AS + A2 Research Methods (Paper 2) Flashcards
1
Q
Independent variable
A
The variable which is deliberately altered/ manipulated to see what its effect is.
2
Q
Dependent variable
A
The variables in which changes occur due to independent variables .i.e. what is being measured/recorded.
3
Q
Extraneous variables
A
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.
4
Q
Confounding Variables
A
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
5
Q
Laboratory experiments
A
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.
6
Q
Laboratory experiments advantages
A
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
7
Q
Laboratory experiments disadvantages
A
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.
8
Q
Field Experiments
A
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
9
Q
Field Experiments advantages
A
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
10
Q
Field Experiments disadvantages
A
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.
11
Q
Natural experiments
A
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.
12
Q
Natural experiments advantages
A
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
13
Q
Natural experiments disadvantages
A
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
14
Q
Quasi-Experiment
A
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
15
Q
Quasi-Experiment advantages
A
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.
16
Q
Quasi-Experiment disadvantages
A
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.
17
Q
Randomisation
A
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.
18
Q
Standardisation
A
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)
19
Q
Independent groups design
A
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).
20
Q
Repeated measures design
A
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.
21
Q
Matched-pairs design
A
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.
22
Q
Control groups
A
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.
23
Q
Independent groups design advantages
A
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).
24
Q
Independent groups design disadvantages
A
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.
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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
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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.
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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
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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
29
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.
30
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
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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.
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Negative correlations
This is when one variable increases, the other decreases. E.g. the hotter the temperature, the less number of clothes you wear.
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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.
34
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
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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.
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Naturalistic observation
This is about observing people in their natural environment without them knowing that they are being observed.
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Controlled observation
This is about observing people in a controlled environment e.g. a lab
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Participant observation
In this observation, the observer actually joins in and observes him/her self as well as others
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Non-participant observation
When the observer does not take part but merely observes others
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Naturalistic observation advantages
Participants will generally behave naturally as they are probably not aware that they are being observe
High levels of ecological validity
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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
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Controlled observation advantages
High level of control over extraneous variables
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Controlled observation disadvantages
Participants more likely to show demand characteristics as they know that they are being observed.
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Participant observation advantages
Rich more detailed data can be obtained The observer will have a better understanding of the group dynamics
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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
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Non-participant observation advantages
More ecological validity especially if observes are not aware of being observed
The observer is likely to be less biased
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Non-participant observation disadvantages
The actual meaning of the behaviour may not be understood by the observer
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Disclosed/overt observation
This is when the participants know that they are being observe
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Undisclosed/covert observation
This is when the participants do not know they are being observed
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Structured observation
When the observers use a predetermined check list to observe certain behaviours
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Unstructured observation
When the observers don't use a predetermined check list to observe certain behaviours
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Disclosed/overt observation advantages
Ethical – since consent has been taken
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Disclosed/overt observation disadvantages
Participants likely to show demand characteristics as they know that they are being observed
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Undisclosed/covert observation advantages
Less likely to show demand characteristics as the participants don’t know that being observed
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Undisclosed/covert observation disadvantages
Ethical issues – such as lack of consent
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Structured observation advantages
It is easier to gather relevant data because you know what you’re looking for
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Structured observation disadvantages
Interesting behaviours could go unrecorded because they haven’t been predefined as important
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Unstructured observation advantages
More likely to produce qualitative data that is more impressionistic and descriptive rather than numerical
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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
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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.
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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
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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
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Disadvantages of event sampling
If the specified event is too complex, the observer may overlook important details leading to incomplete information
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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
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Advantages of time sampling
Time sampling is effective in reducing the number of observations having to be made
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Disadvantages of time sampling
You may miss important behaviours or events in between the times that you are not observing
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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.
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Consent
This is especially important when observing children – it is normally the consent of the parents that is necessary.
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Open questions
require a lengthy answer and generate qualitative data
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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.
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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
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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.
