Data collection methods have evolved dramatically due to the technological advancements being utilized. Technology has brought the ability to collect, analyze and accurately report big data. However, the ability to handle big data comes at a price: bias. Oxford English Dictionary defines bias as a systematic distortion of a statistical result due to a factor not allowed for in its derivation. Bias in data analysis can happen because the humans carrying out data collection have some prejudice against certain variables, or the actual collected data is skewed, or the data collection tools were faulty. The paper is mainly going to focus on three biases that affect the accuracy of the analysis outcome. They include common method variances, the cognitive bias which includes confirmation, availability, selection and interpretation biases, and finally, recall bias.
Common Method Variances
Common Method Variance occurs when responses systematically vary because of the use of a common scaling approach on measures derived from a single data source (Fuller et al., 2016). This means that the CMV happens when variations in the responses are caused by the instrument rather than the actual predispositions of the respondents that the tool attempts to capture. Fuller et al. (2016) also state that CMV may not produce changes in effect sizes and significance levels in an amount that is practically useful. However, CMV is a critical threat to behavioral research as it alters the estimates of reliability and validity of the underlying constructs (Podsakoff, MacKenzie, & Podsakoff, 2012). Hence, it should be controlled whenever possible. Researchers can control this bias by either use of statistical controls or procedural remedies. The statistical power utilized is Harman’s single-factor test. The Harman’s analysis mainly focusses on whether the majority of the variance is accounted for by one general factor (Podsakoff, MacKenzie, & Podsakoff, 2012). However, the test does not control for CMV, but it instead alerts the possibility of its presence in the data collected. This means the test is relatively weak and has even raised questions on its effectiveness. Procedural remedies are multisource techniques extensively used in behavioral research. An appropriate method based on the nature and characteristics of the study is selected, and it compliments the statistical controls rather than replace them. The central factor for the success of a procedural remedy technique is to determine commonalities among the variables and attempt to minimize the likelihood of CMV via the inherent design of the study.
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Cognitive Bias
This form of bias mainly results from the humans handling the data. It has common types that include: confirmation bias, availability bias, selection bias and interpretation bias (Joshi, 2017). Confirmation bias occurs when there is a need to prove a hypothesis; hence the researcher tends to lean heavily on the data that might seem to lead this way. To reduce confirmation bias, the researchers should be willing to reexamine and reconsider all the data and must avoid leaning towards the preconceived notions. Availability bias refers to the way people make decisions based on the readily available information. A change in human perspective has been shown to reduce the chances of this bias (Joshi, 2017) significantly. Selection bias is mainly associated with the sampling techniques employed during data collection. The data collected, based on this bias, is unrepresentative of the population on the ground. Procedural review and pretest of the sampling technique will reduce the selection bias to a minimal. Interpretation bias refers to the difference in understanding the data to come up with conclusions. Elizabeth Loftus at the University of California illustrated this bias when she showed some volunteers a film of car crashes. She divided the group into two and separated them. Each of the groups had a different perspective of what the speed of the cars was when they hit. This form of bias can also be equated to the familiar analogy of the glass and water. Some individuals say the glass is half full and others say the glass is half empty. It all depends on the individual interpretation. To inhibit interpretation bias, one must look at the data from each perspective before coming up with a conclusion.
Recall bias
Response biases could be the primary sources of the measurement error. Response biases mainly mislead results and management policies as they increase internal inconsistencies, decrease validity and produce defective frequency distributions (Arana and Leon, 2013). Contrary to its significance, only some researchers acknowledge response bias as a limitation (Yüksel, 2017). Several procedural remedies such as the careful construction of survey instruments, as well as, statistical resources such as regression-based marker variable technique, can be utilized as a way to alleviate the effects of response bias.
Bias in data handling is a common phenomenon often resulting from human or instrument errors. Prejudice cannot be adequately mitigated. However, the application of multiple forms of control, concurrently, dramatically improves the degree of bias control. Bias affects the findings of research and ultimately interferes with the solutions and policies based on the results. Researchers should adopt multiple procedures to ensure the quality of data obtained from their work is consistent and that sound conclusion can be made.
References
Araña, J. E., & León, C. J. (2013). Correcting for scale perception bias in tourist satisfaction surveys. Journal of Travel Research , 52 (6), 772-788.
Fuller, C. M., Simmering, M. J., Atinc, G., Atinc, Y., & Babin, B. J. (2016). Common methods variance detection in business research. Journal of Business Research , 69 (8), 3192-3198.
Joshi, N. (2017). How to avoid bias in data analytics. Retrieved from http://www.hrmonline.com.au/technology/avoid-bias-data-analytics/
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.
Yüksel, A. (2017). A critique of “Response Bias” in the tourism, travel and hospitality research. Tourism Management , 59 , 376-384.
