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research methods Flashcards

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1
Q

Timeline of psychological experiments

A
  1. Come up with a theory (based on research)
  2. Narrow focus and produce an aim for your investigation
  3. Formulate a hypothesis
  4. Conduct your experiment
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2
Q

An aim

A
  • an aim is developed from a theory
  • a theory has been extensively researched before going ahead with an investigation to test the theory
  • An aim in psych is a general statement that describes the purpose of an investigation
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3
Q

hypotheses

A
  • written after the aims
  • is made at the start of a study that clearly states the relationship between two variables
  • A hypothesis should always include the IV and the DV
  • can either be directional or non-directional
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4
Q

directional hypotheses

A

(“one tailed”)
- used when you can know or predict from previous research which way this piece of research should go
- the researcher makes clear the sort of difference that is anticipated between two conditions or two groups of people
- generally, a directional hypothesis will include words such as higher, lower, less, more, faster, slower
- eg. people who drink red bull will be more talkative than people who don’t

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5
Q

non-directional

A

(“two tailed”)
- used when you don’t know which direction it is going in
- doesn’t state the direction of the research BUT states there will be a difference
- we usually use this if we don’t have any previous research in the area to base our prediction off
- eg. there will be a difference in chattiness dependent on whether people have red bull or water

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6
Q

independent variable (IV)

A
  • factor that is directly manipulated by the experimenter
  • there are at least two levels of IV in an experiment
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7
Q

Dependent Variable (DV)

A
  • measured by the experimenter to assess the effects of the IV
  • all other variables should be controlled/kept constant
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8
Q

Levels of the Independent Variable

A
  • in order to test the effect of the IV we need different conditions
  • For example, if we are looking at energy drinks and happiness, we would need a condition to compare the energy drinks one to
  • the one without an energy drink would be called a control condition and the one with is called the experimental condition
  • there could be multiple experimental conditions
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9
Q

control condition

A

lacks any treatment or manipulation of the independent variable.

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10
Q

experimental condition

A
  • receive treatment or manipulation of the independent variable
  • could be multiple experimental conditions
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11
Q

Operationalisation of Variables

A
  • variables (IV and DV) must be operationalised. This means defined in a way they can be easily tested and measured.
  • eg. if we wanted to see the effect of energy drinks on memory, we would need to operationalise it by saying how we are measuring memory (eg. a test) and how we are going to keep the IV consistent
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12
Q

Writing Hypotheses

A
  • it must be clear and testable
  • make sure that…
    1. The IV and DV are clear and measurable
    2. You have stated the relationship between the IV and DV
    3. You have selected an appropriate hypothesis (directional or non-directional based on the information you have been given in the stem of the question)
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13
Q

how to identify an experiment hypothesis

A
  • experiment
  • ‘causes’
  • effectiveness
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14
Q

how to identify a correlation hypothesis

A
  • relationship
  • link
  • association
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15
Q

sentence starter for experiment (directional) hypotheses

A

there will be an [increase/decrease/more/less/higher/lower] in..

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16
Q

sentence starter for experiment (non-directional) hypotheses

A

there will be a difference in…

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17
Q

sentence starter for correlation (directional) hypotheses

A

there will be a [positive/negative] relationship between…

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18
Q

sentence starter for correlation (non-directional) hypotheses

A

there will be a relationship/association between…

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19
Q

extraneous variables

A
  • other (non-IV) variables that may interfere with the experiment by affecting with the DV and so need to be controlled
  • can be divided into participant and situational variables
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20
Q

participant variables

A

any individual differences between participants that may affect the DV

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21
Q

situational variables

A

any features of the experimental situation that may affect the DV

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22
Q

confounding variables

A

any variable, other than the IV, that may have affected the DV. They vary systematically with the IV (whereas extraneous do not).

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23
Q

features of confounding variables

A
  • They just crop up and we cannot control for them because we don’t know they are going to happen
  • They are usually found once the experiment has been conducted
  • Almost like an unintentional second IV - something else you are changing
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24
Q

demand characteristics

A

this is a type of extraneous variable… Any cue from the researcher or from the research situation that may be interpreted as revealing the purpose of the investigation. This can lead to participants changing their behaviour. Examples include the please-u and screw-u effect.

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25
Q

please-u effect

A

participants may, for example, try to please the researcher by doing what they have guessed is expected of them

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26
Q

screw-u effect

A

They may deliberately try to skew the results, attempting to the opposite of what they think is expected

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27
Q

investigator effects

A

any effect of the investigators behaviour (conscious or unconscious) on the research outcome (the DV). This may include the design of the study, the selection and interaction with participants, the materials and instructions. Leading questions eg. “are you happy with the study?” may also have been an investigator effect.

