4.2.3 Research Methods

Question 1

the experimental method

Answer 1

• looks at how variables affect outcomes
• e.g. Bandura’s Bobo doll experiment looked at how changing the variable of the role model’s behaviour affected how the child played

Question 2

laboratory experiments

Answer 2

• an experiment conducted in an artificial, controlled environment
• the researcher has high levels of control over all variables and environmental factors within the experiment to ensure only the IV changes between conditions
• standardised procedures are used for this control
• e.g. asch’s conformity experiments

Question 3

lab experiments - strengths

Answer 3

• cause and effect conclusions are more possible due to the high levels of control
• the use of a standardised procedure means the research is replicable, which increases reliability
• high internal validity as the IV may be seen to affect the DV

Question 4

lab experiments - limitations

Answer 4

• demand characteristics may be an issue as ppts know they’re in a study which may alter their behaviour, affecting the validity
• often lacks ecological validity due to the artificial nature of the procedure
• often lacks mundane realism, i.e. results can’t be generalised to real-world behaviours

Question 5

field experiments

Answer 5

• an experiment carried out in a naturalistic, real-world setting, e.g. a school
• an IV is manipulated by researchers and the DV is measured quantitatively, as in lab experiments
• e.g. bickman’s study of the effects of uniform on obedience

Question 6

field experiments - strengths

Answer 6

• likely to have higher ecological validity as it’s a real-life setting
• ppts are less likely to show demand characteristics as they’re less likely to know what’s expected of them
• high levels of mundane realism, i.e. results are more likely to be able to be generalised to real-world behaviours

Question 7

field experiments - limitations

Answer 7

• harder to assign ppts, so changes may occur due to ppt variables rather than what the researcher’s measuring
• harder to control extraneous variables within the experiment, which could affect measurements of the DV

Question 8

natural experiments

Answer 8

• where the researcher doesn’t manipulate the IV as it varies naturally
• the researcher takes advantage of the naturally changing IV to measure effects on the DV
• ppts are usually allocated to conditions randomly
• e.g. research into white blood cell activity before and after students take exams

Question 9

natural experiments - strengths

Answer 9

• allows research in areas that controlled experiments can’t research, due to ethical or cost reasons
• high external validity as it’s conducted in a natural setting
• low demand characteristics as natural behaviours are being displayed

Question 10

natural experiments - limitations

Answer 10

• hard to establish causal relationships as other variables aren’t controlled so may have an effect
• lack of reliability as it’s unlikely to be able to replicate the same situation to repeat the test
• ethical issues as deception is often used, making informed consent difficult, and confidentiality may be compromised if research is conducted in an identifiable place

Question 11

quasi experiments

Answer 11

• where experimenters can’t randomly allocate ppts to conditions as the IV is natural and can’t be changed as it’s a particular feature of ppts
• e.g. gender, the existence of a mental disorder, etc.

Question 12

quasi experiments - strengths

Answer 12

• often carried out in controlled conditions
• higher ecological validity as research is often less artificial than lab studies, so results are more likely to be able to be generalised

Question 13

quasi experiments - limitations

Answer 13

• ppts aren’t randomly allocated, so confounding variables may affect results, e.g. where the ppt lives
• hard to establish causal relationships as the IV isn’t being directly manipulated

Question 14

naturalistic observation

Answer 14

• observations made in a real-life setting
• e.g. setting up cameras in a school to see how people interact in those environments

Question 15

controlled observations

Answer 15

• observations made in an artificial setting set up for the purposes of observation
• e.g. zimbardo’s stanford prison experiment

Question 16

covert observation

Answer 16

• ppts are unaware they’re being observed as part of a study
• e.g. setting up hidden cameras in an office
• higher validity as it rules out demand characteristics
• unethical as ppts don’t have informed consent

Question 17

overt observation

Answer 17

• ppts are aware they’re being observed as part of a study
• e.g. zimbardo’s prison study
• ethical as ppts have given informed consent
• social desirability is likely, where ppts present their ‘best selves’ to the researcher
• demand characteristics are more likely, which impacts validity of results

Question 18

participant observation

Answer 18

• when the researcher is actively involved in the situation being observed
• ppts may not realise the researcher isn’t really one of them
• e.g. zimbardo playing the role of a prison warden in his own study
• researcher is able to build a rapport (relationship) with ppts, so they’re more likely to have open conversations and act in a natural way
• researchers can become too involved with ppts which makes their interpretations of ppts’ behaviours biased

Question 19

non-participant observation

Answer 19

• when the researcher isn’t involved in the situation and remains separate from ppts
• e.g. in bandura’s bobo doll study, the observers didn’t interact with the children being observed
• researcher is more likely to remain objective whilst observing and recording the ppts behaviour
• researcher is unable to build rapport (a relationship) with ppts, so they’re less likely to open up fully / show fully natural behaviours

Question 20

self-report techniques

Answer 20

when ppts provide info about themselves, either through questionnaires or interviews

Question 21

questionnaires

Answer 21

• a standardised list of questions answered by all ppts in a study
• questions can either be open or closed;
• open; allows ppts to write their own answer, so often seen as having more validity, and it qualitative data
• closed; ppts have to choose from a fixed set of responses (multiple choice / yes or no), which gives quantitative data

Question 22

questionnaires - strengths

Answer 22

• closed questions give quantitative data which is easier to analyse and spot patterns
• questionnaires are standardised, so studies can be easily replicated which can strengthen reliability

