key terms

Ethics

Ethics

Rules of conduct recognised as appropriate to a particular profession or way of life - set out by the BPS

Participants

Investigators must consider ethical implications and consequences on participants, potential benefits of research must outweigh potential risks

Consent

Researcher must obtain written consent where possible, full understanding of objectives and all risks that would stop them from wanting to participate, consider those who can’t consent for themselves e.g., children, mentally impaired

Deception

Researchers should avoid hiding the nature of their research because it could cause distress after the study, consult for colleagues (that have no status in study) and inform participants as soon as possible - if colleagues question ethical standards, the researcher should re-think procedure

Debriefing

After the experiment, participants should be informed of the true nature of the research, if not before, participants should leave in the state they arrived because anything that could cause harm has been removed, if support is needed, everyone should be treated equally, regardless of their performance

Withdrawal

Participants should be informed they have the right to withdraw at any point, data can be destroyed at any point at the request of the participants

Confidentiality

Any information provided by participant should be kept secret, unless agreed otherwise (Data Protection Act), any released data should not allow participants identities to be revealed and if it will participants should be warned before the study

Protection

Participants protected from physical or mental harm - they should not face anything they wouldn’t normally, follow-up measures if necessary

Observational research

Not all ethical guidelines can enforced but wellbeing and privacy should be respected, no observing in areas where they wouldn’t expect to be observed

Giving advice

Participants must be informed if physical/ psychological problems are found - researchers should only give advice if qualified

Hypothesis

Statement with what you believe to be true

Aim

Statement of what you are intending to investigate

Directional hypothesis

States the kind of difference (more/less) - one-tailed hypothesis

Non-directional hypothesis

States only there is a difference - two-tailed hypothesis

Experimental hypothesis

Hypothesis for an experiment

Research hypothesis

Hypothesis written for any other kind of study

Alternative hypothesis

Hypothesis alternative to null hypothesis

Null hypothesis

Hypothesis with no difference or relationship

Non-directional hypothesis for independent groups/ matched pairs

There will be a difference in (operationalised DV) between participants who (IV1) and participants who (IV2)

Directional hypothesis for independent groups/ matched pairs

There will be a more/ fewer/ lower/ faster/ slower/ increase/ decrease in (DV) between participants who (IV1) and participants who (IV2)

Null hypothesis for independent groups/ matched pairs

There will be no difference in (DV) between participants who (IV1) and participants who (IV2)

Non-directional hypothesis for repeated measures

There will be a difference in (operationalised DV) when participants experience (IV1) and when they (IV2)

Directional hypothesis for repeated measures

There will be a more/ fewer/ higher/ lower/ faster/ slower/ increase/ decrease in (DV) when participants experience (IV1) and when they (IV2)

Null hypothesis for repeated measures

There will be no difference in (DV) between when participants experience (IV1) and when they (IV2)

Non-directional hypothesis for correlation

There will be a relationship/ association/ correlation between CV1 and CV2

Directional hypothesis for correlation

There will be a (positive/ negative) relationship/ association/ correlation between CV1 and CV2

Null hypothesis for correlation

There will be no relationship/ association/ correlation between CV1 and CV2

Variable

Anything that can change or change something else in an experiment

Independent variable

Variable that is manipulated by the researcher - different levels of experimental conditions needed

Dependent variable

The effect that is measured in the experiment

Operationalisation

Defining variables in terms of how they can be measured

Extraneous variables

Variables that can affect DV if it is not controlled

Confounding variables

Any variables other than the IV that have affected the DV - we have to be confident what part of the results are IV or confounding variables

Demand characteristics

Change in participant behaviour due to thoughts not the IV - these should be minimised, or it can affect validity

Investigator effects

Change in participant is due to investigator effect - these effects can reduce validity