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28
Q

variable that is differences between participants

A

participant variables (a type of extraneous)

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29
Q

variable that is features of the experimental situation

A

situational variables (a type of extraneous)

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30
Q

variables that vary systematically

A

confounding

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31
Q

variables that have random effects

A

extraneous

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32
Q

when ppt change their behaviour due to guessing the aim of the experiment

A

demand characteristic

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33
Q

investigators behaviour variable

A

investigator effects

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34
Q

ways to minimise extraneous or confounding variables

A
  • randomisation
  • standardisation
  • single-blind trials
  • double-blind trials
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35
Q

randomisation

A

involves the use of chance in order to control for the effects of bias when designating materials and deciding the order of conditions

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36
Q

standardisation

A

using exactly the same formalized procedures and instructions for all participants.

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37
Q

single-blind

A

participants are not told the aim of the research (or other important details like the presence of another group)

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38
Q

double-blind

A

neither the participant or the researcher who conducts the study is aware of the aims of the investigation (third-party researcher is brought in)

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39
Q

repeated measures

A

The same participants take part in the each condition of the IV. This means that each condition of the experiment includes the same group of participants.

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40
Q

strengths of repeated measures

A
  • participant variables are controlled (no differences between two groups)
  • more economical (less participants)
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41
Q

weaknesses of repeated measures (and ways to deal with these)

A
  • order effects (fatigue or boredom) - practice effect
  • demand characteristics

= could use two different (but similar) tests or randomise the items
= counterbalancing participants (half A then B, half B then A)

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42
Q

independent measures

A

Different participants are allocated to two (or more) experimental groups representing different levels of the IV. There may also be a control group.

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43
Q

strengths of independent measures

A
  • less likely to have demand characteristics
  • shouldn’t have the order effects
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44
Q

weaknesses of independent measures (and ways to deal with these)

A
  • less economical because you need more people
  • extraneous variables/participant variables
    = random allocation
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45
Q

Matched pairs

A

This is where participants are matched for similar key variables eg. same IQ or same age etc. This means that there are two groups of participants. One group do condition A and one group do condition B.

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46
Q

strengths of matched pairs

A
  • less likely to have demand characteristics
  • shouldn’t have the order effects
  • participant variables are more controlled
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47
Q

weaknesses of matched pairs (and ways to deal with these)

A
  • less economical because you have more people
  • time consuming and expensive to match the people
  • do still have participant variables (although should be very low)

= could conduct a pilot study before to identify important variables to match pairs on (reduce participant variables even further)

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48
Q

random allocation

A

participants are allocated to each IV randomly eg. names from a hat

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49
Q

types of experiment and how to identify them

A
  • lab (manipulated in a non-natural/controlled setting)
  • field (manipulated in a natural setting)
  • natural (change but outside of investigators control)
  • quasi (un-manipulatable characteristics)
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50
Q

lab experiments

A

conducted in highly controlled environments. This is not always a laboratory - it could, for example, be a classroom where conditions can be well controlled.
- participants tend to GO to the “lab”

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51
Q

strengths of lab experiments

A
  • high control over extraneous variables (so that any effect on the DV is likely to be the result of manipulation of the IV)
  • can be more certain about demonstrating cause and effect
  • replication is more possible than in other types of experiment because of the high level of control (ensures that new extraneous variables are not introduced)
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52
Q

limitations of lab experiments

A
  • may lack generalisability as the lab environments may be quite artificial (the participants may act in unusual ways) - low external validity
  • participants are usually aware they’re being tested which may also give rise to ‘unnatural’ behaviour (demand characteristics)
  • the tasks participants are asked to carry out may not represent real-life experience (low mundane realism)
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53
Q

example of lab experiments

A

eg. Johnson and Scott (1979) tested the impact of anxiety on eye witness testimony - a weapon in a criminal’s hand distracts attention (because of the anxiety it creates) from other features and therefore reduces the accuracy of identification.
- participants were asked to sit in a waiting room where they heard an argument in an adjoining room
- one group then saw a man run through the room carrying a pen covered in grease (low anxiety condition) or a knife covered in blood (high anxiety condition)
- they were later asked to identify the man from a set of photographs
Mean accuracy was 49% in identifying the man in the low anxiety condition compared with 33% accuracy in the knife condition.

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54
Q

external validity

A

the extent that the results can be generalised to the rest of the population

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55
Q

mundane realism

A

level of everyday reality - effects participant behaviour

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56
Q

field experiments

A

the IV is manipulated in a natural, more everyday setting (in the field)

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57
Q

strengths of field experiments

A
  • higher mundane realism because the environment is more natural
  • may produce behaviour that is more valid and authentic
  • participants may be unaware they are being studied (high external validity)
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58
Q

limitations of field experiments

A
  • loss of control of extraneous variables (cause and effect between the IV and DV is difficult to establish and replication is often not possible)
  • important ethical issues (people don’t know they are being studied so cannot consent - invasion of privacy)
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59
Q

example of a field experiment

A

eg. Rutter et al. (2011) conducted a longitudinal study on 165 Romanian orphans adopted by British parents.
- children were split into 4 groups:
1. Group 1 - 58 children under the age of 6 months
2. Group 2 - 59 children between 6 months and 24 months (2yrs)
3. Group 3 - 48 children over 48 months (4yrs)
4. Group 4 - 52 British adoptees who were the control group
- each group was assessed at the ages of 4, 6, 11, and 15
At the start of the observations, over half of the Romanian children were suffering from severe malnutrition and a low IQ, showing delayed intellectual development, compared to the control group.

