Research methods

The ethical guidelines

• Consent - participants should know the purpose of an experiment before they begin and should agree to take part. Some cases eg children cant give consent so would ask guardian instead. Presumptive consent - ask a group similar to those we are about to study and if they agree we assume our group would too.

• Debrief - explaining true purpose of experiment

• Confidentiality - must protect identities

• Deception - shouldn’t lie

• Right to withdraw - people should be allowed to leave experiment whenever they want

• Protection from harm - shouldn’t harm participants

Ethical principles

• Respect - valuing the dignity and worth of all persons

• Competence - understanding the importance of preserving the ability to function optimally within the recognised limits of their knowledge, skill, training, education and experience

• Integrity - valuing honesty, accuracy, clarity and fairness in their interactions with all persons, and seek to promote integrity

• Responsibility

Questionnaires

• Method of self-report where someone answers questions about themselves and their opinions.

• Non-experimental method, where you do not need an IV or DV (but you can have them).

• Can be used alongside other methods, for example, an experiment may use a questionnaire as a method.

• Can be given to a large amount of people which are referred to as a survey.

• There are two types of questions that can be asked on a questionnaire: open and closed

Strengths of questionnaires

• A large number of questionnaires can be administered quickly → cost efficient and less time consuming

• Can be used to reach a wide range of participants → results can be generalised to the target population

• Results can be completed privately and made anonymous → participants are likely to give an honest, more valid response, reducing both social desirability and demand characteristics

• Responses are direct from the participants → responses are more likely to be more accurate and therefore valid

• Open questions provide depth and detail due to the qualitative data → better understanding why

• Standardised → can repeated to test for reliability

Weaknesses of questionnaires

• Participants may be influenced by social desirability → may lie to provide answers which may look good, which lowers the validity

• Often have low response rates → makes the results harder to generalise to the target population

• May have response bias → only certain types of people may return the questionnaires, making the results less generalisable to the target population

• Open questions may lead to issues of interpretation (subjectivity) → will make the results harder to compare

• Closed questions have forced options, may not agree with any → less valid as not real opinion

Interviews

• Method of self-report where someone answers questions about themselves or their opinions.

• Interviews are a non-experimental method, where you do not need an IV or DV (but you can have them).

• Interviews can be used alongside other methods, for example, an experiment may use an interviews as a method.

• Interviews can be given to a large amount of people which are referred to as a survey.

Strengths of structured interviews

• Every participant receives the same questions, which is standardised → the results from the interview can be repeated and tested for reliability

• Every participant receives the same standardised set of questions → the results are easy to compare

• They are easy to conduct and do not require a lot of training → they are less time consuming.

Weaknesses of structured interviews

• Interviewer has to be trained to administer the interview → may be more costly and time consuming

• You cannot change the questions to follow up on an interesting point → results will be less in depth and therefore less valid

Strength of unstructured interviews

• You can change the questions and add follow up questions if the participants states something interesting → participants will provide more detailed responses, therefore results will be more valid

Weaknesses of unstructured interviews

• Each participant receives different questions and therefore not standardised → interview cannot be repeated and tested for reliability

• Each participant receives different questions and therefore not standardised → makes it harder to compare and analyse results

• Hard to conduct and require training → time consuming and not cost effective

Quantitative data

• Data that is expressed numerically and statistically. You can conduct a statistics test on the data.

• Analysed using statistics and measures of central tendency e.g., mean, mode, median.

• Data that is more likely to be drawn from controlled situations such as laboratory experiments.

• Quantitative data is typically data that is drawn from closed questions.

• Quantitative data deals with ‘what’ rather than ‘why’, for example, no words are provided in their responses.

Strengths of quantitative data

• Gives statistical data which can be analysed statistically → assesses whether results are due to chance

• Can be easily analysed → results can be represented in graphs and charts to analyse to see if its due to chance

• Uses operationalised variables → makes the study easier to repeat and we can test for reliability

• Objective analysis → less open to interpretation so more valid

Weaknesses of quantitative data

• Less depth and detail → less understanding why so lack validity

• Produces data which is narrow, unrealistic information which only discusses on small fragments of behaviour → results will be less valid

Qualitative data

• Qualitative data can be expressed in detailed descriptions/words/images.

