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.
It’s impossible to completely avoid observer bias in studies where data collection is done or recorded manually, but you can take steps to reduce this type of bias in your research.
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.
If you have a small amount of attrition bias, you can use some statistical methods to try to make up for it.
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.
To avoid attrition, applying some of these measures can help you reduce participant dropout by making it easy and appealing for participants to stay.
- Provide compensation (e.g., cash or gift cards) for attending every session
- Minimize the number of follow-ups as much as possible
- Make all follow-ups brief, flexible, and convenient for participants
- Send participants routine reminders to schedule follow-ups
- Recruit more participants than you need for your sample (oversample)
- Maintain detailed contact information so you can get in touch with participants even if they move
Attrition bias can skew your sample so that your final sample differs significantly from your original sample. Your sample is biased because some groups from your population are underrepresented.
With a biased final sample, you may not be able to generalize your findings to the original population that you sampled from, so your external validity is compromised.
Attrition bias is a threat to internal validity. In experiments, differential rates of attrition between treatment and control groups can skew results.
This bias can affect the relationship between your independent and dependent variables. It can make variables appear to be correlated when they are not, or vice versa.
Some attrition is normal and to be expected in research. However, the type of attrition is important because systematic bias can distort your findings. Attrition bias can lead to inaccurate results because it affects internal and/or external validity.
Attrition bias is the selective dropout of some participants who systematically differ from those who remain in the study.
Some groups of participants may leave because of bad experiences, unwanted side effects, or inadequate incentives for participation, among other reasons. Attrition is also called subject mortality, but it doesn’t always refer to participants dying!