Research Methods Year 1 Flashcards
What is an experimental method ?
A scientific method that involves the manipulation of variables to determine cause and effect.
However, establishing this is not easy + requires researchers to conduct studies that are classified as ‘true experiments’.
What is a true experiment ?
There is a control condition + an experimental condition.
participants are randomly assigned to these conditions so the researcher can make fair comparisons between both groups.
The researcher manipulates the IV to test its effect on the DV.
everything else is kept the same (control variable).
If there is a change in the results, the IV caused the change in the DV (cause + effect)
What is the IV ?
Independent variable.
A variable that is manipulated by the researcher, or changes naturally.
What is the DV ?
Dependent variable.
The variable that is measured by the researcher. Any effect on the DV should be caused by changes in the IV.
What is the DV ?
Dependent variable.
The variable that is measured by the researcher. Any effect on the DV should be caused by changes in the IV.
How can we test the effect of the IV ?
- The control condition
- The experimental condition
What is the control condition ?
a baseline measure of behaviour without experimental treatment.
What is the experimental condition ?
One where there has been researcher manipulation.
The researcher is keen to see if a difference in behaviour has occurred.
What is operationalisation ?
Clearly defining variables so they can be measured.
E.g. intelligence, social anxiety…
What is an aim ?
A general statement of what the researcher intends to investigate (the purpose of the study).
They are developed from theories.
What is a hypothesis ?
A testable statement predicting the outcome of the study which is made at the start of the study.
What are the 2 types of hypotheses ?
- Alternate/experimental hypothesis
- Null hypothesis
What is the null hypothesis ?
Predicts that there will be no difference/relationship.
What is an alternate/experimental hypothesis ?
Predicts a difference/relationship and can be directional or non directional
What is a directional hypothesis ? (Or one tailed hypothesis)
The researcher makes it clear what sort of difference that may be seen between the 2 conditions.
The hypothesis may use the words like ‘less’ ‘more’ ‘higher’ ‘lower’
What is a non directional hypothesis ? (Or a two tailed hypothesis)
When there has been no previous research to suggest what direction the research will go in, or the previous findings have been contradictory.
The researcher states ‘there will be a difference’ or ‘there will be a relationship’ between the 2 conditions.
The direction of the outcome isn’t mentioned.
What are the 5 steps to writing an experimental hypothesis ?
- Identify the IV and the DV
- How is the IV manipulated e.g. what are the levels of the IV? (Compare)
- How has the DV been measured exactly? Operationalise the DV.
- Should the hypothesis be one tailed or two tailed?
- Write your hypothesis- Put all the information together.
Template for a non directional hypothesis :
There will be a difference in (DV) , measured by (OPERATIONALISED DV) for participants who (IV - CONDITION 1) compared to those who (IV - CONDITION 2).
How do you write a 1 tailed hypothesis + template for it :
Follow steps I-3, then you will identify in step 4 that previous research has been conducted that has demonstrated the direction the researcher is likely to go in.
+ compare the groups.
Participants who (IV - CONDITION 1) will be more/less (OPERATIONALISED DV) than participants who (IV - CONDITION 2)
What is a correlational hypothesis ?
this looks at a relationship between two co-variables.
there is no IV or DV in a correlation.
it can still be directional or non-directional.
the co-variables must still be clearly operationalised.
template for a correlational hypothesis ?
there will be a correlation between (co variable I) and (co variable 2)
non directional : there will be a correlation…
directional : there will be a positive/negative correlation…
What are the 4 types of experiments ?
- Laboratory experiments
- Field experiments
- Natural experiments
- Quasi-experiments
What are laboratory experiments ?
experiments that are conducted in a highly controlled setting, usually a research laboratory where participants are aware of being observed and part of a study.
the researcher manipulates the IV + records the effects of the DV.
participants can be randomly allocated to conditions (so considered a ‘true’ experiment)
what are the advantages of a lab experiment ?
- High control over extraneous variables meaning cause and effect can be established.
- Replication is possible due to the high level of control. This means results can be checked for reliability.
what are the disadvantages of a lab experiment ?
- Participants are often aware of being tested - possible demand characteristics.
- Artificial environment means it may lack generalisability.
- Investigator effects may occur (unless it is a double blind experiment- this is when both the participant and the researcher conducting the experiment does not know the aims of the investiaation)
what is a field experiment ?
the researcher still manipulates the IV and records the effects on the DV but the experiment is conducted in a real life setting (natural)
what are the advantages of field experiments ?
- High ecological validity due to being conducted in a real-life setting.
- Behaviour is likely to be more valid & authentic (less demand characteristics).
what are the disadvantages of field experiments ?
- There is less control over extraneous variables (these will be different depending on the experiment).
- It is difficult to replicate them completely because they tend to be less controlled.
- Possible ethical issues if participants are unaware they’re being studied.
what is a natural experiment ?
the researcher takes advantage of a naturally occurring IV and the effect it has on the DV.
the experimenter has not manipulated the IV directly; the IV would vary naturally whether or not the researcher was interested.
the researcher cannot randomly allocate participants to conditions and/or has no control over the IV. this is not a ‘true’ experiment.
it is the IV that is natural, not necessarily the setting.
what are the advantages of natural experiments ?
- Provides opportunities for research that may not be otherwise conducted due to practical /ethical reasons
- They have high external validity because they involve the study of real-life.
what are the disadvantages of natural experiments ?
- A naturally occurring event may happen, rarely limiting generalisation to other situations.
- Participants may not be randomly, allocated to conditions.
what is an example of a natural experiment ?
