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research methods Flashcards

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1
Q

define aim

A

description of what you are researching and why

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2
Q

define hypothesis

A

states the relationship between the variables and predicts the results

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3
Q

define directional hypothesis

A

states the direction and correlation the experiment is expected to go in based on previous research

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4
Q

define non directional hypothesis

A

predicts there will be a difference in results but the direction is unknown as there is no previous research

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5
Q

define null hypothesis

A

predicts there will be no difference

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6
Q

define IV

A

variable we change

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7
Q

define DV

A

variable we measure

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8
Q

define operationalisation of variables and how to do it

A

clearly defining the variables and stating how they will be measured by adding values and units

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9
Q

define extraneous variables

A

variables other than IV that may have an effect on DV if not controlled and doesn’t relate to IV

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10
Q

define demand characteristics

A

clues that allow participant to guess the aim and changes their behaviour to help or sabotage the experiment

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11
Q

define social desirability

A

when the participant tries to please the researcher or try to make themselves look better

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12
Q

define the hawthorne effect

A

when people are interested so they show a more positive response which leads to artificially high results

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13
Q

define investigator/experimenter effects

A

experimenter unconsciously conveys to participant how they should behave

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14
Q

examples of investigator effects

A

tone, accent, body language, leading questions

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15
Q

define situational variables

A

aspects of environment that may affect the participants behaviour

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16
Q

examples of situational variables

A

temperature, noise, authenticity of experiment

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17
Q

define participant variables

A

the ways each participant varies and how this affects their results

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18
Q

examples of participant variables

A

trauma, mood, intelligence, anxiety, gender, culture

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19
Q

how can you control extraneous variables

A

single blind design
double blind design
experimental realism
randomisation
standardisation
controls

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20
Q

define single blind design

A

participant is not aware of the research aims

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21
Q

define double blind design

A

participant and experimenter are unaware of aim and hypothesis

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22
Q

define experimental realism

A

researcher makes the task engaging that the participant doesn’t know they are being observed

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23
Q

define randomisation

A

randomly allocating tasks and roles to avoid bias

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24
Q

define standardisation

A

experience of experiment is kept almost identical

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25
Q

define confounding variable

A

variable other than IV that had a direct effect on the DV and is related to IV

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26
Q

define pilot studies

A

small scale practice investigations to help identify potential problems before doing the real experiment, so money and time is saved and stops floor and ceiling effect

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27
Q

define validity

A

the extent to which a study measures what it intends to measure

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28
Q

define internal validity

A

whether the effects observed are due to the IV and not another factor

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29
Q

define mundane realism

A

how realistic the task is

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30
Q

define external validity

A

how well you can even compare your findings to other people, places and times

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31
Q

define ecological validity

A

the extent to which the results reflect real life

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32
Q

define population validity

A

how well the sample can be used to generalise to represent the population as a whole

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33
Q

define temporal validity

A

the extent to which the findings are valid when we consider differences in time progressions

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34
Q

define face validity

A

the test/questionnaire looks like it measures what it intends to

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35
Q

define concurrent validity

A

whether the results can be compared to another existing, well established test which measures the same thing and follows the same correlation

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36
Q

how to improve validity

A
  • control group- compare results with the experimental group to see if IV changes
  • covert observations- participant doesn’t know they are being watched so they are natural
  • questionnaires- keeping them anonymous so they are more truthful
  • qualitative methods- interviews have high ecological validity as they represent humans more accurately
  • standardise procedures and instructions
  • single blind or double blind
  • assure results are anonymous so participants are truthful
  • incorporate a lie scale to assess the consistency of responses
  • triangulation- use of different sources of evidence
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37
Q

define experimental design

A

researcher has to decide how they will use their participants

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38
Q

define a repeated measures experiment

A

same group if participants in all conditions

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39
Q

advantages of repeated measures experiment

A
  • no participant variables
  • fewer participants so more economical
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40
Q

disadvantages of repeated measures experiment

A
  • order effects
  • demand characteristics
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41
Q

define order effects and what are the different types

A

doing the same task twice
boredom, fatigue, practise

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42
Q

solutions to repeated measures experiment

A

counterbalancing and randomisation

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43
Q

define counterbalancing

A

when two groups do the tasks in different order to cancel our order effects

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44
Q

define independent group design

A

different groups perform only one condition

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45
Q

advantages of independent group design

A
  • no practice effects
  • reduces demand characteristics
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46
Q

disadvantages of independent group design

A
  • needs more participants
  • participant variables between groups
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47
Q

solutions to independent group design

A

random allocation as each participant had an equal chance to being in either group and tried to avoid imbalance of participant variables in either group

