Psychology - Research Methods PMT

experimental method

The experimental method concerns the manipulation of an independent variable (IV) to have an effect on the dependent variable (DV), which is measured and stated in results. These experiments can be: field, laboratory, quasi or natural.

aims

An aim is a general statement made by the researcher which tells us what they plan on investigating, the purpose of their study. Aims are developed from theories and develop from reading about other similar research.

hypotheses

A hypothesis is a precise statement which clearly states the relationship between the variables being investigated. The hypothesis can either be non-directional or directional. A directional hypothesis states the direction of the relationship that will be shown between the variables whilst a non-directional hypothesis does not.

example of hypotheses

E.g. If a researcher is carrying out a study to investigate whether sleep helps memory performance:

● A directional hypothesis for this would be - “The more sleep a participant has the better their memory performance.”

● A non-directional hypothesis would be - “The difference in the amount of hours of sleep a participant has will have an effect on their memory performance, which will be shown by the difference in the memory test scores of the participants.”

when you should use each hypothesis

A directional hypothesis tends to be used when there has already been a range of research carried out which relates to the aim of the researcher’s investigation. The data from this previous research would suggest a particular outcome. However if there has been no previous research carried out which relates to the study’s aim or the research is contradictory than a non-directional hypothesis is appropriate.

independent variable

The independent variable refers to the aspect of the experiment which has been manipulated by the researcher or simply changes naturally to have an effect on the DV which is then measured.

dependent variable

The dependent variable is the aspect of the study which is measured by the researcher and has been caused by a change to the IV. All other variables that could affect the DV should be carefully controlled so that the researcher is able to confidently conclude that the effect on the DV was caused by only the IV.

operationalisation of variables

Operationalisation refers to the act of a researcher clearly defining the variables in terms of how they are being measured. This means the variables should be defined and measurable. The hypotheses states should also show this operationalisation.

e.g. “Participants that get at least four hours of sleep will show better performances on the memory test, shown by them achieving higher scores than the participants that got less than four hours of sleep.”

extraneous variable

any other variable which is not the IV that affects the DV and does not vary systematically with the IV, they are essentially nuisance variables. Examples are the lighting in the lab or the age of participants - these variables do not confound the results of a study but just make them harder to detect.

confounding variable

a variable other than the IV which has an effect on the DV. Unlike the extraneous variable, confounding variables do change systematically with the IV. With these variables it becomes difficult for the researcher to be sure of the origin of the impact of the DV as the confounding variable (not the IV) could have been the cause.

An example for the aforementioned sleep study would be time of day the experimental task is done - those who complete the memory test later in the day may be more tired and therefore do worse, obscuring the true relationship between lack of sleep and memory performance

demand characteristics

any cue the researcher or the research situation may give which makes the participant feel like they can guess the aim of the investigation . This can cause the participant to act differently within the research situation from how they would usually act

investigator effects

any unwanted influence from the researcher’s behaviour, either conscious or unconscious, on the DV measured (the research’s results). This includes a variety of factors :- the design of the study, the selection of participants and the interaction with each participant during the research investigation.

randomisation

use of chance to reduce the effects of bias from investigator effects. This can be done for the design of materials, deciding the order of conditions, the selection of participants etc

standardisation

using the exact same formalised procedures and instructions for every single participant involved in the research process. This allows there to eliminate non-standardised instructions as being possible extraneous variables.

types of experiments

laboratory, field, quasi and natural

laboratory

an experiment that takes place in a special environment whereby different vairables can be carefully controlled.

strengths of laboratory experiments

high degree of control - experimenters control all variables, the IV has been precisely replicated, leading to greater accuracy.

replication - researchers can repeat experiments and check results

limitations of laboratory experiments

experimenter’s bias - this bias can affect results and participants may be influenced by these expectations

low ecological validity - high degree of control makes the situation artificial, unlike real life

field experiment

an experiment conducted in a more natural environment, not in a lab but with variables still being well controlled 

strengths of field experiment

naturalistic - so more natural behaviours hence high ecological validity

controlled IV - greater accuracy 

limitations of field experiments

ethical considerations - invasion of privacy and likely to have been no informed consent.

loss of control - over extraneous vairables hence precise replication not possible

Quasi experiment

an experiment whereby the IV has not been determined by the researcher, instead it naturally exists e.g. gender

strengths of quasi experiment

controlled conditions - hance replicable, likely to have high internal validity

limitations of quasi experiments

cannot randomly allocate participants - there may be confounding variables presented. this makes it harder to conclude that the IV caused the DV

natural experiment

an experiment in which the IV is not brought about by the researcher hene would have happened even if the researcher had not been there e.g. studying reactions to earthquakes

strengths of natural experiments

provides opportunities - research that would not have otherwise been impossible due to practical or ethical reasons can be researched 

high external validity - as you are dealing with real life issues 

limitations of natural experiments

natural occuring events - may be rare this means these experiments are not likely to be replicable therefore hard to generalise findings

very difficult to randomise - participants cannot be randomised into groups so confounding and extraneous variables become a problem

sampling methods

opportunity, random, systematic, stratified and volunteer

opportunity sampling

participants happen to be available at the time which the study is being carried out so are recruited conveniently

strengths of opportunity sampling

easy method of recruitment which is time saving and less costly

limitations of opportunity sampling

not representative of the whole population hence lacks generalisability

researcher bias - they control who they want to select

random sampling

this is when all members of the population have the same equal chances of being the one that is selected. random number allocation (pick out of a hat)

strengths of random sampling

no researcher bias - the researcher has no influence over who is picked

limitations of random sampling

time consuming - need to have a list of members of the population and then contacting them takes time

volunteer bias - participants can refuse to take part so can end up with an unrepresentative sample.