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60
Q

natural experiments

A

when the researcher takes advantage of a pre-existing independent variable (this is ‘natural’ because the variable would have changed anyway) - naturally manipulated IV

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61
Q

strengths of natural experiments

A
  • provide opportunities for research that may not otherwise be undertaken for practical or ethical reasons
  • high external validity because they study real-life issues and problems as they happen
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62
Q

limitations of natural experiments

A
  • a naturally occurring event may only happen very rarely, reducing the opportunities for research and limit the scope for generalising findings
  • participants may not be randomly allocated to experimental conditions (so the researcher might be less sure whether the IV affected the DV)
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63
Q

example of a natural experiment

A

eg. Rutter et al. (2011) conducted a longitudinal study on 165 Romanian orphans adopted by British parents.
- children were split into 4 groups:
1. 58 children under the age of 6 months
2. 59 children between 6 months and 24 months (2yrs)
3. 48 children over 48 months (4yrs)
4. 52 British adoptees who were the control group
- each group was assessed at the ages of 4, 6, 11, and 15
At the start of the observations, over half of the Romanian children were suffering from severe malnutrition and a low IQ, showing delayed intellectual development, compared to the control group.

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64
Q

Quasi-experiments

A

have an IV that is based on existing difference between people (eg. age or gender). No one has manipulated this variable, it simply exists. - IV could not be manipulated at all

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65
Q

strengths of Quasi-experiments

A

often carried out under controlled conditions and therefore share the strengths of a lab experiment:
- high control over extraneous variables
- can be more certain about demonstrating cause and effect
- replication is more possible than in other types of experiment (ensures that new extraneous variables are not introduced)

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66
Q

limitations of Quasi-experiments

A
  • cannot randomly allocate participants to conditions and therefore there may be confounding variables
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67
Q

example of a Quasi-experiment

A

eg. Sheridan and King (1972) tested obedience between genders.
- The participants were asked to give genuine electric shocks of increasing strength to a puppy
54% of the male participants gave the maximum (non-fatal) shock in contrast to 100% of the female participants.

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68
Q

what is a ‘true experiment’?

A

the IV is under the direct control of the researcher, lab and field are true experiments

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69
Q

case studies

A
  • to make a detailed and in-depth analysis of an individual, small group, institution or event
  • often unique and involve studying a situation that is unusual (eg. a rare disorder)
  • can also focus on more typical events (eg. a group of elderly people recollecting events of their upbringing)
  • varied but always gather rich data in qualitative form
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70
Q

what might a case study involve?

A
  1. A case history of the individual/group - interviews, questionnaires, observations to gather qualitative data
  2. Gathering other forms of data - psychometric or psychological tests to collect quantitative data that can be used alongside the qualitative
  3. Deciding the length of time the study will run and if any additional participants are needed - most case studies are longitudinal (take place over a long period of time) and may involve gathering additional data from family and friends
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71
Q

strengths of case studies

A
  • rich, detailed insights that can shed light on very unusual forms of behaviour
  • contribute to our understanding of normal functioning (especially in brain damage cases)
  • may generate interest and highlight a need for further, perhaps more scientific methods of investigation
  • allows investigations that would otherwise be unpractical or unethical
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72
Q

limitations of case studies

A
  • cannot generalise results to the wider population
  • researchers own subjective feelings may influence the case study
  • accounts from individuals/friends/family may hold inaccuracies
  • very difficult to replicate (so cannot check for reliability)
  • time consuming, both to conduct and analyse
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73
Q

temporal validity

A
  • may not be applicable to the current time period
  • societal change OR research methods used were less rigorous and controlled
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74
Q

observational techniques

A

there are 6:
- naturalistic
- controlled
- covert
- overt
- participant
- non-participant

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75
Q

naturalistic observation

A
  • type of setting
  • observing in a setting or context that the target behaviour would normally occur in
  • evaluation:
    • This means they have high ecological validity as the findings can be generalised to everyday life. They are in their normal environment
    • lack of control over the situation makes replication difficult. Also uncontrolled extraneous variables making it difficult to judge behaviour
76
Q

controlled observation

A
  • type of setting
  • controlling certain aspects/variables of the observation
  • controlled room (not just a lab) but one abnormal to the normal situation
  • evaluation:
    • Replication is easier and extraneous variables are less of an issue
    • Cannot apply findings to everyday life- not a normal environment
77
Q

covert observation

A
  • do the participants know they are being observed?
  • unaware they are being observed
  • might use a one-way or two-way mirror or hidden camera
  • such behaviour must be in public to be ethical
  • evaluation:
    • Participants do not know they are being observed which removes participant reactivity ensuring behaviour is natural
    • Ethical issues- even in public people may not want to be studied. Observing people without consent isn’t ethical
78
Q

