Bias in research

1. Bias in research Objective 1.6 Prologue

2. Bias is a form of systematic error that can affect scientific investigations and distort the measurement process. • A biased study loses validity in relation to the degree of the bias. • While some study designs are more prone to bias (such as qualitative research), its presence is universal. • It is difficult or even impossible to completely eliminate bias. Building Context

3. In qualitative research, bias affects the validity and reliability of findings, and consequently affects public perception. • In qualitative research, bias is inevitable. The goal of qualitative research is to recognize and reduce it, or at least be aware of how it impacts data. Building Context

4. In qualitative research, there are two major categories of bias: • Researcher bias • Biased questions • Biased samples • Biased reporting • Participant Expectations • Biased actions • Biased answers Building Context

5. Objective 1.6 Explain effects of participant expectations and researcher bias in qualitative research.

6. Researcher bias, also called experimenter bias, is a process where the researcher performing a study consciously or subconsciously influences the results, in order to portray a certain outcome. • Participant expectation or the Hawthorne effect is the process where subjects of an experiment change their behavior, simply because they are being studied.

7. In qualitative research researcher bias is a problematic concept, since by definition the qualitative researcher is part of the process, and all researchers are different. • This human factor has been said to be both the greatest strength and the greatest weakness of qualitative method.

8. Principle 1:Human behavior occurs within a social or cultural context. The social situation can impact our behavior (i.e. the presence of a researcher) • Principle 2: Humans have a tendency to comply or conform to group norms. Group interviews can be impacted by the presence of others. People in a position of “authority” can impact ones response. A trip down memory lane to support our claim: Principles that Define the Socio-cultural level of analysis

9. The researcher collects the data and has a major impact on the quality of the data. • During data collection, the researcher’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. Researcher bias and its effect on credibility

10. The bias of the researcher can create certain “expectations” for the participant. • These expectations can lead to answers that reflect the bias of the researcher as opposed to the perspective of the participant. • This causes a study to lose its credibility. Researcher bias and its effect on credibility

11. Sampling bias is consistent error that arises due to the sample selection. • For example, a survey of high school students to measure teenage use of illegal drugs will be a biased sample because it does not include home schooled students or dropouts. • A sample is also biased if certain members are underrepresented or overrepresented relative to others in the population. Sampling bias and its effect on credibility

12. Both underrepresentation and overrepresentation can lead to skewed qualitative data and cause research to lose credibility. • Research that claims to “represent the voice of a specific group” does not do so if some portion of the group is not represented in the sample. Sampling bias and its effect on credibility

13. Sampling bias is usually the result of a poor or subjective sampling plan. • The most notable is the bias of non response when for some reason some units have no chance of appearing in the sample. • This can be briefly explained in your response. Sampling bias and its effect on credibility

14. Design bias (leading questions or selective probing in interviews) • Content analysis bias (misinterpreting or omitting a response from data analysis) Other types of bias within a qualitative study

15. In some cases, an experimenter might give off hints or cues that might make the participant believe that a particular outcome or behavior is expected, this is considered participant bias. • It is important to note that the participant may or may not be right in their guess. • Even if the individual is wrong about the experimenter's intentions, it can have a profound influence on how the participant behaves. Participant Expectation

16. For example, the subject might take it upon themselves to play the role of the "good participant." • Instead of behaving as they normally would, these individuals strive to figure out what the experimenter wants and then live up to these expectations. • This is especially true with case studies, focus groups, and observations. Participant Expectation

17. Demand characteristics might also motivate participants to behave in ways that they think are socially desirable (to make themselves look better than they really are) • Or in ways that are antagonistic to the experimenter (an attempt to throw off the results or mess up the experiment). • This is also known as the Hawthorn effect. Participant Expectation

18. Researcher Expectations… How can this impact your data?

19. Tomorrow… Credibility in Qualitative Research

20. Classwork Read and outline pp. 36-44 Define all terms Summarize the following sub-sections Experimentation (Skip Obj. 13) Cause and Effect IV/DV Statistical Reasoning (Skip Obj. 19&20) Describing Data