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Aim: a general [blank_start]statement[blank_end] of what the researcher intends to investigate- the [blank_start]purpose[blank_end] of the study. Hypothesis: a [blank_start]testable[blank_end] statement that states the relationship between the [blank_start]variables[blank_end] being investigated.
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statement
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conclusion
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idea
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purpose
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outcome
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conclusion
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testable
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untestable
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variables
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beliefs
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data
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A directional hypothesis: states what kind of difference there will be between the [blank_start]variables[blank_end]. They often include words like 'more' or 'less' e.g. People who drink caffeine will be more intelligent than people who don't. A non-directional hypothesis: simply states that there will be a difference, but not the [blank_start]type[blank_end] e.g. People who drink more caffeine will differ in terms of intelligence when compared to people who don't drink caffeine.
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variables
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outcomes
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researchers
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conclusions
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type
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outcome
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conclusion
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Researchers usually use directional hypotheses when previous research suggests no particular outcome. When previous research suggests a particular outcome, they'd use a non-directional hypothesis.
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In an experiment, the researcher manipulates the [blank_start]independent[blank_end] variable and records the effect of this change on the [blank_start]dependent[blank_end] variable.
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Levels of the IV: the [blank_start]control[blank_end] condition (e.g. No caffeine/ drink of water), the [blank_start]experimental[blank_end] condition (caffeine). An effective directional hypothesis should distinguish between the IV and DV e.g. The group who drink caffeine will be more intelligent than the group who drink water. The only issue with this hypothesis is that it doesn't include the [blank_start]operationalisation[blank_end] of the variables (explained in other question)
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control
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experimental
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operationalisation
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Operationalisation of variables includes ensuring the variables being investigated are [blank_start]measurable[blank_end] and therefore unfuzzy, e.g. Participants who drink 200ml of coffee in one hour can answer 5 more questions in a 20 minute period than participants who drink 200ml of water in one hour.
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measurable
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immeasurable
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subjective
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confounding
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Extraneous variables: any variable other than the [blank_start]IV[blank_end] which may have an effect on the [blank_start]DV[blank_end]. They [blank_start]do not vary[blank_end] systematically with the IV. Confounding variables: any variable other than the [blank_start]IV[blank_end] which may have affected the [blank_start]DV[blank_end] so we are unsure of the true source of the changes to the DV. They [blank_start]vary[blank_end] systematically with the IV.
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IV
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DV
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CV
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DV
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IV
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CV
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do not vary
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vary
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IV
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EV
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DV
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DV
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IV
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EV
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vary
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do not vary
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Demand characteristics: in which the participant guesses the [blank_start]aim[blank_end] of a study, and then acts accordingly e.g. As they believe they are [blank_start]expected[blank_end] to behave, or try to over-perform to [blank_start]please[blank_end] the researcher.
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aim
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outcome
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conclusion
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expected
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not expected
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please
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irritate
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obey
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Investigator effects: how the [blank_start]researcher[blank_end]'s behaviour influences a participant's behaviour e.g. Smiling at certain participants but not others. It can also refer to the [blank_start]actions[blank_end] of the researcher related to the study design e.g. Selection of participants, the materials, the instructions etc. [blank_start]Leading[blank_end] questions are also an example of investigator effects
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researcher
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participant
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general public
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expected
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actions
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beliefs
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opinions
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Leading
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Open
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Closed
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Which technique is used to minimise the effects of extraneous/confounding variables on an outcome?
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Operationalisation
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Randomisation
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Demand characteristics
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Leading questions
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[blank_start]Randomisation[blank_end] refers to the use of [blank_start]chance[blank_end] wherever possible during an experiment to reduce investigator effects. For example, if participants must recall word from a list, the list should be randomly generated- the position is not chosen by the experimenter.
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Randomisation
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Operationalisation
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chance
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objectivity
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rigid structure
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researcher involvement
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participant involvement
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Standardisation: all participants must have [blank_start]the same[blank_end] environment, information and experience. This includes standardised [blank_start]instructions[blank_end].
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the same
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a different
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a similar
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instructions
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beliefs
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ideas
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outcomes
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Experimental Design: 1. [blank_start]Independent[blank_end] groups: [blank_start]two[blank_end] separate groups, one group does control condition, other does experimental condition. Results are compared. 2. [blank_start]Repeated[blank_end] measures: [blank_start]one group[blank_end], does both the control condition and the experimental condition. Results are compared. 3. Matched pairs: choosing one group, then choosing another to [blank_start]match[blank_end] participants in the first group (e.g. Based on IQ, culture etc.) One group does control condition, other does experimental condition. Results are compared.