Question 23

questionnaires - limitations

Answer 23

• open questions provide qualitative data which can make analysis difficult
• ppts may lie in their answers, e.g. on controversial topics
• differences in interpretations of questions
• ppts are less likely to provide full detail in written answers, so interviews may be better suited for gaining detailed info
• responses may be biased, e.g. towards people who have a lot of spare time, as they’re probably more willing to complete it

Question 24

structured interviews

Answer 24

• questions are standardised and pre-set
• the interviewer asks all ppts the same questions in the same order
• means the interviewer doesn’t need intensive training to ask questions
• answers are easy to compare as the same questions were asked
• ppts’ responses can’t be followed up with extra questions to add more detail
• interviewer effects - their appearance or character may bias ppts’ answers, e.g. females may be less comfy answering questions on sex by a male interviewer

Question 25

unstructured interviews

Answer 25

• the interviewer discusses a topic of interest with the ppt in a less structured and more spontaneous way, pursuing avenues of discussion as they come up
• interviewer is able to build a rapport with the ppts, which is more likely to allow for honest answers and higher levels of validity
• answers can be followed up for more info / extra detail
• interviewer has to be highly trained and ready to come up with suitable questions on the spot
• lack of quantifiable data as every interview will be different, making it harder to compare
• interviewer effects

Question 26

semi-structured interviews

Answer 26

• combination of set questions, with the ability to add in extra questions to gain more info
• easy comparison as the same questions are asked
• more info can be gathered from extended questions
• rapport can be built as the interviewer can ask more questions and relax the interviewee
• interviewer has to be highly trained and ready to come up with suitable extending questions

Question 27

correlations

Answer 27

• analysis of relationships between co-variables
• two co-variables are measured and compared to find a relationship, so variables aren’t manipulated
• e.g. age and reaction time
• the relationship between co-variables can be analysed visually through scattergrams, or numerically through the correlation co-efficient

Question 28

scattergrams

Answer 28

• these can provide 3 outcomes;
• positive correlation; when one co-variable increases, so does the other
• negative correlation; when one co-variable increases, the other one decreases
• zero correlation; when there’s no relationship between variables

Question 29

correlation co-efficient

Answer 29

• represents both the direction and strength of the relationship between co-variables as a number between -1 and +1;
• a perfect positive would be +1
• a perfect negative would be -1
• no relationship would be 0
• both positive and negative correlation coefficients can be described as weak, moderate or strong, depending where they fall between the numbers

Question 30

correlational analysis - strengths

Answer 30

• able to show relationships between variables
• data is already easily available for researchers to analyse
• as data is readily available, it’s unlikely for there to be any ethical issues
• allows predictions to be made when looking at relationships between co-variables

Question 31

correlational analysis - limitations

Answer 31

• they don’t show causation, i.e. unable to show which variable impacts the other
• extraneous relationships with other variables may affect the co-variables and the outcome
• only works for linear relationships; unable to work for curvilinear relationships

Question 32

content analysis

Answer 32

• an indirect observational method used to analyse human behaviour
• it’s used to analyse qualitative data by transforming it into quantitative data through coding units, e.g. the amount of times a swear word is said in a film
• can be used for data in different formats, e.g. interview transcripts, films, audio recordings, etc.

Question 33

content analysis procedure

Answer 33

1. data is collected
2. the researcher reads through / examines it to familiarise themselves with it
3. the researcher identifies coding units
4. the data is analysed by applying the coding units
5. a tally is made of the number of times that coding unit appears

Question 34

content analysis - reliability

Answer 34

• after conducting a content analysis, the researcher will need to test reliability, and they can use;
• test-retest reliability; run the content analysis again on the same sample and compare the results - if they’re similar, it shows reliability
• inter-rater reliability; a second rater (researcher) conducts the content analysis with the same set of data and compares them - if results are similar, it shows reliability

Question 35

content analysis - Waynforth & Dunbar (1995)

Answer 35

• they conducted one by analysing lonely heart adverts in newspapers to see if men and women were looking for different things in relationships
• they looked at 881 adverts
• they found men aimed their adverts at younger women and mentions their resources more than attractiveness
• women aimed theirs at older males and mentioned attractiveness more than resources
• they concluded from their content analysis that men and women did want different things in their relationships

Question 36

thematic analysis

Answer 36

• a qualitative method that allows researchers to identify, analyse and report themes
• an alternative to content analysis
• researchers attempt to identify deeper meanings in data by organising, describing and interpreting it

Question 37

thematic analysis procedure

Answer 37

1. the researcher familiarises themselves with the data
2. codes are created to identify the data and important features of it
3. the data / codes are examined to look for themes and identifying patterns
4. the researcher reviews themes and patterns to see if they can explain the behaviour and answer the research question
5. the researcher then names and defines each theme
6. they make a formal write up of the research analysis and the themes found
   - themes must emerge from the data and not be pre-described by the researcher

Question 38

content and thematic analysis - strengths

Answer 38

• reliability is established as it’s easily replicable
• high levels of external validity as the data is taken from the real world and not made just for the study
• data is readily available so results are usually generalisable to other real world situations
• quick, easy and non-invasive to perform as it doesn’t need contact with ppts
• can be used to verify results from other research