Romanian Orphan studies (Attachment topic)
IV = adoption before or after the age of 6 months (naturally occurring/varying)
What are quasi experiments ?
Studies that are ‘almost experiments. The IV is not something that varies at all - it is simply a difference between people that exists.
The researcher records the effects of this ‘quasi-IV’ on the DV.
The researcher cannot randomly allocate participants to conditions and/or has no control over the IV.
This is also not a ‘true’ experiment.
The IV is naturally occurring.
the DV may be measured in a laboratory.
e.g. a study of gender where males and females are compared.
what are the advantages of quasi experiments ?
- Carried out under controlled conditions
- High control over extraneous variables meaning cause and effect can be established.
- Replication is possible due to the high level of control. This also means results can be checked for reliability.
what are the disadvantages of quasi experiments ?
- Participants are aware of being tested - possible demand characteristics.
- Participants cannot be randomly allocated so there may be confounding variables.
What is an extraneous variable ?
Any variable, other than the IV, that may have an effect on the DV if it is not controlled.
These variables can come from the participant (e.g. age, intelligence), the experimental situation (e.g. noise levels, temperature) or the experimenter (e.g. personality, appearance or conduct of the researcher)
What are confounding variables ?
Any variable, other that the IV, that has not been controlled so do affect the DV.
Therefore, we can’t be sure of the true reason for the changes to the DV/difference found.
What are demand characteristics ?
Participants are not ‘passive in experiments and they may work out what is going on and change their behaviour to please the experimenter or even act negatively.
They occur when a participant may receive a ‘cue from the researcher or the situation and so the participant changes their behaviour as a result.
What are investigator effects ?
Any effects of the investigator’s behaviour (conscious or unconscious) on the research outcome (DV).
This may include everything from the design of the study, to the selection of and interaction with the participants during the research process.
What are 2 ways to reduce extraneous/confounding variables ?
- Randomisation
- Standardisation
What is randomisation ?
the use of ‘chance’ in order to control for the effects of bias.
i.e. in a memory experiment that may involve participants recalling words from a list.
The order of the list should be randomly generated so that the position of each word is not decided by the experimenter.
What is standardisation ?
using the same procedures for all participants, such as the same environment, instructions and experience.
What are reliability and validity used for?
to assess how good a piece of research is.
they can affect the credibility of research findings.
What is validity ?
Validity refers to how accurate and representative the results are.
Or
The degree to which something measures what it claims to.
What are the 2 types of validity ?
- Internal
- External
What is internal validity ?
whether the results are due to the manipulation of the IV and not affected by confounding variables.
What is external validity ?
The extent to which the results can be generalised to other settings.
What are the 2 types of external validity ?
- Temporal validity
- Ecological validity
What is temporal validity ?
how well we can generalise the results across different periods of time
What is ecological validity ?
whether the experimental results can be generalised to other settings, particularly from artificial/controlled settings to real life environments
What is reliability ?
how consistent the results are.
it can be improved by developing consistent forms of measurement.
What are the 2 types of reliability ?
- Internal reliability
- External reliability
What is internal reliability ?
the extent to which a test is consistent within itself.
E.g. if someone was completing a questionnaire measuring high levels of obedience they should have the same score on each question on the questionnaire for it be considered to have internal reliability.
What is external reliability ?
the extent to which a test is consistent over time.
E.g. if someone achieved 120 on the IQ test, if they were tested again in say 8 months time, we would expect them to achieve the same result. This would show the test to have external reliability.
What is inter-observer reliability ?
the extent to which 2 or more observers are in agreement on the behaviours they observe.
we check for inter-rater reliability by correlating the 2 (or more) sets of observations to see if they correlate positively. If their correlation is +0.8 or above we would conclude that inter-observer reliability is high and that they have consistently observed the same behaviours.
What is an experimental design ?
how the participants in an experiment will be used.
What are the 3 types of experimental design ?
- Independent groups
- Repeated measures
- Matched pairs
What are independent groups ?
When 2 separate groups of participants experience 2 different conditions of the experiment.
What are the strengths of the independent group design ?
- Order effects are avoided
- There are less likely to be demand characteristics as participants only take part in 1 condition of the experiment and are therefore less likely to pick up on cues.
What are the limitations of the independent group design ?
- Individual differences between groups, otherwise called “participant variables”, may affect the results.
To deal with this, random allocation is used. - A larger amount of participants are needed in this experimental design.
What are repeated groups ?
When all participants take part in both conditions.
What are the strengths of repeated groups ?
- Participant variable problems are avoided because all participants take part in both conditions.
Therefore, it doesn’t matter if they have different IQs or memory abilities because they are kept constant through both conditions. - This requires fewer participants because the same group is re-used.
What are the limitations of repeated groups ?
- Order effects are very likely to occur; participants may become bored, aware of aims or tired because they carry out a task twice.
They would need to control for this by using counterbalancing - Demand characteristics are more likely to occur because participants have been exposed to both conditions of the experiment and therefore may pick up on cues or figure out the aim of the experiment.
- The researcher will need to ensure they have different test materials for condition 1 and 2.
For example, they would not be able to use the same list of words in a memory test in both conditions. To control for this they have to use a different set of words but make sure they are of similar difficulty.
How do you deal with order effects ?
Counterbalancing
What is counterbalancing ?
It is an attempt to deal with order effects in which half the participants take part in condition A and then B, and the other half take part in condition B and then A. (ABBA technique).
This doesn’t prevent order effects, but attempts to balance out the effects of order between conditions.
What are order effects ?
when participants become aware of or bored with an experimental procedure.