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Design of Questionnaires
1. Type of data –whether you want qualitative data and/or quantitative data will affect whether you ask open and/or closed questions.
a) Open questions – the participant can answer in any way and in as much detail as they want. This gives detailed, qualitative information, although it may be hard to analyse, as the participants could give very different answers
b) Closed questions – limit the answers that can be given. They give quantitative data that is relatively easy to analyse. However, less detail is obtained about each participant.
2. Ambiguity – you have to avoid questions and answer options which are not clearly defined e.g. do you listen to music frequently? What is meant by frequently?
3. Double barrelled questions – best not to use them, since a person may wish to answer differently to each part. For example, Do you agree that modern music is not as good as the music of the 1960s and that there should be more guitar based music in the charts?
4. Leading questions – these are questions that lead the participant for a particular answer e.g.how tall was the man? This is assuming the man was tall?
5. Complexity – whenever possible clear English should be used, avoiding jargon. If specialist terms are to be used, then they should be clearly defined.
6. Filler questions – it may help to include some irrelevant questions to distract the respondent from the main purpose of the survey – this may help to reduce demand characteristics.
7. Sequence of questions – it is best to start with easy ones, saving questions that might make someone feel anxious or defensive until the middle or end of the questionnaire by which time they will feel much more relaxed
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Strengths of open questions
1.Respondents can expand on their answers which increases the amount of detail of information collected
2.Open questions can provide unexpected answers, thus allowing researchers to gain new insights into people’s feelings and attitudes
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Weaknesses of open questions
1.Respondents who are less literate may find open questions difficult and most respondents may simply avoid lengthy, complex answers, therefore in practice, open questions may not actually provide detailed extra information
2.Open questions can produce qualitative data which is more difficult to summarise because there is likely to be such a wide range of responses – this then limits the researcher to establishing trends or patterns because the data is so detailed and differentiated.
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Strengths of closed questions
An advantage of using closed questions is the nature of the data collected which is quantitative. This type of data makes it easy to analyse the results statistically or in a graphical format. This is useful because direct comparisons can be made between groups of individuals. This means the researcher can look for patterns and trends in the data that can lead to further research being conducted.
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Weaknesses of closed questions
There are limitations to adopting a closed question format in questionnaire research. By sticking to a predetermined list of questions, the researcher is unable to pursue and explore responses that are of particular interest. Additionally, closed questions often produce a response bias. This can happen because the participant doesn’t take the time to read all the questions properly and, for example, selects ‘yes’ for each of their answers. This means that the data generated may lack internal validity.
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Structured/formal interview
Set questions with closed answers
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Unstructured/informal
Not set questions – it is like a casual conversation
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Semi- structured
Some open and some closed questions
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Clinical
Questions are based on the previous answer given
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Structured/formal interview advantages
Quick and cost effective as can gather a large sample
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Structured/formal interview disadvantages
Not enough detail therefore limited analysis
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Unstructured/informal advantages
Participants will be more honest as they feel relaxed
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Unstructured/informal disadvantages
Participants may go off the topic and waste time
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Semi- structured advantages
Lots of data will be gathered in terms of both qualitative and quantitative
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Semi- structured disadvantages
Interviewer bias may occur when the interview asks questions in a certain way
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Clinical advantages
Really get to understand how the person is feeling
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Clinical disadvantages
Unethical – person may feel uncomfortable when questioned so intensely
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Design of interviews
1. How structured the interview will be – in other words is it structured with closed questions, semi-structured with both open and closed or unstructured? For example, if an unstructured interview is being carried out, then the interviewer should be skilled in knowing what follow-up questions to ask, not probing too much into each answer given. Another really important tip is to make sure not to ask questions like, ‘what was it really like?’ or ask ‘why’?
2. Recording the interview – the interviewer may take notes throughout the interview but this is likely to lead to distractions thus it probably makes more since to audio record or video record the interview which can then be play backed as many times as needed
3. Using a question checklist to make sure no questions are asked twice
4. The behaviour or appearance of the interviewer – could influence how the participants react – for example, if the interviewer is dressed in a casual or business dress, the interviewees may/ may not feel more at ease and answer more honestly. Also maintaining eye contact is important and of course having good listening skills is important.
