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A-level Psychology > research methods > Flashcards

research methods Flashcards

1
Q

non-experimental research methods

A

observations
self report techniques-questionnaire
interviews
correlation

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2
Q

eg. experimental research methods

A

lab experiments
field experiments
natural experiments
quasi experiments

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3
Q

self report

A

person asked or explained their own feelings, opinions, behaviours or experiences-related to given topic

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4
Q

lab experiments

A

highly controlled researcher manipulates IV + records effect on DV

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5
Q

lab experiments-strengths

A

establish causes + effect
easy to replicate
remove extraneous variables

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6
Q

lab experiment-weakness

A

demand characteristics
low ecological validity

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7
Q

field experiment-strengths

A

higher ecological validity
less demand characteristics-less artificial

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8
Q

field experiment

A

takes place in natural everyday setting-researcher manipulates IV + records effect on DV

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9
Q

field experiment-weaknesses

A

not possible control + eliminate extraneous variables in field so impact on DV

difficult to replicate-in natural environment-not same if replicated

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10
Q

natural experiment

A

takes place in natural setting

IV not manipulated by researcher to have an effect on DV

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11
Q

natural experiment-strengths

A

higher ecological validity

less likely to demonstrate demand characteristics

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12
Q

natural experiment-weaknesses

A

not possible control + eliminate extraneous variables in field so impact on DV

difficult to replicate-in natural environment-not same if replicated

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13
Q

quasi experiment

A

IV based on existing differences in ppl-no one has manipulated variable it simply exists

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14
Q

quasi experiment-strengths

A

highly controlled-establish cause + effect-if lab

high ecological validity-if natural/field

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15
Q

quasi experiment-weakness

A

if lab-low ecological validity
demand characteristics
have confounding variables

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16
Q

naturalistic observation

A

natural setting

Ps in own environment + interference-kept to minimum

can be observed or done secretly

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17
Q

naturalistic observation-strengths

A

high in ecological validity
less demand characteristics

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18
Q

naturalistic observation-weaknesses

A

cannot control extraneous variables
difficult to replicate

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19
Q

controlled observation-strength

A

easy to replicate

easy to check reliability of findings

unwanted extraneous variables eliminated

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20
Q

controlled observation

A

highly controlled researcher manipulates variables + observes Ps behaviour

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21
Q

controlled observation-weakness

A

demand characteristics
low ecological validity

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22
Q

covert observations-strengths

A

no demand characteristics

allows to explore behaviour-private or secretive eg.criminal behaviour

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23
Q

covert observations

A

observations done secretly

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24
Q

covert observations-weaknesses

A

ethical issues eg.lack of informed consent

difficult to record behaviour w/x being discovered

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25
Q

overt observations

A

observations done openly

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26
Q

overt observations-strength

A

fewer ethical issues-Ps know that their taken part in an observation

researcher can found out more info about them-find out reasons for Ps actions

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27
Q

overt observations-weaknesses

A

behaviour not natural-observer/investigator effect-can lead to demand characteristics

researcher might find it difficult to recruit Ps willing to take part

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28
Q

double blind procedure

A

researcher assistant-doesnt know full aim
so can’t give clues to Ps
record observation in less bias way

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29
Q

participant observation

A

observer gets involved + Ps in behaviour of group observed

can be done overt or covert

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30
Q

participant observation-strengths

A

researcher will have fuller understanding of actions of group

Ps will have natural behaviour

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31
Q

participant observation-weakness

A

researcher becomes more of P than observer-difficult to be objective + step back about observation

difficult to record behaviour w/x being discovered

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32
Q

non-participant observations

A

researcher follows group around but doesn’t get involved

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33
Q

non-participant observations-strengths

A

researcher is not interfering w/behaviour being observed

able to remain objective

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34
Q

extraneous variables-situational variables

A

aspect of research situation that might influence Ps behaviour

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35
Q

non-participant observations-weaknesses

A

might not fully understand actions of group

presence of observer can change behaviour of group

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36
Q

confounding variables

A

uncontrolled extraneous variables that have affected at least 1 of condition

researchers could not be sure whether differences in homework performance was due to presence of music or intelligence

