AS + A2 Research Methods (Paper 2) Flashcards
Independent variable
The variable which is deliberately altered/ manipulated to see what its effect is.
Dependent variable
The variables in which changes occur due to independent variables .i.e. what is being measured/recorded.
Extraneous variables
these are variable that could interfere with the measuring of the IV and can be subdivided into participant variables and situational variables. Participant variables are any individual differences between participants that may affect the measuring of the DV e.g. personality, age whereas situational variables are any features of the experimental situation that may affect the DV.
Confounding Variables
these are variables that actually interfere with the measuring of the DV e.g. imagine you are doing an experiment on the effects of a new energy drink on levels of talkativeness – you pick 20 participants, 10 will consume the new energy drink and the other 10 will consume water only. You them measure how much both groups of participants talk for the next hour. Imagine group A (the energy drink group) are all extroverts and group B (the water group) are introverts. You will find that all the participants in group A talk much more than participants in Group B. However, was this due to the drink or the fact that all participants in group A were extroverts – thus personality now becomes a confounding variable
Laboratory experiments
Research is carried out in a controlled way.
The aim is to control all variables except one key variable, which is deliberately altered/ manipulated to see what its effect is.
For an experiment to be successful, the dependent and independent variables must be operationalised .i.e. clear, specific and testable. For example, if measuring aggression in children this must be testable .e.g. the number of times the child shows displays a verbal or physical act of aggression in a 10 minute period.
There may also be confounding variables which the experimenter will try and control such as noise, light etc
The aim of controlling EVs is to minimize their possible impact on the results of the investigation. An example of extraneous variables may be the participant’s personality or personal experiences
Laboratory experiments are conducted in an artificial setting.
Laboratory experiments advantages
Control- the effects of extraneous variables are minimized, so the experimenter can be more confident that is the independent variable which has affected the dependent variable.
Replication- strict controls means it is easier to replicate the study to test to reliability of findings.
Cause and effect – the cause and effect can be determined since the cause would be the IV and effect would be DV
Laboratory experiments disadvantages
Lack of ecological validity- because the setting is artificial, experiments may not be a reflection of real-life behaviour.
Demand characteristics- participants may either accurately or inaccurately guess the aim of the experiment and respond and behave according to what they think is being is investigated.
Field Experiments
Behaviour is measured in a natural environment like a school or street. The independent variable is manipulated by the experimenter (i.e. participants are put into conditions) so that its effect can be measured through the dependent variable
Field Experiments advantages
Ecological validity- field experiments are less artificial than those done in a laboratory, so they relate better to real life.
Demand characteristics- these can be avoided in a field study if participants aren’t aware that they’re in a study.
Cause and effect can still be determined since the manipulation of the IV is the cause and the measuring of the DV is the effect
Field Experiments disadvantages
Less control- it is harder to minimize extraneous variables in a field study, making it harder to come to a conclusion. Also less control over the sample (people being used in the experiment)
Ethics- participants who didn’t agree to take part might experience distress and can’t be debriefed. Observation must respect privacy.
Harder to replicate fully – because this is being carried out in the real world, you will never get the same sample.
Natural experiments
A natural experiment is a study that measures variables that aren’t directly manipulated (caused) by the experimenter, for example comparing behaviour in a single-sex and mixed school. This then means that the IV is naturally occurring. Effectively the experimenter is finding participants who already meet the conditions of the experiment, rather than allocating participants to conditions themselves.
Natural experiments advantages
Ethics - makes it possible to study variables that it would be unethical or impossible to manipulate e.g. comparing schizophrenic to non-schizophrenic individuals or comparing a community that has TV with a community that doesn’t to see which is the most aggressive
High level of ecological validity – because the experiment is carried out in a natural environment and the IV is not manipulated but naturally occurring, this allows for natural behaviour to be measured
Natural experiments disadvantages
Participant allocation - you can’t randomly allocate participants to each condition, and so extraneous variables (e.g. what area the participants live in) may affect results making it very difficult to reach conclusions.