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Aims
An aim is a statement of a study’s purpose- for example the Peterson and Peterson’s study aimed to find out the maximum duration of the STM. The aim will also state whether your study is a test of difference or a relationship between two variables.
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Experimental hypothesis (He)
An experimental hypothesis (He) is when the researcher is carrying out an experiment with an IV (independent variable) and DV (dependant variable) with IV being manipulated and controlled whilst the DV is being measured (you are normally looking for a difference in scores between conditions or groups)
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Alternative hypothesis (Ha)
An alternative hypothesis (Ha) is when the two variables are just being measured but not manipulated. For example, when you are carrying out correlational research. (you are normally looking for a relationship between the two variables (known as co variables)
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Null hypothesis (Ho)
A null hypothesis (Ho) is stating that there will be no difference or correlation between the two variables
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Directional (One-tailed) hypothesis
A specific prediction is made about whether, for example the relationship between two variables will be positive or negative or that one group will perform better than the other group.
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Non-directional (two tailed) hypothesis
Predicts that there will be a relationship between two variables but does not state whether it will be positive or negative, or states there will be a significant difference but not state which group will perform better
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Demand characteristics
Participants guess the aim of the experiment and try to please the experimenter for example, in a memory test, a participant might do badly because this is what S/he thinks the experimenter wants from him/her
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Social desirability bias
When participants try to present themselves in a good light e.g. when a participant may say the only smoke once a week when in reality they are a chain smoker!
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Experimenter effects
A situation where the experimenter, deliberately, or on an unconscious level, influences the results he/she wants to obtain. E.g. the experimenter may smile more and show more eye contact with female participants than male participants which might make the females perform better/worse than males
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Investigator or researcher effects
When the person carrying out the study either consciously or unconsciously influences the participants answers in a questionnaire e.g. when a question is worded in such a way that influences a certain answer from the respondents
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Interviewer effects
When the person conducting the interview influences the interviewees to answer in a certain way either through non verbal or verbal communication.
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Observer bias
For example, in a naturalistic observation, the observer may only observe certain aspects of the observation (e.g. watching more closely when males show aggressive behaviour than females (because his hypothesis may state that males are more aggressive than females)
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Pilot studies
A pilot study is an initial run-through of the procedures to be used in an investigation; it involves selecting a few people and trying out the study on them. It is possible to save time, and in some cases, money, by identifying any flaws in the procedures designed by the researcher. A pilot study can help the researcher spot any ambiguities (i.e. unusual things) or confusion in the information given to participants or problems with the task devised. Sometimes the task is too hard, and the researcher may get a floor effect, because none of the participants can score at all or can complete the task – all performances are low. The opposite effect is a ceiling effect, when the task is so easy that all achieve virtually full marks or top performances and are “hitting the ceiling”.
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Target Population
Who your study is aimed at e.g. students, adults, children, men or women etc?
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Sample
Participants that actually take part in your research selected from your target population.
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Representative
The sample used should be a fair representation of your target population for example if your target population is students aged 11 -19 in a mixed sex secondary school and your sample has more 11- 13 year olds than 18 year olds, then your sample is not representing all ages.
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Generalisation
The sample used in your study should be representative enough for you to be able to generalise (apply) your results to all secondary school students
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Bias
Bias means distorted and when looking at the topic of sampling, we can have two main forms of bias – sample bias and volunteer bias. Sample bias describes the fact that even though all sampling methods aim to produce a representative sample, they are inevitably biased or distorted. For example an opportunity sample is biased because it is chosen on the basis of who is available at that time. Volunteer bias describes the fact that people who volunteer are likely to be different to other members of the population and this biases the data produced.
