Observer bias occurs when the researcher’s assumptions, views, or preconceptions influence what they see and record in a study, while actor–observer bias refers to situations where respondents attribute internal factors (e.g., bad character) to justify other’s behavior and external factors (difficult circumstances) to justify the same behavior in themselves.
Research bias affects the validity and reliability of your research findings, leading to false conclusions and a misinterpretation of the truth. This can have serious implications in areas like medical research where, for example, a new form of treatment may be evaluated.
Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous, so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous, as units share characteristics.
Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population.
This allows you to gather information from a smaller part of the population (i.e., the sample) and make accurate statements by using statistical analysis. A few sampling methods include simple random sampling, convenience sampling, and snowball sampling.
It is especially likely to occur in self-report questionnaires, as well as in any type of behavioral research, particularly if the participants know they’re being observed. This research bias can distort your results, leading to over-reporting of socially desirable behaviors or attitudes and under-reporting of socially undesirable behaviors or attitudes.
The observer-expectancy effect occurs when researchers influence the results of their own study through interactions with participants.
Researchers’ own beliefs and expectations about the study results may unintentionally influence participants through demand characteristics.
The observer-expectancy effect is often used synonymously with the Pygmalion or Rosenthal effect.
You can use several tactics to minimize observer bias.
- Use masking (blinding) to hide the purpose of your study from all observers.
- Triangulate your data with different data collection methods or sources.
- Use multiple observers and ensure interrater reliability.
- Train your observers to make sure data is consistently recorded between them.
- Standardize your observation procedures to make sure they are structured and clear.
Observer bias occurs when a researcher’s expectations, opinions, or prejudices influence what they perceive or record in a study. It usually affects studies when observers are aware of the research aims or hypotheses. This type of research bias is also called detection bias or ascertainment bias.
Multiple imputation involves using simulations to replace the missing data with likely values. Alternatively, you can use sample weighting to make up for the uneven balance of participants in your sample.