Bias in Qualitative and Quantitative Studies

Both qualitative and quantitative researchers admit that the potential for bias does exist in research. However, they uphold diverse positions on the probability of bias significantly influencing their research results. They also have different points of view on the probability of successfully eliminating bias in research. Most researchers, who engage in quantitative research, promote objectivity, which is an important aspect in science; they argue that it is possible to tackle all the research questions in a study without partiality. Conversely, the majority of the researchers who engage in qualitative studies argue that bias is intrinsic, but the reader can easily identify it by critiquing a particular research.

Intentional bias is uncommon in research; it occurs in situations where the researcher intentionally and consciously manipulates the research to accomplish a hidden purpose (Easterbrook, Berlin, Gopalan, & Mathews, 1991). According to most researchers, unintentional bias is more likely to happen as opposed to the deliberate one. Bias can take a number of forms, some of which are intentional; however, several of them are not deliberate. Unintentional bias occurs during the compilation of data or as a result of the sampling technique used.

There are various examples of biases; the first one, information bias, occurs where the data is recorded in an erroneous manner. On the other hand, assessment bias is a meticulous problem where an individual consciously looks for a subjective response; this can be eliminated by double blinding, which ensures the persons conducting the experiment and the subjects of the research are unaware of crucial information that could prejudice the study (Easterbrook et al., 1991). The other type, cherry picking, is an intentional inclination toward a particular research topic. Timing bias mainly concerns carrying out a study at a period that is likely to compromise the results. Bias can also arise when a conclusion is determined using a diminutive sample that is not representative of the whole population. Bias can also arise from excluding critical support data in order to interfere with the credibility of the research by making the inferences weak. The last type, confirmation bias, is caused by respondents who have the predisposition to recall only the points they agree with (Bruno, 2009).

When critiquing a particular study, one should take into account the strengths and weaknesses of the study, especially the research methods used. For instance, if a researcher is interested in studying the health benefits associated with a particular food, he or she should be specific on the gender and age of the respondents (Bruno, 2009); omitting certain genders or age groups could give rise to bias. This should be avoided by accounting for all the unavoidable omission biases, which can be realized by changing the untested plan.

Researchers should make certain that the respondents receive the respect they deserve; they should also be allowed to be autonomous to safeguard them from abuse by researchers with selfish motives. This makes sure that respondents are chosen in a fair manner and not for selfish purposes (Bruno, 2009). Concentrating on one point of view when interrogating respondents should be avoided as it interferes with the neutrality of the research.

Respondents should be given enough time to fill in the questionnaires given to them during surveys; subjecting them to excessive strain can result in procedural bias. For example, respondents who take part in a survey over tea break are likely to respond without comprehending the questions well.

Knowing, in advance, the errors that are likely to crop up in the course of measuring processes and data collection is also critical in avoiding bias. For instance, when gathering information on prejudice against people of a certain race, it is important to know that most people are unenthusiastic about giving responses in an interview for fear of being judged wrongly (Easterbrook et al., 1991). An example of bias in a qualitative research is where previous research studies, rather than clinical trials, are used in the identification of the positive and negative effects of certain medical interventions (Gluud, 2006). Most quantitative studies are considerably affected by measurement bias, which can be mitigated by using secret questionnaires and using a significant number of respondents. Qualitative researchers know that most respondents tell the interviewer what they think he would want to hear instead of remaining faithful to the truth (Gluud, 2006).

Cross-checking all the variables obtained from a study is important to get rid of all the tentative errors, given that false negatives and positives lead to prejudiced results. The results obtained should also be put in writing in a precise manner to avoid repetition, which can also lead to prejudiced inferences. During data analysis, the researcher should mention and account for all the types of bias present in the study.

In summary, quantitative and qualitative study techniques are the two main categories of research; although they are mostly used concurrently, some scholars prefer one to the other. However, both proponents of quantitative research and those of qualitative studies agree on one thing: there are cases where each research method is the most applicable; only a few people totally dismiss either qualitative or quantitative research. Consequently, it is clear that both research methods have unique advantages and disadvantages.


Bruno, R. (2009). Evidence of bias in the Chicago Tribune coverage of organised labor: A quantitative study from 1991 to 2001. Labor Studies Journal, 34(3), 385-407.

Easterbrook, P. J., Berlin, J. A., Gopalan, R., & Mathews D. R. (1991). Publication bias in clinical research. Lancet , 337(8746), 867-872.

Gluud, L. L. (2006). Bias in clinical intervention research. American Journal of Epidemology, 163(6), 493-501.