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37
Q

extraneous variables-participant variables

A

characteristics or traits of Ps that may affect results

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38
Q

investigator effects

A

unwanted influence of investigator on DV

eg. personality, gender, age of researcher

researcher may also be biased when selecting/allocating Ps + when recording their data

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39
Q

randomisation-strengths

A

minimise effect of extraneous/confounding variables

prevents investigator effects in allocation of Ps + reduces unconscious bias

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40
Q

counterbalancing

A

used to deal w/order effects when using repeated measures design

Ps sample is divided in half w/1 half completing 2 conditions in 1 order + other half completing conditions in reverse order

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41
Q

standardisation

A

all Ps should be subject to same environment, information + experience

ensure this all procedures + instructions are standardised + kept same

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42
Q

behaviour categories

A

behaviour checklist w/different behaviour categories

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43
Q

behaviour categories-strengths

A

tallies= easy to quantify data + use graphs-compare to qualitative data

more scientific + objective way of caring out observation-standardised way

easy to replicate + check for reliability

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44
Q

time sampling

A

observing at different time intervals

eg.1h observe, 1h not observe

strength-reduces no. of observations made

weakness-but could miss important info

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45
Q

behaviour categories-weaknesses

A

lack of inter-observer reliability-different results obtained by 2 different observers-have different views

observers have quite lengthy training=costly

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46
Q

other methods of recording data in observations

A

note taking-notes taken away-observer tries to identify patterns in behaviour

audio/video recording-not always practical

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47
Q

event sampling

A

observer focuses on specific pre-selected behaviour-their interested in + record every time it occurs

strength-useful when event happen infrequently

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48
Q

questionaries

A

standardised questions-handed out to Ps-supposed to be filled by Ps

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49
Q

closed questions

A

set of pre-determined answers

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50
Q

open questions

A

Ps express their ideas + opinions

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51
Q

Likert scales

A

indicates strength of agreement

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52
Q

rating scale

A

indicates strength of feeling

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53
Q

fixed choice option

A

Ps just tick from range of options

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54
Q

questionaries-strength

A

easily disturbed to Ps
obtain large sample of Ps
generate lot of data

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55
Q

questionnaires-weakness

A

socially desirable answers-appear in favourable light to researcher

lead to leading questions-urge Ps to give certain response

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56
Q

open question-strength

A

Ps can fully express themselves-in depth + meaning

generate lots of qualitative data

fuller understanding of behaviour observed

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57
Q

open question-weakness

A

very time consuming to analyse + draw conclusions from

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58
Q

pilot study

A

small scale trial run of any method eg.observation, lab experiment

done to ensure that Ps understand all Qs, material + instructions

help iron out any difficulties before main study

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59
Q

closed question-strength

A

easy to draw + analyse conclusions from

easy to statistically show data

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60
Q

closed question-weakness

A

lack deep meaning + data

no full understanding of behaviour researched

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61
Q

structured interviews

A

interviewer verbally asks questions
questions=pre determined

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62
Q

structured interviews-strength

A

contain standardised questions
easy to replicate
check for reliability
interviewer can explain Qs Ps don’t understand

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63
Q

structured interviews-weakness

A

socially desirable answers-appear in favourable light to researcher

interviewer effect-where age, personality, gender, ethnicity of interviewer affect responses

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64
Q

unstructured interview

A

conversation between Ps + interviewer

no standardised questions

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65
Q

unstructured interview-weakness

A

not standardised-diffucult to replicate

findings-not consistent + unreliable

difficult to analyse + draw conclusions from

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66
Q

unstructured interview-strength

A

rich, detailed, qualitative data

researcher can steer interview-in any direction-researcher can probe + ask Ps to expand on it

