The quality of any study is reliant on the neutrality of the partakers (Tracy, 2010). However, it is common for bias to find its way into any research study, and potentially affect its outcome. In a study, there are always researchers and respondents, and both can be victims of bias leading to potentially inaccurate findings. However, whenever we review this kind of biases which affect the respondents, the researcher or both and look into ways on which we can curb them, we improve our chances of minimizing the probability of getting inaccurate findings from the study. Respondents are vulnerable to some biases. Acquiescence bias is when a respondent tends to be highly positive about anything, and as such tends to agree with anything that comes his or her way (Guan, & Wong, 2012). They just view an idea, product or anything before them as good and can buy it.
Respondents can also be vulnerable to sponsor bias (Song, Parekh, Hooper, Loke, Ryder, Sutton, & Harvey, 2010). Whenever they know the sponsor or suspect the author of a study, respondents tend to respond based on what they feel about the sponsor, instead of answering the questions. Researchers too can fall victim to cultural bias, where they base their judgment on their cultures. Every culture has its assumptions, motivations and other variations, however, leaders can fall victim by judging respondents based on their cultures assumptions and motivations (Vijver, 2012). Researchers can also be vulnerable to confirmation bias, where researchers from a hypothesis and use the respondents answers to confirm the hypothesis. What makes such a confirmations a bias is that fact that researchers can quickly dismiss alternative evidence solely because they are only viewing the whole study from one side.
Understanding the role of bias in qualitative research is essential to avoiding it (Podsakoff, MacKenzie, & Podsakoff, 2012). Avoiding bias enable the study to give final accurate findings as a purpose in the study. Even though it is not practically easy to separate the data from the individual researcher, there are some tactics which can be used to protect the integrity of the research. Using multiple people to code the data, and checking on consistency may enable one to know he or she is on the right track. Asking respondents to review your data, specifically in providing feedback whether your interpretation reflects what they meant is a good way to avoid bias. The same can be said for comparing the data or finding with other peers. This is not exhaustive, but a blueprint to ensure the role of biasness is not underrated in research.
Even though biases do happen, it is imprudent to assume they affect almost every research. Most research is cognizant of such concerns and tends to act to ensure they avoid them. However, the methodology used in the article by Shondrick, Dinh, & Lord, (2010), there is no clear evidence of action to avoid the biases on the side of the respondents. This, however, is common in other leadership articles, including the article by Stentz, Clark, & Matkin, (2012), where the research explored the different leadership practices and their impact. The presumptive in this critique is that researchers tend to minimize loopholes which cause biases, and that can be observed in their findings, by vastly considering alternative views and peers results. However, the same cannot be said in an area where researchers have to engage respondents, many of whom who are unaware of the concerns posed by the bias to the study. Therefore, verification, through returning the findings to be reviewed by respondents can provide a basis for validating the results.
Guan, Y., Lu, H., & Wong, M. F. (2012). Conflict-of-interest reforms and investment bank analysts research biases. Journal of Accounting, Auditing & Finance, 27(4), 443-470.
Song, F., Parekh, S., Hooper, L., Loke, Y. K., Ryder, J., Sutton, A. J., ... & Harvey, I. (2010). Dissemination and publication of research findings: an updated review of related biases. Health Technol Assess, 14(8), 1-193.
He, J., & van de Vijver, F. (2012). Bias and equivalence in cross-cultural research. Online readings in psychology and culture, 2(2), 8.
Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2012). Sources of method bias in social science research and recommendations on how to control it. Annual review of psychology, 63, 539-569.
Stentz, J. E., Clark, V. L. P., & Matkin, G. S. (2012). Applying mixed methods to leadership research: A review of current practices. The Leadership Quarterly, 23(6), 1173-1183.
Shondrick, S. J., Dinh, J. E., & Lord, R. G. (2010). Developments in implicit leadership theory and cognitive science: Applications to improving measurement and understanding alternatives to hierarchical leadership. The Leadership Quarterly, 21(6), 959-978.
Tracy, S. J. (2010). Qualitative quality: Eight big-tent criteria for excellent qualitative research. Qualitative inquiry, 16(10), 837-851.
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