Randomisation

Minimise variable effects by randomly organising the experiment not selectively

Standardisation

Controls variables by keeping everything the same e.g. writing down instructions

Target population

The group of people the researcher is interested in

Sample

Group taken from target population intended to represent them

Representative sample

Obtain a sample that is un-biased so generalisations can be made - the larger the sample size the less chance of bias (15 participants is best)

Random sampling

Number each member of target population from a complete list, choose numbers using random selection methods, everyone has equal chance of being chosen

Pros of random sampling

Produces potentially unbiased sample, this means CVs/ EVs are controlled, enhances internal validity

Cons of random sampling

Difficult to obtain and time consuming, complete list of population is hard to get, also some participants may refuse to take part

Systematic sampling

Every nth number of the target population is selected starting at a random point

Pros of systematic sampling

Potentially unbiased, the first item is usually selected at random, objective method

Cons of systematic sampling

Difficult to carry out and time consuming, a complete list of the population is required, may as well use random sampling

Stratified sampling

The proportion of factions in target population should be accurately represented in the sample, randomly choose the correct proportion from each subgroup

Pros of stratified sampling

Representative sample that allows generalisation

Cons of stratified sampling

People within subgroup may differ and therefore sample could be biased

Opportunity sampling

Sample is whoever is in a certain place and is free at a certain time

Pros of opportunity sampling

Cheaper and less time consuming

Cons for opportunity sampling

Often produces a biased sample, researcher might approach certain people (researcher bias)

Volunteer sampling

Researcher puts out an advert and sample group are the people who respond

Pros of volunteer sampling

Easy to produce and less time consuming

Cons of volunteer sampling

Bias sample, volunteers are likely to be a certain type of person (volunteer bias)

Laboratory experiments

Takes place in a controlled environment where the researcher can manipulate the IV and control EV

Pros of lab experiments

High internal validity where the effect of the IV and DV is more certain because EVs and CVs can be controlled

Replication of experiment is possible, greater control means less chance of new EVs introduced and findings can be confirmed - supports validity

Cons of lab experiments

Artificial tasks so stops generalisability and low external validity

Demand characteristics can occur due to cues which means findings may be due to the cues not the IV - low internal validity

Field experiments

Takes place in a natural setting where the researcher can manipulate the IV and record the effect on the DV

Pros of field experiments

More natural environment, Ps are more comfortable, and behaviour is more authentic, results can be generalised

Participants are unaware of being studied, behave normally so generalise findings so increases external validity

Cons of field experiments

More difficult to control CVs/ EVs, observed changes in the DV may not be due to IV, more difficult to establish cause and effect

Important ethical issues, no informed consent, invasion of privacy

Quasi experiment

The IV cannot possibly be manipulated but does exist

Pros of quasi experiment

Practical/ ethical option, unethical to manipulate IV, only way causal research can be done

Greater external validity, involve real-world issues, findings are more relevant to real experiences

Cons of quasi experiment

Hard to claim IV effects DV

Can’t allocate participants so confounding variables may affect DV

Natural experiments

IV is naturally occurring and is not manipulated by the researcher

Pros of natural experiments

High control, replication is possible

Comparisons can be made between people, IV is a difference between people

Cons of natural experiments

Can’t allocate participants so confounding variables can affect DV

Causal relationships are not demonstrated, researcher does not manipulate the IV, cannot say for certain that any change in the DV was due to the IV

Independent groups design

Participants are split into groups where each group does one condition, there is no order effect (cannot use knowledge from condition 1 to influence condition 2), individual differences can influence findings, have to find double the participants - time consuming and expensive

Repeated measures design

Participants take part in all conditions of the study, individual differences are controlled because group is the same for all conditions, creates order effect

Counterbalancing

2 different groups, one does condition 1 first, one does condition 2 first - balances out order effects of both

Matched pairs design

Pairs assigned due to similarity in a variable that could affect DV - each person in the pair does a different condition, no order effects, less individual differences, matching participants is difficult, time consuming and expensive