overt observation

A
  • do the participants know they are being observed?
  • participants know they are being observed and give informed consent beforehand
  • evaluation:
    • Ethical issues aren’t an issue- informed consent is gained
    • Because ppts know they are being observed they may act in a desirable way- social desirability
79
Q

participant observation

A
  • does the observer take part in the research?
  • become part of the group being studied
  • eg. if you were in a bus stop queue and did the observation there
  • evaluation:
    • The researcher can experience the situation as the ppts do, having insight into their lives, increasing the validity of results
    • Because they are becoming part of the group, they may lose their objectivity
80
Q

non-participant observation

A
  • does the observer take part in the research?
  • remain separate from group being studied and record behaviour more objectively
  • it may be impractical to join a group and so non-participant observation may be the only way
  • evaluation:
    • Objective stance- less bias
    • Loose the insight you gain being a participant. May also see less and miss bits out!
81
Q

unstructered observation

A
  • recording all relevant behaviour but with no system eg. just writing down everything you see
  • a problem with this is that there is so much to record
  • this would be suitable if you were observing one or very few people or if you wanted rich information on one person/event
82
Q

structured observation

A
  • use of behavioural categories (breaking the target behaviour into components that can be observed)
  • behavioural categories:
    • allow for more structure and objectiveness
    • should be clear, self-evident, and unambiguous
    • should cover most of the behaviour being watched (there should be no dustbin category where many behaviours are put)
    • should be ‘exclusive’ and not overlap, you shouldn’t have to mark two at once
  • event and time sampling
83
Q

what is observational design?

A

how a researcher would actually plan their observational study

84
Q

types of sampling procedures for structured observation

A

event sampling, time sampling

85
Q

event sampling

A

counting the number of times a certain behaviour occurs in a target individual/s eg. counting how many times somebody smiles

86
Q

time sampling

A

recording behaviours in a given time frame eg. noting how often someone talks during a 30 sec period - the observer would tick one or more categories from a checklist or behaviour categories

87
Q

evaluation points for event sampling

A

+ useful when the target behaviour or event happens quite infrequently and could be missed if time sampling was used
- if the event is too complex, the observer may overlook important details if using event sampling

88
Q

evaluation points for time sampling

A

+ effective in reducing the number of observations that have to be made
- those instances where behaviour is sampled may be unrepresentative of the observation as a whole

89
Q

evaluation points for structured observation

A

+ uses behavioural categories which make the recording of data easier and more systematic
+ the data produced is also more likely to be quantitative so analysing the data and comparing behaviour is more straightforward
- may not provide the depth of information that unstructured does so info may be overlooked or unrecorded

90
Q

evaluation points for unstructured observation

A

+ the data collected is much more rich and detailed, providing a greater insight into the behaviour being observed
- tends to produce qualitative data which is much more difficult to record and analyse
- may be open to observer bias as objective behavioural categories are not present, the researcher may only record the behaviours that catch their eye and these may not be important or useful

91
Q

inter-observer reliability

A
  • situations could potentially be hard to replicate because they take place at one moment in time and involve naturally occurring events
  • equally single-observers could be biased in their observations
  • if there is more than one observer, their observations could be compared to check for consistency (reliability)
  • the separate sets of data are recorded and then tested to see if there is a correlation
  • if they are similar in their findings, this would be a high inter-observer reliability which is a strength
  • inter-observer reliability is considered good if a score of 0.81-1.00 is found - this score is called a “Kappa” score
92
Q

measures of central tendency

A

mean, median, mode

93
Q

what is a normal distribution graph?

A
  • a normal distribution is represented by a bell curve (an inverted U)
  • within a normal distribution, most people are located in the middle area of the curve with very few people at the extreme
  • mean, median, and mode are all located at the peak
94
Q

skewed distribution

A
  • not all distributions form such a symmetrical and balanced pattern as a normal distribution
  • skewed distributions are those that appear to lean to one side or another
  • some data sets that derive from psychological scales or measure may produce skewed distributions
  • you can either have a positive skew or a negative skew
    In order to determine whether it is a negative or positive skew, look at where the “tail” is
95
Q

what is a positive skew?

A

A positive skew is where most of the distribution is concentrated towards the left of the graph resulting in a long tail on the right. Eg. if you were given a psych test in which most people got low marks, this would create a positive skew.
- The mode remains at the highest point of the peak
- The median moves to the right of (higher than) the mode
- The mean moves to the right of (higher than) the median (remember extreme scores affect the mean)

96
Q

what is a negative skew?

A

A negative skew is where most of the distribution is concentrated towards the right of the graph resulting in a long tail on the left. Eg. if you were given a psych test in which most people got high marks, this would create a negative skew.
- The mode remains at the highest point of the peak
- The median moves to the left of (lower than) the mode
- The mean moves to the left of (lower than) the median (remember extreme scores affect the mean)

97
Q

what is a target population?