Question 39

content and thematic analysis - limitations

Answer 39

• researcher bias may occur as they have to interpret the data
• may lack validity as the data hasn’t been collected under controlled conditions
• also lacks causality for this reason
• results may be flawed due to the over representation of events and using readily available material, e.g. negative events usually have much more coverage than positive, which may skew the data to show an invalid representation of behaviour

Question 40

case studies

Answer 40

• detailed and in depth investigations of an individual, a group of people, or an event
• researchers may use observation, questionnaires, interviews, etc. to collect data
• they usually provide qualitative data as they’re studies of a single subject
• this data can be turned into quantitative data through different analysis techniques
• researchers use data to build a case history of the subject and then interpret this to draw conclusions
• snapshots; case studies conducted over a short period of time
• longitudinal studies; follow ppts’ behaviour over a long period of time

Question 41

case studies - strengths

Answer 41

• holistic approach as the whole individual and their experiences are considered
• allows researcher to study unique and otherwise difficult to research behaviour, e.g. it’s unethical to remove half a toddler’s brain just for an experiment, but if such a procedure is medically necessary anyway, then researchers can use this as an opportunity to learn more about the brain

Question 42

case studies - limitations

Answer 42

• results aren’t generalisable or representative as it only looks at single examples
• researcher bias as researchers may be biased in their interpretations when drawing conclusions from the case studies
• information / details can be easily missed
• difficult, if not impossible, to replicate

Question 43

scientific processes

Answer 43

looks at how scientific studies are organised and reported, and covers ways of evaluating a scientific study

Question 44

aims

Answer 44

• a precise statement about what the researchers are investigating and why
• e.g. ‘to investigate the effect of SSRIs on symptoms of depression’

Question 45

hypothesis

Answer 45

• a testable prediction of what the researchers expect to happen
• it needs to contain the IV and the DV which must be operationalised (you must have a way of testing them)
• e.g. subjects are more likely to comply when orders are issued by someone wearing a uniform

Question 46

types of hypothesis

Answer 46

• directional hypothesis (one-tailed); states what the experimenter thinks will happen
• non-directional hypothesis (two-tailed); when the experimenter isn’t sure what will happen and is ‘sitting on the fence’
• experimental / alternative hypothesis (H₁); a prediction that changing the IV will cause a change in the DV, i.e. a directional or non-directional hypothesis
• null hypothesis (H₀); a prediction that changing the IV won’t have an effect on the DV

Question 47

sampling

Answer 47

• used to select the ppts who will take part in the study
• the sample is selected from the target population (everyone that the study is supposed to apply to)
• the researcher uses the sample to draw from the target population then generalises the findings to them

Question 48

sampling techniques

Answer 48

• random
• systematic
• stratified
• opportunity
• volunteer

Question 49

random sampling

Answer 49

• selecting people in a way that everyone has a fair chance of being selected, e.g. by pulling names out of a hat
• it’s an unbiased selection, so is more likely to be a representative sample
• as results are fairly representative, results can be generalised to the target population
• time consuming and impractical as it’s not always possible for the entire target population to want to participate
• it may be non-representative as one specific group may be coincidentally selected randomly, e.g. all males, which wouldn’t be a true representation of the population

Question 50

systematic sampling

Answer 50

• selecting every nth person from a list to form a sample
• unbiased selection, so more likely to be representative, and so results are more able to be generalised
• not always truly random or representative, as the list may involve hidden periodic traits, e.g. if every 10th person is a 19 year old, then they’re the only person in the sample which isn’t a true example of the target population

Question 51

stratified sampling

Answer 51

• a small-scale reproduction of the target population
• involves dividing and categorising the population by characteristics important to the study, e.g. age, gender, etc., then working out what % of the population is in each group, then randomly sampling them according to these percentages
• i.e. if 20% of the whole population is aged 0-18, then the representative sample will randomly select 20% of 0-18 year olds from that group
• the sample is representative of the target population, which also makes it easier to generalise results
• selection is unbiased as it’s based on the subgroups in society
• time-consuming to know the subgroups, divide the ppts, then select them to match the population
• the researcher requires knowledge of the subgroups / categories of the population which may not be available

Question 52

opportunity sampling

Answer 52

• selecting those who are the most convenient, willing and able to take part, e.g. by approaching people in the street and asking them to take part
• used in natural experiments as the researcher has no control over who’s being studied
• a quick and easy way to get info as it’s using people who are readily available to use
• can’t generalise results as the sample is likely to be unrepresentative, e.g. if you conduct it in a school, it will exclude people who work in jobs elsewhere
• it’s a self-selected sample as ppts can agree or decline to join in the study

Question 53

volunteer sampling

Answer 53

• ppts volunteer to take part in a study, e.g. often by replying to adverts
• variety of ppts as they’re choosing to take part
• easy way to get info as it’s using people who’re willing to be involved in the study
• less likely to have people who want to jeapordise the study
• volunteer bias as ppts can offer differ from non-ppts as they chose to take part, so results won’t be able to be generalised
• demand characteristics are an issue as volunteers are often eager to please their researcher

Question 54

pilot studies

Answer 54

• small-scale practice investigations carried out prior to the research to identify any issues which could arise
• they identify problems in the design, method or analysis, allowing them to be altered and fixed which ensures a higher level of validity
• ppts can also help identify issues and propose changes to prevent demand characteristics
• they also identify if there’s a chance of significant results being found, so help to see if the research will be worth it

Question 55

repeated measures design

Answer 55

• when the same ppts complete each of the experiment conditions
• ppts are competing with themselves, producing related data

