WHAT YOU SHOULD ALREADY KNOW
METHODS
1. EXPERIMENTAL
This manipulates the independent variable (IV) to see the effect on the dependent
variable (DV). The IV is what the experimenter manipulates or alters and the DV is
what is measured. Extraneous variables are any other variables that may have an
effect on the DV. They need to be controlled to avoid affecting the experiment.
Uncontrolled extraneous variables can become confounding variables.
Type of
Definition
Advantages
experiment
Laboratory Done in a
 High degree
experiment controlled
of control
environment
 Replicable
usually a lab.
 Cause and
Participants should
effect
be randomly
 Easier to use
Allocated to groups
technical
equipment
Field
Performed in the
experiment “real” world
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High
ecological
validity
Low or no
demand
characteristic
Weaknesses
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Natural
The IV occurs
experiment naturally this is
really a quasi
experiment as
random allocation
of participants are
not possible
Experimenter
bias
Problems
operationalising
IV and DV
Low external
(ecological )
validity
Demand
characteristics
Less control
Replication
harder
Ethics e.g.
informed
consent
Time consuming
and more
expensive
See Field experiment See Field experiment
2. Studies using correlational analysis
Not a research method as such, but a method of data analysis. It involves
measuring the strength of the relationship between 2 variables to see if a trend or
pattern exists between them.
 A positive correlation is where one variable increases as the other variable
increases
 A negative correlation is where one variable increases while the other
variable decreases.
 A correlation coefficient is a number that expresses the degree to which 2
variables are related. The measurement ranges from +1 (perfect positive) to
-1 (perfect negative) 0 = no correlation
Advantages
Allows predictions to be made
Allows quantification of relationship
No manipulation of behaviour is needed so
can be quick and ethical
Weaknesses
Quantification problem i.e. strength of
relationship is affected by the number of
scores
Cannot prove cause and effect
Extraneous relationships, other variables
may have influenced both measured
variables
Only works for linear relationships
3. Observational techniques
Observations can form part of an experiment etc. but in this context it refers to a
study where observation is the main research method involving the precise
(objective) measurement of naturally occurring and relatively unconstrained
behaviour and will take place in the participants natural environment
a) Participant observation involves the observer becoming actively involved in the
activities of people being studied. It can be disclosed or undisclosed
b) Non-participant observation involves the researcher observing the behaviour
from a distance. It can be overt or covert.
Advantages
High external validity
Practical method especially where
manipulation of behaviour would be
unethical or impractical
Few demand characteristics
Weaknesses
Cause and effect cannot be inferred
Observer bias especially if observer knows the
purpose of the study or if more than one
observer in which case need to check using inter
(across) and/or intra (within)
-rater reliability
Replication very difficult
Ethics especially if unaware of observation
Practical problems e.g. categorising behaviour
4. Self report techniques
a) Questionnaires
 Closed questions: responses are fixed by the researcher
 Open questions: these allow participants to answer in their own words
Advantages
Quick and cheap
Large samples
Quantitative and qualitative analysis
Easy to replicate as use standardised
questions
Weaknesses
Misunderstandings or misinterpretations of
the question
Biased samples
Low response rates
Superficial issues (closed questions are not
suitable for sensitive issues requiring
detailed understanding)
Social desirability bias
b) Interviews
Asking questions in a face to face situation
 Structured (formal) interviews. A questionnaire is read to the participant
and the interviewer writes down the answer.
 Unstructured (informal) interviews. Involves an informal discussion on
predetermined topic. The questions, however are not predetermined
allowing the researcher to explore other areas.
 Semi- structured interviews. A combination of structured and unstructured.
Advantages
Complex issues best dealt with face to face
Misunderstandings can be clarified
Both quantitative and qualitative data can
be analysed
The more structured the interview the
easier it is to replicate
Weaknesses
Interviewer effect
Interview training
Ethical issues
Respondents may not be able to put into
words their true feelings
5. Case Studies
An in-depth, detailed investigation of an individual or group.
Advantages
Rich detail
May be the only possible method
Useful for theory contradiction
Weaknesses
Unreliable and cannot be generalised
easily
Researcher bias/subjective
INVESTIGATION DESIGN
Aims
A reasonably precise statement of why a study is taking place. It should include what’s
being studied and what the study is trying to achieve.
Hypotheses
A hypothesis is much more precise than an aim and predicts what’s expected to happen.
They are testable statements.
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Experimental hypothesis. Used for experiments only. This predicts significant
differences in the DV as a result of manipulation of the IV. Any difference or effect
found is not due to chance. Alternative hypothesis is used for all other research
methods.
a) Directional/one-tailed hypothesis. This states the direction of the results. It is
called directional because it states one direction in which the results can go.
b) Non-directional/two-tailed. This states there will be a difference, but doesn’t
state the direction of the results.
Null hypothesis. It predicts that the IV will not affect the DV, the results will simply
be due to chance
EXPERIMENTAL DESIGN
Repeated measures design. The same participants are tested in the 2 (or more) conditions.
Advantages
No individual differences between the 2
groups.
Fewer participants are needed (half as
many)
Weaknesses
Order effects. Can be controlled by
counterbalancing “ABBA”
Lost participants (may drop out)
Demand characteristics – more likely to
guess the purpose of the study
Takes more time. May need a gap between
conditions
Independent groups design. Different participants are used in each of the conditions.