What are matched pairs ?
Pairs of participants are first matched on a key variable/s (i.e. IQ).
Then, one member is assigned to condition A and the other assigned to condition B.
What are the strength of matched pairs ?
- The issue of participant variables is greatly reduced.
- Order effects are totally avoided.
- Demand characteristics less likely.
What are the limitations of matched pairs ?
- It is impossible to match people exactly on every characteristic.
- It is very time-consuming to find lots of people that match each other so closely.
Give an example of using matched pairs design in psychological research :
Bandura et al. investigated the effect of observing aggressive and non-aggressive role models on children’s behaviour. Would they imitate the aggression they had seen?
In order to control for naturally occurring aggression levels in the children (so it would not confound the DV) he got the children’s parents and teachers to rate their aggression on a 1-5 scale.
He then matched the children on their aggression levels so each condition had the same number of highly aggressive children (5), medium aggression (4-2), and non aggressive children (1).
What do most face to face interactions involve in an interview ?
An interview and interviewee.
What are the types of interviews ?
- Structured
- Semi structured
- Unstructured
What are structured interviewed ?
They are made up of pre determined questions that are asked in a fix order.
This is like a questionnaire but conducted face to face (or over the phone) in real time.
What are semi structured interviews ?
Many interviews are likely to fall between structured and unstructured interviews.
There is a list of questions prepared in advance, but interviewers can follow up answers (like a job interview).
What are unstructured interviews ?
It is like a conversation.
There are no ser questions, but there is an aim that a certain topic will be discussed.
The interview will be free-flowing (like a job interview).
The interviewee is encouraged to expand on their answers.
Strengths & limitations of structured Interviews :
Easy to replicate due to their standardised format (increases reliability).
However a problem is that it’s difficult for interviewers to deviate from the topic or for interviewees to expand on their answers (lacks depth and therefore validity)
Strengths & limitations of unstructured interviews:
Much more flexible; an interviewer can follow up on points if and when they arise gaining more insight and understanding (increase validity)
However, trying to analyse the data can be challenging often because open ended questions are used (qualitative data)
There is always the risk of interviewees being untruthful for reasons of social desirability.
Questionnaires :
These involve a pre-set list of questions (or items) to which the participant responds through written answers. These are used to assess a person’s thoughts and/or experiences.
A questionnaire may be used as part of an experiment to measure the DV. There are different styles of questions that can be designed. Open and closed questions.
Strengths of questionnaires :
questionnaires are can be given to a large sample of people and so large amounts of data can be gathered relatively easily. They can also be done without the researcher being present ie postal questionnaires.
Limitations of questionnaires :
However, limitations include respondents wanting to show themselves in a positive light (social desirability) rather than being truthful. Or respondents may show reponse bias’ where they respond in a particular way,
Open questions :
Don’t have a fixed range of answers and respondents are free to answer in any way that they wish.
Open questions tend to produce qualitative data (rich in depth, difficult to analyse).
Closed questions :
Offers a fixed number of responses and produce numerical data by limiting the answers respondents can give.
They produce quantitative data (easy to analyse, but lacks the depth associated with open questions).
Or respondents may be asked to rate how often they…
Open questions evaluation :
Respondents can expand on their answers, which increases the amount of detailed information collected.
• Open questions can reveal unexpected answers; therefore researchers can gain new insight into people’s
feelings and attitudes.
• They also provide qualitative data (non-numerical data) which alchough may be rich in information, it can be more difficult to summarise and/or detect patterns to draw conclusions.
Closed questions evaluation :
They have a limited range of answers and produce quantitative data (numerical data. This means the answers are easier to analyse using descriptive statistics (mean, mode, graphical representation).
However, respondents may be forced to select answers that don’t represent their true thoughts or behaviour, therefore the data collected may lack validity.
What type of method are questionnaires ?
Self report method
They are usually used to produce quantitative data for statistical analysis, but can also be used to collect qualitative data
What to avoid - questionnaires + interviews :
- Overuse of jargon (technical terms) that only those familiar with the field will understand
- Emotive language and leading questions (guiding the respondent to a particular response)
- Double barrelled questions and double negatives
e.g. contains two questions in one; respondents may agree with one half of the question but not the other and therefore would not know how to respond
What method are interviews ?
Self report method
They are more likely to collect qualitative data than questionnaires, but certain types of interview will lead to quantitative data being gathered.
What does a good interview involve ?
- Recording information - this can be done in various ways e.g. writing down answers, using a video recorder, using an audio recorder.
• Ethical issues - Informed consent is needed from the participant for the researcher to obtain and keep the data. The participant should be reminded that their answers will be kept confidential.
• Location - A quiet room away from other people is the most appropriate as this location is likely to get the participant to feel comfortable and open up.
• Neutral questions - These are usually started with to make the participant feel relaxed and help establish a rapport.
Features of a good questionnaire :
- Clarity - Clear questions that are easy to understand for respondent (reader)
- Bias - Questions do not lead respondents to give a particular answer (e.g. ‘don’t you think violent films make children more aggressive?”)
- Assumptions - Avoids making assumptions about respondents
- Non-intrusive - Avoids questions that are too personal.
- Checked - Questionnaire is piloted to make sure questions are understood and interpreted correctly.
3 types of closed questions :
- Fixed choice questions
- Likert scale (e.g. strongly agree)
- Rating scales
Observational methods :
Researchers might decide to conduct an observation to see for themselves how people behave rather than using an experiment or self-reports. There are two types of observational method to choose from
Types of observations :
- Naturalistic
- Controlled
- Overt
- Covert
- Participant
- Non participant
Naturalistic observation :
Watching and recording behaviour in the setting where it would normally take place.
