Bias is "a form of systematic error that can affect scientific investigations and distort the measurement process" (Sica, 2006, p. 780). Sica (2006) stated that the presence of bias in a study can affect its validity and evidence quality. It can also limit the relevance and applicability of a given study. More often in research studies, the goal is to minimize bias as its complete elimination is difficult or even impossible.
Although there is a tendency for qualitative research to have more bias than quantitative due to its subjective nature, bias can still exist in both of these methodologies. Several factors that can result in bias include: participants' conscious or subconscious change in behavior/statement to present themselves in best light; researchers may unintentionally communicate their expectations which influence responses; imbalanced sample; flawed method of data collection; inadequate study design; and faulty implementation (Polit & Beck, 2012)
Random bias may occur by chance and may be a result of statistical fluctuations in measured data. Although it can affect reliability of results, because it is random, it may be corrected by statistical analysis. In order to correct or reduce random bias or error, a larger number of measurements or larger data sets can be used in the study. According to Malone and colleagues (2014), "the larger the size of study sample, the more closely the sample means will be dispersed around the true population mean" (p. 279).
Systematic bias, by contrast, are errors/bias that persist throughout the entire study. These can not be diminished by increasing sample size. Systematic bias may occur during the design stage, sample selections, data collection, data assessment and reporting of results. Presence of systematic bias can undermine study's validity and lead to unfavorable outcomes in the research findings. It may result in failing to establish association between two variables or mask the real association...