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67
Q

correlations

A

relationship between 2 variables-not cause + effect

2 variables= co-variables

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68
Q

positive correlation

A

high score on 1st variable associated w/high score on 2nd variable

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69
Q

negative correlation

A

1 variable increases= other variable decreases

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70
Q

no correlation

A

no relationship between data score

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71
Q

correlation co-efficient

A

number between 1 + -1 which shows strength or relationships-closer to 0 weaker= relationship between 2 variables

sign (+ or -) shows whether relationship is strong or weak

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72
Q

correlation-strength

A

allows relationship of 2 variables to be examined-when controlled experiment not possible due to ethical issues

good starting point for further research-produces quantitative data

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73
Q

correlation-weakness

A

can be misused-as finding correlation between 2 variables tell us very little other than relationship just exists

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74
Q

operationalisation

A

making sure variable can be easily measured

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75
Q

aim

A

general statement of intended purpose of study

investigate theories that have been developed

contain variables being investigated

aim-what researcher wants to find out

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76
Q

hypothesis

A

prediction about what will happen in study-precise + testable statement

can be directional OR non-directional

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77
Q

directional hypothesis

A

AWARE of any past research-results have similar outcome

makes clear sort of difference that is anticipated

predict why way results will go

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78
Q

non-directional hypothesis

A

UNAWARE of any past research OR findings unclear or contradictory

safer to use non-directional hypothesis in case findings go in either direction

states there is difference-but doesn’t predict which way results go

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79
Q

null hypothesis

A

statement which predicts that IV will NOT affect DV-so NO significant difference

80
Q

directional correlation

A

specific type of relationship between 2 co-variables-eg. positive OR negative

81
Q

non-directional correlation

A

states that there will be relationship between 2 co-variables-but doesn’t state whether positive OR negative

82
Q

null correlation

A

NO correlation between 2 co-variables

83
Q

ethical issues

A

potential for Ps to be harmed in some way during research-role of BPS encourages researchers to follow BPS guidelines

84
Q

ethical issues: informed consent

A

researcher must attempt to get real content from Ps-only possible when Ps fully understand what they are agreeing to do-consent for children should be from parents

85
Q

ethical issues: deception

A

Ps should be given all info + not lied to before the study-but in order to collect valid data Ps may not be told entire truth-minimal degree of deception should be used

86
Q

ethical issues: confidentiality

A

data from Ps should be protected under Data Protection Act- Ps should be aware of what their data is used for-their confidentiality= respected

87
Q

ethical issues: right to withdraw

A

Ps must be allowed to stop participating in study OR stop study altogether in order for research to follow ethical guidelines

88
Q

ethical issues: protection from harm

A

Ps should be protected from harm by researcher + study should not be designed to deliberately cause harm-harm is both physical + emotional distress

89
Q

ways of dealing with ethical issues: debriefing

A

used for deception OR psychological harm

fully inform Ps about nature of research + Ps allowed to discuss any issues they have

Ps have experienced any harm-debriefing offering counselling + advice

90
Q

ethical issues: privacy

A

names of Ps should not be recorded so they cant be identified

91
Q

repeated measure- strength

A

P variables are minimised bc same Ps take part in both conditions of experiment
strength bc it increases internal validity of research

half number of Ps is needed compared to other 2 designs bc Ps take part in both conditions of experiment
strength bc it means researcher can save time money by using same Ps for both conditions

92
Q

repeated measures- weakness

A

order effects may occur means that order in which Ps complete conditions may affect their performance eg. boredom, practise or fatigue effects can occur as Ps are taking part in more than 1 condition

Demand characteristics can also be more likely- limitation bc if order effects do occur internal validity of research is lowered

93
Q

repeated measures

A

1 group of Ps who take part in both conditions of study

94
Q

independant groups

A

Different Ps are used in each condition of study

each P only experiences 1 condition of IV

95
Q

independant groups-strengths

A

no order effects bc Ps are only taking part in 1 condition of experiment-reduces possibility of boredom, practice + fatigue effects occurring + reduces chance of Ps guessing aims of experiment + changing their behaviour

96
Q

independant groups-weakness

A

Ppt variables can occur bc there may be individual differences between 2 groups of Ps that could affect results

97
Q

matched pairs

A

different Ps used in each condition but they are matched on variable that could affect results if left unchecked