Strength of naturalistic observation :
- High ecological validity
- High external validity as done in a natural environment
Weakness of naturalistic observation :
- Low ecological validity if participants become aware that the are being watched.
- Replication can be difficult.
- Uncontrolled confounding and extraneous variables are presented.
Controlled observation :
Watching and recording behaviour in a structured environment e.g.
lab setting.
Strength of controlled observation :
- Researcher is able to focus on a particular aspect of behaviour.
- There is more control over extraneous and confounding variables
- Easy replication.
Weakness of controlled observation :
- More likely to be observing unnatural behaviour as takes place in an unnatural environment.
- Low mundane realism so low ecological validity.
- Demand characteristics presented.
Overt observation :
Participants are watched and their behaviour is recorded with them knowing they are being watched.
Strengths of overt observation :
Ethically acceptable as informed consent is given.
Weakness of overt observation :
- More likely to be recording unnatural behaviour as participants know they are being watched.
- Demand characteristics likely which reduces validity of findings.
Covert observation :
the participants are unaware that their behaviour is being watched and recorded.
Strength of covert :
- Natural behaviour recorded hence high internal validity of results.
-removes problem of participant reactivity whereby participants try to make sense of the situation they are in, which makes them more likely to guess the aim of the study.
Weakness of covert :
Ethical issues presented as no informed consent given.
Also could be invading the privacy of the participants.
Participant observations :
The researcher who is observing is part of the group that is being observed.
Strength of participant observations :
Can be more insightful which increases the validity of the findings.
Weakness of participant observations :
-There’s always the possibility that behaviour may change if the participants were to find out they are being watched.
- Researcher may lose objectivity as may start to identify too strongly with the participants
Non participant observations :
The researcher observes from a distance so is not part of the group being observed.
Strength of non participant observations :
Researcher can be more objective as less likely to identify with participants since watching from outside of the group.
Weakness of non participant observations :
- Open to observer bias for example of stereotypes the observer is aware of.
- Researchers may lose some valuable insight.
Problem with observational designs :
Observer bias is easily presented.
This is when an observer’s reports are biased by what they expect to see.
A solution to this problem is checking the inter observer reliability of the observation.
This is done by many researchers conducting the observational study, their reports are then compared and a score calculated using the formula :-
Total number of agreements / total number of observations × 100.
The score that shows high inter observer reliability is any score above 80%.
Planning an observational study - unstructured observations :
The researcher records all relevant behaviour, but has no system. They may simply write down everything they see!
..clearly there may be too much to record as well as recording behaviour that may not be that important.
Planning an observational study - structured observations :
It is preferable to use these observations; they aim to be objective and rigorous.
The researcher uses a list of pre-determined list of behaviour categories and sampling methods.
Planning an observational study - structured observations :
It is preferable to use these observations; they aim to be objective and rigorous.
The researcher uses a list of pre-determined list of behaviour categories and sampling methods.
Operationalising :
Breaking up behaviour in a set of components so it can be measured.
Developing behavioural categories - structured :
For structured observations one of the hardest tasks before carrying it out is deciding how the behaviour should be categorised. The researcher needs to be very clear on exactly what behaviour they’re looking for.
When forming a behavioural categories list, it is important to make sure that behaviours do not overlap with other behaviours, so very similar behaviours should not be listed e.g. grin and smile. They should be clearly operationalised. During structured interviews there are different types of sampling methods
Behavioural categories meaning :
a target behaviour which is being observed is broken up into more precise components which are observable and measurble
3 Categories for behavioural categories :
• Objective - the researcher should not have to make guesses about behaviour. The categories must be observable.
• No waste basket - loads of different behaviour is thrown in because it’s unclear where the behaviour should be categorised.
• Independent of each other - categories should not overlap, meaning that the researcher has to mark two categories at one time.
2 types of sampling :
Structured observations have a systematic (a clear organised system) way of observing behaviour using sampling. There are two methods: event sampling and time sampling
what is event sampling ?
this involves the counting of the number of times a particular behaviour is carried out by the target group or individual you are watching.
event sampling advantage :
It is good for infrequent behaviours that are likely to be missed if time sampling was used.
event sampling disadvantage :
- If complex behaviour is being observed, important details of the behaviour may be overlooked by the observer.
- If the behaviour is very frequent, there could be counting errors.
- It is difficult to judge the beginning and ending of a behaviour.
what is time sampling ?
this is the recording of behaviour within a timeframe that is
pre-established before the observational study.
time sampling advantage :
- It reduces the number of observations that has to made so it is less time consuming.
time sampling disadvantage :
The small amount of data that you collect within that time frame ends up being unrepresentative of the observation as a whole.
evaluation of observational methods : structured vs unstructured
structured observations are designed to use behavioural categories that make the recording of behaviour easier. The data is likely to produce quantitative data which means analysing and comparing the behaviour observed is straightforward.
unstructured observation design will tend to produce qualitative data, which may be harder to analyse. There is also a higher risk of observer bias’ in unstructured design as behavioural categories aren’t used. Researchers may record behaviour that simply ‘catches their eye and could also miss important behaviours.
evaluation of observational methods : behavioural categories
the categories need to be very clear avoiding the ‘waste basket’
evaluation of observational methods : sampling
Event sampling is useful when the target behaviour or event happens infrequently and could be missed if time sampling was used however, if the event is too complex, the observer may overlook important details if using event sampling.