Rare events - some groups of interest are hard to find e.g. a community which doesn’t have TV
Quasi-Experiment
This is very similar to a natural experiment in that the Independent Variable is not directly manipulated. However, Quasi experiments are generally carried out in a lab setting. An example of a variable that cannot be directly manipulated but can still be carried out under controlled conditions is gender – to test gender differences in memory
Quasi-Experiment advantages
Control - the effects of extraneous variables are minimized, so the experimenter can be more confident that is the independent variable which has affected the dependent variable.
Replication - strict controls means it is easier to replicate the study to test to reliability of findings.
Quasi-Experiment disadvantages
Lack of ecological validity - because the setting is artificial, these experiments may not be a reflection of real-life behaviour.
Demand characteristics - participants may either accurately or inaccurately guess the aim of the experiment and respond according to what they think is being is investigated.
Randomisation
The use of chance in order to control for the effects of bias when designing materials and deciding the order of conditions. For example, to make sure that a list of words are not too easy or too hard, it is a good idea to put them in random order which can be done through a computer or manually.
Standardisation
Using exactly the same formalised procedures and instructions for all participants in a research study – this improves the reliability of the study (the ability to repeat the study again and get the same findings)
Independent groups design
This is where there are different participants in each group/condition. In other words, there are a different set of participants in all the conditions. Normally (but not always) an independent groups design is used to compare gender differences, age differences or any differences between people. But the participants only do the condition once, this avoids the problem that if all the participants did the test in both conditions any improvement in performance may be due to them having had a second opportunity to complete the task (which would be an extraneous variable).
Repeated measures design
This is where the same participants are used in each condition. The researcher can therefore compare the performances in each condition knowing that the differences weren’t due to participant variables (in other words, one group of pps were not better than the other as in an independent groups design) An example of an experiment when a repeated measure design could be used when we are comparing the performance of our pps when in condition A – they have consumed and energy drink and after some time interval e.g. a day, the same pps are tested but this time, they are given water. This allows for direct comparisons to be made without worrying about extraneous variables such as one group better than the other as would be the case in an independent groups design.
Matched-pairs design
This is where there are different participants in each condition, but they are matched on important variables (e.g. age, sex and personality, IQ). This then allows comparisons to be made but without one group just being better than the other since the participants in each group have been matched with each other so that participants in group A are equally matched with participants in group B. A matched pairs design avoids order effects as well as less chance of demand characteristics.
Control groups
Some studies use control groups. These are groups which have not experienced any of the manipulations of the IV that the experimental group might have. This allows the researcher to make a direct comparison between them in order to assess the impact of the IV.
Independent groups design advantages
No order effects through either getting better through practice (learning effects) or getting worse through being bored or tired (fatigue effects)
Less likely to guess the aim of the experiment and change behaviour to please the experimenter (demand characteristics).
Independent groups design disadvantages
Participant variables - Differences between people in each group may affect the results e.g. one group may just happen to be composed of individuals who have a better memory
Twice as many participants are needed to obtain the same amount of data compared to having everyone do both conditions.
Repeated measures design advantages
Less chance of participant variables- because the same people do the test in all conditions any differences between individuals shouldn’t affect results.
Fewer participants are needed to get the same amount of data as opposed to an independent groups de
Repeated measures design disadvantages
Order effects- any improvements in later conditions could be due to practice rather than due to the effects of the independent variables. Alternatively participants could perform worse due to fatigue or boredom. But, can be overcome by counterbalancing when half of the participants do condition 1 first and half do condition 2 and then alternate.
Greater risk for demand characteristics as they are participating on more than one condition.
Matched-pairs design advantages
No order effects because the participants are only doing the condition once.
Less chance of Participants variables- important differences are minimized through matching
Matched-pairs design disadvantages
As with independent groups design, twice as many participants are required compared to repeated measures.