Question 56

repeated measures design - strengths

Answer 56

• no group differences as the same ppts are used, so ppt variables won’t affect the measure of the IV
• less ppts are needed as they take part in all conditions and produce more data per ppt

Question 57

repeated measures design - limitations

Answer 57

• order effects may be an issue as ppts may perform better as they learn what’s expected (positive order effect) or they may perform worse as they’re bored or tired (negative order effect)
• however this can be controlled by counterbalancing; splitting ppts into groups and changing the order of conditions per group
• higher level of demand characteristics as ppts are more likely to guess the purpose of the research
• more time is usually needed to allow for ppts to take part in multiple conditions

Question 58

independent measures design

Answer 58

• different ppts complete each condition of the research, i.e. each set of ppts only experience one condition of the IV
• ppts are allocated randomly to each condition to avoid researcher bias
• ppts are competing with each other, producing unrelated data

Question 59

independent measures design - strengths

Answer 59

• less likely to have demand characteristics as ppts are less likely to guess the purpose of the study from just one condition
• order effects are less likely as ppts can’t guess what happens next and change their behaviour
• time efficient as all sets of ppts can be tested at the same time

Question 60

independent measures design - limitations

Answer 60

• there may be ppt variables, e.g. if more ppts with a specific characteristic are all randomly assigned to one condition, it can affect the measure of the IV, leading to group differences affecting the results
• more ppts are needed to ensure there are enough to take part in the different conditions, which can be difficult to access

Question 61

matched pairs design

Answer 61

• different ppts complete each condition
• ppts are assessed and matched on characteristics that are important for the research, e.g. age, ethnicity, gender, etc.
• MZ (monozygotic / identical) twins are often used as they create the perfect matched pair
• the matched paired ppts are then randomly assigned to one condition each
• this produces related data

Question 62

matched pairs design - strengths

Answer 62

• less likely to be order effects as ppts can’t predict what happens next and change their behaviour
• lower level of demand characteristics as ppts are less likely to guess the purpose of the study if they only participate in 1 condition

Question 63

matched pairs design - limitations

Answer 63

• matching is difficult, and is usually impossible to match all characteristics
• even well matched ppts may have different levels of motivation in the study, affecting the outcome
• more ppts are needed to ensure there are enough, which can be difficult to access
• matching ppts is very difficult and time-consuming

Question 64

observational design

Answer 64

• the choice of behaviour to record and how it will be recorded / measured
• different ways to record data, e.g. audio recordings, note taking, videos, pictures, etc.
• it’s decided by the observer when designing the observation

Question 65

behavioural categories

Answer 65

• created by researchers when they agree on the behaviours that should be recorded
• they ensure that different researchers are consistent in what they’re looking for
• categories must be operationalised (reflect what’s being studied) and can be coded, e.g. when studying aggression relevant codes may include; AGL (aggressive language), AGB (aggressive behaviour), NABG (non-aggressive behaviour), etc.
• categories either show characteristics, e.g. M for male, or the behaviour being observed, e.g. H for hitting
• can help improve inter-observer reliability

Question 66

sampling procedures

Answer 66

• it can be hard to record every single behaviour during the observation period, so observers will also decide when to record a behaviour, e.g. through time or event sampling

Question 67

event sampling

Answer 67

• recording every time a behaviour (from the behaviour categories) occurs
• however, behaviours that aren’t on the categorised list means relevant behaviours can be missed

Question 68

time sampling

Answer 68

• recording behaviours in a set time frame at regular intervals
• e.g. recording each ppts’ behaviour for 20 secs every 15 mins over a 2 hour observation
• allows the researcher flexibility to record behaviour and has the opportunity to record unexpected behaviours
• however it also means behaviours not recorded in the set time will be missed

Question 69

inter-observer reliability

Answer 69

• when data is less likely to have observer bias, so is a valid representation of behaviour
• even with clear behavioural categories, interpretations are subjective and can be affected by researcher bias
• researchers can test reliability of their own observation by comparing it with another researcher’s observation and seeing if it’s consistent
• 2 or more trained observers conduct the same observation separately, after agreeing on the list of behavioural categories
• they compare the 2 data sets and test the correlation with a statistical test, e.g. spearman’s rho, which if they get a correlation score of 0.8 or above, then it would be accepted

Question 70

questionnaire construction

Answer 70

• they can consist of either open (free) or closed (multiple choice) questions
• the researcher must consider the following elements when designing a questionnaire;
• aims; there must be a clear aim to write relevant questions
• length; relatively short so ppts don’t get bored
• question formation; they should be concise, easy to understand and unambiguous
• previous questionnaires; look at structures of these as the basis of their own
• pilot study; it should be tested with a small group of people first to enable the researcher to make any modifications needed

Question 71

interview construction

Answer 71

• structured; identical closed questions which provide quantitative data that’s easy to analyse
• unstructured; a free-flowing discussion consisting of questions about a particular topic of interest, which provides qualitative data that’s harder to analyse
• semi-structured interviews; combination of both techniques as there are set questions for ppts, but extra follow-up ones may also be asked to gain extra detail, producing both quantitative and qualitative data

Question 72

variables

Answer 72

• independent; the variable changed by the scientist to see the effects of it
• dependant; the variable that changes in response to the IV being changed
• control; variables that remain constant and unchanged throughout the experiment