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48
Q

define matched pairs design

A

pair up participants on a certain quality that is believed to affect the performance on the DV and their results are compared

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49
Q

advantages of matched pairs design

A
  • participant variables reduced
  • no order effects
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50
Q

disadvantages of matched pairs design

A
  • larger number of participants needed
  • difficult to match on characteristics like personality
  • difficult to know which variables are relevant
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51
Q

solutions to matched pair design

A

pilot study to help choose which variables are most important to match on

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52
Q

define ceiling effect

A

task is too easy so all the scores are high

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53
Q

define floor effect

A

task is too difficult so all scores are low

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54
Q

define construct validity

A

extent to which a test captures a specific construct or trait and it overlaps with some other aspects of validity

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55
Q

define experiment

A

IV that is changed so that the effect on DV can be observed and aims to establish cause and effect relationship

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56
Q

define laboratory experiment

A

takes place in a carefully controlled lab where the IV is manipulated by the experimenter so the DV can be measured

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57
Q

pros of laboratory experiment

A
  • extraneous variables are closely controlled so increases internal validity
  • easily repeated as it is controlled so increases reliability
  • shows cause and effect relationship
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58
Q

cons of laboratory experiment

A
  • artificial nature so lacks ecological validity
  • know they are tested so may lead to demand characteristics
  • lacks mundane realism
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59
Q

define field experiment

A

conducted in natural setting where the IV is still manipulated so the DV can be measured

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60
Q

pros of field experiment

A
  • higher mundane realism
  • naturalistic so high ecological validity
  • demand characteristics are less likely
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61
Q

cons of field experiment

A
  • harder to control extraneous variables
  • ethical issues as the participant don’t know they are being studied
  • harder to replicate
  • IV may be operationalised in a way that lacks mundane realism
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62
Q

define natural experiment

A

IV naturally occurs, and would take place even without the research taking place, and DV is then measured

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63
Q

pros of natural experiment

A
  • high external validity
  • provides opportunities for research that would otherwise be impossible to replicate
  • reduced demand characteristics
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64
Q

cons of natural experiment

A
  • less control over extraneous variables
  • very unlikely to be able to replicate
  • random allocation of participant not possible so there may be bias and lead to participant variables
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65
Q

define quasi experiment

A

participants are automatically assigned to a condition depending on their characteristics or features that don’t change

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66
Q

pros of quasi experiment

A
  • controlled experiments so can be replicated
  • high ecological validity as you can compare to real life
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67
Q

cons of quasi experiment

A
  • cannot randomly allocate so more chance of extraneous variables
  • demand characteristics as they may become more aware
  • DV may be articulate and reduced ecological validity
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68
Q

define sampling

A

choosing a group of people to represent the target population

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69
Q

define target population

A

population to which the researcher would like to generalise their results to

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70
Q

define opportunity sample

A

using people who are available at the time of testing

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71
Q

define random sample

A

each member of the population has an equal chance of being selected like names in a hat or random generator

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72
Q

define stratified sample

A

subgroups are identified and participants are chosen at random from each group in proportion to target population

