research methods

aim

-what the researcher intends to investigate

hypothesis

-a testable and operationalised statement about what will happen

directional hypothesis (one-tailed)

-predicts the specific nature of the difference between two or more variables
-typically based on previous research
-key words: higher, lower, positive and negative

non-directional hypothesis (two-tailed)

-predicts a difference between two variables without predicting the exact direction of the difference -usually when past research has been inconclusive -key word used is difference

operationalisation

-defining variables in a way they can be measured

independent variable

-manipulated by the researcher
-produces the two conditions of the experiment

dependant variable

-variable that is measured to see if it has been affected by the IV

extraneous variables

-unwanted variables which might have an affect on the DV and give false results
-do not vary systematically with the IV
-eliminated or controlled as much as possible

confounding variables

-unwanted variables that vary systematically with the IV

demand characteristics

-cues from the researcher/research situation that reveal the aim of the study
-thus participants change behaviour accordingly

investigator effects

-effect of researcher's behaviour on DV
-can be conscious/unconscious behaviour
-controlled with single or double blind

randomisation

-the use of chance to control for the effects of bias when designing materials and deciding order of conditions
-randomised for each participant

standardisation

-ensure the same instructions and procedure for participants to minimise the influence of extraneous variables

independent groups design

-two groups of participants experience two different conditions of the experiment

independant groups evaluation

+ order effects not an issue
- participant variables
- need more participants, twice as many as repeated measures

repeated measures

-all participants experience both conditions of the experiment

repeated measures evaluation

+ participant variables are controlled
+ fewer participants are needed
- order effects
- demand characteristics

matched pairs

-participants are paired together on a variable relevant to the experiment

matched pairs evaluation

+ order effects and demand characteristics are reduced, only take part in one condition
- still participant variables, cannot be matched perfectly

lab experiment

-conducted in highly controlled environment

strengths of lab experiments

-high control over extraneous variables
-replication is more possible, increasing internal validity

weaknesses of lab experiments

-lack generalisability to real life, artificial setting so low external validity
-usually aware they are being tested so show demand characteristics

field experiments

-IV is manipulated in a natural setting

strengths of field experiments

-higher mundane realism than lab experiments as environment is more natural
-high external validity as participants may be unaware they are being studied

weaknesses of field experiments

-less control over extraneous variables, more difficult to establish cause and effect
-ethical issues as participants are unaware they are being observed

natural experiments

-when the researcher takes advantage of a pre-existing variable

strengths of natural experiments

-provides opportunities for research that may not otherwise be undertaken for practical or ethical reasons
-high external validity because they involve real-life issues and problems as they occur

weaknesses of natural experiments

-chances for research occurs very rarely
-limits scope for generalising findings to similar situations

quasi experiments

-have an IV that is based on an existing difference between people

strengths and weaknesses of quasi experiments

+ often carried out under controlled conditions, so share strengths of lab experiment
- cannot randomly allocate participants, so there may be confounding variables

target population

-the total group to be studied or described and from whom samples may be drawn

sample

-representative group from the target population
-should be large enough to represent the target population adequately

biased sample

-certain groups may be over or under represented

opportunity sample

-researcher decides on the type of participant needed and approaches anyone who appears suitable until a sufficient number is reached

opportunity sample evaluation

• less time consuming, easy and convenient

• researcher bias as researcher may only approach those they know or look friendly, therefore those that take part may not be representative of the target population so harder to generalise

random sampling

-a sample that fairly represents a population because each member has an equal chance of inclusion

random sampling evaluation

• no researcher bias as everyone has an equal chance of being selected, makes the sample more representative and generalisation is easier -time consuming and still may not be representative of the target population -some may refuse to take part

stratified sampling

-classifying the target population into categories and then choosing a sample which consists of participants from each category in the same proportions

stratified sampling evaluation

• very representative as the sample is in the same proportions as the target population

• very time consuming, must make sure the same proportions are in place before randomly sampling the target population

systematic sampling

-consists of selecting participants at fixed intervals from the target population

systematic sampling evaluation

• less bias than opportunity sampling because researcher has no control over who takes part, potentially makes the sample more representative

• list used may contain a hidden order by researcher so certain participants are still selected making the sample less representative

volunteer sampling

-involves asking for volunteers

volunteer sampling evaluation

• easy and convenient, less time consuming than other sampling methods

• those who volunteer may not be typical of the general population because they have an unusual interest in the investigated area, so the sample may be biased and not representative

ethical issue in research

-arise when a conflict exists between the rights of participants in research studies and the goals of research to produce authentic, valid and worthwhile data

ethical issues

-informed consent
-deception
-protection
-privacy and confidentiality

informed consent

-participants should be able to make a judgement about whether or not to take part -but too much information may affect their behaviour, ways to prevent this are:

-presumptive consent, asking a similar group if they would consent

-prior general, agreeing to be deceived

-retrospective, asking for consent afterwards

deception

-deliberately misleading or withholding information
-participants should be given a full debrief at the end, including true aims, details of study, use of the data and the right to withhold data

protection from harm

-participants should be at no more risk than they would in daily life
-should be told their right to withdraw at any point and reassured that behaviour is typical
-offer counselling if distressed

privacy and confidentiality

-participants allowed to control information about themselves and if invaded confidentiality is respected
-refer to participants using numbers or aliases
-participants data cannot be shared with other researchers

dealing with ethical issues

-the BPS code of ethics in psychological research is a set of ethical guidelines to respect the rights of participants
-guidelines are implemented by the ethical committees in universities

how to write a briefing statement

1. state the aims but keep it vague
2. briefly state the procedure
3. be mindful of ethical guidelines
4. ask for consent to take part

how to write a debriefing statement

1. state details of the study's aims
2. state details of the procedure, such as conditions
3. let the participants know what you wanted to find
4. ask if their data can be used in the findings
5. ask if there are any further questions and thank them for taking part

pilot study

-a small scale preliminary study conducted before any large-scale research in order to evaluate the potential for a future, full-scale project

aims of pilot studies (experimental studies, questionnaires and observational studies)

-important that they are not just restricted to experimental studies -when using self-report methods it is helpful to try out questions beforehand to remove those that may be ambiguous or confusing -in observational studies, it can check coding systems before the real investigation occurs -important part of training observers -allows the researchers to identify any potential issues and to modify the design or procedure, saving time and money

naturalistic observation

-takes place in a natural setting
+ high external validity because behaviour is studied in the environment it would naturally occur
- more difficult to control extraneous variables so harder to detect a pattern in behaviour

controlled observation

-takes place in an artificial environment
+ extraneous variables have less influence as there is some control over variables, making replication easier
- less external validity because the environment is artificial

covert observation

-participants aren't aware they're being observed
+ high validity because participants will act naturally, removes participant reactivity
- ethical issues such as deception or invasion of privacy

overt observation

-participants are aware they're being watched
+ ethically acceptable because participants are aware of being observed and they have given consent
- behaviour may not be natural because they know they're being watched, affecting the validity of the results

participant observation

-researcher takes part
+ higher validity because the researcher can experience it as the participant does, so giving more insight into behaviour
- researcher doesn't remain objective because the line between researcher and participant may become blurred

non-participant observation

-researcher doesn't take part
+ researcher remains objective because they maintain a distance from participants
- may have less insight into motivations behind behaviour because distance is maintained

structured observation

-use a premeditated list of behaviours

-using a behaviour checklist tends to produce quantitative data which makes comparisons easier

unstructured observation

-researcher writes down everything they see

• produce accounts of behaviour that are rich in detail

• greater risk of observer bias because there is an absence of behavioural categories

behavioural categories

-target behaviour is broken down into components that are observable and measurable

-behaviour checklist is used

event sampling (structured observations)

-observer records the number of times that the target behaviour occurs

• every behaviour of interest will be recorded - possibility that some behaviours may be missed if there is too much occurring

time sampling (structured observation)

-observer records behaviour at prescribed intervals

• allows for better use of time - not every behaviour of relevance will be counted if it occurs inbetween the time allocated

self-report technique

-methods of gathering data where participants provide information about themselves without interference from the experimenter
-e.g. questionnaires and interviews

questionnaire

-a written set of questions to be answered by a research participant
-used to assess thoughts and/or feelings
-may be used to assess dependant variable as part of an experiment

questionnaire strengths

-may share more personal information than in a face-to-face interview, less self-conscious
-distributed to more people, so can gather large amounts of data quickly

questionnaire weaknesses

-answers may not be truthful because of social desirability effect -may produce a response bias e.g. replying in a similar way because they favour a particular response

writing good questionnaire questions

-no leading questions -avoid ambiguous questions -avoid emotive questions -avoid jargon -avoid double barrel questions

structured interview

-made up a pre-determined set of questions that are asked in a fixed order
-conducted face-to-face in real time