systematic sampling

a predetermined system is used whereby every nth member is selected from the sampling frame. this numerical selection is applied consistently 

strengths of systematic sampling

avoids researcher bias and usually fairly representative of population

limitations of systematic sampling

not truly unbiased unless you use a random number generator and then start the systematic sample

stratified sampling

with this method the composition of the sample reflects the varying proportions of people in particular subgroups within the wider population

strenghts 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

limitations of stratified sampling

time consuming - takes time 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

volunteer sampling

involves self selection whereby the participant offers to take part either in response to an advert ot when asked to 

strengths of volunteer sampling 

quick access to willing participants which makes it easy and not time consuming 

as particiants are willing to take part, they are more likely to cooperate in the study

limitations of volunteer sampling

volunteer bias - the study may attract a particular profile of a person, this means that generalisability is then affected 

motivations like money could be driving participants so participants may not take study seriously, influencing the results

experimental design

independent group design, repeated measures and matched pairs

independent groups design

the participants only perform in one condition of the IV

strengths of independent groups design

- there are no order effects presented

- participants are less likely to guess the aims of the study (demand characteristics eliminated)

limitations of independent groups design

- no control over participant variables whereby different abilities of participants in the various conditions can cause changes to the DV

- you need more participants than other designs to gather the same amount of data

solution to the limitations of independent group design

random allocation solves the first limitation, this is as it ensures that each participant has the same chance of being in one condition of the IV as another

repeated measures

the same participants take part in all conditions of the IV

strengths of repeated measures

- Eliminates participant variables.

- Fewer participants needed, so not as time consuming finding and using them.

limitations of repeated measures

- Order effects presented e.g. boredom may mean in second condition done participant does not do as well on task.

solution to limitations of repeated measures

Counterbalancing - this is when half of the participants do conditions in one order and the other half do it in an opposite order.

matched pairs

Pairs of participants are first matched on some variable that has been found to affect the dependent variable (DV), then one member of each pair does one condition and the other does another.

strengths of matched pairs

- No order effects.

- Demand characteristics are less of a problem.

limitations of matched pairs

- Time consuming and expensive to match participants.

- A large pool of potential participants is needed which can be hard to get.

- Difficult to know which variables are appropriate for the participants to be matched.

pilot study

a small-scale version of an investigation which is done before the real investigation is undertaken. They are carried out to allow potential problems of the study to be identified and the procedure to be modified to deal with these. This also allows money and time to be saved in the long run.

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.

Double blind studies are particularly 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 gorup/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.

types of observation

naturalistic, controlled, overt, covert, participant and non-participant

naturalistic observation

watching and recording behaviour in the setting where it would normally take place

strengths of naturalistic observation

- high ecological validity 

- high external validity as done in a natural environment 

limitations of naturalistic observations

- low ecological validity if participants become aware that they 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

strengths 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

limitations 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

limitations 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 obsrvations

the participants are unaware that their behaviour is being watched and recorded

strengths of covert observation

- 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

limitations of covert observation

- ethical issues presented, as no informed consent given. also could be invading the privacy of the participants

participant observation

the researcher who is observing is part of the group that is being observed

strength of participant observation

- can be more insightful which increases the validity of the findings

limitations of participant observation

- 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 observation

the researcher observes from a distance so is not part of the group being observed

strength of non-participant observation

- researcher can be more objective as less likely to identify with participants since watching from outside of the group

limitation of non-participant observation

- open to observer bias for example of stereotypes the observer is aware of

- researchers may lose some valuable insight

observer bias

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.

observational designs

unstructured and structured

unstructured observation design

consists of continuous recording where the researcher writes everything they see during the observation

strengths of structured observation design

- more richness and depth of detail

limitations of structured observation design

- produces qualitive data which is more difficult to record and analyse

- greater risk of observer bias e.g. only record ‘catch eye’ behaviours 

structured observation design

the researcher quantifies what they are observing using predetermined list of behaviours and sampling methods

strengths of structured observation design

- easier as more systematic

- quantitative data is collected which is easier to analyse and compare with other data

- there is less risk of observer bias

limitations of structured observation design

- not much depth of detail

- difficult to achieve high inter observer reality as filling the predetermined lists in is subjective

behavioural categories

when a target behaviour which is being observed is broken up into more precise components which are observable and measurable e.g. aggressive behaviour can be broken down to - shouting, punching, swearing etc

during structured observations there are different types of sampling methods

time sampling and event sampling

time sampling

this is the recording of behaviour within a timeframe that is pre-established before the observational study

strengths of time sampling

- it reduces the number of observations that has to be made so it less time consuming

limitations of time sampling

- the small amount of data that you collect within that time frame ends up being unrepresentative of the observation as a whole

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

strength of event sampling

- it is good for infrequent behaviours that are likely to be missed if time sampling was used

limitations of event sampling

- 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

correlation

a mathmatical technique that is used to investigate an association between two variables which are called co-variables

correlations differ to experiments as…

the variables are simply measured, not manipulated like in experiments and only an association is found, no cause-and-effect relationship is found hence the terms DV and IV aren’t used

correlation coefficients

a numerical value, ranging from -1 to +1, that measures the strength and direction of the relationship between two variables. A value of +1 indicates a perfect positive correlation (both variables increase together), −1 indicates a perfect negative correlation (as one variable increases, the other decreases), and 0 indicates no correlation at all.

negative correlation

when one variable increases the other decreases, when the data is presentedon 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 postive 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 correlatioon equal to 0.