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Independent
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Repeated
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Matched
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two
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three
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Repeated
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Independent
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Matched
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one group
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two groups
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match
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contrast
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compete against
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Name two advantages of the independent group design
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Order effects are not a problem
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Participants less likely to guess aims
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Cost- effective
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Lack of participant variables
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Name two disadvantages of the independent groups design
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Quite expensive
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Participant variables
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Order effects
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Easy to guess aims
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Name two advantages of the repeated measures design
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Name two disadvantages of the repeated measures design
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Order effects
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Demand characteristics
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Expensive
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Participant variables
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Name two advantages of the matched pairs design
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Name two disadvantages of the matched pairs design
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[blank_start]Random[blank_end] allocation: allocating participants randomly to the conditions. This should evenly distribute participant characteristics (e.g. Names in a hat). [blank_start]Counter[blank_end]-balancing: half participants take part in condition A then B, the other half do B then A. This helps control [blank_start]order[blank_end] effect, although it doesn't remove them entirely.
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Lab experiment: takes place in a [blank_start]controlled[blank_end] environment in which the [blank_start]researcher[blank_end] manipulates the [blank_start]IV[blank_end], while maintaining [blank_start]strict control [blank_end]of the extraneous variables.
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controlled
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natural
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abnormal
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researcher
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environment
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IV
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DV
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CV
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PV
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EV
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strict control
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moderate control
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no control
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Field experiment: takes place in a [blank_start]natural[blank_end] setting in which the [blank_start]researcher[blank_end] manipulates the IV.
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natural
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lab
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outdoor
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field
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controlled
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researcher
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environment
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general public
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Natural experiment: takes place in a [blank_start]natural[blank_end] setting in which the change in the IV [blank_start]is not[blank_end] brought about by the [blank_start]researcher[blank_end], but would occurred anyway.
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natural
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lab
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controlled
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well-populated
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is not
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is
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researcher
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environment
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general public
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Quasi experiment: there is [blank_start]no manipulation [blank_end]of the IV, it exists anyway (e.g. Age or gender)
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no manipulation
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manipulation
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minimal manipulation
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Name three advantages of lab experiments
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Name four disadvantages of lab experiments
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Name two advantages of field experiments
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Name two disadvantages of field experiments
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Name two advantages of natural experiments
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Name two disadvantages of natural experiments
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Difficult to generalise
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Can't randomly allocate
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Low external validity
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Too artificial
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Name an advantage of quasi experiments
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Carried out in controlled conditions
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Carried out in natural environment
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Easy to identify cause and effect
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Few confounding variables
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Name a disadvantage of quasi experiments
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Cannot randomly allocate
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Lack of control
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Difficult to replicate
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The target population: a [blank_start]subset[blank_end] of general population e.g. Male students for Idaho. The sample: a small group that is ideally representative of the [blank_start]target[blank_end] population.
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subset
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type
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sample
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target
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general
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Random sampling: [blank_start]all[blank_end] members of target population [blank_start]have equal[blank_end] chance of being selected. Each person is added to a list and then given a number, and the sample is generated via a [blank_start]computer[blank_end] (e.g. Computer-based randomiser)
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all
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most
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some
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half of the
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have equal
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haven't got an equal
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computer
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researcher
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Systematic sampling: every [blank_start]nth[blank_end] person is selected e.g. Every 5th pupil on a school register. A sampling frame (alphabetised list of target population) is produced and every nth person is selected.
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Stratified sampling: the sample reflects the proportions of people in particular sub-groups ([blank_start]strata[blank_end]). The researcher calculates what percentage each strata is of the [blank_start]target population[blank_end] (e.g. 40% female) and then participants are [blank_start]randomly[blank_end] sampled accordingly. With reference to the example above, if you were to have 20 participants, 8 would need to be female in order to be representative.
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strata
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omega
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gamma
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stratifiers
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general population
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world
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randomly
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systematically
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target population
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Opportunity sampling: selecting anyone who is [blank_start]willing[blank_end] and able to participate.