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Random sampling
Every person in target population has equal chance of being selected e.g. names in a hat
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Random sampling advantages
Fair way to select participants as every member of the population has
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Random sampling disadvantages
May not be representative of target population as we might get too many participants from one particular sub group Often impractical if target population is large as it could end up being time consuming and expensive
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Opportunity sampling
Choose people who are available and willing to take part
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Opportunity sampling advantages
Quick and easy as it is easy to approach people and ask them to take part. Less ethical issues as the participant is consenting to take part
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Opportunity sampling disadvantages
Very unlikely to be representative of target population because only certain types of people ever do agree to take part – this then leads to a biased sample
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Systematic sampling
When every nth person is chosen
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Systematic sampling advantages
Easy to do as you can get the computer to pick every nth person or you can do it yourself from a register
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Systematic sampling disadvantages
May not be representative of target population as only certain people will be selected.
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Self- selected/volunteer sampling
When participants put themselves forward to take part in study after replying to advert/flyer
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Self- selected/volunteer sampling advantages
Ethical as the participants have replied to the advert and put themselves forward large sample to choose from.
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Self- selected/volunteer sampling disadvantages
Only certain people with certain personalities put themselves forward so not representative. For example, it is possible that people who are unemployed will reply to an advert and agree to take part as they may have more time on their hands compared to employed people – leading to a biased sample.
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Stratified sample
Participants chosen according to their proportion in target population. Participants are chosen randomly
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Stratified sample advantages
Very representative of target population as every person has an equal chance of being selected and all subgroups will be selected equally but still using random methods.
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Stratified sample disadvantages
Time consuming and expensive because if you have a very large target population, it may be difficult to get consent of the participants that are chosen randomly.
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Snowball sampling
When participants recommend others like rolling a snowball.
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Snowball sampling advantages
Quick way to gather participants as the participants themselves will recommend so there is no need for the researcher to spend time and money finding participants.
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Snowball sampling disadvantages
Not representative of target population.
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Deception
There are two types of deception that may be used in psychological research. Active deception is deliberately lying to the participants whilst passive deception is withholding important information. The BPS guidelines state that deception should be avoided wherever possible and only used when it’s scientifically justified – where the study would be meaningless otherwise.
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Informed consent
This should be before the study begins and is basically when the participant agrees to take part in the study knowing what the aim and procedure is. The BPS guidelines state participants should be given all information they need to decide whether to participate in research and should not be coerced or pressured. Some participants cannot give their informed consent such as children. In this case the parents should be give their informed consent for their children to participate.
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Protection from psychological and physical harm
This means any negative emotion (e.g. stress, distress, embarrassment) or any physical harm such as pain etc.. The BPS guidelines state researchers have a responsibility to protect participants from physical and psychological harm during the study. Any risk of harm should be no greater than what the participant might experience in their normal life.
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Confidentiality
This means keeping information private and all participants should remain anonymous so that they cannot be recognised
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Privacy
People do not to expect to have their privacy invaded at any time unless they have consented to it. For example: being observed in your own home without your consent is contravening the ethical issue of privacy
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BPS Code of Ethics and Conduct (2009) four principles
respect, competence, responsibility and integrity.
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Peer review
Peer review is the process by which psychological research papers, before publication, are subjected to independent scrutiny by other psychologists working in a similar field who consider the research in terms of its validity, significance and originality. The intention of peer reviewing is to ensure that any research conducted and published is of high quality
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Purposes of Peer review
1. Allocation of research funding – Research is paid by various government and charitable bodies. For example the MRC (Medical Research Council) funds research
2. Publication of research in scientific journals and books – Scientific or scholarly journals provide scientists with the opportunity to share the results of their research
3. Assessing the research rating of university departments – all university science departments are expected to conduct research and this is assessed in terms of quality (Research Assessment Exercise, RAE)
4. To validate the quality and relevance of research – all elements of research are assessed for quality and accuracy: the formulation of hypotheses, the methodology chosen, the statistical tests used and the conclusions drawn
5. To suggest amendments or improvements –reviewers may suggest minor revisions of the work and thereby improve the report or in extreme circumstances they may conclude that the work is inappropriate for publication and should be withdrawn.