• Might use open questions where participants can give answers in their own words with no formal measure.

• May come from case studies and real-world settings (blogs, magazine articles).

• It deals with ‘why’ rather than ‘what’, for example participants give their opinions.

• Can be analysed using methods such as thematic analysis

Strengths of qualitative data

• Depth and detail as qualitative data → results will be more valid as better understanding why

• Can be transformed into quantitative data → results can then be analysed statistically to see if results are due to chance

• Can be conducted in more natural circumstances increasing the ecological validity → results can be applied to real life situations.

Weaknesses of qualitative data

• Subjective analysis → open to interpretation so less valid

• Not often standardised → is lack of control so cannot test for reliability, and is often considered less scientific and more subjective in comparison to quantitative data.

Levels of measurement

• Nominal: categorical data

• Ordinal: ordered categories and the distances between the categories is not known

• Interval/ ratio: scale data, there are set intervals between each point

Volunteer sampling

Consists of participants becoming part of a study they volunteered for when asked or when responding to an advert they saw - usually in public places

Strengths of volunteer sampling

• Convenient and no bias from researcher → less time consuming as the participant chooses to take part themselves

• There’s consent from participants → researcher knows participant is consenting to take part in the study

Weakness of volunteer sampling

Some bias in where the experimenter pits their flyer or advert → sample of participants who volunteer may have shared characteristics reducing the generalisability of the findings

Random sampling

Identifying everyone in the target population (getting a list) and then selecting the number of participants needed in a way that gives everyone an equal chance of being chosen

Strengths of random sampling

Provides an unbiased and representative sample of the target population → results can be generalisable to the wider population

Weakness of random sampling

Difficult to do when there is a large target population and you need to identify everyone’s names → may be time consuming and also may still not be representative

Opportunity sampling

Consists of taking the sample from people who are available at the time of when the study is being carried out and if they fit the criteria

Strengths of opportunity sampling

Quick to complete → convenient and less time consuming for the researcher

Weakness of opportunity sampling

Not representative of the target population → less generalisable as the participants may all have shared characteristics

Stratified sampling

Classifying the population into categories and then choosing a sample which consists of participants from each category in the same proportions as they are in the population

Strength of stratified sampling

Results will be more representative of the wider target population → results can be generalised to the target population

Weaknesses of stratified sampling

• Can be time consuming → takes a lot of time to calculate the specific numbers needed

• Certain groups may be overlooked → there may be bias in the sample so results not generalisable

Thematic analysis

• Way of analysing qualitative data

• Performed through the process of coding in 6 phases to create established, meaningful patterns

Phases of thematic analysis

• Familiarization with data (read stuff)

• Generating initial codes (see what jumps out as interesting)

• Searching for themes among codes (are any of those things related/similar)

• Reviewing themes (is there enough evidence to say it's a pattern?)

Strengths of thematic analysis

• Allows for in-depth results → results gathered from identifying themes will be more valid

• Encourages researchers to derive themes rather than imposes pre-selected ones → improves the validity of the findings

• Reduces large amounts of data into manageable summaries → results can be managed but without losing validity

• Can gather large samples of data → results may be more generalisable from a larger sample

• Multiple researchers can code at the same time → results can be tested for inter-rater reliability

Weaknesses of thematic analysis

• May be interpretation bias → there is subjectivity in analysis of results, making the results less valid

• Criticised as not being scientific → results cannot be analysed to see if they were due to chance

• Requires skills from the researcher to identify representative themes → will be time consuming to train someone to code

Content analysis

• Converting qualitative data into quantitative data

• Done by converting the data into categories or themes. You would then count the frequencies of the occurrence or categories in the data.