Question 73

extraneous and confounding variables

Answer 73

• extraneous; different factors that could affect the outcome of the DV, but these can be controlled, e.g. temperature, light, etc.
• confounding; factors that affect the outcome of the DV but also affect the IV, thus affecting the relationship between these variables, and these can’t be controlled, e.g. a hypothesis of alcohol leading to lung cancer may be affected by the confounding variable of smoking if many alcoholics were also smokers

Question 74

operationalising variables

Answer 74

• the process of defining variables into measurable factors
• i.e. a way of measuring a variable is defined
• e.g. measuring aggression in a school by recording the amount of times students shout or hit

Question 75

controls

Answer 75

• used in scientific experiments to prevent factors other than those being studied from affecting the outcome
• they’re needed to eliminate alternate explanations of experimental results

Question 76

controls - random allocation

Answer 76

• all ppts having an equal chance of taking part in a study
• reduces ppt variables, so individual differences are less likely to impact the results
• reduces bias and systematic errors in the experiment

Question 77

controls - counterbalancing

Answer 77

• the researcher splits ppts in half and both groups are presented the IV in different orders
• this helps to reduce order effects

Question 78

controls - randomisation

Answer 78

• ppts are randomly assigned to conditions
• used to reduce systematic errors that the order of the trials may present
• i.e. reduces order effects

Question 79

controls - standardisation

Answer 79

• when all elements of the method and procedure of the study are kept identical for all ppts, e.g. instructions, briefing / debrief, the order of the study, timings of the study
• allows for the research to be replicated and reliable, which allows for meaningful data

Question 80

demand characteristics

Answer 80

• when ppts identify a subtle cue as to what the experimenter expects, so they change their behaviour
• they usually change behaviour to match the aim, and try to please the researcher
• or they may try to annoy the researcher and purposely give the wrong results
• differs from social desirability bias, which is when ppts change behaviour to seem likeable

Question 81

investigator effects

Answer 81

• when the researcher influences the results of the study, and may occur if;
• the researcher is (unconsciously) biased in their interpretations of the study, which impacts results
• the investigator’s physical characteristics may influence how the ppts interact with them, e.g. females may feel less comfy discussing certain topics with a male researcher
• other personal characteristics of the researcher, e.g. voice tone, accent, etc. may impact how ppts react to them

Question 82

ethics

Answer 82

• ethical considerations are in place to ensure the health and dignity of ppts are protected during psychological research
• the British Psychological Society (BPS) has a published code of conduct that all psychologists must follow
• it’s based on the 4 principles of respect, competence, responsibility and integrity
• ethical committees are in place to review proposed research and ensure they abide by the BPS code of conduct
• animal rights are also an ethical issue

Question 83

BPS (2009) code of ethics

Answer 83

• informed consent; ppts should have detailed info about the study to then provide full consent, and if they’re under 16, parental consent must be gained
• protection of ppts; ppts must be protected from both physical and mental harm
• avoidance of deception; ppts shouldn’t be deceived, but if it’s necessary, then consent should still be gained by;
• prior general consent; ppts agree to be deceived but they don’t know how
• presumptive consent; asking people with similar backgrounds to ppts if they would’ve given consent
• retrospective consent; getting formal consent after the study has taken place
• anonymity / confidentiality; ppts have a right to remain anonymous in publication of a study, and all info should remain confidential, but in some cases they may be identifiable from their characteristics
• briefing / debriefing; all relevant info should be explained to ppts before and after the study - if deception has been sued, a debrief is necessary to ensure ppts understand the purpose of the study
• right to withdraw; ppts should be aware they have the right to withdraw from the study at any time, even after it’s finished
• incentives to participate; ppts shouldn’t be offered bribes / promises to take part

Question 84

how studies can protect from harm

Answer 84

• full informed consent where possible
• avoid deception, but if used then debrief ppts immediately afterwards
• ensure ppts are aware of the right to withdraw at anytime
• anonymised data
• ensure ppts have full info on the use of data

Question 85

peer review

Answer 85

• plays a key part in the verification of research before it’s published as it helps determine if it can be deemed scientifically acceptable
• it’s an independent assessment carried out by a few experts who review the research and suggest edits or identify issues with it

Question 86

peer review process

Answer 86

1. a paper is submitted to the journal
2. the editor sends it to other experts in the field
3. they read and critique the paper, checking for hypothesis and methodology, accuracy of reporting, accuracy of statistical testing, quality, validity, significance and errors
4. they may accept the paper as it is, accept it with a few changes, reject it but suggest alterations for re-submission, or reject it completely
5. the paper is returned to the editor

Question 87

types of peer review

Answer 87

• open review; the researchers and reviewers are known to each other
• single-blind; the reviewers don’t know the researchers name, in hopes that it’ll be an unbiased review (however many reviewers often hide behind the anonymity to be undeservedly harsh) - the most common type
• double-blind; reviewers and researchers are anonymous to each other, so the review is free from any bias of them influencing each other (however researchers are often identifiable by their writing / research styles)

Question 88

peer review - strengths

Answer 88

• prevents falsified work from being accepted
• provides valuable feedback for the author
• process is accepted by majority of researchers
• enables journal editors to select the most important research findings for publication
• helps to prevent scientific fraud
• promotes / maintains high standards in research

