved from https://www.biochemia-medica.com/en/journal/23/1/10.11613/BM.2013.003/fullArticle

Walia, A. (2017, March 1) Peer-reviewed science losing credibility as large amounts of research shown to be false. Collective-evolution.com. Retrieved from https://www.collective-evolution.com/2017/03/01/peer-reviewed-science-losing-credibility-as-large-amounts-of-research-shown-to-be-false/?fbclid=IwAR3Bs4SSvC4Wv0bLBJSb_iaTWI0aLUEa9oRpcBOzgROay6bmYwbk3QlHchY

Wills, B. J., Brewster W. Z., & Gerald, R. N (2018) Students religiosity and perceptions of professor bias: Some empirical lessons for sociologists. The American sociologist 50(1) pp. 136-153. Retrieved from https://link-springer-com.ezproxy.liberty.edu/article/10.1007%2Fs12108-018-9388-y