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73
Q

define volunteer sample

A

participants out themselves forward to take part

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74
Q

define systematic sample

A

using a system to pick a pattern of participants e.g every nth time

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75
Q

pros of opportunity sampling

A
  • quick and cheap
  • can have face to face ethical debriefings
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76
Q

cons of opportunity sampling

A
  • researcher bias as they bc hoods who they want
  • depends on who’s available, different factors eliminate who is free
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77
Q

pros of random sampling

A
  • avoids bias
  • aims to be fair and representative
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78
Q

cons of random sampling

A
  • impossible to have all names of target populayion
  • doesn’t guarantee full representation
  • time consuming
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79
Q

pros of stratified sampling

A
  • highly representative so has population validity
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80
Q

cons of stratified sampling

A
  • time consuming and difficult to gather
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81
Q

pros of volunteer sampling

A
  • give their informed consent
  • will be interested and less likely to withdraw
  • large number may apply so it gives more accurate results and in depth analysis
  • helpful to find people who can be seen as atypical
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82
Q

cons of volunteer sampling

A
  • biased as it is not representative of the whole population
  • hawthorne effect
  • demand characteristics
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83
Q

pros of systematic sampling

A
  • normally representative
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84
Q

cons systematic sampling

A
  • may not be able to identify all members of the population
  • unexpected bias that has a pattern
  • starting point and deciding on list type may be biased
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85
Q

what are the ethical issues

A
  • informed consent
  • deception
  • right to withdraw
  • protection from harm
  • privacy and confidentiality
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86
Q

features of informed consent

A
  • making participants aware of the aims of the research, procedures, risks, rights and what their data will be used for
  • use consent forms
  • under 16s need parental consent
  • consent cannot be given by those under the influence
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87
Q

define presumptive consent

A

similar group of people are told the details of the study and asked if it is acceptable, and their answer will presume the answer of the actual participants

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88
Q

define prior general consent

A

participants give their permission to be deceived but not knowing how

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89
Q

define retrospective consent

A

asking them after they have taken part of their data can be used

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90
Q

limitations of informed consent

A
  • invalidate purpose of study
  • participants do not know fully what they are getting in to
  • demand characteristics
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91
Q

features of deception

A
  • BPS only allows when there is scientific justification and no alternative procedure
  • full debrief after to discuss concerns
  • cost-benefit analysis
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92
Q

limitations of deception

A
  • cost- benefit decisions are flawed
  • debriefing cant turn back time
  • distrust in psychologists
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93
Q

features of right to withdraw

A
  • enticed by financial incentives
  • fully informed consent so they know what they are doing and less likely to withdraw
  • volunteer samples as people are more eager and won’t withdraw
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94
Q

limitations of right to withdraw

A
  • time consuming
  • guilty to withdraw
  • economic pressure because they are getting paid
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95
Q

features of protection from harm

A
  • physical or psychological
  • should be in same state after the experiment as they were before
  • no greater harm than what they would experience in every day life
  • offer therapy and counselling at the end
  • stop the study immediately if the participant is harmed too much
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96
Q

limitations of protection from harm

A
  • harm may not be apparent or obvious yet
  • don’t always know what will be harmful beforehand
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97
Q

features of privacy and confidentiality

A
  • protected under data protection act
  • using code names and anonymity
  • deleting unnecessary daya
98
Q

limitations of privacy and confidentiality

A

still work out participants from limited amount of information

99
Q

define naturalistic observation

A

behaviour in natural situation or environment without any intervention

100
Q

advantage of naturalistic observation

A

high external and ecological validity

101
Q

disadvantages of naturalistic observation

A
  • less control over extraneous variables
  • replication is difficult
102
Q

define controlled observations

A

some variables are controlled by the researcher and participants are likely aware that they are being studied

103
Q

advantages of controlled observation

A
  • more control over extraneous variables
  • easy replication
104
Q

disadvantage of controlled observation

A
  • low ecological validity and mundane realism
  • demand characteristics
105
Q

define overt observation

A

participants are aware they are being observed, but observers may try to be as unobtrusive as possible

106
Q

advantages of overt observation

A

infrom participants and ask for consent

107
Q

disadvantage of overt observation

A
  • demand characteristics
  • social desirability bias
108
Q

define covert observation

A

participants are unaware they are being observed

109
Q

advantages of covert observation

A
  • higher ecological validity
  • natural behaviour so high internal validity
110
Q

disadvantages of covert observation

A

ethical concerns because they cannot give consent

111
Q

define non participant observation

A

observer is merely watching or listening to the behaviour of others and not interacting with