Stages of content analysis

• Get qualitative data (gather sample)

• Read data (familiarise yourself)

• Code data (highlight key bits)

• Count occurrence of those codes

Strengths of content analysis

• You have quantitative data which you can then analyse statistically → you can then assess whether your results are due to chance

• The process is standardised → can re-do the analysis and test for reliability

• It is an unobtrusive method of analysis → it is ethical as you typically use secondary data

Weaknesses of content analysis

• There is some subjectivity involved → researcher is selecting themes and deciding how it fits the data and may therefore be bias

• May be issues with internal validity → how do we know that the categories are measuring what they claim to measure

• It reduces complex information down to a simpler form → important information may be loss and therefore may reduce the validity

Lab experiments

• Have variables which researchers are able to manipulate to determine cause and effects.

• High level of control to eliminate extraneous variables.

• Take place in an artificial setting.

• Follow a standardised procedure such as set timings, everything kept the same for every participant, which means that the study can be easily replicated

• Typically collects quantitative data

Strengths of lab experiments

• Variables can be controlled → increases validity and reliability

• Can gain information on cause and effect → we can determine the relationship between variables

• Standardised → can repeat again to see if reliable

Weaknesses of lab experiments

• Low in ecological validity → results will not be reflective of the real-life situations

• High in demand characteristics → results will lack validity as participants may lie or change their behaviour

• May be ethical concerns → researchers are responsible for the participants during the experiment

Field experiments

• The researcher has control over the IV to see if the results on the DV help to gain cause and effect.

• Lower levels of control as it takes place in the real world, therefore they can be affected by extraneous variables.

• Take place in a naturalistic setting.

• The procedure of a field experiment is not standardised, there is less control over aspects such as timings, which means that the study is harder to replicate.

Strengths of field experiments

• High in ecological validity → results can be applied to real life situations

• Reduction in demand characteristics → results will be higher in validity because they’re not changing their behaviour

• Gain information on cause and effect → can determine the relationship between variables

Weaknesses of field experiment

• Low in reliability as they are not standardised → can’t be replicated to test for reliability

• Harder to control extraneous variables → results will lack validity

Independent measures

Participants only take part in one condition of the experiment

Strengths of independent measures

• It avoids order effects → the participant will not become bored or tired in the experiment therefore increasing validity

• There is less chance of demand characteristics → the participant is less likely to know the aims as they only do one condition, thus increasing the validity.

Weaknesses of independent measures

• More people are needed due to separate conditions in comparison to a repeated measures design → will be more time consuming to conduct

• There are participant variables → the differences between the groups may affect the results, for example variations in age, sex or social background

Repeated measures

One group of participants take part in both conditions of the experiment

Strengths of repeated measures

• Avoids participant variables, as we compare scores from one condition to another for the same participant → it increases validity of the results.

• Fewer people are needed, as the same people can take part in group conditions → less time consuming

Weaknesses of repeated measures

• Demand characteristics are more likely as participants are involved in all conditions → validity is reduced because they might change behaviour

• Order effects are more likely to occur → reduces validity

Counter balancing

Alternating the order in which participants perform in different conditions of an experiment to eliminate order effects

Matched pairs

People are matched in each condition for characteristics that may have an effect if their performance, eg. IQ, age

Strengths of matched pairs

• Avoids participant variables, as we compare scores from one condition to another for the same participant → increases validity of the results

• Avoids order effects → participant will not become bored or tired in the experiment therefore increasing validity

• Less chance of demand characteristics → participant is less likely to know the aims as they only do one condition, thus increasing the validity

Weaknesses of matched pairs

• It may be hard to find appropriate matches for all participants → may be very time consuming to do and there may still be participant variables

• Requires more participants → will be very time consuming to recruit

Case studies

• Unique cases (usually studying brain damage)

• Naturally occurring circumstances

• Useful in studying rare cases where the situation cannot be tested experimentally

• Intensive and detailed

• Usually longitudinal, occurring over long periods of time

• Often has a variety of research methods to establish validity of findings - triangulation of data

Strengths of case studies

• You can obtain triangulation of data by using a variety of research methods → increases the concurrent validity.

• More ethical → they are naturally occurring events that otherwise would not be able to be tested experimentally

Weaknesses of case studies

• They are unique cases → due to the rarity; we can not generalise to the wider population.