98
Q

matched pairs-strengths

A

P variables are minimised bc Ps are matched on important variables therefore individual differences between groups are unlikely + so there is higher internal validity

not affected by order effects as Ps only take part in 1 condition P performance cannot be affected by boredom, practise, or fatigue as they do not take part in 2 conditions as in repeated measures design strength bc it increases internal validity of research

99
Q

matched pairs-weakness

A

time consuming bc Ps are often pre-tested to match them up eg. to match Ps on intelligence all Ps must take an IQ test

if 1 partner of a pair drops out researcher risks losing both members- makes it less economic than other designs

100
Q

random sampling

A

every member of target population has an equal chance of being selected

101
Q

opportunity sampling

A

selecting people who are willing + available to take part at time of research

102
Q

systematic sampling

A

every nth member of target population is selected

103
Q

volunteer sampling

A

people put themselves forward to participate

Volunteers usually respond to newspaper or university noticeboard adverts that are placed by researchers

104
Q

stratified sampling

A

involves researcher dividing population into subpopulations

Researchers then ensure each subgroup is represented in their sample

researcher 1st identifies different strata that make up population proportions needed for sample to be representative are worked out

105
Q

random sampling-strength

A

unbiased bc researcher does not have any influence over who will be selected for sample means that sample will be free from researcher bias

equal opportunity of being selected increasing representativeness of sample

106
Q

random sampling-weakness

A

Ps selected may not be available/ refuse take part-researcher will have small sample size so time consuming

random sample could just contain only males OR females Ps which makes sample bias

107
Q

opportunity sampling-strength

A

Quick + convenient method bc researcher just makes use of people who are available at time most popular

108
Q

opportunity sampling-weakness

A

likely to be bias- bc researcher influences who is asked to take part

Ps may support their hypothesis-unrepresentative + lack population validity

109
Q

volunteer sampling-strength

A

Ps more motivated to take part-volunteer w/interest

Ps more likely to take it more seriously

110
Q

volunteer sampling-weakness

A

biased sample bc often Ps who volunteer share certain characteristics or traits eg. are keen + helpful

problems when attempting to generalise findings from such biased sample

111
Q

systematic sampling-strengths

A

researchers selection of Ps is not biased

112
Q

stratified sampling-weakness

A

very complex + time consuming

113
Q

systematic sampling-weakness

A

time consuming + not everyone in target population has an equal chance of being selected

Ps may refuse to take part

114
Q

stratified sampling-strengths

A

representative of target population since characteristics of target population are represented proportionally

more likely that findings from this sample can be generalised

115
Q

ways of dealing with ethical issues: confidentiality

A

personal details are held these must be protected

more usual to simply record no personal details eg. maintain anonymity

Researchers usually refer to Ps using numbers or initials when writing up investigation

116
Q

purpose of conducting pilot study

A

aims to find out if aspects of design do or don’t work

eg. if Ps understand instructions if timings for tasks are appropriate or if parts of design make aims of research obvious.

conducting pilot study on small group of people it is possible for researcher to see what needs to be adjusted before investing time + money in larger scale research study

117
Q

case studies

A

detailed study of an individual, group, or situation

often involve an analysis of unusual individuals or events such as person w/rare disorder

Case studies tend to take place over long period of time

When constructing case study researchers often include case history of individual concerned, using interviews, observations + questionnaires

118
Q

case studies-strengths

A

Case studies provide rich + valuable insights on very unusual + atypical forms of behaviour allows researcher to investigate topic in far more detail than might be possible if they were trying to deal w/many research Ps as in an experiment findings are based on real life problems + issues increasing ecological validity of research

Case studies provide great deal of qualitative data that often generates ideas for future research

119
Q

case studies- weakness

A

possible to generalise findings from single individual or small sample to wider population limitation bc it means findings are only representative of person whom study is focused lacking population validity

case studies are criticised due to their subjective nature bc researcher must decide which information to include in final report + must interpret vast quantities of qualitative data which is produced personal accounts from P + their family + friends may be prone to inaccuracy + memory decay especially if childhood experiences are being relayed lowers the validity of evidence from case studies

120
Q

event sampling-strengths

A

relevant behaviours are not missed

121
Q

event sampling-weakness

A

Observations based on event sampling may not take in account broader contextual factors that influence child’s behaviour

