4.2.3 Research Methods Flashcards

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1
Q

the experimental method

A
  • 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
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2
Q

laboratory experiments

A
  • 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
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3
Q

lab experiments - strengths

A
  • 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
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4
Q

lab experiments - limitations

A
  • 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
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5
Q

field experiments

A
  • 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
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6
Q

field experiments - strengths

A
  • 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
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7
Q

field experiments - limitations

A
  • 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
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8
Q

natural experiments

A
  • 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
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9
Q

natural experiments - strengths

A
  • 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
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10
Q

natural experiments - limitations

A
  • 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
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11
Q

quasi experiments

A
  • 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.
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12
Q

quasi experiments - strengths

A
  • 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
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13
Q

quasi experiments - limitations

A
  • 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
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14
Q

naturalistic observation

A
  • observations made in a real-life setting
  • e.g. setting up cameras in a school to see how people interact in those environments
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15
Q

controlled observations

A
  • observations made in an artificial setting set up for the purposes of observation
  • e.g. zimbardo’s stanford prison experiment
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16
Q

covert observation

A
  • 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
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17
Q

overt observation

A
  • 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
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18
Q

participant observation

A
  • 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
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19
Q

non-participant observation

A
  • 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
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20
Q

self-report techniques

A

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

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21
Q

questionnaires

A
  • 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
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22
Q

questionnaires - strengths

A
  • 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
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23
Q

questionnaires - limitations

A
  • 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
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24
Q

structured interviews

A
  • 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
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25
Q

unstructured interviews

A
  • 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
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26
Q

semi-structured interviews

A
  • 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
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27
Q

correlations

A
  • 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
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28
Q

scattergrams

A
  • 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
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29
Q

correlation co-efficient

A
  • 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
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30
Q

correlational analysis - strengths

A
  • 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
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31
Q

correlational analysis - limitations

A
  • 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
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32
Q

content analysis

A
  • 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.
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33
Q

content analysis procedure

A
  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
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34
Q

content analysis - reliability

A
  • 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
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35
Q

content analysis - Waynforth & Dunbar (1995)

A
  • 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
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36
Q

thematic analysis

A
  • 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
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37
Q

thematic analysis procedure

A
  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
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38
Q

content and thematic analysis - strengths

A
  • 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
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39
Q

content and thematic analysis - limitations

A
  • 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
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40
Q

case studies

A
  • 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
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41
Q

case studies - strengths

A
  • 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
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42
Q

case studies - limitations

A
  • 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
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43
Q

scientific processes

A

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

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44
Q

aims

A
  • a precise statement about what the researchers are investigating and why
  • e.g. ‘to investigate the effect of SSRIs on symptoms of depression’
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45
Q

hypothesis

A
  • 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
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46
Q

types of hypothesis

A
  • 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
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47
Q

sampling

A
  • 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
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48
Q

sampling techniques

A
  • random
  • systematic
  • stratified
  • opportunity
  • volunteer
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49
Q

random sampling

A
  • 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
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50
Q

systematic sampling

A
  • 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
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51
Q

stratified sampling

A
  • 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
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52
Q

opportunity sampling

A
  • 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
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53
Q

volunteer sampling

A
  • 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
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54
Q

pilot studies

A
  • 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
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55
Q

repeated measures design

A
  • when the same ppts complete each of the experiment conditions
  • ppts are competing with themselves, producing related data
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56
Q

repeated measures design - strengths

A
  • 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
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57
Q

repeated measures design - limitations

A
  • 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
58
Q

independent measures design

A
  • 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
59
Q

independent measures design - strengths

A
  • 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
60
Q

independent measures design - limitations

A
  • 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
61
Q

matched pairs design

A
  • 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
62
Q

matched pairs design - strengths

A
  • 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
63
Q

matched pairs design - limitations

A
  • 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
64
Q

observational design

A
  • 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
65
Q

behavioural categories

A
  • 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
66
Q

sampling procedures

A
  • 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
67
Q

event sampling

A
  • 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
68
Q

time sampling

A
  • 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
69
Q

inter-observer reliability

A
  • 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
70
Q

questionnaire construction

A
  • 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
71
Q

interview construction

A
  • 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
72
Q

variables

A
  • 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
73
Q

extraneous and confounding variables

A
  • 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
74
Q

operationalising variables

A
  • 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
75
Q

controls

A
  • 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
76
Q

controls - random allocation

A
  • 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
77
Q

controls - counterbalancing

A
  • the researcher splits ppts in half and both groups are presented the IV in different orders
  • this helps to reduce order effects
78
Q

