Experiment= involves the manipulation of an IV to see what effect it has on the DV, whilst attempting to control the influence of all other extraneous variables. (The only method that has variables, controls and can help to establish C & E)
Lab experiment= researcher deliberately manipulates the IV while maintaining strict CONTROL over extraneous variables through STANDARDISED procedures in a controlled (artificial) environment to establish cause and effect
Strengths:
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Weaknesses:
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Field experiment= researcher deliberately manipulates at least one IV, to see its effect on a dependent variable; conducted in the ps NATURAL environment and attempts to control some variables
Strengths:
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Weaknesses:
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Quasi experiment= An experiment where the investigator either does not manipulate the independent variable (IV) directly/ where the experimental conditions (IVs) already exist/naturally occurring, or does not have full control over extraneous
variables that might influence results.
(The IV is changed by NATURAL OCCURRENCE, the researcher just records the effect it has on the DV)
variables that might influence results.
(The IV is changed by NATURAL OCCURRENCE, the researcher just records the effect it has on the DV)
Strengths:
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Weaknesses:
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IV= the factor manipulated (change) by the researcher
DV= the factor measured (result) by the researcher
Extraneous variable= all other factors other than the IV that could affect the DV and influence the result
Confounding variables= extraneous variables which DO influence the DV and affect the results
DV= the factor measured (result) by the researcher
Extraneous variable= all other factors other than the IV that could affect the DV and influence the result
Confounding variables= extraneous variables which DO influence the DV and affect the results
- Ps variables- characteristics of the individual ps that may affect the outcome (age, gender, mood, IQ, personality)
- Situational variables- features of the situation that may affect the ps (time of day, noise, DCs, boredom)
- Investigator variables- ways the researcher can influence the outcome of the research (cues/prompts, bias, design, time constraint, leading Qs)
Controls= ways to minimise the effect of extraneous variables on the DV in order to establish cause & effect
Here are some controls to minimise confounding variables:
Here are some controls to minimise confounding variables:
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Experimenter:
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Hypothesis= a prediction/testable statement
Alternate hypothesis= a statement of difference. E.g 'there will be a SIGNIFICANT DIFFERENCE between the time it takes boys and girls to do 10 star jumps'
Null hypothesis= a statement of NO DIFFERENCE. E.g 'there will be no difference between the time it takes boys and girls to do 10 star jumps'
Operationalised hypothesis= to precisely define ALL variables (define the IV & precisely measure the DV)
One-tailed hypothesis= a directional statement of difference. E.g 'males will do 10 star jumps in a faster time than females'
(remember that this fishy has ONE tail so knows which direction it is going)
Two-tailed hypothesis= a non-directional statement of difference. E.g 'there will be no difference between the time it takes boys and girls to do 10 star jumps' (remember this fishy has TWO tails so it doesn't know what direction it is going in)
Independent measures design= ps take part in ONE condition
Repeated measures design= ps take part in ALL conditions
Matched pairs/grps design= ps take part in ONE condition, but are MATCHED on a relevant criteria to a ps(s) in another condition
Conditions= experimental grps created by the manipulation of the IV
DC= where ps answer or act in a way they feel the researcher wants them to
Order effects= something that the ps can have (boredom and fatigue) which influences the results and is caused by taking part in MORE THAN ONE condition/task
Counterbalancing= mixing the order of tasks to reduce order effects influencing the result of the same task
Individual differences= ps variables, characteristics each ps brings to the exp (experience, age, gender, emotions, intelligence)
Ecological validity= the extent to which the experiment reflects real life
Reliability= consistency, getting the same results again and again
Replicability= ability to repeat the study EXACTLY again and again
Descriptive statistics= used to summarise and describe findings from quant data to identify patterns and trends
Measures of central tendency= ways of measuring averages (mean, median, mode)
Measures of dispersion= ways of measuring how spread out results are (range, interquartile range, standard deviation)
Graphical representations= ways of presenting data (bar chart, scatter graph)
Quantitative data= numerical results
Alternate hypothesis= a statement of difference. E.g 'there will be a SIGNIFICANT DIFFERENCE between the time it takes boys and girls to do 10 star jumps'
Null hypothesis= a statement of NO DIFFERENCE. E.g 'there will be no difference between the time it takes boys and girls to do 10 star jumps'
Operationalised hypothesis= to precisely define ALL variables (define the IV & precisely measure the DV)
One-tailed hypothesis= a directional statement of difference. E.g 'males will do 10 star jumps in a faster time than females'
(remember that this fishy has ONE tail so knows which direction it is going)
Two-tailed hypothesis= a non-directional statement of difference. E.g 'there will be no difference between the time it takes boys and girls to do 10 star jumps' (remember this fishy has TWO tails so it doesn't know what direction it is going in)
Independent measures design= ps take part in ONE condition
Repeated measures design= ps take part in ALL conditions
Matched pairs/grps design= ps take part in ONE condition, but are MATCHED on a relevant criteria to a ps(s) in another condition
Conditions= experimental grps created by the manipulation of the IV
DC= where ps answer or act in a way they feel the researcher wants them to
Order effects= something that the ps can have (boredom and fatigue) which influences the results and is caused by taking part in MORE THAN ONE condition/task
Counterbalancing= mixing the order of tasks to reduce order effects influencing the result of the same task
Individual differences= ps variables, characteristics each ps brings to the exp (experience, age, gender, emotions, intelligence)
Ecological validity= the extent to which the experiment reflects real life
Reliability= consistency, getting the same results again and again
Replicability= ability to repeat the study EXACTLY again and again
Descriptive statistics= used to summarise and describe findings from quant data to identify patterns and trends
Measures of central tendency= ways of measuring averages (mean, median, mode)
Measures of dispersion= ways of measuring how spread out results are (range, interquartile range, standard deviation)
Graphical representations= ways of presenting data (bar chart, scatter graph)
Quantitative data= numerical results