Practicalities: time-consuming and can be difficult to find participants who match on key variable
Random allocation
An attempt to control for participant variables in an independent groups design which ensures that each participant has the same chance of being in one condition or the other – the process of random allocation occurs when for example, if there are to be 10 participants in Condition A and 10 in Condition B, then we could put 10 pieces of paper with A and 10 pieces of paper with B in a hat, each pp then picks a paper out of the hat to assign them to that condition.
Counterbalancing
An attempt to control for the effects of order in a repeated measures design; half the participants experience the conditions in one order while the other half in the opposite order. For example, you could have 10 pieces of paper with Condition A and 10 with condition B. You could put these papers in a hat, Each pp picks a paper and then does that condition first, then do the other condition afterwards so that 10 participants can do condition A first and 10 do condition B, then you alternate. Another way that random counter balancing could occur is the ABBA method where the first pp does A then B and the second pp does B then A and so on
Positive correlations
This is when both variables decrease or increase e.g. the hotter it is, the more ice-creams are eaten, or the lower the temperature, the less ice-creams are eaten.
Negative correlations
This is when one variable increases, the other decreases. E.g. the hotter the temperature, the less number of clothes you wear.
Correlation coefficients
When analysing the correlational data using tests, you will come up with one number – known as the ‘observed or calculated’ value which is then compared against a critical value in a table relevant to those two tests. This observed value is always a number between +1 and -1 and can be any of the following numbers:
-1, -0.9, -0.8, -0.7, -0.6, -0.5, -0.4, -0.3, -0.2, -0.1, 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 +1
A strong positive correlation coefficient will be a number closer to +1 whereas a strong negative correlation coefficient will be a number closer to -1. If a correlation coefficient is a number which is closer to 0, then this is normally shown as no correlation.
Strengths of Correlations
Useful preliminary tool for research – by assessing the strength and direction of a relationship, they provide a precise and quantifiable measure of how two variables are related
Correlations are often used as a starting point to assess possible patterns between variables and can provide a tool for further study.
Correlations are often quick and economical to carry out as there is no need for a controlled environment or manipulation of variables.
Correlational studies tend to be ethical as opposed to experiments where deception could be an issue
Weaknesses of correlations
Correlations do not show cause and effect, they only show a relationship.
There could be a third variable – which is actually causing the relationship between the two variables, For example, it could be that rather than high anxiety levels making you drink more caffeine, it could actually be being in a high pressured job that makes you anxious and thus drink more caffeine.
Correlational research is often exaggerated in the media as showing cause and effect which can sometimes have negative consequence for certain groups of people. For example although there may be a relationship between single parent families and crime, this does not mean that all children in single parent families will commit crime but in the media this is not clarified – so the research data can be misused.
Naturalistic observation
This is about observing people in their natural environment without them knowing that they are being observed.
Controlled observation
This is about observing people in a controlled environment e.g. a lab
Participant observation
In this observation, the observer actually joins in and observes him/her self as well as others
Non-participant observation
When the observer does not take part but merely observes others
Naturalistic observation advantages
Participants will generally behave naturally as they are probably not aware that they are being observe
High levels of ecological validity
Naturalistic observation disadvantages
There may be ethical issues such as ‘consent’ especially if the pps are not aware that they are being observed
No control over extraneous variables
Controlled observation advantages
High level of control over extraneous variables
Controlled observation disadvantages
Participants more likely to show demand characteristics as they know that they are being observed.