Time sampling is effective in reducing the number of observations that have to be made however those instances when behaviour is sampled might be unrepresentative of the observation as a whole.
possible sources of data when compiling a case study :
Interviews with the subject
Medical records
Tests of intelligence
Tests of personality
Advantages of case studies :
- Rich in detail- provide great depth and understanding about individuals.
- The only possible method to use- case studies allow psychologists to study unique behaviours or experiences that could not have been studied any other way. The method allows ‘sensitive areas to be explored, where other methods would be unethical.
- Useful for theory contradiction- just one case study can contradict a theory.
Disadvantages of case studies :
• Not representative- as no two case studies are alike, results cannot be generalised to others.
• Researcher bias- researchers conducting case studies may be biased in their interpretations or method of reporting, making findings suspect.
• Reliance on memory- case studies often depend on participants having full and accurate memories.
what is a pilot study ?
small-scale version of an investigation which is done before the real investigation is undertaken.
carried out to allow potential problems of the study to be identified and the procedure to be modified to deal with these.
allows money and time to be saved in the long run.
can also be used for self reports and can be used to check if behavioral categories are effective.
single-blind procedure :
A research method in which the researchers do not tell the participants if they are being given a test treatment or a control treatment.
This is done in order to ensure that participants do not bias the results by acting in ways they “think” they should act-avoids demand characteristics.
double-blind procedure :
a research procedure in which neither the participants nor the experimenter knows who is receiving a particular treatment.
this procedure is utilised to prevent bias in research results.
useful for preventing bias due to demand characteristics or the placebo effect.
gives a way to reduce the investigator effects as the investigator is unable to unconsciously give participants clues as to which condition they are in.
control group/condition :
sets a baseline whereby results from the experimental condition can be compared to results from this one.
if there is a significantly greater change in the experimental group compared to the control than the researcher is able to conclude that the cause of effect was the IV.
what is a correlation ?
a mathematical technique that is used to investigate an association between two variables which are called co-variables.
plotted on scatter grams. one co variable on the x axis, and the other on the y.
the closer the crosses are clustered around a line of best fit, the stronger the correlation.
How do correlations differ to experiments ?
- The variables are measured, not manipulated like in experiments.
- Only an association is found, no cause-and-effect relationship found hence the terms
DV and IV are not used.
correlation coefficients
correlation coefficients are calculated.
the value determines the strength and the relationship between two variables.
this doesn’t mean that one variable is causing another, but that there is a relationship of some sort.
always a figure between +1 and -1 where +1 shows a perfect positive correlation and -1 shows a perfect negative correlation.
0 means there isn’t a correlation.
the closer the correlation coefficient to 0, the weaker the coefficient.
the closer the correlation coefficient to 1 or -1 , the stronger the correlation.
negative correlation :
- when one variable increases the other decreases. When the data is presented on a scattergram the line of best fit has a negative gradient. It has a correlation coefficient of less than 0.
positive correlation :
when one variable increases the other also increases. When the data is presented on a scattergram the line of best fit has a positive gradient.It has a correlation coefficient of more than 0.
zero correlation :
no relationship is found between the co-variables. When the data is presented on a scattergram, no line of best fit can be drawn as the points on the scattergram are random. It has a correlation coefficient equal to 0.
explain +0.36 as a correlational coefficient :
the sign tells you the direction of the correlation
the number tells you the strength.
curvilinear relationship :
as one variable increases, so does the other but only up to a certain point after which as one variable continues to increase the other begins to decrease.
on a graph this forms an inverted U shape.
examples of directional and non directional hypothesise:
Directional correlational hypothesis
There will be a significant positive correlation between temperature and ice-cream sales or
There will be a significant negative correlation between temperature and scarf sales.
Non-directional correlational hypothesis
There will be significant correlation between average time spent reading per week and scores on an I.Q. test.
non directional and directional hypothesis definitions :
A directional hypothesis for a correlation states whether the relationship will be a positive or a negative correlation.
A non-directional hypothesis simply states that there will be a correlation.
what does a correlational hypothesis predict ?
A relationship between two variables not a difference, and they are worded differently to experimental hypotheses.
strengths of correlations :
- They can be used as starting points to assess patterns between co-variables before committing to
conducting an experimental study. - Quick and economical to carry out. No need for controlled environment and no manipulation of variables is required, so less time consuming.
- Secondary data can be used in the correlational study which makes it less time consuming.
weaknesses of correlation :
- It is difficult to establish a cause and effect relationship, really only an association is found.
-The third variable problem is presented - this is when there is a chance that there is another variable, a third variable which the researcher is unaware of that is responsible for the relationship between the co-variables.
- correlations tend to be misused or misinterpreted especially when made public by the media - correlation is often presented as causation.
what is content analysis ?
allows a researcher to take qualitative data and to transform it into quantitative data (numerical data).
the technique can be used for data in many different formats e.g. interview transcripts.
the researcher conducting a content analysis will use ‘coding units’ in their work.
these units vary widely depending on the data used.
the aim of content analysis is to summarise and describe the data in a systematic way so overall conclusions can be drawn.
procedure for a content analysis :
Data is collected
Researcher reads through or examines the data, making themselves familiar with it
The researcher identifies coding units
The data is analysed by applying the coding units
A tally is made of the number of times that a coding unit appears
Coding and quantitative data :
- Coding process begins after data collection and familiarisation.
- Identify coding units: Find key parts of the data to focus on.
- Categorise data based on these units.
- Reason: Large data sets (e.g., 40 interview transcripts) need to be organised.