• Not reliable → the cases are unique, we can not test them for reliability

• They are uncontrolled → naturally occurring cases which can not be checked for reliability

• Intensive and intrusive → they can be intrusive for the individual and impact their daily life

• Cause and effect can not be established → not all factors can be account or controlled for

Correlations

• Looking for a relationship between two variables

• Use quantitative data of at least ordinal

• Use either primary or secondary data

• No IV or DVs, just co-variableS

• Correlation coefficients is measure from -1, 0, 1

Types of correlations

• Positive - as one variable increases, so does the other

• Negative - as one variable increases, the other decreases

• No correlation - if no line of best fit can be drawn, then there is no correlation, correlation coefficient is 0

Strengths of correlations

• They are ethical → allows us to conduct research which would otherwise have been unethical or impossible

• We can identify new areas to research → allows us to find relationships, we never realised existed, so we can test this further

• They use existing data → research will be quick and easy to carry out

• High in ecological validity → findings will be able to be applied to real life situations

Weaknesses of correlations

• We cannot assume cause and effect → just because there is a relationship does not mean there are not other important and influencing variables

• They do not account for extraneous variables → would lower the validity of the findings

• A lack of correlation does not mean that there is no relationship → correlations fail to account for non-linear relationships

Twin studies

• We find identical twins where one of them has a trait we're looking for i.e. schizophrenia, the other twin is studied to measure the frequency with which both of them have that trait

• This is compared with the incidence of both of a pair of non-identical twins (again we find a case where we have one twin with that trait and measure the second)

• Twins are sometimes genetically tested to ensure they are identical

• Researchers will use hospital records to identify the first individual of the pair to be diagnosed

Strengths of twin studies

• They help us determine whether a behaviour is due to nature or nature → it is useful in helping us determining why a behaviour may occur.

• Can use DNA testing to determine if the twin is MZ or DZ → findings regarding twins would be more valid.

• They often use triangulation of data to measure a specific trait → results on the twin study will be more valid.

Weaknesses of twin studies

• Twins are relatively rare in the population → results from the twins may not be generalisable to the wider population.

• Historically it has been hard to determine whether twins are MZ or DZ → findings may not be valid. OTOH, DNA testing can now be used.

• It is assumed that the difference between MZ and DZ twins is the amount of DNA that they share → in many cases MZ twins may be treated more similarly than DZ twins.

Adoption studies

• Find a person who was adopted who has a certain trait, eg schizophrenia

• Measure their biological family members to see the rate that they have the trait

• Measure their adopted family members/ parents to see the rate that they have the trait

• Might look at a control group of adoptees without the trait/ people who were never adopted

• Might use a concordance rate

Strengths of adoption studies

• They help us determine whether a behaviour is due to nature or nature → useful in helping us determining why a behaviour may occur.

• They are considered ethical → adoption is a naturally occurring events that does not require manipulation of groups from the researcher.

Weaknesses of adoption studies

• Recruitment may take a long time to find children who meet the criteria and match to families → it becomes time consuming as it may take several years.

• May be difficult to establish what is due to nature or nurture → prenatal environment of the children may be different.

• May be difficult to establish what is due to nature or nurture → adopted children may be adopted by a family culturally similar to their biological one and so may end up with some shared environment.

• There may be attrition → results in the adoption studies may not longer be representative.

Observations

• Someone's behaviour is monitored and recorded based on what is visually seen.

• You often use a coding scheme (list of behaviours you are looking for) when conducting an observation.

Sampling methods in observations

• Event sampling: Use a coding scheme to tally events when they occur.

• Continuous sampling: Making notes of everything which is happening.

• Time sampling: Recoding their behaviours every nth time, for example every 30s or once every 3 minutes over a set period of time - you write down exactly what they're doing at that moment.

Covert observations

• Participant doesn’t know they are being observed

• One-way mirrors can be used to prevent participant’s awareness or CCTV can also be used

Strengths of covert observations

• Less chance of demand characteristics, as participants do not know they are being observed→ findings from the covert observation would be more valid.

• Less chance of social desirability as the participants do not know they are being observed → findings from the covert observation will be more valid.