Question 89

peer review - limitations

Answer 89

• peer reviews aren’t unbiased as the research world is relatively small, so researchers are often known by most
• researchers are often funded by different organisations who push for certain research to be deemed acceptable
• reviewers may plagiarise the work of others’ or they may refrain from accepting work so their own work can be published first
• it’s argued that the ability to publish research is in control of the ‘elites’, so they reject contradictory research as they don’t like to change ideas
• an expensive, slow, time-consuming process

Question 90

implications of psychological research for the economy

Answer 90

• psychological research studies human behaviour so it creates practical applications for use in everyday life
• it supports the development of effective therapies which means more people can return to work, which supports the economy as it reduces the burden on employers, the NHS and the taxpayer
• e.g;
• psychopathology - CBT, REBT, depression treatments, drug therapy for OCD
• memory - EWT research led to the use of cognitive interviews which reduce wrongful convictions thus reducing space in jail and a waste of money / resources

Question 91

reliability

Answer 91

• focuses on the consistency of a study
• if a study is completed in the same way and gets the same results, it’s seen to be reliable
• internal reliability; the extent something is consistent with itself
• external reliability; the extent a test measure is consistent over time

Question 92

assessing reliability

Answer 92

• test-retest method;
• measures external reliability
• gives the same test to the same ppts twice, on different occasions, and if the same result is gained then reliability is established
• split-half method;
• measures internal reliability
• splits the test in half and has the same ppts complete both halves, and if a similar result is provided from both halves then it has internal reliability
• inter-observer reliability;
• designed to stop observer bias
• 2 or more observers complete independent observations on the same study, and if their results are similar then they’re reliable results

Question 93

improving reliability

Answer 93

• practice through conducting a pilot study
• standardising procedures (reduces investigator effects)

Question 94

validity

Answer 94

• focuses on the accuracy of a study
• internal validity; the extent to which the results represent the truth and aren’t due to methodological errors
• external validity; measures whether the results can be generalised beyond the research setting;
• ecological validity; generalisable to real-life settings
• population validity; generalisable to other people
• temporal validity; generalisable over time

Question 95

assessing validity

Answer 95

• temporal validity; assesses how valid it remains over time
• face validity; assesses the extent to which a test appears to measure what it’s supposed to measure
• criterion validity; refers to the extent to which results / conclusions are valid compared with other measures. it’s split into 2 types;
• predictive validity; assesses the ability of a test to predict future behaviour, so it’s typically established through repeated results over time
• concurrent validity; assessing through correlation by correlating scores from existing research to see if it’s similar

Question 96

improving validity

Answer 96

• internal validity can be improved by reducing investigator effects and demand characteristics
• external validity can be improved by conducting experiments in naturalistic settings

Question 97

features of science

Answer 97

• objectivity and the empirical method
• replicability
• falsifiability
• theory construction and hypothesis testing
• paradigms and paradigm shifts

Question 98

features of science - objectivity and the empirical method

Answer 98

• objectivity should be an aim for all scientists as it ensures research hasn’t been affected by their personal biases, opinions, etc.
• the empirical method collects quantitative data by using observable evidence and strictly controlling variables to test a theory, e.g. lab experiments or observational methods
• methods such as case studies or interviews generate more qualitative data, so couldn’t claim to be objective or empirical

Question 99

features of science - replicability

Answer 99

• refers to research which could be carried out again and would be likely to show consistent results, which increases the validity of the findings, i.e. they show that the IV has affected the DV
• it’s only possible when the original research has a standardised procedure with highly controlled variables, as it’s easier to repeat
• e.g. lab experiments, questionnaires, controlled observations, etc.

Question 100

features of science - falsifiability

Answer 100

• the ability of a study to be found to be false, so scientific methods can be used to test this
• this is why significance testing is based on either rejecting or accepting the null hypothesis
• the more that theories / studies are tested and found to withstand the testing, the more scientific they are
• e.g. experiments on memory, such as Loftus and Palmer (1974) have been tested repeatedly using controlled methods

Question 101

features of science - theory construction and hypothesis testing

Answer 101

• a theory is a set of general rules that intend to explain certain behaviours or events
• theories can be constructed using empirical evidence gathered via research to support its central assumptions, as they can’t exist on the basis of beliefs alone
• a hypothesis is a prediction of what the researcher expects to find after conducting an experiment, and it must be objective and measurable
• when the findings have been analysed, a clear decision can be made whether to accept or reject the null hypothesis - if it’s rejected then the theory is strengthened

Question 102

features of science - paradigms and paradigm shifts

Answer 102

• a paradigm is a set of shared assumptions / a generally accepted way of thinking within a subject, such as science
• thomas kuhn (1962) argues that science isn’t as objective as it seems, as the majority of scientists just accept existing scientific theories (paradigms) as true and then find data that supports these, while ignoring data that refutes them
• a paradigm shift occurs when an existing paradigm is challenged and replaced with a new one
• it’s more rare, but as time passes these ideas gain support as more scientists begin to challenge the old theory, adding more research to support the new paradigm
• e.g. when the behaviourist idea took over from psychoanalytic theory which prevailed from the late 19th century

Question 103

scientific report

Answer 103

• this presents the findings of a study which has been designed, conducted and analysed by one / more researchers
• it uses a standardised format and follows a specific structure