- May involve counting words/phrases for quantitative analysis.
What is thematic analysis ?
qualitative data analysis method that involves reading through a data set (such as transcripts from in depth interviews or focus groups), and identifying patterns in meaning across the data.
the researcher starts off with a source of qualitative data to generate more meaningful qualitative data.
procedure/steps of thematic analysis ?
- Familiarize yourself with the data - e.g. if the data is in the form of audio files, transcribe them
- Create your initial codes
- Collate codes with supporting data
- Group codes into themes
- Review and revise themes
- Write your narrative
strengths of content + thematic analysis :
- High in external validity because the data is obtained from real life experience e.g. journal entry.
- Content analysis is flexible in the sense that it may produce quan. and qual. data depending on the aims of the study.
weaknesses of content + thematic analysis :
- Communication analysis often happens outside its original context, leading to potential misinterpretation of intentions.
- Coding challenges: Choosing codes can be hard and time-consuming.
- Lack of objectivity: Descriptive thematic analysis may introduce bias.
sampling :
The researcher needs to decide how they select participants to take part in their investigation.
The population is a group of people from whom the sample is drawn.
Target population = subset of the general population.
what is a sample ?
A sample is a group of people who take part in the research and is taken from the target population.
Researchers aim to obtain a representative sample so that the findings can be generalised.
bias :
bias can occur if certain groups may be over or under-represented within the sample selected.
This limits the extent to which generalisations can be made to the target population.
generalisation :
the extent to which the findings and conclusions from a study can be applied to the population.
This is made possible if the sample of participants is representative of the population
what is opportunity sampling ?
where a researcher decides to select anyone who is available and willing to participate in their study. Students are often used in psychological research for this reason.
strengths + weaknesses of opportunity sampling :
Strengths
• This method is convenient as it saves time, effort and is less costly
Limitations
• The sample is likely to be unrepresentative of the target population as it’s drawn from a specific area such as one street in one town.
• The researcher has complete control over the selection of participants, they may simply avoid people they don’t like the look of (researcher bias)
what is random sampling ?
This is when all members of the population have the same equal chances of being the one that is selected.
The method used is :
a complete list of all the members of the target population is obtained
each member of the population is assigned a number then either a random number table or a random number generator or the lottery method is used to randomly choose a partner.
strengths + weaknesses of random sampling :
Strengths
No researcher bias - researcher has no influence of who is picked.
Limitations
Time consuming-need to have a list of members of the population (sampling frame) and then contacting them takes time.
Volunteer bias-participants can refuse to take part so can end up with an unrepresentative sample.
What is systematic sampling ?
A predetermined system is used whereby every nth member is selected from the sampling frame. This numerical selection is applied consistently.
A sampling frame is produced, which is a list of people in the target population organised into e.g. alphabetical order.
The researcher then works through selecting every 5th, 3rd, 9th person etc.
strengths and weaknesses of systematic sampling :
Strengths
Avoids researcher bias and usually fairly representative of population.
Weaknesses
Not truly unbiased unless you use a random number generator and then start the systematic sample.
What is stratified sampling ?
From the wider population a subgroup is created (strata) based on age, social class etc. Then the population is randomly sampled within each strata.
To carry out a stratified sample the researcher first identifies the different strata that make up the population.
The proportions needed for the sample to be representative are worked out.
Finally, the participants that make up each strata are selected randomly
Strengths of stratified sampling ?
No researcher bias-the selection within each stratum is done randomly.
Produces representative data due to the proportional strata hence generalisation is possible.
Weaknesses of stratified sampling ?
Time consuming to identify strata and contact people from each.
A complete representation of the target population is not possible as the identified strata cannot reflect all the differences between the people of the wider population.
What is volunteer sampling ?
Involves self selection whereby the participant willing participants offers to take part either in response to an
advert or when asked to.
Weaknesses of volunteer sampling ?
Volunteer bias- the study may attract a particular profile of a person. This means generalisability is then affected.
Motivations like money could be driving participation so participants may not take study seriously, influencing the results.
Strengths of volunteer sampling ?
Quick access to willing participants which makes it easy and not time consuming.
As participants are willing to take part they are more likely to cooperate in the study.
why do ethical issues arise ?
when conflicts arise between the rights of participants in research studies and the goals of researchers to produce valid data.
The BPS code of ethics (British Psychological Society) is a legal document instructing Psychologists in the UK about what behaviour is and is not acceptable when dealing with participants.
Psychologists have a professional duty to observe the guidelines, which are closely matched to the ethical issues and attempt to ensure all participants are treated with respect and consideration during a piece of research.
What is informed consent ?
Participants must be told the purpose of the investigation (their aims) and about any potential risks they may be subject to when taking part in it.
This allows them to make an informed decision on whether they want to participate in the research study.
Researchers don’t always wish to disclose this information as it could lead to demand
characteristics being presented hence result bias.
e.g. Loftus didn’t tell participants the true aim of the study (investigating anxiety)
solutions to informed consent
- Prior general consent-participants give permission to take part in many studies whereby one of them involves deception so they are consenting to getting deceived,
- Presumptive consent- researcher gathers opinions from a group like the participants in the study but does not inform the actual participants. demand characteristics are eliminated
- Retrospective- participants are asked for consent after they have participated in the study.
what is deception ?
deliberately withholding information from participants or misleading them during the research study.
This is only seen as acceptable when the participants knowing the true nature could guess the aims of the investigation or when the deception will not cause distress.
e.g. Milgram deceived his participants by telling them it was a study on ‘learning’, not obedience.