Weakness of covert observation

Potential ethical issues as the participants do not know they are being observed → no consent from the participant

Overt observation

In an overt observation the participant is aware that they are being observed, for example, they volunteer to be observed or you inform them beforehand

Strength of overt observation

Informed consent → participants agreeing to take part

Weaknesses of overt observation

• High chance of demand characteristics → lowers the validity as the participant may try to behave in line with what they think the observer wants to see

• High chance of social desirability → lowers the validity as the participants may try to behave in a way they believe is socially acceptable

Participant observation

The observation is conducted by someone who is part of the group being observed - you join the group

Strength of participation observation

Gathers in depth information → findings will be more valid

Weaknesses of participant observation

• May be difficult to replicate → cant test the findings for reliability

• Findings may be bias → findings may not be valid

• High chance of demand characteristics → lowers the validity as the participants may try to behave in line with what they think the observer wants to see

Non-participant observation

Conducted by someone that is not part of the group they are observing

Strength of non-participant observation

• May be less bias → observer can view the situations objectively

Weaknesses of non-participant observations

• May be demand characteristics as the observer may influence the behaviour → findings from the observation may be less valid

• Gather less detail → findings may lack validity

Structured observation

• Researcher controls some variables, potentially in a lab setting, reducing the naturalness of the behaviour

• Observer is causing the situation

Strengths of structured observation

• Variables can be controlled → less chance of extraneous variables, increasing validity of findings

• Often standardised → findings can be tested for reliability

• Less time consuming → we are causing the situation so we know what behaviours to look out for

Weaknesses of structured observation

• Often lower in ecological validity → people know they are being observed and findings will be less valid

• May be high in demand characteristics → person knows they are being observed and may change their behaviour, reducing validity of the findings

• May be ethical concerns → observer is responsible for what is happening in the observation

Naturalistic observations

Observe behaviour in a natural situation where everything happens as it typically would

Strengths of naturalistic observation

• High in ecological validity → findings can be applied to real life

• Reduction in demand characteristics → findings from a naturalistic observation will be more valid

Weaknesses of naturalistic observation

• Don’t follow a standardised procedure → cant test the findings for reliability

• Time consuming → waiting for behaviour to occur naturally

Animal research

• The UK has the Animals (Scientific Procedures) Act 1986 to adhere to.

• There was apparatus and a qualified person would be needed to run the study, with anaesthetic as required.

• Often research is done in labs in controlled and standardised settings.

• Most often mice and primate are used.

• Mice models are used to study the genetic influence in illnesses i.e., Schizophrenia.

• There are ethical issues with using non-human animals, such as needing to have a licence and having to use appropriate caging.

• Also endangered species are avoided, and there has to be minimal use.

• Other ways of studying the area must be considered such as using humans or computer simulation.

• There are also practical issues with non-human animals such as them representing human processing to an extent but not fully.

Batesman cube

• the degree of animal suffering

• the quality of the research

• the potential medical benefit

Ethical issues working with animals

• Replacing the use of animals: other ways of studying the area must be considered such as using humans or computer simulation.

• Choice of species or strain: must have been bred in captivity, animals that will suffer the least, endangered species avoided

• Reduction in number of animals: must use minimum number of animals possible.

• Procedures: scientific procedures involving a protected animal that may have the effect of causing pain, suffering, distress or lasting harm is regulated under the Animals Act 1986, researchers need a licence for the research

• Housing conditions: caging conditions should take into account the social behaviour of the species

• Deprivation: experimenter should consider the animal’s normal eating and drinking habits and its metabolic requirements; a short period of deprivation for one species may be unacceptably long for another.

• Getting the animals: Common laboratory species must come from Home Office Designated Breeding and Supply Establishments. Other species should only come from high quality suppliers

• Disposing of animals: If an animal has been used in a procedure its reuse is tightly controlled and requires specific HomeOffice approval. In other circumstances, when research projects or teaching exercises using captive animals are completed, it may sometimes be appropriate to distribute animals to colleagues for further study, breeding or as companion animals.