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willing
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unwilling
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randomly sampled
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Volunteer sampling: involves the researcher advertising the study, and participants selecting [blank_start]themselves[blank_end] to take part (volunteer)z
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themselves
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others
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the researcher
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Name and advantage of random sampling
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Name three disadvantages of random sampling
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Difficult and time consuming
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Sample can still be unrepresentative
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Participants can refuse to take part
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Researcher bias can affect sample
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Name two advantages of systematic sampling
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Avoids researcher bias
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Quite representative
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Entirely representative
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Name two advantages of stratified sampling
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Name a disadvantage of stratified sampling
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Name an advantage of opportunity sampling
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Convenient
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Representative
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Avoids researcher bias
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Name two disadvantages of opportunity sampling
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Name an advantage of volunteer sampling
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Easy+ quick
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Representative
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Easy to generalise
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Name a disadvantage of volunteer sampling
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Volunteer bias
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Researcher bias
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Time consuming
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Informed consent: making participants aware of the [blank_start]aims[blank_end], procedures, their [blank_start]rights[blank_end] and the use of the data. It can make a study seem [blank_start]unnatural[blank_end] if the participant knows the aims.
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aims
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beliefs
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researcher's name
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rights
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lack of rights
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unnatural
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more natural
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Deception: [blank_start]deliberately[blank_end] misleading or withholding information. This means participants can't [blank_start]give informed consent[blank_end]. It can be justified if it means participants' behaviour is more [blank_start]natural[blank_end] and they are not suffering.
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deliberately
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accidentally
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give informed consent
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behave naturally
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interact adequately
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natural
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artificial
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Protection from [blank_start]harm[blank_end]: participants should not suffer any form of harm during the experiment. The harm can be psychological e.g. Feeling embarrassed, guilty or inadequate.
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harm
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help
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researcher
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public
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Privacy: [blank_start]participants[blank_end] control information about themselves. Confidentiality: this involves the right to have our [blank_start]personal[blank_end] data protected.
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participants
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researchers
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the general public
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personal
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medical
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educational
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public
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To deal with informed consent, researchers should send a consent [blank_start]letter[blank_end], and only go ahead when this is signed.
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Dealing with protection from harm and deception: [blank_start]debriefing[blank_end] can be used to ensure the participants know the aims and [blank_start]details[blank_end] of the study. It should also reassure participants that they have the right to [blank_start]withhold[blank_end] information, and that they can be provided counselling if necessary.
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debriefing
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standardisation
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operationalisation
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sampling
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details
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problems
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withhold
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enclose all
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Dealing with confidentiality: this often done by referring to participants by [blank_start]numbers[blank_end] or [blank_start]initials[blank_end]. They are also reminded during debriefing that their data will be protected throughout.
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numbers
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first names
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last names
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initials
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code names
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A pilot study is a small-scale version of the actual investigation.
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Pilot studies often use [blank_start]fewer[blank_end] participants, and are utilised to test if the [blank_start]investigation[blank_end] runs smoothly. This also involves identifying any [blank_start]issues[blank_end] so they can be modified in order to save time and money in the future.
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fewer
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more
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male
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female
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investigation
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aim
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hypothesis
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issues
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positives
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participants
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Single blind trial: only [blank_start]researcher[blank_end] knows aim, controls [blank_start]demand characteristics[blank_end]. Double blind trial: both researcher and participant [blank_start]don't know [blank_end]aim, preventing demand characteristics and [blank_start]investigator effects[blank_end].
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researcher
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participant
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demand characteristics
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participant variables
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order effects
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researcher bias
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don't know
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know
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investigator effects
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participant variables
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order effects
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Control group: group of participants whose purpose is for [blank_start]comparison[blank_end]. The experiment group tests the effects of changing the IV, and this is compared to results from the control group.
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Naturalistic observation: watching and recording behaviour in the setting in which it would [blank_start]normally[blank_end] be performed. Controlled observation: watching and recording behaviour within a [blank_start]structured[blank_end] environment e.g. In which some variables are managed
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normally
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not normally
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never
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structured
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natural
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Covert observation: participants' behaviour is recorded and watched [blank_start]without[blank_end] their knowledge or consent. Overt observation: participants' behaviour is recorded and watched [blank_start]with[blank_end] their knowledge and consent
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without
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with
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with
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without
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Participant observation: researcher [blank_start]becomes[blank_end] member of group whose behaviour he/she is recording. Non-participant observation: researcher [blank_start]doesn't become [blank_end]a member of group whose behaviour he/she is recording.
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becomes
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doesn't become
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doesn't become
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becomes
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Naturalistic observations have high [blank_start]external[blank_end] validity as findings [blank_start]can[blank_end] be generalised to everyday life. Lack of control decreases [blank_start]replicability[blank_end] and extraneous variables could also be present. Controlled observations [blank_start]can't[blank_end] be easily generalised, but extraneous variables are [blank_start]less[blank_end] common so replication is [blank_start]easier[blank_end]
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external
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internal
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can
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can't
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replicability
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generalisability
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can't
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can
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less
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more
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easier
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harder