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Criticisms of Peer review
1. Unachievable ideal – it isn’t always possible to find an appropriate expert to review a research project or proposal
2. Anonymity – using the single blind technique is usually practised so that reviewers may be honest and objective. However, if the reviewers are doing it for revenge then this creates more problems. Also as research is conducted in the social world where people compete for grants, inevitably they will make friends and enemies. Hence this may interfere with objectivity
3. Publication bias – peer review tends to favour the publication of positive results, possibly because editors want research that has important implications in order to increase the standing of their journal
4. Preserving the status quo – peer review is about maintaining or confirming what existing research has already showed. Thus peer review may be one of the elements that slows down change in scientific theories thus burying the findings of ground-breaking research
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Role of the father (economy)
Psychological research has shown that both parents are equally capable of providing the emotional support necessary for healthy psychological development, and this understanding may promote more flexible working arrangements within the family. It is now the norm in lots of households that the mother is the higher earner and so works longer hours, whilst many couples share childcare responsibilities across the working week. This means that modern parents are better equipped to maximise their income and contribute more effectively to the economy (by paying taxes)
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The development of treatments for mental illness (economy)
Absence from work costs the economy an estimated £15 billion a year. A recent government report revealed that a third of all absences are caused by mild to moderate mental health disorders such as depression, anxiety and stress (The Telegraph, 2014). Psychological research into the causes and treatments of mental illness therefore has an important role to play in supporting a healthy workforce. Many conditions are treated through the use of drugs such as SSRIs for depression and OCD. Anti-anxiety drugs are used for stress conditions.
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Social Change (economy)
Understanding social influence has been used to change people’s behaviour and attitudes. For example, the US state of Montana had a problem with alcohol-related car crashes among 21-34 year olds. While only 20% of people in this age group had driven after drinking, 92% of this age group believed that the majority of their peers did this. By correcting this misperception with adverts stating that most young adults (4 out of 5) do not drink and drive, alcohol-related car crashes were massively reduced
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Improving Memory (economy)
The cognitive interview was developed based on psychological research into the way in which memory works. It has improved the amount of accurate information collected from eyewitnesses. The implication for the economy is that the amount spent on wrongful arrests and wasted police time will be vastly reduced.
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Qualitative data
This is data in the form of information which is lengthy and has lots of detail. Interviews, observations and questionnaires can all generate qualitative data. In addition qualitative data can also be found in: books, pictures, diaries, reports and newspapers etc. Qualitative data is normally collected on the basis on how people think or feel. For example: how do you feel about Theresa May as our PM? or what happened in your dream?
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Qualitative data advantages
1. You can gain lots of detailed data which will help you to appreciate the complexity of human behaviour
2. You can generate new hypothesis from this data
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Qualitative data disadvantages
1. It may be difficult to analyse because the information is so detailed
2. You may be quite subjective when you analyse the detail and of course it may be difficult to generalise and make conclusions.
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Quantitative data
This is data that is expressed numerically. Quantitative data collection techniques usually gather numerical data in the form of individual scores from participants such as the number of words a person remembers in an experiment. Data is open to being analysed statistically and can be easily converted to numerical data.
Some examples of questions that generate quantitative data: On a scale of 1-10 with 1 being not at all likely to 10 being definitely , how likely are you to read a war novel?
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Quantitative data advantages
1. Easy to analyse so that comparisons between groups can be easily drawn
2. Data is more objective and less open to bias
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Quantitative data disadvantages
1. It is narrower in scope and meaning
2. May not always represent ‘real life’
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Primary data
Primary data refers to original data that has been collected specifically for the purpose of the investigation by the researcher. It is data that arrives first-hand from the participants themselves. Data which is gathered by conducting an experiment, questionnaire, interview or observation would be classed as primary data.
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Secondary data
Secondary data refers to data that has been collected by someone other than the person who is conducting the research. In other words, this is data that already exists before the psychologist begins their research. This may be the case when looking at individual case studies where the researcher may analyse data about the individual that others have gathered.
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Meta-analsyis
This refers to a process in which the data from a large number of studies, which have involved the same research questions and methods of research, are combined. The researchers may simply discuss the findings and conclusions – a form of qualitative analysis or they may also use a form of quantitative analysis by carrying out a statistical test on all of the data.