A

a large group of individuals that the researcher is interested in, this group is still too large so sampling techniques are used

98
Q

sampling

A

Ideally we want a sample of participants that will be as representative of the target population as possible. This is so that we can prevent bias and thus allow for generalisation of findings.
The five main sampling techniques used by psychologists are:
1. systematic
2. stratified
3. opportunity
4. volunteer
5. random

99
Q

what is random sampling?

A

This method gives every member of the target population an equal chance of being selected eg. by assigning a number to each member, and then selecting from the pool at random.

100
Q

What are the strengths and weaknesses of random sampling?

A

+ widely accepted that since each member has the same probability of being selected, there is a reasonable chance of achieving a representative sample
- small minority groups within your target group may distort the results, even with a random sampling technique
- it can be impractical (or not possible) to use a completely random technique, eg. the target group may be too large to assign numbers to

101
Q

What is systematic sampling?

A

A systematic method is chosen for selecting from a target group, eg. every fourth person in a list could be used in a sample. It differs from random sampling in that it does not give an equal chance of selection to each individual in the target group.

102
Q

What are the strengths and weaknesses of systematic sampling?

A

+ assuming the list order has been randomised, this method offers an unbiased chance of gaining a representative sample
- if the list has been assembled in any other way, bias may be present for example if every 4th person is male
- time-consuming

103
Q

what is stratified sampling?

A

Here the sampler divides or ‘stratifies’ the target group into sections, each showing a key characteristic which should be present in the final sample. Each of those sections is sampled individually (eg. using random sampling). The sample thus created should contain members from each key characteristic in a proportion representative of the target population.
[(no. in strata)/(no. in population)] x sample size

104
Q

What are the strengths and weaknesses of stratified sampling?

A

+ avoids the problem of misrepresentation sometimes caused by purely random sampling
- takes more time and resources
- care must be taken to ensure each key characteristic present in the population is selected across strata, otherwise this will design a biased sample

105
Q

What is opportunity sampling, what are the strengths and weaknesses?

A

Participants who are both accessible and willing to take part are targeted.
+ easy and inexpensive
- consequent sample may not be representative as it could be subject to bias

106
Q

What is volunteer sampling, what are the strengths and weaknesses?

A

Here the sample consists of people who have volunteered to be part of the study.
+ often achieves a large sample size through reaching a wide audience eg. with online ads
- those who respond may all display similar characteristics so the chances of an unrepresentative sample may be higher

107
Q

ethical issues

A
  • ethical issues arise when a conflict or dilemma exists between participants’ rights and researchers’ needs to gain valuable and meaningful findings
  • moral responsibility to protect participants and respect their rights and dignity, no matter the topic being researcher
  • BPS is responsible for the promotion of excellence and ethical practice, it publishes ethical guidelines which must be adhered to
  • all research has to be cleared by an ethics committee
108
Q

what are the four main ethical issues?

A
  1. informed consent
  2. deception
  3. protection from harm
  4. privacy and confidentiality
109
Q

informed consent (ethical issues)

A
  • they should know what they are getting into before they become part of it
  • they should know the aim, procedure, what their data will be used for, and any rights they have eg. right to withdraw at any point
  • ppts can then make an informed judgement about whether to take part without feeling coerced or obliged to consent.
  • ppts have to sign a letter of consent, if they are under 16 then it is up to parents to consent (with willingness from that child)
110
Q

deception (ethical issues)

A
  • deliberately misleading or withholding information at any stage of the study
  • linked to consent because the participant hasn’t been fully informed of the aim and procedure
  • deception can be used if it does not cause the participant undue distress
  • if deception is used, there has to be a full debrief with the true aims and any details revealed, they should also be reminded throughout and at the end that they can withdraw (or withdraw data)
  • counselling will also be offered if needed
111
Q

protection from harm (ethical issues)

A
  • participants should not be placed at any more risk than they would be in their daily lives and should be protected from physical and/or psychological harm
  • this includes being made to feel embarrassed, inadequate, or being placed under undue stress or pressure
  • participants should be reminded of their right to withdraw and offered counselling after the experiment
112
Q

privacy and confidentiality (ethical issues)

A
  • participants have the right to control information about themselves and have their personal data protected
  • the right to privacy extends to where the study took place eg. institutions or geographical locations
  • if personal details are held these must be protected however it is more usual to simply record no personal details (remain anonymity) eg. referring to ppts with numbers or initials
  • ppts should also be reminded that their data will be protected and told that it will not be shared with other researchers
113
Q

alternative ways to get consent in psych research

A
  • presumptive consent
  • prior general consent
  • retrospective consent
114
Q

presumptive consent and issues with it

A

rather than getting consent from the participants themselves, a similar group of people are asked if the study is acceptable. If this group agree, then consent of the original participants is assumed.
- the real participants don’t actually consent and have no say
- presuming that the real participants will have a similar mindset

