variables -> demand characteristics

variables

• IV = Some aspect of the experimental situation that is manipulated by the researcher/changes naturally so effect on DV can be measured

• DV = The variable that is measured by the researcher, any effect on DV should be caused by the change in IV

• Levels of IV - need different experimental conditions in order to test effect of IV on DV

• Variables must be operationalised - this makes the hypothesis testable

  • Must make things that are fuzzy like social behaviour measurable and so do this in the hypothesis

• EV - Any variable other than the IV that may affect the DV if not controlled - nuisance variables that do not vary systematically with the IV

  • any other variables that may influence the DV and so these should be identified by start of study and minimised as much as possible

  • Many are straightforward - may not influence findings but just make it harder to detect a result

• Confounding variables - An EV but it varies systematically with the IV - can't tell if any change in DV is due to IV or confounding variable

control

• Randomisation - simple steps the researcher can take to minimise EV's - one of these is randomisation which uses chance methods to remove researcher bias when designing investigation

  • Example - memory word list, order of list randomly generated so position of each word is not decided by researcher

  • Multiple conditions - order of conditions should be randomly chosen

  • If ppts take part in all conditions order of these should be randomised

• Standardisation - all ppts should be in same environment, information and experience

  • List of what will be done should be completed, standardised instructions to read out to ppts

• Counterbalancing - used in repeated measures design so that ppts do the tasks in opposite orders as order effects may be an issue and cause boredom or fatigue that might cause deterioration in performance in second task

  • Balances out order effects but does not remove them

• Random allocation - coin flip/removing names from a hat to decide which condition ppts are in - this balances out ppt variables

  • Attempts to evenly balance ppt variables

demand characteristics

• Ppts are not passive and usually try and make sense of the situation

  • Ppt reactivity is a significant EV in experiments

  • The cues that may tell the ppts about the aim of a study may cause them to second-guess the experimenter's intentions

• Please you effect - ppts may act in a way that they think is expected of them, deliberately over-perform

• Screw you effect - ppts may act in a way that they think will sabotage the study, deliberately under-perform

  • Both of these mean the behaviour is not authentic

• Investigator effects

  • Ppt reactivity may lead to investigator effects

  • Unconscious behaviour that investigators do that may encourage behaviour (accidentally smiling too much)

  • Coolican 2006 - may also refer to actions of researcher in experimental design - selection of ppts, materials, instructions, leading questions

• Double blind procedure - don't inform ppts about what condition they are in and don't even make them aware that there is another condition

  • Same standardised instructions for all

• Investigator - don't let them know whether they are in one group or the other, anonymise ppts

investigator effects and demand c definitions

Any effect of investigator's behaviour on research outcome - can include design of study to selection and interaction with ppts

Any cue from researchers or the research situation that may be interpreted by ppts as revealing the purpose of an investigation