112
Q

advantage of non participant observation

A

observer effects less likely

113
Q

disadvantage of non participant observation

A

less insightful

114
Q

define participant observation

A

observer is part of the group being observed

115
Q

advantage of participant observation

A

more insightful

116
Q

disadvantage of participant observation

A
  • demand characteristics
  • lose objectivity
117
Q

advantages of observations

A
  • high validity
  • captures spontaneous and unexpected behaviour
118
Q

disadvantages of observation

A

-observer bias
- only observable behaviour can be recorded
- hard to replicate

119
Q

define inter-observer reliability

A

if several observers are coding behaviours, their codings agree with each other

120
Q

features of inter observer reliability

A
  • should agree beforehand the behavioural categories and their interpretations of them
  • carry observations at the same time but in different places
  • total number of agreements / number of observations
121
Q

define unstructured observation

A

all relevant behaviours is recorded and no system is used

122
Q

evaluation of unstructured observation

A

+ greater insight
- observer bias, unnecessary behaviours noted (time wasting)

123
Q

define event sampling

A

counting the number of times a certain behaviours occurs

124
Q

evaluate event sampling

A

+ focuses on an event, find averages
- cant note abnormal behaviours, no indication of when it happened

125
Q

define time sampling

A

recording behaviour at preset intervals of time

126
Q

evaluate time sampling

A

+ frequencies within observation, more objective
- may miss something, demand characteristics, observer bias, social desirability bias

127
Q

define structured observations

A

use various systems to record behaviour

128
Q

evaluate structured observations

A

+ smaller risk of observer bias
- less insight as they are nothing frequencies of behaviour, interesting behaviours unwritten

129
Q

define behavioural categories and criteria of them

A

target behaviour is operationalised so it’s more reliable and measurable
- objective- no inferences have to be made
- cover all possible behaviours and no waste basket
- criteria shouldn’t overlap

130
Q

define self report techniques and why they are useful

A

participants give information about themselves, including their experiences, beliefs and feelings

131
Q

types of closed questions

A

likert scale- indicated agreement from strongly agree to strongly disagree
ranked scale- from 1 to 10
semantic differential scale- indicate where they fall between two extremes
multiple choice- choose from
options

132
Q

advantages of closed auestions

A

easy to analyse

133
Q

disadvantages of closed questions

A
  • forced to pick an option that doesnt represent them
  • waste baskets
134
Q

advantages of open questions

A
  • more detail and can expand on answers
  • allow for unexpected answers
135
Q

disadvantages of open questions

A
  • worry of confidentiality
  • qualitative data not produced
136
Q

advantages of questionnaires

A
  • can be distributed to large numbers cheaply and quickly
  • may be more willing to participate
137
Q

disadvantages of questionnaires

A
  • not accessible to all (literate)
  • social desirability bias
  • leading questions so response bias
  • takes a long time to design
  • participant bias
  • sample not representative
  • acquiescence bias (tendency to agree with things)
138
Q

criteria for questionnaire design

A
  • easily analysed so more likely closer questions
  • free from bias and leading questions
  • should be clear and avoid using double negatives
  • make language understandable for all
  • contain filler questions to reduce demand characteristics
  • sequence questions sensibly
  • avoid double barrelled questions with more than one answer
  • pilot study
139
Q

define correlation

A

relationship and strength between two variables

140
Q

define positive correlation

A

as one co-variables increases, the other increases too

141
Q

define negative correlation

A

as one co-variable increases, the other decreases

142
Q

define no correlation

A

no relationship between the two variables

143
Q

define intervening variables

A

another variables that had not been studied

144
Q

define continuous variable

A

variable that can take on any value within a certain range and not categorised

145
Q

explain correlation coefficients

A
  • between -1 and +1
  • show strength of the co-variables
  • coefficients above 0.8 have a strong correlation and are reliable and valid
146
Q

define quantitative data

A

data in the form of numbers

147
Q

strengths of quantative data

A
  • reliable
  • can be analysed statistically
  • easy to compare and analyse
148
Q

weaknesses of quantitative data

A
  • lacks detail
  • may oversimplify
149
Q

define qualitative data

A

data in the form of words

150
Q

strengths of qualitative data

A

-detailed

151
Q

weaknesses of qualitative data

A
  • subjective
  • unreliable
  • hard to compare
  • time consuming
  • researcher bias
152
Q

define triangulation

A

use of a mixture of qualitative and quantitative data

153
Q

define primary data

A

information observer and collected directly from first hand experience, including designing and carrying out the study