Deception solution :
- Debriefing-
all participants would be debriefed after the study, it can be a written or verbal debrief. During the debrief the true nature of the study must be said and the participants should be told what their data will be used for.
After the debrief participants have the right to choose to withhold or withdraw their data
What is protection of harm ?
Participants must be protected from physical and psychological harm. It is the job of the researcher to make sure of this.
All through the investigation, participants are also reminded should be done before a study that they do have the right to withdraw, especially if the study is causing them harm.
Protection of harm solution :
- If the participants have been subject to any stress or psychological harm, the researcher should provide counselling if it is required.
- A Cost-Benefit Analysis should be done before a study is carried out. This is done by the ethics committee whereby the pros and cons of the study are weighed up to determine whether the study will be ethical. This can be difficult and an example of where this was done but went wrong is for Zimabardo’s Stanford Prison Experiment in 1973. The study was stopped after 8 days due to this.
Social Influence topic.
What is privacy + confidentiality ?
Right of privacy refers to the right that participants have to controlling information about themselves- how much is released and how it is used. It can be difficult to avoid invading a participant’s privacy for example if it is a field study-these are done in natural environments. The right of privacy can extend to the location of the study whereby the institution is not named.
Confidentiality refers to the right participants have which concerns any personal data of theirs being protected.
Privacy + confidentiality solution :
- Anonymity can be maintained. This is achieved by the researchers not recording any personal details of their participants so that none of the results data can be traced back to them. Instead the researchers can refer to the participants using numbers or initials when writing up the investigation e.g. HM case study.
-The participant should be reminded during both the briefing and debriefing of the investigation that their data will be protected
What is the right to withdraw ?
Participants should be aware they can leave a study at any time, and even withdraw their data after the study is finished.
For example, in Milgram’s study participants were made aware that they could leave the study at any time but due to the nature of the
research participants felt they did not have this right.
Difference between qualitative and quantitative data :
Qualitative : data which is displayed in words, is non-numerical.
Quantitative data- data that is displayed numerically, not so converted to graphs or in words.
Qualitative data strengths :
- More richness and depth of detail.
- Allows participants to further develop their opinions hence has greater external validity.
- A more meaningful insight into the participants’ views is achieved.
Qualitative data weaknesses :
- Difficult to analyse.
-difficult to make comparisons with other data. - Researcher bias presented as conclusions relv on the subjective interpretations of the researcher (interpretative bias).
Quantitative data strengths :
- Can be analysed statistically so converted to graphs or charts.
- This makes it easy to make comparisons with other data
Quantitative data weaknesses :
- Lack of depth in detail.
- No meaningful insight into participants’ views.
- As participants are not able to develop their opinions the results have low external validity.
Differences between primary and secondary data :
Primary data - this is when information is obtained first hand by the researcher for an investigation.
Secondary data - this is when information is collected by someone else other than the researcher yet is used by the researcher for their investigation. Also known as ‘desk research’.
Primary data strengths :
-Targets the exact information which the researcher needs, so the data fits their aims and objectives.
Primary data limitations :
- Requires time and effort.
- Can be expensive.
Secondary data strengths :
Inexpensive and easily accessed.
Data has probably already been statistically tested and peer reviewed
Secondary data limitations :
May be likely that the data is outdated/incomplete.
The data may not be reliable- the researcher was not there when the study was conducted so is likely to be unsure of the validity of the results.
What is a meta analysis ?
Researcher combines results from many different studies and uses all the data to form an overall view of the subject they are investigating.
Meta analysis limitations :
- Publication bias such as the file drawer problem may be presented- this is when the researcher intentionally does not publish all the data from the relevant studies but instead chooses to leave out the negative results. This gives a false representation of what the researcher was investigating.
Meta analysis strengths :
- More generalisability is possible as a larger amount of data is studied.
- The researcher is able to view the evidence with more confidence as there is a lot of it.
Effect size :
Kohnken (1999) conducted a meta-analysis of 53 studies related to the cognitive interview. They were exploring the effectiveness of the cognitve interview compared to standard interview techniques. The effect-size was 34%.
This means that of all the studies, the cognitive interview technique improved recall by 34%, when compared to the standard interview technique.
So, it is an overall statistical measure of the difference or relationship between variables across a number of studies.
What is peer review ?
The assessment of scientific work by experts in the same field, it is done to make sure that all research intended to eventually be published is of high quality.
The experts should be objective and unknown to the author.
3 main purposes of peer review :
- To know which research is worthwhile hence funding can be allocated to it.
- To validate the relevance and quality of research. This is important to prevent fraudulent research from being released to the public.
- To suggest possible improvements or amendments to the research study.
evaluation of peer review - anonymity is a problem
reviewers sometimes use it to settle old scores or bury rivals, especially if they’re competing for funds. This means that anonymity affects the objectivity of reviewers. Due to this, some journals have started doing open reviewing to avoid this problem.
evaluation of peer review - difficult to find an expert
Smith (1999) argues that because of this a lot of poor research is passed as the reviewer didn’t really understand the work.
evaluation of peer review - publication bias
Editors tend to prefer to publish ‘headline grabbing’ findings and positive results.
This brings about the file drawer problem whereby negative results are intentionally not published. All this causes there to be a misconception of the current state of psychology.
evaluation of peer review - burying ground breaking research
any research that opposes mainstream theories tends to be suppressed. This means that established scientists’ work is more likely to be published and the new and challenging ideas are usually rejected. This means that the rate of change in scientific fields is slowed down.
descriptive statistics :
the use of tables, graphs, and summary statistics to analyse data.
measures of central tendency
any measure which calculates an average value within a set of data.
include mean, median, mode.
mean :
arithmetic average.
