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High in ecological validity
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No demand characteristics
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Problems with ethics (consent)
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Interpretation of behavior problems
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Specialised equipment can be used
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High amounts of control, confounding variables can be limited, easy to replicate.
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Low in ecological validity as artificial setting
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High in demand characteristics.
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Shows the relationship between variables.
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Can provide background for further study.
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Correlation does not causation
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Quick and easy to do.
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Ask people directly and they know themselves well so accurate answer.
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People may lie and give socially desirable responses.
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Answers are not always representative of what people actually do.
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Avoids bias
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Sample may not be representative (all random sample may be male)
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People picked through random sample may not want to be included in study
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Random sample is hard to set up.
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Participants want to do the experiment
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May be bias (only rich people volunteer themselves etc)
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May be affected by demand characteristics as they volunteered themselves and want to please the experimenter
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Quick and easy to organise
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Very bias (only people there at the time)
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Not very generalisable
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Not effected by individual differences as same PP's do both conditions of exp.
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PP's may get bored
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PP's may get practice effects and get better.
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Participants don't get practice effects
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Participants don't get bored as haven't done condition before.
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Effected greatly by individual differences.
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Matching participants on characteristic, less individual differences
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No practice effects
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No boredom
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High in ecological validity
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No demand characteristics
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Problems with ethics (consent)
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Interpretation of behavior problems
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Specialised equipment can be used
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High amounts of control, confounding variables can be limited, easy to replicate.
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Low in ecological validity as artificial setting
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High in demand characteristics.
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Shows the relationship between variables.
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Can provide background for further study.
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Correlation does not causation
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Quick and easy to do.
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Ask people directly and they know themselves well so accurate answer.
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People may lie and give socially desirable responses.
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Answers are not always representative of what people actually do.
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Avoids bias
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Sample may not be representative (all random sample may be male)
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People picked through random sample may not want to be included in study
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Random sample is hard to set up.
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Participants want to do the experiment
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May be bias (only rich people volunteer themselves etc)
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May be affected by demand characteristics as they volunteered themselves and want to please the experimenter
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Quick and easy to organise
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Very bias (only people there at the time)
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Not very generalisable
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Not effected by individual differences as same PP's do both conditions of exp.
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PP's may get bored
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PP's may get practice effects and get better.
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Participants don't get practice effects
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Participants don't get bored as haven't done condition before.
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Effected greatly by individual differences.
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Matching participants on characteristic, less individual differences
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No practice effects
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No boredom
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Participant can give own answer to the question, not forced choice.
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Much more detailed
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More likely qualatative data
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Very difficult to analyse
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People less likely to put accurate answer as it takes more effort
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Quick and easy to analyse
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People will always answer as it doesn't take long
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Quantatitive data
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Not very detailed
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Forced choice
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Also very quick and easy to analyse
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Allows participants to personalise their answer slightly
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Interpretation of scale, meaning one persons 8/10 may be anothers 6/10
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Also forced choice
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Records every instance of the behaviour you are measuring
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Doesn't take into account time, as a person on the phone for 2000 minutes would be counted as 1.
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Very quick and easy to do.
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Records how long the behaviour you are measuring occurs for.
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Problem with how long you measure for, if you measure once every minute, and someone was on their phone for 59 seconds, it wouldn't be counted.
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Quite difficult to do.
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Basically the average
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Can be affected heavily by outliers
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Isn't very good on small samples
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Better on large samples
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The middle number of results
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Isn't affected by anomalous high or low values
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Better to do on large samples
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May not give an accurate result if there is a large jump in the values
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Effected the most by anomalies
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Provides the difference between high and low result.
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Most often number
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Better to do on larger range of values as the mode would more likely represent the result
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Can be very effected by anomalous results
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