Participant observation advantages
Rich more detailed data can be obtained The observer will have a better understanding of the group dynamics
Participant observation disadvantages
The observer may lose his/her objectivity and be biased and subjective in his/her views The observer may have to rely on their memory when they recall facts about the observation
Non-participant observation advantages
More ecological validity especially if observes are not aware of being observed
The observer is likely to be less biased
Non-participant observation disadvantages
The actual meaning of the behaviour may not be understood by the observer
Disclosed/overt observation
This is when the participants know that they are being observe
Undisclosed/covert observation
This is when the participants do not know they are being observed
Structured observation
When the observers use a predetermined check list to observe certain behaviours
Unstructured observation
When the observers don’t use a predetermined check list to observe certain behaviours
Disclosed/overt observation advantages
Ethical – since consent has been taken
Disclosed/overt observation disadvantages
Participants likely to show demand characteristics as they know that they are being observed
Undisclosed/covert observation advantages
Less likely to show demand characteristics as the participants don’t know that being observed
Undisclosed/covert observation disadvantages
Ethical issues – such as lack of consent
Structured observation advantages
It is easier to gather relevant data because you know what you’re looking for
Structured observation disadvantages
Interesting behaviours could go unrecorded because they haven’t been predefined as important
Unstructured observation advantages
More likely to produce qualitative data that is more impressionistic and descriptive rather than numerical
Unstructured observation disadvantages
It may be difficult to come up with conclusion since the data may be so detailed which therefore may lead to problems with generalisation
Design of observation
Recording data - if you want qualitative data you could just make written notes. But video or audio recording means that you have a more accurate permanent record
Categorising data - you must define the behaviours you observe – known as behavioural categories. For example, if you are observing aggression in children then you must operationalise or define what constitutes as aggression for example physical aggression is punching hitting etc, and verbal aggression is swearing, shouting etc
Rating behaviour - you could use a rating scale e.g. from 1 - 10. You could put each participant’s behaviour into several categories. You could use a coding system where every participant is given a number. Behaviour rated in this way provides quantitative data in the form of numbers.
Inter-observer reliability - where the scores of all the observers correlate highly with each other. This is one way that you can overcome observer bias – when observers only observe what they want to find. The way that inter-observer reliability is measured is when a structured observation is carried out and behavioural categories are predefined. Two observers then, will record the observation separately and then correlate their data using a Spearman’s rho correlation coefficient. If the correlation coefficient is more than +0.8 – then there is high inter observer reliability between the researchers and one could argue that the observation is reliable.
Event sampling
This involves counting the number of times a particular behaviour (the event) occurs in a target individual or group – for instance the number of times students recycle in a school canteen
Advantages of event sampling
Event sampling is useful when the target behaviour or event happens quite infrequently and could be missed if time sampling was used
Disadvantages of event sampling
If the specified event is too complex, the observer may overlook important details leading to incomplete information
Time sampling
Involves recording behaviour in a pre-established time frame. For example, we may record recycling behaviour of students in a school canteen every 1 minute using a checklist or just write down everything that is happening for that 1 minute
Advantages of time sampling
Time sampling is effective in reducing the number of observations having to be made
Disadvantages of time sampling
You may miss important behaviours or events in between the times that you are not observing
Invasion of privacy
This cannot be avoided but observers are not allowed to observe people in a non-public place unless they get consent of the participants.
Consent
This is especially important when observing children – it is normally the consent of the parents that is necessary.
Open questions
require a lengthy answer and generate qualitative data
Closed questions
Require one worded answers. For example, yes/no, or using a rating scale from 1 – 5. These types of questions generate quantitative data.
Advantages of questionnaires
Cheap – you don’t need trained people to give the questionnaires as you would need in an interview
Large sample can be asked meaning that your results will be representative hence you can then generalise
Quick and easy to do – if the questionnaires generally have closed questions, respondents will be able to complete them quickly saving time
Disadvantages of questionnaires
People can lie
People can show demand characteristics (they guess what the aim of the questionnaire is and answer in a way to please the researcher)
People will show social desirability bias (respondents may answer in a way so that they are seen in a more positive light – e.g. how often do you smoke? Respondents may say they smoke less than they actually do)
Not all people may respond and those who do, may be of a certain personality which means that your sample will be biased – for example, it could be that more older people (aged 65+) complete the questionnaire than younger people – this then means that researchers will not have a full picture of a topic but only have a viewpoint more suited to older people which then means that the data gathered is not representative of all age groups.