154
Q

strengths of primary data

A
  • more reliable
  • can cater to your research
155
Q

weaknesses of primary data

A
  • time consuming and costly
  • many not have access to groups and data you need
  • ethical considerations
156
Q

define secondary data

A

information that was collected from other studies

157
Q

benefits of secondary data

A
  • use data from bigger samples
  • access to information you wouldn’t be able to reach
  • meta- analysis
  • quicker
  • objective and detached
158
Q

weaknesses of secondary data

A
  • may not be exactly what you are researching
  • may not understand research in detail
  • takes time to analyse
  • outdated
  • unreliable
159
Q

define meta analysis

A

analyse results from loads of different studies and come up with general conclusions

160
Q

benefits of meta analysis

A
  • help to identify trends
  • increase sample size and reliability of findings
161
Q

weaknesses of meta analysis

A
  • publication bias like file drawer problem where researcher intentionally does not publish all the data
  • some research may contradict each other
162
Q

what are the measures of central tendency and how do you work them out

A

mean- add all number together and divide by how many values there are
median- putting the numbers in order and finding the middle number
mode- most common number

163
Q

evaluation of mean

A

+considers all data, used for further calculations
- can be skewed by extreme values and make it unrepresentative, can give unrealistically precise values that don’t work for discrete data

164
Q

evaluation of median

A

+ will not be affected by extreme values
- may not be representative, little further use

165
Q

evaluation of mode

A

+ will not be affected by extreme values, makes more sense when presenting discrete values, easy to use
- does not use all data, may have more than one mode, little further use

166
Q

what are the dispersion techniques and how to work them out

A

range- highest minus lowest
standard deviation- spread around the mean

167
Q

evaluation of range

A

+ can see consistency, easy to calculate
- affected by extreme values, fails to account distraction, does not account numbers in the middle

168
Q

evaluation of standard deviation

A

+ precise measure where all values are taken into account
- difficult to calculate, affected by extreme values

169
Q

define longitudinal studies

A

studies conducted over a long period of time to observe long term effects between the same individual

170
Q

evaluation of longitudinal studies

A

+ in depth, reduces participant variables
- extraneous variables, people might drop out

171
Q

define cross sectional studies

A

group of participants are compared to another group at the same point in time

172
Q

evaluation of cross sectional studies

A

+ efficient, more control over experiment
- participant variables

173
Q

define cross cultural studies

A

compare behaviours in different cultures

174
Q

how to display quantitive data

A
  • table
  • line graph
  • histogram
  • bar chart
  • scattergram
  • pie chart
175
Q

features of tables

A
  • clearly present data and show any patterns
  • raw data to show scored before analysis
176
Q

features of line graphs

A
  • can show more than one set of data
  • continuous data in list form
  • independent on x and dependent on y
  • join each point up
  • see trends over time
177
Q

features of histograms

A
  • continuous scale
  • uses class intervals
  • columns touch each other
  • frequencies of scores
178
Q

features of bar charts

A
  • non- continuous data
  • columns do not touch
179
Q

features of scattergrams

A
  • show relationship and correlation between two variables
  • continuous data on both axis
  • draw line of best fit
180
Q

features of pie chart

A
  • sectors of a circle to show proportion
181
Q

define content analysis

A

quantifying qualitative data through the use of coding units

182
Q

features of content analysis

A

-indirect form of observation as you analyse artefact people have produced
- put into categories or typologies, quotations and summaries

183
Q

what sampling method in content analysis

A

analysing content every n number of times

184
Q

how to carry out coding in content analysis

A
  1. watch/read the sample and identify potential categories which have emerged
  2. compare categories/ coding unit with another psychologist and use the ones they have agreed upon
  3. give examples of the categories that they would be looking for and operationalise
  4. carry out content analysis separately and counting the number of examples that fall into each category
  5. compare examples to look for agreement
185
Q

define thematic analysis

A

recurring themes identified during coding and are described further in greater detail, perhaps by conducting further analysis