controls - randomisation

A
  • ppts are randomly assigned to conditions
  • used to reduce systematic errors that the order of the trials may present
  • i.e. reduces order effects
79
Q

controls - standardisation

A
  • 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
80
Q

demand characteristics

A
  • 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
81
Q

investigator effects

A
  • 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
82
Q

ethics

A
  • 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
83
Q

BPS (2009) code of ethics

A
  • 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
84
Q

how studies can protect from harm

A
  • 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
85
Q

peer review

A
  • 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
86
Q

peer review process

A
  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
87
Q

types of peer review

A
  • 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)
88
Q

peer review - strengths

A
  • 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
89
Q

peer review - limitations

A
  • 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
90
Q

implications of psychological research for the economy

A
  • 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
91
Q

reliability

A
  • 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
92
Q

assessing reliability

A
  • 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
93
Q

improving reliability

A
  • practice through conducting a pilot study
  • standardising procedures (reduces investigator effects)
94
Q

validity

A
  • 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
95
Q

assessing validity

A
  • 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
96
Q

improving validity

A
  • internal validity can be improved by reducing investigator effects and demand characteristics
  • external validity can be improved by conducting experiments in naturalistic settings
97
Q

features of science

A
  • objectivity and the empirical method
  • replicability
  • falsifiability
  • theory construction and hypothesis testing
  • paradigms and paradigm shifts
98
Q

features of science - objectivity and the empirical method

A
  • 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
99
Q

features of science - replicability

A
  • 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.
100
Q

features of science - falsifiability

A
  • 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
101
Q

features of science - theory construction and hypothesis testing

A
  • 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
102
Q

features of science - paradigms and paradigm shifts

A
  • 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
103
Q

scientific report

A
  • 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
104
Q

the structure of a scientific report

A
  • 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
105
Q

quantitative data

A
  • data in the form of numbers (numerical)
  • often produced from lab experiments or closed questions
  • however it’s less detailed
106
Q

qualitative data

A
  • 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
107
Q

primary data

A
  • 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
108
Q

secondary data

A
  • 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
109
Q

meta-analysis

A
  • 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
110
Q

measures of central tendency

A
  • these are ways of reducing large data sets into averages
  • includes mean, median and mode
111
Q

mean

A
  • 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
112
Q

median

A
  • 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
113
Q

mode

A
  • 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
114
Q

measures of dispersion

A
  • they quantify how much scores in a data set vary
  • includes range and standard deviation
115
Q

range

A
  • 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
116
Q

standard deviation

A
  • 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
117
Q

percentages

A
  • 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
118
Q

correlation

A
  • 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
119
Q

presentation of quantitative data

A
  • tables
  • scattergrams
  • bar charts
  • histograms
  • line graphs
  • pie charts
120
Q

tables

A
  • data tables record raw results
  • summary results tables summarise results and present descriptive statistics, e.g. mean, mode, etc.
121
Q

scattergrams

A
  • used to represent correlational data, showing the relationship between 2 variables on the x and y axis
122
Q

bar charts

A
  • 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
123
Q

histograms

A
  • 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
124
Q

line graphs

A
  • 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
125
Q

pie charts

A
  • provide a visual representation of percentages
126
Q

normal distribution

A
  • 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
127
Q

skewed distribution

A
  • 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
128
Q

nominal data

A
  • 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
129
Q

ordinal data

A
  • 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
130
Q

interval data

A
  • 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
131
Q

coding, content and thematic analysis

A
  • 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
132
Q

inferential testing

A
  • 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
133
Q

the sign test

A
  • 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
134
Q

how to conduct a sign test

A
  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
135
Q

probability

A
  • 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
136
Q

significance

A
  • 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
137
Q

use of statistical tests

A
  • 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
138
Q

type 1 and type 2 errors

A
  • 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
139
Q

factors affecting the choice of statistical test

A
  • 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
140
Q

when to use different statistical tests

A

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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