122
Q

time sampling-strengths

A

manage observations more rather than being overwhelmed by every single behaviour that occurs

123
Q

time sampling-weakness

A

behaviours sampled may be unrepresentative bc relevant behaviours displayed outside time frame are missed

124
Q

Inter-observer reliability

A

data recording more objective + unbiased observations should be carried out by at least 2 researchers

improve reliability observers should familiarise themselves w/ behaviour categories being used

After observing same behaviour at same time observers should compare + discuss any differences in interpretation

124
Q

qualitative data-strengths

A

rich in detail so you are more likely to find out more about topic being studied

more holistic understanding of phenomena under study

125
Q

qualitative data-weakness

A

conducted w/small sample sizes
difficult to draw conclusions from
time consuming

126
Q

quantitative data-strengths

A

objective
easy to draw conclusion from
not time consuming

126
Q

quantitative data-weakness

A

less detailed data
open to misrepresentation

127
Q

primary data

A

data collected by researcher specifically for purposes of their study

data comes first hand from the Ps

Data gathered using an experiment, questionnaire, interview, or observation would be classed as primary data

128
Q

primary data- strengths

A

more accurate + reliable bc it comes from direct source

faster + easier to collect primary data than secondary data which can take weeks or even months to collect

129
Q

primary data-limitation

A

Requires considerable planning, preparation + resources on behalf of researcher

130
Q

secondary data

A

data that is collected by someone other than primary user

131
Q

secondary data- strengths

A

allows researchers to investigate phenomena that cannot be tested now

Inexpensive requiring minimal effort and easy to access

132
Q

secondary data- weakness

A

may be missing data that researcher is interested in investigating-limits utility

133
Q

meta-analysis

A

research method that uses secondary data

where data from lots of studies already carried out is combined to provide an overall view on subject

meta-analysis may produce qualitative data eg. review of conclusions from research or quantitative data

134
Q

meta-analysis: strengths

A

Results can be generalised across much larger populations

135
Q

meta-analysis: weakness

A

difficult process to undertake bc it requires use of sophisticated tools

136
Q

measure of dispersion: SD

A

Measures dispersion of scores around mean

higher standard deviation greater spread of scores from mean

low standard deviation number indicates that scores are close to mean

137
Q

mean-strength

A

mean uses every value in data + hence is good representative of data

138
Q

mean-weakness

A

unrepresentative if there are extreme values

139
Q

median-strength

A

Not affected by extreme values

140
Q

median-weakness

A

time consuming w/lot of data as it has to be put in order

141
Q

mode-strength

A

Not affected by extreme values

142
Q

mode-weakness

A

can be more than 1 mode + all values can be modal which means mode is not always representative of data

143
Q

range-strength

A

Easy to calculate

144
Q

SD-strength

A

Shows whether or not data is clustered around mean

Not affected by extreme values or outliers

144
Q

range-weakness

A

Doesn’t take into account distribution spread of all numbers

145
Q

SD-weakness

A

Difficult to calculate
Does not show full range of data

146
Q

theory construction

A

develop theories all time to explain things we observe in our everyday life Scientific theories are constructed by gathering evidence

eg. may develop theory regarding capacity of short-term memory after series of experiments reveals that memory span is around 7

should be possible to make clear + precise predictions based on scientific theory an essential component of theory is that it can be scientifically tested

147
Q

falsifiability

A

always be possible to prove a theory wrong eg. must have testable hypothesis

Popper says that rather than finding evidence to support theory scientists should actively try to find evidence to show that it is false

Freud’s psychodynamic theory is regarded as unscientific such as unconscious mind are impossible test + therefore cannot be proven wrong

Popper drew clear line between good science in which theories are constantly challenged + what he called ‘pseudoscience’ which could not be falsified

148
Q

paradigm

A

Thomas Kuhn suggested that what distinguishes scientific disciplines from non-scientific disciplines is shared set of assumption + methods- paradigm