Question 104

the structure of a scientific report

Answer 104

• title; concise but informative
• abstract; a brief (150-200 word) summary of the entire research process, including results
• introduction; introduces the reader to the topic area and allows them to place the study into context
• method; describes the ppt sample, the design, materials used, ethical guidelines adhered to, and the procedure which should be standardised so it’s easily replicable
• results; summarise the findings of the study and often includes inferential analyses to determine whether results are significant - if quantitative data was collected, it’s analysed statistically but for qualitative data, content or thematic analysis are used
• discussion; begins with a summary of the findings, then presents an evaluation of the research, and then discusses its potential applications and may even make suggestions for further research
• referencing; acknowledging all sources used throughout the process to avoid plagiarism

Question 105

quantitative data

Answer 105

• data in the form of numbers (numerical)
• often produced from lab experiments or closed questions
• however it’s less detailed

Question 106

qualitative data

Answer 106

• data in the form of words (non-numerical), and is often rich and detailed
• often produced from case studies or unstructured interviews
• however, it’s harder to compare and analyse as it can’t be compared mathematically and scientifically

Question 107

primary data

Answer 107

• data collected by the researcher for the purposes of the study
• e.g. conducting interviews, running a lab experiment, etc.
• allows for more accurate and reliable results as it’s closer to the source
• however it requires more time and effort for the researcher

Question 108

secondary data

Answer 108

• data collected by someone other than the researcher, i.e. it’s pre-existing
• e.g. census information
• researchers use it as part of their study, e.g. when conducting a meta-analysis
• however the quality can’t be controlled by the researcher so it may not fully match the aims / needs of the study

Question 109

meta-analysis

Answer 109

• when a researcher looks at the results of a number of studies on a particular topic to establish general trends and conclusions
• can be useful as it reflects a large sample, making it easier to generalise results
• however the quality of studies may vary, and studies may only be included if they show significant results (due to publication bias), so all studies with insignificant results are excluded, so it’s not an accurate representation

Question 110

measures of central tendency

Answer 110

• these are ways of reducing large data sets into averages
• includes mean, median and mode

Question 111

mean

Answer 111

• calculated by adding all numbers in a set together and dividing the total by the amount of numbers in the set
• accurate as it provides a precise number based on all data in the set
• however it can be skewed by one or two freak scores (very high or very low numbers) which isn’t representative

Question 112

median

Answer 112

• calculated by arranging all numbers in a set from smallest to biggest and finding the middle number
• won’t be skewed by freak scores
• however it may not be representative of all numbers, so it’s less accurate than the mean

Question 113

mode

Answer 113

• calculated by identifying the most commonly occurring number in a set
• won’t be skewed by freak scores
• however it doesn’t use all the data in a set
• a data set may also have more than one mode

Question 114

measures of dispersion

Answer 114

• they quantify how much scores in a data set vary
• includes range and standard deviation

Question 115

range

Answer 115

• calculated by subtracting the smallest number in the data set from the largest number
• easy and quick to calculate
• however it can be skewed by freak scores, as the difference between the biggest and smallest numbers can be skewed by an anomalous result which may give an exaggerated impression of the data
• doesn’t account for different distributions in data sets

Question 116

standard deviation

Answer 116

• a measure of how much numbers in a data set deviate from the mean
• calculated by;
• working out the mean
• subtracting the mean from each number in the set
• squaring these numbers
• adding all those numbers together
• dividing the result by the amount of numbers
• the square root of this number is the standard deviation
• less skewed by freak scores, compared to the range, as it works out the average difference from the mean
• however it takes longer to calculate than the range

Question 117

percentages

Answer 117

• a percentage describes how much out of 100 something occurs, and is calculated by a/b x100
• a percentage change is calculated by;
• (old-new) / old x100

Question 118

correlation

Answer 118

• refers to how closely two (or more) things are related
• measured mathematically using correlation coefficients (r);
• r=+1; perfectly positive correlation; when one increases, so does the other by the same amount
• r=-1; perfectly negative correlation; when one increases, the other decreases by the same amount
• r=0; no correlation
• the closer to 1, the stronger the correlation is, and the closer to -1, the weaker it is

Question 119

presentation of quantitative data

Answer 119

• tables
• scattergrams
• bar charts
• histograms
• line graphs
• pie charts

Question 120

tables

Answer 120

• data tables record raw results
• summary results tables summarise results and present descriptive statistics, e.g. mean, mode, etc.

Question 121

scattergrams

Answer 121

• used to represent correlational data, showing the relationship between 2 variables on the x and y axis

Question 122

bar charts

Answer 122

• used for discrete data (data that’s been divided into categories) to allow for differences in data to be seen clearly
• the bars don’t touch each other to show we’re dealing with separate conditions
• the x-axis lists the categories and the y-axis illustrates the different results for them

Question 123

histograms

Answer 123

• used to show continuous or interval data, so the bars touch each other
• the x-axis categorises the continuous data (i.e. ranges of numbers like 1-20, 20-40, etc.) and the y-axis shows the different results for them

Question 124

line graphs

Answer 124

• also represents continuous data
• it compares trends and changes between two variables via a continuous line through the points on a grid
• different lines can be added to show different categories / conditions, i.e. person A, B and C’s results would be shown by 3 different lines

Question 125

pie charts

Answer 125

• provide a visual representation of percentages

Question 126

normal distribution

Answer 126

• a symmetrical pattern of data which has the majority of scores on / near the mean average
• scores become rarer the more they deviate from the mean
• there are an equal number of scores above the mean as there are below it
• it forms a bell shaped pattern when plotted