structured interview strengths

-easy to replicate and reduces differences between interviews
-easy to analyse quantitative data, so more likely to discover trends

structured interview weaknesses

-less in depth information because can't deviate from topic, limits richness of data collected
-increased risks of investigator effects because of how the investigator may ask questions

unstructured interview

-no set questions
-general aim that a certain topic will be discussed and interaction will be free-flowing
-interviewee is encouraged to expand and elaborate on their answers

unstructured interview strengths

-gain more insight into motivations behind behaviour because points can be followed up
-less chance of demand characteristics and investigator effects

unstructured interview weaknesses

-difficult to analyse the data because questions not standardised so it's more difficult to make comparisons between individuals
-risk they may lie due to social desirability effect

closed questions

pre-determined set of answers to choose from
+ easy to analyse quantitative data
+ easy to replicate and find trends
- limits responses ability to explore interesting answers
- response bias

open questions

no restriction on how participants make their response

• less chance of researcher bias

• detailed answers obtained

• participants may answer in a socially desirable way, reducing validity

case studies

-in-depth detailed study of an individual or small group
-could also be of an institution or a single event
-researcher can use observation, questionnaires, interviews and medical records to collect the data

strengths of case studies

-useful for investigating the effects of unusual experiences, such as deprivation or hospitalisation
-give detailed information about reasons behind behaviour by examining personal accounts from participants and family

weaknesses of case studies

-findings can't be generalised due to sample being small
-psychologist may become too involved and lose objectivity, so report would be biased

content analysis

-converting qualitative data into quantitative data by coding the information

coding (content analysis)

-read/observe relevant material
-from this identify potential categories of behaviour to sort material into
-work through communication to be studied and identifying each instance of the chosen categories
-can be done by counting up the number of times it appears (quantitative data)

content analysis strengths

-easily replicated by using the same coding system
-statistical analysis can be conducted because data has been tallied

content analysis weaknesses

-only describes data so cause and effect cannot be determined -researcher bias can be a problem as different researchers may interpret the categories differently

thematic analysis

-refers to any idea that keeps appearing in the communication being studied
-looking for words/behaviours that appear repeatedly
-from this themes could emerge
-such themes could be combined into broader categories
-data should stay in qualitative format and wouldn't be reduced to numbers, conclusions would be drawn from the themes

thematic analysis strengths

-provides richer and more detailed data than content analysis
-allows for categories to emerge from the data rather than being pre-determined

thematic analysis weaknesses

-can be subjective, although researches often include a reflexivity sections within their final report
-being so flexible may make it difficult to decide which aspects of the data to concentrate on

positive correlation

-as one variable increases, the other also increases
-or as one decreases so does the other
-both variables move in the same direction

negative correlation

-as one variable increases, the other variable decreases

zero correlation

-occurs when a correlational study finds no relationship between variables

difference between correlations and experiments

-correlational techniques are non-experimental methods used to measure how strong the relationship is between two variables
-in an experiment, effect of IV on DV is measured

strengths of correlational methods

-useful preliminary tool, provides a quantifiable measure of how two variables are related, suggests idea for future research if variables are strongly related -usually quick and less expensive to carry out as no need for controlled environment and no manipulation of variables, secondary data can be used too so less time consuming

weaknesses of correlational methods

-can't demonstrate cause and effect because we do not know which co-variable is causing the other to change, so establishing the direction of the effect is an issue
-third variable problem where a third untested variable may be causing the relationship between the two variables

peer review

-before publication all aspects of the study are scrutinised by experts in the field
-they should be objective and not known to the researcher

aims of peer review

-ensures that only high quality research is shared as such evidence becomes part of mainstream thinking and practice -poor research shared could damage the integrity of the field -research often has practical applications, if research wasn't reviewed to ensure quality, then any recommendations may have negative consequences

advantages of peer review

-maintains high standards in research and ensures that funding allocation is assigned to high quality research
-helps to prevent scientific fraud as submitted work is scrutinised
-promotes the scientific process through the development and sharing of accurate knowledge
-contributes new knowledge to the field

disadvantages of peer review

-if anonymity isn't maintained, experts with a conflict of interest might not approve research to further their own reputation or career
-response bias, only statistically significant findings are published, this means that findings which challenge existing understanding might be overlooked as they are not published

sections of a scientific report

-abstract

-introduction

-method

-results

-discussion

-referencing

abstract (scientific report)

-short summary 150-200 words

-includes all major elements: aims, hypotheses, method, results and conclusions