186
Q

define test-retest reliability

A

conduct the content analysis and then recode them at a later date and compare the two sets of data

187
Q

evaluation of content analysis

A

+ easy to perform, non-invasive and ethical, high ecological validity, easily repeated and reliable
- observer bias, subjective, non-descriptive, cultural bias, may not have ecological validity compared to real life, choice of content can be biased

188
Q

define case study

A

in depth investigations of a single person, group of people or event

189
Q

features of a case study

A
  • represents thoughts, emotions, experiences and abilities
  • longitudinal- follow over an extended period of time
  • qualitative data like interviews, observations and questionnaires
  • examples like HM and KF
190
Q

evaluation of case studies

A

+ rich detail, help construct theories, help study the unusual
- hard to generalise, ethical issues like confidentiality and psychological harm, objectivity, past records may be biased or incomplete, hard to establish cause and effect

191
Q

how to assess validity

A

face validity or concurrent validity

192
Q

define reliability

A

measure of consistency

193
Q

how to assess reliability

A
  • test-retest reliability- administering the same test or questionnaire in the same person on different occasions, 2 week time frame
  • inter-observer reliability- assess observations by conducting content analysis
194
Q

how to improve reliability

A
  • questionnaires can be rewritten so they aren’t ambiguous
  • use same interviewer
  • properly trained interviewers
  • no leading or ambiguous questions
  • structured interviews
  • operationalised behavioural categories that do not overlap
195
Q

define a normal distribution curve

A

symmetrical pattern of data that creates a bell shaped curve, all measures of central tendency are th same or similar and in the middle

196
Q

what is a positively skewed distribution

A

data is concentrated to the right (ceiling effect) and mean is mean higher

197
Q

what is a negatively skewed distribution

A

data is concentrated to the right (floor effect) and mean is much lower

198
Q

what is nominal data

A

data is in separate categories

199
Q

what is ordinal data

A

data is ordered in some way but the scores do not use standardised scales

200
Q

what is interval data

A

data that is measured using equal intervals and can go into minuses

201
Q

what is ratio data

A

data measured with equal intervals but cannot go into minuses

202
Q

define statistical testing

A

provides a way of determining whether a hypothesis should be accepted or rejected

203
Q

factors affecting choice of statistical test

A

levels of measurement- nominal, ordinal, interval or ratio
type of test- difference or correlation tion
design- related (repeated measures/matched pairs) or unrelated (independent groups)

204
Q

test for unrelated, nominal data

A

chi squared

205
Q

test for related, nominal data

A

sign test

206
Q

test for unrelated, ordinal data

A

mann whitney

207
Q

test for related, ordinal data

A

wilcoxon

208
Q

test for correlated, ordinal data

A

spearmans

209
Q

test for unrelated, interval data

A

unrelated t test

210
Q

test for related, interval data

A

related t test

211
Q

test for correlated, interval

A

pearson’s

212
Q

how to conduct a sign test

A
  1. state the hypothesis
  2. find out if each participants score increased, decreased or stayed the same
  3. find s value- number of participants with least frequent sign
  4. find n value- number if participants either change in results
  5. check results in statistical table to find the critical value
  6. state a conclusion
213
Q

how do you know if your results are significant or not

A

s value must be equal to or less than critical value for it to be significant and reject the null
otherwise, it is not significant and you accept the null

214
Q

what is peer review

A

a way of assessing the scientific credibility of a research paper by other psychologists who work in a similar field

215
Q

ways of conducting a peer review

A

single blind- names of reviewers not revealed
double blind- both reviewers and researchers are anonymous
open- both reviewers and researchers are known to each other

216
Q

purpose of peer review

A
  • allocation of research funding
  • publication of research in journals and books
  • assess research rating of a univeristy
217
Q

evaluation of peer review strengths

A

+ check validity of research and determine how important it is
+ anonymity means reviewers can be honest