Total of all values in a set of data is divided by the number of values
advantages :
- Makes use of all values.
- Good for interval data
disadvantages :
- It is influenced by outliers (extreme scores) so it can be unrepresentative.
median :
Arrange data from lowest to highest then find the central value.
Strengths :
- not affected by extreme scores.
- Good for ordinal data.
Limitations :
- Not as sensitive as mean, does not use all data.
mode :
The most frequently occurring value in a set of data.
Strengths :
- Useful for nominal data (data in categories).
Limitations :
- Is not useful when there are several modes.
measures of dispersion :
any measure that calculates the variation in a set of data.
include the range and standard deviation
range :
Minus the lowest score from the highest score.
Strengths
- Easy to calculate.
Limitations
- Affected by extreme values.
- Does not use all data.
standard deviation :
The square root of the variance calculates the SD.
A low SD means that more data is clustered close to the mean hence there is less data spread.
Strengths :
- precise measures where all data values are taken into account.
Limitations :
- Difficult to calculate.
- Affected by extreme values.
data in a table :
usually not in the form of raw scores but the data has been converted into descriptive statistics
Bar charts :
This way of representing data allows for differences in data to be seen more clearly.
They are used for discrete data, which describes data that has been divided into categories.
The bars do not touch each other which shows that we are dealing with separate conditions.
The amount of frequency for each category is plotted on the y-axis (vertical axis) whilst the categories (below these are condition A and B) are plotted on the x-axis (horizontal axis).
Histograms :
the bars touch each other and this represents that we are dealing with continuous data rather than discrete.
the x-axis has equal sized intervals of one category whilst the y-axis represents the frequency.
Line graphs :
represents continuous data, whereby points are connected by lines to show the change of values.
the IV is plotted on the x-axis while the DV is plotted on the y-axis
Scattergrams :
used to show associations between co-variables rather than differences (correlation)
either of the co-variables can occupy the x-axis or the y-axis, and each point displayed on the graph coincides with the x and y position of the co-variables.
skewed distribution
a spread of frequency data that is not symmetrical, instead the data all clusters to one end. There are two types of these: positive and negative skews
positive skew :
most of the distribution of data is concentrated on the left.
negative skew :
most of the data distribution is concentrated on the right.
normal distribution :
symmetrical pattern of frequency data that forms a bell-shaped pattern.
mean, median, mode all occupy the same midpoint on the curve.
3 categories of quantitative data?
nominal, ordinal, interval
nominal data :
in the form of categories.
It is discrete- one item can only appear in one category.
It does not enable sensitive analysis as it does not yield a numerical result for each participant.
measure of central tendency : mode
e.g. country of birth
ordinal data :
represented in a ranking form
e.g. 1= hates maths, 10= loves maths.
There are no equal intervals between each unit.
A weakness of it is that it lacks precision as is based on the subjective opinion of people.
measure of central tendency : median
measure of dispersion : range
Interval data :
based on numerical scales which include equal units of precisely defined size.
This is the most sophisticated form of data as it is based on objective measures.
It is needed for the use of a parametric test.
measure of central tendency : mean
measure of dispersion : standard deviation
e.g. time in seconds
critical value :
When the statistical test has been calculated, the researcher is left with a number, the observed value (what they found) or sometimes called the calculated value.
This needs to be compared with a critical value (sometimes called a table value) to decide whether the result is significant or not.
to use the table you need to have the following information:
1. The significance level desired (always 0.05 or 5% except in cases mentioned above)
2. The number of participants in the investigation (the N value)
3. Whether the hypothesis is directional (one-tailed) or non-directional (two-tailed)
table of critical values :
The calculated value of S must be equal to or less than the critical value (in this case 0) for it to be significant.
I is not the same or less than 0, therefore the results are not significant.
the sign test :
Statistical testing is a way of determining whether hypotheses should be rejected or accepted.
tell us whether relationships between variables that have been found during experiments are statistically significant or if they have only occurred due to chance.
sign test can only be used for a study that :
• Looked for a difference not an association.
• Used a related experimental design- repeated measures design.
• Collected nominal data.
procedure of the sign test :
Step 1: Subtract each participant’s score in condition B from A. Clearly note the sign of each result (+ or -)
Step 2: Work out the number of participants (N). Exclude any participants with the same score in both conditions.
Step 3: work out (S). This is the LEAST frequent sign (+ or -)
Step 4: Use the critical value table to find the critical value, read across from N calculated in step 3 and down from the level of significance required
Step 5: Compare the critical value to S.
S value is more than the critical value, accept the null hypothesis.
S value is equal to or less than the critical value, reject the null hypothesis and accept alternative hypothesis.
Converting interval to ordinal :
Start with participant interval scores on a standardised test.
Each participant is assigned a rank score.
This is done by listing each participant from the highest scoring to lowest.
Any participants with the same interval score share the same rank position.
Converting ordinal to nominal :
Separate the categories created.
Highest ranked half of the participants are assigned to the first category and another half to the other.
Difference between type 1 and type 2 errors :
A Type I Error is likely to happen when the researcher uses a probability value that is too high e.g.
0.1.
when the null hypothesis is rejected when it should have been accepted
claims that the results are significant when in fact they are not
A Type II Error occurs when the null hypothesis is accepted when it should have been rejected
The researcher claims that the results are not significant when in fact they are. 0.01 e.g.
How not avoid type 1 and 2 errors ?
Using a 0.05 significance level
