BIAS

 

INTRODUCTION:  Bias is a major concern in designing a study because it can threaten the study’s validity and trustworthiness. In general, a bias is an influence that produces a distortion in the study results. Biases can affect the quality of evidence in both qualitative and quantitative studies.

SOURCES OF BIAS:  Bias can result from both the participants and the researcher him/herself however, in most cases it can be prevented by the researcher.  Bias can result from a number of factors, including the following:

1.  Study participants:  Sometimes people distort their behavior or their self-disclosures which may be consciously or subconsciously, in an effort to present themselves in the best possible light.  Take an example you are researching on the behavior of smokers and alcoholics, it may be difficult for the respondents to dispose all the effects of exposure if they are causalities.

2.  Subjectivity of the researcher:  Investigators may distort information in the direction of their preconceptions, or in line with their own experiences.  It will be very common for a researcher to assume the respondents will mention the same difficulty he/she found in a given intervention.

3.  Sample characteristics:  The sample itself may be biased; for example, if a researcher studies abortion attitudes but includes only members of right-to-life (or pro-choice) groups in the sample, the results would be distorted.  On the other hand, it will be hard to generate good results on Circumcision, if in the sample population, the majority are Muslims of from Lumasaba region.

4.  Faulty methods of data collection:  An inadequate method of capturing key concepts can lead to biases; for example, a flawed paper-and pencil measure of patient satisfaction with nursing care may exaggerate or underestimate patients’ complaints.

5.  Faulty study design:  A researcher may not have structured the study in such a way that an unbiased answer to the research question can be achieved.

CONCLUSION:  To some extent, bias can never be avoided totally because the potential for its occurrence is so pervasive. Some bias is haphazard and affects only small segments of the data. As an example of such random bias, a handful of study participants might fail to provide totally accurate information as a result of extreme fatigue at the time the data were collected.  Systematic bias, on the other hand, results when the bias is consistent or uniform. For example, if a spring scale consistently measured people’s weights as being 2 pounds heavier than their true weight, there would be systematic bias in the data on weight.

 

RELATED;

1.  RELIABILITY

2.  QUALITATIVE RESEARCH

3.  QUANTITATIVE RESEARCH

4.  RESEARCH STUDY DESIGNS

5.  DATA COLLECTION

6.  SAMPLING

REFERENCES