Some psychologists argue psychology is science bc it has paradigm but other say Psychology has too much internal disagreement + too many conflicting approaches to qualify as science

progress w/in scientific discipline occurs when there is a scientific revolution- handful of researchers begin to question accepted paradigm this critique begins to gather popularity + pace + eventually

paradigm shift occurs when there is too much contradictory evidence to ignore

149
Q

peer review

A

assessment of scientific work by others who are experts in field prior to publication

149
Q

aim of peer reviews

A

allocate research funding: Independent peer evaluation takes place to decide whether to award funding for proposed research project

All elements of research are assessed for quality + accuracy: formulation of hypothesis methodology chosen statistical tests used + conclusions drawn

Reviewers may suggest minor revisions of work + thereby improve report or extreme circumstances they may conclude that work is inappropriate for publication + should be withdrawn

150
Q

peer review- weakness

A

Reviewers may use their anonymity as way of criticising rival researchers especially if findings contradict their own beliefs or research

slows down publication process especially when research findings are new + ground-breaking

not always possible to find experts in new area it can result in such work being judged by researchers who do not fully understand research

Publication bias is tendency for editors of journals to publish ‘headline grabbing’ findings to increase their credibility + sales tend to publish positive results- could mean that research which does not meet these criteria is ignored

151
Q

peer review-strengths

A

acts as control mechanism to help prevent flawed or fraudulent research from being published

ensures that research published is academically rigorous + therefore can be trusted in comparison to opinion + speculation

encourages sharing of ideas between experts + collaboration on improvement of research

process is anonymous it is likely to produce an honest appraisal

152
Q

format of scientific report

A

Title
Abstract
Introduction
Method
Results
Discussion
References
Appendices

153
Q

characteristics of a normal distribution

A

mean, median + mode are all at exact same mid-point

data is symmetrical

consistent spread of scores on either side of mid-point eg. approximately 68% of data lies w/in 1 standard deviation of mean

Approximately 95% of data lies w/in 2 standard deviations of mean + 99.7% w/in 3 standard deviations of mean-‘empirical rule’

153
Q

positive skew

A

most of scores are distributed left of graph

eg. very difficult test in which most people got low marks w/only handful at higher end

would produce positive skew positive skew, mean, mode + median are no longer in same mid- position

mode remains at highest point of peak mean is dragged to right towards tail

bc extreme scores affect mean very high scoring candidates in test have had effect of pulling mean to right

153
Q

normal distribution

A

If you measure certain variables eg. height or IQ frequency of these measurements should form bell-shaped curve symmetrical spread of data is called normal distribution

W/normal distribution most people are in middle area of curve w/very few at extreme ends

mean, median + mode are in same midpoint of curve

154
Q

negative skew

A

very easy test would produce distribution where bulk of scores are concentrated to right of graph

mean is pulled to left towards tail due to a few low scoring candidates

mode is not affected by extreme scores + is therefore at highest peak

both cases median lies between mode + mean

154
Q

improve test retests

A

If correlation between 2 tests is lower than 0.8 researcher would need to review measures + then carry out another test-retest on new test

154
Q

skewed distribution

A

spread of frequency data that is not symmetrical-data cluster to 1 end

155
Q

ways of testing reliability-test retest

A

Ps are given questionnaire to complete + are then given same task on later occasion eg. 1 week later

Ps responses are then correlated to identify if they have given similar responses on both occasions

If correlation of 0.8 is established between tasks it is considered reliable measure

156
Q

improving reliability-questionnaires

A

Rewrite confusing, leading or complicated questions

Avoid open questions as they could be misinterpreted

157
Q

Internal validity

A

refers to whether research is measuring what it intended to measure

affected by presence of extraneous/confounding variables

157
Q

improving reliability-interviews

A

Use same interviewer each time

Ensure interviewers are properly trained

Use structured interview

157
Q

improving reliability-experiments

A

Use standardised procedure

Reword any confusing instructions

Use single or double-blind procedure

158
Q

improving reliability-observations

A

Operationalise behavioural categories

Ensure categories do not overlap

Ensure observers are familiar w/ categories

159
Q

external validity

A

refers to whether research findings can be generalised to other people, places + times

