Week 2

Person’s belief rather than actual experimental change alters their behavior

• Experimenter’s expectations influence outcome of study • they “see” what they want to “see” • Not necessarily malicious or intentional • Might behave or treat participants differently • Rosenthal & Fode: smart rats vs dull rats – “smart” rats did better than “dull” rats despite being the same

Observers’ expectations influence what they BELIEVE they observed and what they ACTUALLY observed

People change their behavior when they know they’re being studied

• Naturalistic observation: observe ppl in natural conditions • Privacy and control: ppl less likely to be influenced by demand characteristics if they won’t be held responsible for their own actions • Unawareness: ppl being observed unaware of true nature of observation

Specific example of demand characteristics – Hawthorne noticed ppl worked harder in factory when they knew they were being watched

Give someone a little exposure to something –> it subconsciously affects their behavior

• Method of avoiding bias • neither researcher nor participant knows placement in IV • Script

Method where multiple operational defs of a variable are used in or across different studies (e.g. examining data when wealth is measured with net worth, yearly income, etc and seeing how relationship holds)

Repeating a study to find similar outcome

MRI - brain and structure fMRI - active areas – measures blood flow in brain EEG - electrical activity of brain

spatial resolution – ability to determine location temporal resolution – ability to determine timing

Neg: right-leaning Pos: left-leaning Unskewed: left half mirror image of right half

• Bell curve • Freq highest in middle and decreases at extremities

Central tendency: “typical” value of a dataset Variability: the “spread” of values across dataset

• “Typical” value of a dataset • Mean, median, mode • When a distribution is skewed, MMM will have different values, so you should never calculate only one of them

• The distribution of values of a dataset • Range: largest measurement minus smallest measurement • Standard deviation: how each of the measurements differ from the mean

Observed correlation might be caused by an unknown third variable

A problem where something abt the participant determines which level if IV they’re in

Way for scientists to indicate statistically significant results – means there’s less than a 5% chance the results occurred by pure dumb luck

Internal: how valid the results of the study itself are External: how applicable the results are to the real world

• “As we defined that variable…” • Accounts for the fact that the results of a study depend on how the variable was defined

• “In the people we studied…” • Accounts for fact that studied sample may not be representative of population

• “It is likely that…” • Accounts for the fact that results may be incorrect