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Meta-analysis advantages
Strengths of carrying out a meta-analysis is that it allows us to view data with much more confidence and results can be generalised across much of the larger population
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Meta-analysis disadvantages
Limitations of using a meta-analysis is that it may be prone to ‘publication bias’ – also referred to as the ‘file drawer problem’. This means that the researcher may not select studies with negative or non-significant results – thus the data from the meta-analysis will be biased because it only represents some of the relevant data and incorrect conclusions may be drawn.
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Mean
- takes all scores into account
- can do further analysis
- may be skewed by anomalies
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Median
- Quick and easy
- Not affected by extreme scores
- Does not take all scores into account
- No further analysis can be carried out
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Mode
- Very easy
- Gives researcher knowledge quickly of what is prevalent
- Becomes meaningless if too many modes
- further analysis cannot be done
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Range
- makes data more meaningful
- easy to do
- doesn’t show frequency of scores
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Standard deviation
- very precise and accurate
- makes data more meaningful
- hard to calculate
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Measures of Central Tendency
Measures of Central Tendency are ‘averages’ which give us information about the most typical values in a set of data. The three averages are: mean, median and mode.
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Measures of Dispersion
Measures of Dispersion are based on the spread of scores, that is, how far scores vary and differ from one another. The two measures are range and standard deviation.
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Sign test
Step 1 – State the hypothesis: people are more happier after going on holiday than before (this was tested two weeks before participants went on holiday and two weeks after) This is a one tailed, directional hypothesis
Step 2 – Record the data on the table, then find the difference between the two conditions.
Step 3 work out whether the difference was more + or less – or the same (0)
Step 4 total the number of pluses and total the number of minuses - in this case, the number of + signs = 10 and the number of – signs = 3.
Step 5 – Find the calculated value of the Sign Test (s). For the Sign test, we always use the total of the less frequent sign which in this case is 3. Thus the calculated value of S = 3
Step 6 – calculate your N value which is the total of the pluses and minuses (which in this case is 10+3 = 13)
Step 7 – check your calculated value against the critical value in the table for the Sign Test.
Step 8 – state your conclusion
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Content Analysis
Content analysis can be defined as: a technique for analysing qualitative data of various kinds. Data can be placed into categories and counted (quantitative) or can be analysed in themes (qualitative).
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Coding
Coding is an important step in conducting content analysis and involves the researcher developing categories for the data to be classified. Qualitative data can be extensive in its nature, for example interview transcripts, and so coding can be helpful in reaching succinct conclusions about the data. These categories provide a framework to convert the qualitative material into quantitative data, which can then be used for further (statistical) analysis.
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Content analysis advantages
1.Content analysis tends to have high ecological validity because it is based on observations of what people actually do – real communications, such as recent newspapers or the books that people read
2.When sources can be accessed by others (e.g. videos of people giving speeches), the content analysis can be replicated, and therefore the observations can be tested for reliability
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Content analysis disadvantages
1.Researchers can still be biased when putting the data into categories which reduces the reliability and validity of the data because different researchers may interpret the meaning of the categories differently
2.Cultural differences may contribute to inconsistent interpretation of behaviour coding since language may be translated and therefore interpreted differently by someone of a different nationality. As a result, the validity of findings from a content analysis can be questioned since it may not have been measuring what it intended to with accuracy.
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Thematic analysis
Thematic analysis is a technique that helps identify themes throughout qualitative data. A theme is an idea or a notion, and can be explicit (such as stating that you feel depressed) or implicit (for example, using the metaphor of a black cloud for feeling depressed).
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Thematic analysis advantages
A strength of thematic analysis is high ecological validity. Much of the analysis that takes place within these research methods are basing their conclusions on observations of real‐life behaviour and written and visual communications. For example, analysis can take place on books people have read or programmes that people have watched on television. Since records of these qualitative sources remain, replication of the content/thematic analysis can be conducted. If results were found to be consistent on re‐analysis then they would be said to be reliable.