115
Q

prior general consent and issues with it

A

participants give their permission to take in a number of different studies - including one that will involve deception. By consenting, participants are effectively consenting to be deceived.
- not consenting to the specific situation that they will be subject to, not fully informed
- it could be that, given the specifics, a participant would not consent

116
Q

retrospective consent and issues with it

A

Participants are asked for their consent (during debriefing) having already taken part in the study. They may not have been aware of their participation or they may have been subject to deception.
- already taken part in the experiment, whether they would have consented or not, they cannot go back to what has happened

117
Q

points to cover in a consent form

A
  • what the research will explore
  • what they will do if they take part
  • risk of psychological distress (if applicable)
  • reiterate the right to withdraw and that if they do their data will be immediately removed
  • data will remain confidential, no personal details recorded and any data kept anonymous if published
  • they will be debriefed
  • contact email for if they have questions
118
Q

points to cover in a debrief form

A
  • what the real aims and methods were
  • reiterate the right to withdraw
  • reiterate privacy, confidentiality
  • (for protection of harm-) following support/counselling if needed
  • check again that it is okay for the data to be used
119
Q

qualitative data (plus strengths and weaknesses)

A
  • descriptive, non-numerical data - words rather than numbers
  • descriptions of thoughts, feelings, opinions, etc.
  • often interviews or unstructured observation
    + rich detail
    + greater external validity
    • difficult to analyse
    • open to subjective interpretation
120
Q

quantitative data (plus strengths and weaknesses)

A
  • numerical data - numbers rather than words
  • scores, statistics, no. of items recalled, etc.
  • experiments
    + easy to analyse and draw patterns
    + less open to bias/objective
    • narrow/limited detail
    • less application to real life
121
Q

primary data (plus strengths and weaknesses)

A
  • original data arriving first-hand from participants themselves
  • an experiment, observation, questionnaire or interview
    + authentic, first-hand data
    + specifically targets intended audience/info
    • lots of time and effort
    • can be costly to run
122
Q

secondary data (plus strengths and weaknesses)

A
  • collected by someone else - often already analysed and a conclusion known
  • includes journal articles, books, websites, gov. statistics, population/employee records, etc.
    + inexpensive
    + easily found with minimal effort
    • info might not fit the exact purpose
    • can be hard to access the value of research
123
Q

what is a meta-analysis?

A
  • a form of research method that uses secondary data
  • a process in which a number of studies are identified that have similar aims/hypotheses, their results are pooled together and a joint conclusion is produced
  • when the independent variable has been measured in the same way, it is possible to calculate an effect size (basically the dependent variable of a meta-analysis) which gives an overall statistical measure of difference/relationship between variables across a number of studies
124
Q

strengths and weaknesses of meta-analysis

A

+ meta-analysis allows us to create a larger, more varied sample and results can then be generalised across much larger populations, increasing validity
- may be prone to publication bias (AKA the file drawer problem) in which the researcher may not select all the relevant studies, choosing to leave out those with negative or non-significant results. Therefore the conclusion from the meta-analysis is biased because it only represents some of the relevant data.

125
Q

mean (how to calculate, strengths, weaknesses)

A
  • add all the scores/data and then divide by the total number of scores
  • the most sensitive measure of central tendency as it includes all values, it is therefore also most representative
  • easily distorted by extreme values which subsequently lower how representative the mean is
126
Q

median (how to calculate, strengths, weaknesses)

A
  • middle value is taken when all scores/values are arranged in order from lowest to highest
  • if there is no middle value then the halfway point between the middle two is taken
  • extreme scores do not affect it
  • very easily calculated
  • less sensitive as not all the values are included, may therefore be less representative
127
Q

mode (how to calculate, strengths, weaknesses)

A
  • the most frequently occurring score/value, data can be bi-modal if two are identified
  • easy to calculate and in some forms of categorical data is the only method that can be used
  • very crude measure that is often unrepresentative
128
Q

range (how to calculate, strengths, weaknesses)

A
  • a simple calculation of the spread by subtracting the lowest score from the highest score and adding 1 (to allow for the rounding of raw scores)
  • very easy and quick
  • only takes into account the most extreme values so may not be representative
129
Q

standard deviation (how to calculate)

A
  • provides a single value that tells us how far scores move away from the mean
  • calculated by:
    1. find the difference between the mean and each score
    2. add up all these differences and divide the total by the number of scores, this gives variance
    3. standard deviation is then the square root of the variance
  • the lower the value, the more tightly clustered the data is
130
Q

standard deviation (how to calculate)

A
  • provides a single value that tells us how far scores move away from the mean
  • calculated by:
    1. find the difference between the mean and each score
    2. add up all these differences and divide the total by the number of scores, this gives variance
    3. standard deviation is then the square root of the variance
  • the lower the value, the more tightly clustered the data is
131
Q

standard deviation (strengths and weaknesses)

