Controls for extraneous variables

• Define an extraneous variable

  -any variable that can have an impact on the DV other than the IV
• Participant variable

  -includes anything that makes a ppt unique or different to another ppt and means they may perform differently  
• Situational variable

  -relates to anything that changes about the situation or environment you are in during an experiment(temp, time of day)
• Investigator effects

  -any effect of the investigators behavior on the outcome of the study
• Demand characteristics

  -change in ppts behaviour when they ppt thinks they know the aim of the experiment
• Demand characteristics

  -When a ppt thinks they knew the aim of an experiment and change their behaviour
• Social desirability bias

  -When a ppt wants to be viewed favourably so adjusts behaviour accordingly
• Hawthorne effect

  Ppts changes natural behaviour bc they know they are being observed

  Often occurs in Overt observation research  
• Ppt reactivity

  -refers to the fact that ppts are active parts of the research process and are always looking for cues on what they should be doing at any given moment
• Extraneous variables directly affect

  -The internal validity of research

  -bc we can no longer be confident we have measured what we have intended to measure  
• Outline how standardisation can control EVS

  -ensuring that all ppts have exactly the same experience within the study

  -they should all have exactly the same instructions procedures and environment
• Randomisation

  -the use of chance in order to control for the effects of bias when designing materials for research
• Single blind technique

  -ppt is not made aware of the true aims or conditions of the experiment but the experimenter does
• Double blind technique

  -where neither the ppt nor experimenter know the aims or conditions within the experiment
• Counterbalancing

  -can be used to balance the effect of some types of ev across all the conditions of the experiment
• To control ppts variables

  Sample-use large and randomly selected samples

  Design-use repeated measures or matched pairs

  Allocation-randomly allocate to a condition