Naturalistic observation

Takes place in the setting where the behaviour would naturally happen

Pros = high external validity, each to generalise

Cons = hard to replicate, many confounding/ extraneous variables

Controlled observation

Some variables are controlled and manipulated

Pro = less confounding/ extraneous variables

Con = harder to generalise and apply to everyday life (low external validity)

Covert observation

Participants are unaware they are being observed

Pro = higher internal validity and no demand characteristics

Con = ethical problems (consent)

Overt observation

Participants are aware they are being observed

Pro = more ethically sound and acceptable

Con = demand characteristics

Participant observation

Researcher joins target group to record observations

Pro = higher external validity and better insight into participants

Con = may become too invested and not objective

Non-participant observation

Researcher observes target group while staying separate

Pro = remains objective

Con = lose some valuable data due to less knowledge of participants

Factors affecting design behaviour

Importance of access to physical response - behaviour recorded in observation, thoughts are not

Type of people being studied - children or those iwth disabilities may only be able to be studied through observation

Event sampling

Make a record every time the behaviour occurs

Time sampling

Record when it happens in a time frame

Pilot studies

Trial run of an experiment with a few participants to check the investigation will run correctly

Pro = saves time and money in the future

Questionnaires/ interviews = check questions are effective, ensure number of questions and time period are correct

Observation = trial and train observers

Self-report techniques

Ways in which participants can express their views or opinions

Questionnaires

Collect quantitative and qualitative data

Closed questions = limited options and participants choose which is most relevant - quantitative data

Open questions = let the participant write down their opinion, not limited - qualitative data

Avoids jargon, emotive language, double negatives, leading questions, double-barrelled questions

Pro of questionnaires

Gathers lots of information quickly and cheaply

Researcher bias doesn’t have to be present

Data is easy to analyse

Con of questionnaires

Social desirability bias = change response for a specific answer

Response bias = same answer each time

Interviews

Collects qualitative data

Structured interviews

Pre-determined questions

Pro = easy to replicate, little difference between interviews so easy to compare

Con = more limited data as there is no deviation from set topic

Semi-structured interviews

Some set questions with un-set follow ups

Unstructured interviews

No set questions, more conversational

Pro = wider range of data, responses in real life more likely to be truthful

Con = more chance of interview bias, some irrelevant information that you will have to sift through

Correlation

Technique of data analysis, measures strength (number) and direction (+/-) of the relationship between 2+ variables (with numerical variables), information is presented on a scattergram, correlation doesn’t mean causation

Positive correlation

Greater than 0, +1.00 is perfect positive correlation, as one variable increases so does the other

Negative correlation

Less than 0, -1.00 is perfect negative correlation, as one variable increases the other variable decreases

Zero correlation

Numerical value of 0, no relationship/ correlation between variables

Curvilinear correlation

Positive to a point and then negative

Factors affecting design decisions for correlations

Type of data = variables must have a numerical value and exist over a range

Possibility of manipulation = likely to choose this analysis when variables can’t be manipulated

When a similarity is being assessed, e.g. twin study

Assess reliability = if there are 2 sets of results for one variable then the scores should correlate

Pros of correlation

Preliminary research tool = gives a starting point and helps to write a hypothesis

Can use stats from previous studies so is quick and easy to work out the correlation

Precise and quantifiable data for the relationship between 2 variables

Cons of correlations

Correlations can be misinterpreted

Only states there is a relationship, not why or which variable affects the other

Interviewing variable = something not being tested could cause the relationship between 2 variables

Directional correlational hypothesis

States whether relationship is positive or negative

Non-directional correlational hypothesis

Only states there is a relationship

Statistical tests

Tests used to determine if results are significant, significance level of 0.05 used in psychology, alternative hypothesis accepted if p < 0.05, from tests you will get a calculated value that you compare to critical value in a table, to get the critical value you need - significance level, number of participants, one-tailed or two-tailed, if test has an ‘R’ in the name calculated > critical