A

+ a much more sophisticated and precise measure than the range as it includes all the data
- can be distorted by anomalous values

132
Q

two types of self-report methods

A
  1. questionnaires
  2. interviews
133
Q

questionnaires

A

> pre-set list of written questions which assess thoughts and/or feelings
could be used as part of an experiment
open and closed questions

134
Q

open questions

A
  • no fixed answer
  • can answer as you wish
  • yields qualitative data
135
Q

closed questions

A
  • fixed answer from options
  • yields quantitative data
    Eg:
    > rating scales (1-5, operationalised eg. little-lots)
    > Likert scales (level of agreement or disagreement)
    > fixed-choice options (includes yes and no or a range of options).
136
Q

types of interviews

A

Structured - pre-determined set of questions, fixed order - like a questionnaire conducted “face-to-face”
Unstructured - no set questions, free flowing and conversation-like, the interviewee is encouraged to elaborate
Semi-structured - in-between the two with a list of questions but giving the interviewer freedom to ask follow-up questions

137
Q

strengths and weaknesses of questionnaires

A

+ cost-effective, quick and do not need the researcher present
+ generally easy to analyse
- can lead to response bias eg. ticking all yes or all no
- written responses may not always be truthful (social desirability bias)

138
Q

strengths and weaknesses of open questions

A

+ rich and detailed responses
+ may collect unexpected findings
- interpretation is subjective meaning there is a risk of researcher bias
- harder to analyse

139
Q

strengths and weaknesses of closed questions

A

+ easier to analyse
+ answers are more objective so are likely to be interpreted in the same way by any researcher
- oversimplifies potentially very complex subjects
- doesn’t produce as detailed info as open does

140
Q

strengths and weaknesses of structured interviews

A

+ replication is easier
- cannot deviate from the topic

141
Q

strengths and weaknesses of unstructured interviews

A

+ more insight gained
- harder to analyse

142
Q

strengths of questionnaires over interviews

A

+ Qs are easier to analyse
+ Qs are easier to distribute and more time-effective
+ experimenter doesn’t need to be there/the responses can be anonymous for a questionnaire - easier to get honest responses especially on taboo topics

143
Q

strengths of interviews over questionnaires

A
  • can adapt as the interview goes on
  • generally more in depth
  • limited response bias
  • can pick up on cues of how they are feeling
144
Q

errors to avoid when writing questions for an interview/questionnaire

A
  • overusing jargon (technical language)
  • using emotive language
  • leading questions
  • double barrelled questions
  • double negatives
145
Q

developments on questionnaire designs

A
  • may also use filler questions which distract the ppt from the main purpose of the questionnaire which can reduce demand characteristics
  • may be tested on a small group (pilot study) to determine any questions that need to be refined.
146
Q

developments on interview designs

A
  • standardised schedule of questions to reduce interviewer bias
  • either recorded or notes made
  • if a one-to-one interview is being used, it should be conducted in a quiet room away from others to increase the likelihood of honest answers
  • interviewees should be reminded that answers will be treated in the strictest confidence
147
Q

what are correlations?

A
  • correlations show the strength and direction of an association between two or more co-variables (IVs, DVs)
  • correlations are plotted on a scatter graph
  • not interested in cause and effect, just relationship/correlation
148
Q

types of correlation

A
  • positive (both go up or both go down)
  • negative (one goes up, the other goes down)
  • zero (no correlation)
149
Q

intervening variables

A

variables that has interfered in some way with your co-variables, CORRELATION only

150
Q

evaluating correlations

A

+ useful, precise and quantifiable measure for how variables are related (strength and direction)
+ quick and economical
- cannot tell us why or how data is related
- cannot demonstrate cause and effect
- there could be an intervening variable causing the relationship (the Third Variable Problem)
- can be misused or misinterpreted

151
Q

analysing correlations

A
  • Spearman’s Rho and Pearson’s R
  • correlational coefficient
  • can also be used to calculate reliability (need a correlation above +0.8
152
Q

correlational coefficient

A
  • produced by statistical tests, a numerical value between -1 and +1
  • tells you the strength and direction of your correlation
  • “r =” (r for ‘relationship’)
153
Q

what is the process of peer reviewing?

A
  1. prepare a manuscript
  2. journal editor examines topic
  3. copies sent to other expert psychologists
  4. experts act as peer reviewers
  5. reviewer carefully edits manuscript
  6. aspects are assessed and comments made
  7. manuscript returned to editor
  8. reviewers comments read
  9. publication decision made
  10. either publish, revise, or reject
154
Q

benefits of peer reviewing

A
  • ensure that research has been adequately conducted
  • ensure the research is correct in its results and conclusions
  • spot any major discrepancies or potential or false results before it is published
155
Q

aims of peer reviewing

A
  • allocate research funding
  • validate the quality and relevance of research
  • Suggests amendments or improvements
156
Q

types of fraudulent research

A
  • Fabrication (data is made up)
  • Falsification (data exists but has been altered)
  • Plagiarism (work has been copied from others)
157
Q

issues with peer reviewing

A
  • it doesn’t always catch fraudulent research
  • anonymity of the reviewer can be abused (eg. reviewers being unfairly negative because of the effect on their research)
  • publication bias for headline grabbing findings meaning that some research never gets published (slow progress)
  • burying groundbreaking research to maintain the status quo/the reviewers theory
158
Q

what is reliability?