Question 127

skewed distribution

Answer 127

• a non-symmetrical data set where scores are distributed unevenly either side of the mean;
• positive skew; most scores have a skew (tail) to the right, below the mean which is after the median and mode
• negative skew; most scores have a skew (tail) to the left, above the mean which is before the median and mode
• skewed distributions are caused by outliers, i.e. freak scores that throw off the mean;
• a positive skew can be caused by a freakishly high score which makes the mean much higher than most scores
• a negative skew can be caused by a freakishly low score which makes the mean much lower than most scores

Question 128

nominal data

Answer 128

• data in the form of categories
• discrete as one item can only appear in one category
• e.g. number of males and females in a class
• an appropriate measure of central tendency would be the mode

Question 129

ordinal data

Answer 129

• data represented in a ranking form
• no equal intervals between each rank, so it lacks precision
• e.g. 1st, 2nd and 3rd in a race
• an appropriate measure of central tendency would be the median
• an appropriate measure of dispersion would be the range

Question 130

interval data

Answer 130

• data represented in a ranking order, but there are equal, precise intervals between each value
• e.g. temperature (C°/F°) - the difference between 21° and 22° is the same as the difference between 54° and 55° as each value has an equal distance of 1°
• an appropriate measure of central tendency would be the mean
• an appropriate measure of dispersion would be standard deviation

Question 131

coding, content and thematic analysis

Answer 131

• coding; provides quantitative data as data is categorised into units for analysis
• content analysis; analysing data, looking to identify examples of generated codes
• thematic analysis; generates qualitative data; recurring themes will be identified using coding, then these will be described in greater detail

Question 132

inferential testing

Answer 132

• the purpose is to see whether a study’s results are statistically significant, i.e. whether any observed effects are as a result of whatever’s being studied, or if they’ve only occurred due to chance

Question 133

the sign test

Answer 133

• a statistical test which is used when;
• looking for a difference (not correlation)
• using a related experimental design, i.e. repeated measures
• collecting nominal data

Question 134

how to conduct a sign test

Answer 134

1. state the hypotheses - both the alternative and null, and identify whether its directional (requires a one-tailed test) or non-directional (requires a two-tailed test)
2. record the data and work out the sign - record a (+) if the second data set has increased, put a (-) if it’s decreased, and a (0) if it’s stayed the same
3. find the N value - add how many + and - there were, excluding all 0s, and find the calculated value (S) - this is the number of times the less frequent sign occurs
4. locate the critical value of S - use the 0.05 value column for either a one or two-tailed test, depending on the hypothesis, and use the row of the N value to find it
5. compare the calculated and critical values of S; if the calculated value is equal to or less than the critical, then the result is significant, and if it’s greater than the critical, there is no significant difference
6. state the conclusion - if results are significant, accept the alternative hypothesis and reject the null, supporting it with the S, N, and critical values, and vice versa if results aren’t significant

Question 135

probability

Answer 135

• a calculation of how likely it is for an event to occur
• probability is denoted by the symbol ‘p’
• the lower the p value, the more statistically significant your results are
• the usual probability level of significance in psychology is 0.05 (5%), meaning there’s a less than 5% chance the observed effect is due to luck and a 95% chance it’s a real effect
• if there’s any risks attached to the research, e.g. like a ‘human cost’ with clinical drug trials, then the p value is set at 0.01 (1%) instead to be even more sure that results are significant

Question 136

significance

Answer 136

• tells us how sure we are about a correlation or difference existing
• if significant, we reject the null hypothesis and accept the alternative hypothesis
• null states there’s no relationship between the variables and alternative states there is a relationship

Question 137

use of statistical tests

Answer 137

• used to determine whether a significant difference or correlation exists
• this is discovered by comparing the calculated value (the result of the statistical test) to a table of critical values (the numerical threshold that stands between accepting or rejecting the null hypothesis)
• to find the critical value to use from the table, the researcher needs to know the probability level (p value), no. of ppts (n value), and whether it was a one-tailed (directional) or two-tailed (non-directional) test
• when there’s an R in the name of the statistical test, the calculated value has to be greater than / equal to the critical value to be significant
• for tests with no R in the name, the calculated value must be lower / equal to the critical value to be significant

Question 138

type 1 and type 2 errors

Answer 138

• when conducting inferential statistical tests, researchers can make 2 types of errors;
• type 1 error; when researchers conclude an effect is real, i.e. they reject the null hypothesis, but it’s actually not (optimistic / false positive) - e.g. when the p threshold is <0.05, but the researchers’ results are part of the 5% of fluke outcomes
• type 2 error; when researchers conclude there is no effect, i.e. they accept the null hypothesis, but there actually is a real effect (pessimistic / false negative) - e.g. the p threshold is set too low and the data just falls short

Question 139

factors affecting the choice of statistical test

Answer 139

• the study design; whether the experimental design is related (i.e. repeated measures or matched pairs) or unrelated (i.e. independent groups)
• the level of data collected; whether the data is nominal (discrete categories), ordinal (ranked with no precise measurements), or interval (standardised units of measurement)
• whether a difference or correlation is being measured

Question 140

when to use different statistical tests

Answer 140

Unrelated (IG): |Related (RM/MP): | Correlational:
N: Chi squared | Sign test | Chi squared
O: Mann Whitney | Wilcoxon |Spearman’s rho
I: Unrelated t-test |Related t-test |Pearson’s r

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