160
Q

external validity-ecological validity

A

Generalising findings to real life settings

161
Q

external validity-population validity

A

Generalising findings to other people in target population

162
Q

external validity-temporal validity

A

Generalising findings to present day/modern

162
Q

ways of assessing validity- face validity

A

extent to which test looks like it will measure what it is supposed to be measuring

could ask someone who has knowledge of area being investigated about if they think measure looks like it is measuring that topic area

163
Q

ways of assessing validity- concurrent validity

A

extent to which test produces same results as another established measure

would compare score on new test w/score on test that has been proven to be valid

If valid 2 scores should be similar You can measure degree of similarity by correlating 2 sets of scores

correlation coefficient of above 0.8 would tell you that new measure/score is similar to valid measure/score + therefore you can assume new measure is valid

164
Q

improving validity-questionnaires

A

Lie scales to assess consistency of responses

Anonymity to reduce social desirability bias

164
Q

improving validity-observations

A

Covert observations

Ensure behavioural categories are not too broad

164
Q

improving validity-experiments

A

Control groups

Standardised procedures

Single + double-blind procedures

165
Q

content analysis

A

type of observational research in which people are studied INDIRECTLY

eg. instead of observing what people do in certain situation communications they produce are studied

aim of content analysis is to summarise this qualitative data + convert it to quantitative data

165
Q

procedure for content analysis

A

data is collected

researcher reads through/ examines data-making themselves familiar w/it

researcher identifies coding units

data analyse by applying coding units

tally made of no. of times that a coding unit appears

165
Q

coding

A

first stage of content analysis
Some data to be analysed may be extremely large + so there is need to categorise this information into meaningful units

may involve simply counting number of times particular word or phrase appears to produce quantitative data

coding units used will depend on data eg. newspaper reports may be analysed for number of times derogatory terms for mentally ill are used such as ‘crazy’ or ‘mad

eg. would be number of positive or negative words used by mother to describe her child’s behaviour or number of swear words in film

166
Q

thematic anaylsis

A

analysing qualitative data by identifying patterns w/in material

material to be analysed might be diary, TV advertisements or interview transcripts

main process involves identification of themes theme refers to recurrent idea which keeps ‘cropping up’ in communication being studied

eg. mentally ill may be represented in newspapers as ‘drain on resources of NHS’

Such themes may then be developed into broader categories eg. ‘control’ or ‘stereotyping’ of mentally ill

in their final report researcher will use direct quotes from data to illustrate each theme

data here is NOT converted into quantitative data but stays in its written form

166
Q

Content Analysis + Thematic Analysis-strengths

A

Content + thematic analysis can get around many of ethical issues usually found in psychological research
many resources eg. books + TV programmes already exist there is no issue w/getting permission to use it

resources used often have high external validity as they were designed for real life purposes

166
Q
A

1) Familiarisation w/data – involves intensely reading the data + becoming immersed in its content

2) Coding – involves generating codes that identify interesting features of the data- Questions to consider whilst coding may include:
What are people doing?
What are they trying to accomplish?

3) Generating themes – involves combining codes to potential themes in order to identify meaningful patterns in data researcher then reviews themes to see if they work in relation to data

4) Defining themes - researcher then defines what each theme is + what is interesting about theme

5) Write up – researcher will write up final report typically using quotes from data to illustrate each theme

166
Q

Content Analysis + Thematic Analysis-weakness

A

info is often studied outside of context in which it occurred + therefore researcher may attribute opinions or motivations that did not exist

research can lack objectivity as resources could be chosen which can reflect researchers aims

167
Q

nominal data

A

‘discrete’ in that 1 item can only appear in 1 of categories

It is least precise level of measurement

eg. placing Ps into categories based on their gender + grade obtained in Year 1 Psychology
Counting no. of people who support Man United or Man City

168
Q

ordinal data

A

does not have equal intervals between each unit

it would not make sense to say that someone who rated psychology as 8 out of 10 enjoys it twice as much as someone who gave it 4

Ordinal data also lacks precision because it is based on subjective opinion rather than objective measures

eg. what constitutes ‘4’ or an ‘8’ for people doing rating may be quite different