• Refinement alternatives: procedures that are likely to cause pain or discomfort should be performed only on animals that have been adequately anaesthetised, and analgesics should be used before and after such procedures to minimise pain and distress whenever possible.

Practical strengths of animal research

• It is possible to have more control over extraneous variables when using non-human animals compared to humans → This allows us to be more certain about the cause of a specific behaviour as only one thing is changed between the groups of non-human animals.

• Non-human animals reproduce at a faster rate than humans → we can study the effect of something such as genes over the generations.

• Animals are easy to handle and control, meaning they will be easier to test on.

Ethical strengths of animal research

• It is possible to do things to non-human animals that would be unethical in humans

• Using Bateson’s cube, non-human animal studies are ethical if we are certain there will be a benefit, their suffering is low and the research is of high quality.

• Pro-speciesism- Humans are more important than animals so it is ok to do research into them (within reason) if it benefits people

• Uses lab bred animals only which means it is more ethical than if we used animals which had been caught in the wild.

• Some non-animal studies are not in a laboratory and animals are observed in their natural setting, which is more ethical than when non-human animal studies are laboratory-based.

Practical weaknesses of animal research

• The human brain is more complex than an animal's brain (such as our use of emotions, consciousness and reasoning), which is higher-order compared with animals → findings from non-animal studies might not be generalisable enough to humans.

• Results from non-human animals such as rats may not be true for humans → means that they have been used in vain so making it unethical.

• There may be issues of cost (caging, facilities).

Ethical weaknesses of animal research

• Some people argue that we should never do things to non-human animals that we would not do to humans, and all non-human animal studies are unethical

• While we may expect a benefit to come from the research, we cannot know there will be any benefit until after the research has been completed.

• Important to make sure there is no alternative way to carry out the study without the use of non-human animals

Primary data

• Primary data is data that the researcher has gathered themselves

• They have designed/operationalised it for their purposes

• They gather the participants, conduct the experiment and analyse the results themselves

• The data can be quant or qual and could be gathered in any method e.g. experiment, interview

Strengths of primary data

• A strength of primary data is it has been operationalised for the purpose of the study → makes it more valid as it has been gathered and structured for this specific study

• A strength of primary data is that you know exactly how it was collected, for example, what extraneous variables were controlled and what measures were taken to avoid bias → makes the data more valid because you have full control over the research process.

• A strength of primary data is that it has temporal validity because it is up-to-date because it was gathered by the researcher → makes the findings more applicable to current populations and situations rather than secondary data which might be out dated.

Weakness of primary data

A weakness is that it is more costly and time consuming than secondary data as it has to be gathered e.g. a sample → can make it more difficult to gather than pre-existing data and might make it less easy to replicate to check for consistency.

Secondary data

• Secondary data is data that has already been gathered by someone other than the researcher.

• The data was not designed or operationalised for the researcher's specific purposes.

• The researcher obtains and analyses data that was collected by others, such as a census, government records, or academic studies.

• The data can be quantitative or qualitative and could have been gathered using any method, such as surveys, experiments, or interviews.

Strengths of secondary data

• A strength of secondary data is that you can get larger samples since you're using pre-existing data → can make the findings more representative of the population.

• A strength of secondary data is that it is less time-consuming and costly to obtain than primary data because it is pre-existing and gathered by another researcher → means that the study can be more easily repeated to check for consistency.

Weaknesses of secondary data

• A weakness of secondary data is that you don't know exactly how it was collected, for example, what extraneous variables were controlled or what biases might have been present → makes the data less valid as extraneous variables of researcher bias might influence the results

• A weakness of secondary data is that it may lack temporal validity because it might not be up-to-date e.g. using old criteria to diagnose schizophrenia → makes the findings less applicable to current populations and situations.

Longitudinal studies

• A study which takes place over a long period of time

• Study development and change (usually in one person or cohort)

• Don't try to manipulate the person variables (usually)

• Collects quantitative and qualitative data

• Can employ several methods

• Example = genie

• Effects of daycare over time etc

• This type of research is ideal for observing changes and developments, such as a child's cognitive growth or the

  long-term effects of a particular intervention