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Thematic analysis disadvantages
There is the possibility that thematic analysis can produce findings that are very subjective. For example, the researcher may interpret some things said in an interview in a completely different manner from how they were intended, due to their own preconceptions, judgements or biases.
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Case studies
The purpose of a case study is to provide a detailed analysis of an individual, establishment or real‐life event. A case study does not refer to the way in which the research was conducted, as case studies can use experimental or non‐experimental methods to collect data. For example, a researcher may want to interview the participants, provide a questionnaire to their family or friends and even conduct a memory test under controlled conditions to provide a rich and detailed overview of human behaviour.
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Case studies advantages
A strength of the case study approach is that it offers the opportunity to unveil rich, detailed information about a situation. These unique insights can often be overlooked in situations where there is only the manipulation of one variable in order to measure its effect on another.
Further to this, case studies can be used in circumstances which would not be ethical to examine experimentally. For example, the case study of Genie (Rymer, 1993) allowed researchers to understand the long‐term effects of failure to form an attachment which they could not do with a human participant unless it naturally occurred.
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Case studies disadvantages
There are methodological issues associated with the use of case studies. By only studying one individual, an isolated event or a small group of people it is very difficult to generalise any findings to the wider population since results are likely to be so unique. This therefore creates issues with external validity as psychologists are unable to conclude with confidence that anyone beyond the ‘case’ will behave in the same way under similar circumstances, thus lowering population validity.
An issue in case studies, particularly where qualitative methods are used, is that the researcher’s own subjectivity may pose a problem. In the case study of Little Hans, for example, Freud developed an entire theory based around what he observed. There was no scientific or experimental evidence to support his suggestions from his case study. This means that a major problem with his research is that we cannot be sure that he objectively reported his findings. Consequently, a major limitation with case studies is that research bias and subjectivity can interfere with the validity of the findings/conclusions.
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Test‐Retest Reliability
One very straightforward way of testing whether a tool is reliable is using the test‐retest method. Quite simply, the same person or group of people are asked to undertake the research measure, e.g. a questionnaire, on different occasions.
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Inter‐observer reliability
Inter‐observer reliability refers to the extent to which two or more observers are observing and recording behaviour in a consistent way. This is a particularly useful way of ensuring reliability in situations where there is a risk of subjectivity. For example, if a psychologist was making a diagnosis for a mental health condition, it would be a good idea for someone else to also make a diagnosis to check that they are both in agreement.
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Improving Reliability: Questionnaires
For questionnaires, it will be possible to identify which questions that are having the biggest impact upon the reliability, and adjust them as necessary. If it is deemed that they are important items that must remain in the questionnaire, then rewriting them in a manner that reduces the potential for them to be incorrectly interpreted may be enough. For example, if the item in question is an open question, it may be possible to change it into a closed question, reducing possible responses and thereby limiting potential ambiguity.
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Improving Reliability: Interviews
If reliability needs improving in an interview, there are several factors that can be adjusted. Firstly, ensuring that the same interviewer is conducting all interviews will help reduce researcher bias; there is the potential for variation in the way that questions are asked which can then lead to different responses. Equally, some researchers may ask questions that are leading or are open to interpretation. If the same interviewer cannot be used throughout the interviewing process, then training should be provided in order to limit the potential bias. Further to this, changing the interview from unstructured to structured will limit researcher bias
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Improving Reliability: Observations
Observations can lack objectivity, since they are relying on the researcher’s interpretations of a situation. If behavioural categories are being used, it is important that the researcher is applying them accurately and not being subjective in their interpretations. One way to improve reliability in this instance would be to operationalise the behavioural categories. This means that the categories need to be clear and specific on what constitutes the behaviour in question. There should be no overlap between categories leaving no need for personal interpretation of the meaning.
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Internal validity
Internal validity is a measure of whether results obtained are solely affected by changes in the variable being manipulated (i.e. by the independent variable) in a cause and effect relationship.
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External validity
External validity is a measure of whether data can be generalised to other situations outside of the research environment.