168
Q

interval data

A

most precise level of measurement + consists of data that is measured on fixed, numerical scale w/equal distances between points on scale

Interval data is measured using equipment such as stopwatches, thermometers, weighing scales, which produce data based on accepted units of measures

eg. distance in centimetres, time in seconds

168
Q

Levels of measurement + descriptive statistics

A

norminal=mode=n/a
ordinal=median=range
interval=mean=SD

169
Q

sign test

A

research is looking for difference
data is nominal
research has used either repeated measures or matched pairs design

170
Q

how to do the sign test

A

Once you have worked out sign for each pair of data you will need to find out calculated value represented as S for sign test
It is calculated by adding up number of plus signs in your table adding up number of minus signs in your table + selecting smaller value

Now you will need to find out if this result is significant or not
decide if it is significant you will need to compare your calculated value w/critical table value
critical table values are already worked out but you will need to select the correct value

decide what the critical table value is you will need to know:
1) total number of scores
This is your N value
2) Whether your hypothesis was directional or non-directional
3) level of significance to be used always 0.05 unless you are specifically asked to use different 1

171
Q

paramedic tests

A

related t-test, unrelated t-test + Pearson’s r are collectively known as parametric tests
Parametric tests are more powerful + robust than other tests -3 criteria that must be met to use parametric test:

  1. Data must be interval level – actual scores, rather than ranked data is used
  2. data should be normally distributed
    Variables that would produce skewed distribution are not appropriate for parametric tests
  3. should be homogeneity of variance - set of scores in each condition should have similar dispersion or spread 1 way of determining variance is by comparing standard deviation in each condition if they are similar parametric test may be used
171
Q

significance levels

A

significant result is 1 that is unlikely to be due to chance factors statistical tests use significant level – point at which researcher can reject null hypothesis + accept alternative hypothesis

significance level measures amount of chance factors that are permitted in research
It is chosen BEFORE research is carried out + is expressed as decimal

Psychologists have concluded that for most purposes in psychology 5% level of significance is appropriate

written as: P< 0.05. The 0.05 significance level means that probability of results of study occurring by chance is less than 5% We can therefore be 95% confident that IV caused change in DV
95%

occasions when it is necessary to use very strict level of significance eg. when testing new drug

researchers will occasionally allow for more chance factors by choosing less stringent level of significance

171
Q

choosing a statistical test

A

test of difference
unrelated design related design
O=Mann-whitney wilcoxon
I=unrelated t-test related t-test
N=chi-squared sign test

test of association/correlation
N=chi-squared
O= spearman rho
I=pearsons r

172
Q

Type I + Type II Errors

A

Sometimes researchers choose wrong hypothesis-mistakes are known as type 1 or type 2 errors
errors are more likely to occur when 0.1 or 0.01 significance level is used 0.05 is preferred significance level in psychology

Using level of significance which is too lenient eg. p<0.1 may lead to type 1 error where null hypothesis is rejected when it should in fact be retained as results are due to chance

Likewise using level of significance which is too strict eg. p<0.01 may lead to type 2 error where null hypothesis is retained when it should have been rejected

main reason for using 5% level in psychology is that it is neither too strict nor too lenient preventing type 1 + type 2 errors

172
Q

using statistical data

A

statistical test has been calculated result is no.- calculated value check for statistical significance calculated value must be compared w/critical value– numerical cut-off point that tells us whether we can reject null hypothesis + accept alternative hypothesis

Critical value – no. created by statisticians

Calculated value – no. obtained from results of stats test

3 criterias:

  1. no. of Ps- usually appears as N value on table some tests use degrees of freedom instead
  2. One or two-tailed test? -one-tailed test if your hypothesis was directional + two-tailed test
    for non-directional hypothesis
  3. Significance level
    discussed above 0.05 level is standard level used in psychology

calculated value is greater than critical table value in Chi squared test, related t-test, unrelated t-test, Pearson’s or Spearman’s Rho tests then null hypothesis can be rejected

calculated value is less than critical table value in Mann Whitney U Test, Wilcoxon Signed Ranks test or sign test then null hypothesis can be rejected

A-level Psychology (10 decks)

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