218
Q

evaluation of peer review weaknesses

A
  • appropriate experts may not conduct the review
  • may be biased towards prestigious researchers
  • anonymity may mean some are too harsh or critical
  • potential for research to be stolen- affect social relationships
  • publication bias where only positive results are published
  • can be misleading as once published it is in the public domain, even if it is wrong, like MMR link to autism
  • prevents progress of new ideas as radical ideas are often overlooked
  • takes a long time and may become out of date
219
Q

what does p<=0.05 mean

A

5% likelihood of the results occurring by chance, so you can be 95% certain your results are significant

220
Q

what is a type 1 error

A

when experimental hypothesis is accepted when actually results were chance findings so null should have been (usually due to high probability)

221
Q

what is type 2 error

A

when null hypothesis is accepted when there was a real difference (due to low probability)

222
Q

what to remember when completing other statistical tests

A
  • independent groups designs have two N values
  • N is number of participants
  • for correlation tests, ignore the sign but focus on the magnitude
  • for t-tests and pearsons, degree of freedom (N) is N-2
  • for chi squared, degrees of freedom (N) is (number of rows - 1) x (number of columns - 1)
223
Q

how are the different methods of central tendency useful when designing a study

A
  • mean- cannot be used with nominal data
  • median- appropriate for ordinal data
  • mode- only method that can be used for nominal data
  • range- useful for ordinal data
  • SD- best used with mean to describe interval/ratio data that is normally distributed
224
Q

tips when designing a study

A
  • stick to what they ask you to do
  • use bullet points as subheadings
  • correct terminology
  • justify why you have chosen to do what you suggest
  • suggestions must be well thought out, practical and ethical
  • link everything to study
  • plan
225
Q

sections of a psychology report

A
  • title
  • abstract
  • introduction
  • method
  • results
  • discussion
  • references
  • appendices
226
Q

what is a title in a psychology report

A

short clear description of study

227
Q

what is in the abstract of a report

A
  • summary of study including aims, hypothesis, method and results
  • allows reader to determine if the report is worth reading
228
Q

what is in the introduction of a report

A
  • review of previous research that is relevant
  • start eternal and become more specific (funnel)
  • end with stating aims and hypothesis
229
Q

what’s in the method of a report

A
  • detailed descriptions of what the research did
  • in great detail that it could be replicated
  • design, participants, material, procedures, ethics
230
Q

what is in the results of a study

A
  • what the research found
  • descriptive statistics- graphs and measures of central tendency and dispersion
  • inferential statistics with significance levels and justification with hypothesis rejected or accepted
231
Q

what is in the discussion of a study

A
  • interpret the results
  • relationship to previous research
  • strengths and weaknesses of methodology
  • implications for theories and real work application
  • suggestions for future research
  • contribution to research in the current field
232
Q

how to write references for a journal

A

last name. first initial, middle initial. (year). title of journal article. name of journal, volume number (issue number), page numbers.

233
Q

how to references for a book printed

A

last name. first initial, middle initial. (year). book title. place of publication: publisher

234
Q

how to write reference for book online

A

last name. first initial, middle initial. (year). book title. retrieved from URL

235
Q

how to write reference for website

A

last name. first initial, middle initial. (year, month date published). article title. retrieved from URL

236
Q

what is in the appendix of a study

A

supporting material like raw data

237
Q

features of science

A
  • empirical method- method of gaining knowledge through direct observation or testing rather than unfounded beliefs
  • objectivity- empirical data should not be affected by bias
  • replicability- ability to repeat research to check the validity of results
  • theory construction- explanation or theories must be constructed to make sense of facts
  • hypothesis testing- validity is tested to see if results are significant
  • falsifiability- prove a hypothesis wrong in order to be sure of results
238
Q

define theory

A

collection of general principles that explain observations and facts

239
Q

define inductive research

A

begins with research question which helps form hypotheses and theory

240
Q

define deductive research

A

research is theory driven which guides data collection

241
Q

define a paradigm and paradigm shift

A

-shared set if assumptions about the subject matter of a discipline
- paradigm shift is when a new minority idea is accepted
- Kuhn argues something is a science if it has a paradigm

Psychology (9 decks)

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