Question

Discuss sources of bias for both quantitative and qualitative research. For quantitative research, be sure to address both random and systematic bias. You may use examples from the articles you selected as illustrations of bias and/or preventing bias.

Answer 1

There are two types of error associated with most forms of Quantitative research: random and systematic.Random errors, i.e., those due to sampling variability or measurement precision, occur in essentially all quantitative studies and can be minimized but not avoided. Systematic errors,or biases, are reproducible inaccuracies that produce a consistently false pattern of differences between observed and true values. Both random and systematic errors can threaten the validity of any research study. However, random errors can be easily determined and addressed using statistical analysis; most systematic errors or biases cannot. This is because biases can arise from innumerable sources, including complex human factors. For this reason, avoidance of systematic errors or biases is the task of proper research design

Major Categories of Quantitative Research Bias
There are many different types of biases described in the research literature. The mostcommon categories of bias that can affect the validity of research include the following:
1.Selection biases- which may result in the subjects in the sample being unrepresentative of the population of interest.
2.Measurement biases- which include issues related to how the outcome of interest was measured.
3.Intervention (exposure) biases- which involve differences in how the treatment or intervention was carried out, or how subjects were exposed to the factor of interest.

In qualitative research, bias affects the validity and reliability of findings, and consequently affects business decisions. Bias distorts truth. Bias slants and skews data in qualitative research. In research, bias is inevitable. You need to recognize bias and reduce it, or at least be aware of it. In qualitative research, there are five major categories of bias:

Moderator bias - The moderator collects the data and has a major impact on the quality of the data. The moderator’s facial expressions, body language, tone, manner of dress, and style of language may introduce bias. Similarly, the moderator’s age, social status, race, and gender can produce bias.

Biased questions - A biased question influences respondents’ answers. And the way you ask a question can bias a question.

Biased answers - A biased answer is an untrue or partially true statement. Bias influences and skews answers, masking truth. An untrue statement can be intentional or unintentional. It doesn’t matter; it is bias. And it happens for various reasons.

Biased samples - A sample is a subgroup or segment of respondents you interview. A biased sample consists of respondents who don’t represent the group of interest. You interview the wrong people.

Biased reporting - Moderators and analysts sometimes produce bias when reporting the results of qualitative research. They can’t help it. Keeping an open mind requires extraordinary discipline. Experiences, beliefs, feelings, wishes, attitudes, culture, views, state of mind, reference, error, and personality can bias analysis and reporting. The conscious and subconscious are at work. Moderators and analysts are human.

Random Bias
Random Biases are those results which occur due to sampling variability or measurement precision. They occur in essentially all quantitative studies and can be minimized but not avoided.

Systematic Bias
Systematic Biases are reproducible inaccuracies that produce a consistently false pattern of differences between the observed and the true values.