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Ecological Validity
This is a type of external validity, and refers to the extent to which psychologists can apply their findings to other settings – predominantly to everyday life. A lack of ecological validity is typically a point made when discussing weaknesses of laboratory based studies. Due to the artificial and contrived setting of a laboratory, it stands to reason that it is difficult to generalise the findings to a more natural situation since behaviour may be very different as a result. Realistically, it is a multitude of variables that make a laboratory experiment low in ecological validity, including the use of artificial stimulus materials.
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Temporal Validity
Temporal Validity is another form of external validity, which refers to the extent to which research findings can be applied across time. For example, Asch’s research into conformity is often said to be lacking temporal validity because the study was a ‘child of its time’, that is, the findings were a product of the fact that the study was conducted in a conformist era, and thus the findings might not be as applicable in today’s society.
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Population Validity
This is another form of external validity, which refers to the extent to which the research can be applied to different groups of people apart from the group that were used in the study. For example, Asch’s study was carried out on males – could the study also be applied to females?
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Improving Validity: Experiments
In experimental research, a control group is often used. This allows psychologists to see whether the independent variable influences the dependent variable. For example, if researchers are testing the efficacy of a new anti‐depressant drug, they will often have an experimental group (who receive the true medication) and a control group (who receive a placebo). In this case, using a control group would allow a comparison to see whether the medication was truly effective, thus giving greater confidence in the validity of the research.
Research can also include single‐blind or double‐blind procedures as a means to improve validity. The use of a single‐blind procedure means that participants do not know which condition they are in, whilst double‐blind means that neither the participant nor the researcher knows which condition participants are in. This ensures that the knowledge of the conditions does not result in demand characteristics on behalf of the participants or investigator effects from the direct or indirect behaviour of the experimenter.
Another way of improving validity in experimental research is to use standardised instructions, which involves giving all participants the same instructions in exactly the same format. By ensuring that participants receive identical information, psychologists can minimise investigator effects. In this way participants are less likely to have a different interpretation of what they are required to do, whilst the researcher is at less risk of giving a higher level of information to some participants compared to others.
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Improving Validity: Questionnaires
The validity of questionnaires can also be improved using relatively straightforward techniques. Often, researchers will include a lie scale to check the consistency of participants’ responses.
One way in which this can be done is by having two items that are asking the same thing, but in opposite ways. For example, on a scale measuring depression imagine that each item asks participants to rate from 1 to 5, with one being ‘completely disagree’ and 5 being ‘completely agree’. There might be one item in the scale that says, ‘I generally sleep well at night’ and another that says, ‘my sleeping has become worse’. A participant could not respond to both items honestly with a rating of with 5, ‘strongly agree’, because they contradict each other. Such items are then used to check the validity of an individual participant’s scores.
Another way of improving validity in a questionnaire is to ensure that participants know that their responses are going to be kept anonymous because, by remaining unidentifiable, participants are less likely to give answers that are socially desirable.
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Improving Validity: Observations
When it comes to observations, psychologists can improve validity, in particular ecological validity, by making sure that the researchers have minimal impact on the behaviour that they are observing.
One way of doing this is to conduct a covert observation, where the researcher is not seen. By doing this, researchers increase the likelihood that the behaviour observed is natural, as participants will not be acting in a way that they deem correct or desirable for the sake of the study.
Another way of improving validity in observations is the use of behavioural categories. In this instance, researchers will tick off behaviours when they are seen which helps to improve validity by reducing the chance of researcher subjectivity. Ensuring that the categories are clearly defined, and do not overlap, would also further improve validity in observations.
Research that employs qualitative methodology as opposed to quantitative methodology is often regarded as having higher ecological validity due to the depth of data that is collected, often through the use of case studies or interviews.
However, validity can be lowered because analysis is more subjective and open to the investigator’s interpretation. To strengthen the validity here, there are several things that can be done.
First of all, simply including direct quotes from participants can help to improve validity, as it provides evidence that what was being inferred from the data is accurate. Also, validity can be improved by collecting data from a variety of sources; for example, having data that has come from interviews, observations and written reports from participants which is a process called triangulation.