A

reliability refers to the consistency of a measure, for something to be reliable it must always measure in the same way.
> test-retest
> inter-rater

159
Q

inter-rater

A
  • (inter-observer/inter-interviewer)
  • extent of agreement between two (or more) individuals based on a measure
159
Q

test-retest reliability

A
  • assessing the same person or people on two (or more) occasions and finding the extent to which the measure produces the same outcome
159
Q

how is reliability measured?

A
  • by correlating the outcomes of the two individuals/repeated use of the measure
  • a correlational statistical test is conducted on the two sets of data
  • if the correlational coefficient (r value) produced from the test is 0.8 or above, then the data has high inter-rater/test-retest reliability.
160
Q

reliability in questionnaires

A
  • in order to increase the reliability, certain items may be deselected or rewritten
  • eg. if questions are complex/ambigious and therefore interpreted differently by the same person on separate occasions
  • open questions are more prone to misinterpretation whereas the use of fixed choices reduces ambiguity
161
Q

reliability in experiments

A
  • types that enable strict control, like lab, are seen as the most reliable due to their replicability (test-retest)
  • reliability can be increased by standardising procedures
162
Q

reliability in interviews

A
  • using the same interviewer for all interviews is best, otherwise ensuring they are all adequately trained
  • reliability is more easily ensured in structured interviews than the “free-flow” of unstructured
163
Q

reliability in observations

A
  • reliability is high if two or more observers agree on the behaviours they have recorded
  • it can be increased by ensuring that all behavioural categories are operationalised, do not overlap, and that all behaviours are covered in the checklist
  • without these things, there is room for subjective judgement which results in inconsistent records
164
Q

what is validity?

A

Validity refers to the extent to which an observed effect is genuine: does it measure what it is meant to measure (internal) and can it be generalised beyond the research setting within which it was found (external)?
> Can be reliable and invalid but not valid without being reliable.

165
Q

confounding vs extraneous variables

A

Confounding - can’t control it, just affects the DV
Extraneous - could have been controlled but failed to.

166
Q

internal validity

A
  • whether the effects observed in an experiment are due to the manipulation of the IV and not some other factor
  • eg. Bandura’s Bobo Doll (demand characteristics)
167
Q

external validity

A
  • Refers to whether you can apply the conclusions of a scientific study outside of the context of the study.
  • eg. Freud’s psychosexual stages
168
Q

temporal validity

A
  • the extent to which findings can be generalised to another time or era
  • eg. Jacob’s digit span test (conducted in 1887 so not as controlled)
169
Q

ecological validity

A
  • the extent to which findings can be generalised to other settings/situations
  • eg. Bandura’s Bobo Doll study (lab setting)
170
Q

mundane realism/validity

A
  • whether the task and materials used to measure the DV represent the real world events/use that is being studied
  • eg. Milgram’s obedience study (completely not representative of real tasks)
171
Q

population validity

A
  • whether the sample represents the wider population and therefore whether the study can be applied more generally
  • eg. Asch’s line study (all male sample)
172
Q

face validity

A

Face validity - does the test appear to test what it aims to? Does it look right?
> pass to an expert

173
Q
A
174
Q

construct validity

A
  • how well a test measures the concept it is designed to
175
Q

predictive validity

A
  • the degree to which a test can accurately predict an outcome
    > eg. if the prediction is born out then the test has predictive validity (longitudinal studies)
176
Q

main validity limitations of observations

A
  • ambiguous, broad behavioural categories
  • demand characteristics/social desirability
177
Q

improving the validity of observations

A
  • operationalise/specific categories
  • covert observation
178
Q

main validity limitations of questionnaires

A
  • social desirability bias
  • underepresentative/ non-inclusive sampling
179
Q

improving the validity of questionnaires

A
  • a lie scale (indicates whether participants are being truthful/indicates consistency
  • assurance of anonymity
180
Q

main validity limitations of experiments

A
  • demand characteristics
  • unrealistic setting
  • investigator effects
  • confounding variables
181
Q

improving the validity of experiments

A
  • standardisation
  • mundane realism
  • double/single-blind
  • counterbalancing
  • use of a control group
182
Q

main validity limitations of qualitative methods

A
  • subject interpretation
183
Q

improving the validity of qualitative methods

A
  • triangulation of numerous methods
  • direct quotes, coherent report
184
Q

what is a lie scale?

A
  • a set of items included in a questionnaire to indicate whether the participants are being truthful, eg. with sets of nearly identical questions to test consistency
185
Q
A

psychology (15 decks)

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