Participants are often randomly allocated to each condition.
Advantages
No order effects
Less chance of demand characteristics
Time saved
Weaknesses
Need more participants
Any differences between the groups may be
due to individual differences not the
manipulation of the IV. Random allocation
would reduce this problem.
Matched pairs design. Different but similar participant are used in each of the conditions.
Participants are matched across groups on any characteristics judged to be important for
that particular study e.g. age, gender, ethnicity.
Advantages
See independent group design
Less differences between the groups
Weaknesses
See independent group design
Matching is difficult. It is impossible to
match all variables between participants.
The variable missed might be vital.
( Identical twins would be best)
Time consuming. Takes a long time to
accurately match participants on all
variables.
Design of naturalistic observations
Data can be collected by using visual recordings, audio recordings (usually analysed later)
or on the spot note taking using previously agreed rating scales or coding categories. One
difficulty with naturalistic observations involves the development and use of appropriate
behavioural categories.
Behavioural categories. – Observers have to agree on a grid or coding sheet on which to
record the behaviour they wish to study. It is often easier to code or rate the behaviour
e.g. T = talking. M-P = using mobile phone. Or a rating scale 1-5 e.g. on a scale of “safe
driving”.
One of the main problems is to achieve standardisation between different observers and a
lot of training is required. Checks that all observers are coding behaviour in the same way
ensure inter-observer reliability. One way to assess this is to conduct a correlation of all
the observers scores. High correlation indicates reliability ( may not be correct but is
consist as a group).
Design of questionnaire
There are a number of essential factors in questionnaire design.
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Aims – Be very clear about the aim of the research, only ask questions that address
these aims
Length – The longer the questionnaire the less likely it will be completed. Make it
short and to the point.
Advice. Examples of questionnaires that have proved successful in the past should
be used as a basis for the questionnaire design
Statistical analysis – this should be considered at the design stage
Presentation – looks matter
Question order – useful to start with some simple factual biographical questions
before moving on to more probing questions. Usually best to put essential
questions in first half as many are sent back unfinished. Keep it interesting
Question formulation – questions should be simple, to the point and easily
understood. Avoid ambiguity, complicated terms, double barrelled questions etc.
Incentives – offering an incentive to complete a questionnaire can help with return
rates. It should also be easy to return e.g. a pre paid envelope.
Pilot study – A test of the questionnaire should be done on people who can
provide detailed and honest feedback.
Measurement scales. When using scales e.g. 1 strongly agree 2 Agree 3 undecided
4 disagree 5 strongly disagree – many will choose the “undecided” but unclear
whether this is because they have no opinion or cannot decide between their
attitudes. Likert scale possibly best known attitude scale.
Design of interviews
It is important to consider who will be the interviewer.
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Gender and age of the interviewer affect the answers of respondents especially if
the topic is of a sensitive sexual nature.
Ethnicity – some interviewers have more difficulty interviewing people drawn from
a different culture and have less rapport with the participant
Personal characteristics – some people are easier to get on with, interviewers may
adopt different roles. Use of language, accent and appearance can also affect how
someone comes across to the interviewee.
Interviewers need training. Need to be able to listen. Non-verbal communication is also
important. Need to be able to relax interviewee. The more difficult and probing questions
are usually best left to the end
Operationalisation of variables
This means being able to define variables simply and easily in order to manipulate them
(IV) and measure them (DV). Sometimes this can be difficult to do and if the issue is
complex then when you operationalise you may only measure one aspect of the variable.
If the IV and DV are not operationalised accurately and objectively the results may not be
reliable or valid.
Pilot studies.
These are small scale ‘practice’ investigations where all aspects of the research can be
checked and any changes can then be made.
Control of extraneous variables
Extraneous variables must be carefully and systematically controlled. Researchers should
consider:
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Participant variables – age, intelligence, personality etc. should be controlled
across the different groups taking part.
Situational variables - the experimental setting and surrounding environment must
be controlled.
Experimenter variables – the personality, appearance and conduct of the
experimenter should be controlled
If the extraneous variables are not controlled they can become confounding variables
which minimise the value of any results.
Reliability and validity
Reliability – If results are reliable they are said to be consistent i.e. you would get similar
results if you repeated the study. Results can be reliable but not accurate (faulty
equipment) or valid
Validity – Measure what they’re supposed to be measuring and are accurate.
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Internal reliability – is a test consistent within itself.
External reliability – is test reliable over time. Check with test retest method.
Internal validity – are results valid and directly attributed to the manipulation of
the IV (not affected by confounding variables). To be internally valid there should
be : no investigator effects, no demand characteristics, standardised instructions
should be used as well as random sampling.
External (ecological) validity. Can the results be generalised to the wider
population or to different settings or different historical times.
ETHICS – Should follow The British Psychological Society’s Code of Ethics
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Informed consent. Where possible participants should be informed of the
objectives and if participant parental consent should also be given
Avoidance of deception – deception should be avoided where possible this
includes withholding information or misleading participants especially if
participants likely to object or show unease in a debrief. To deal with this issue
can use a) presumptive consent, b) prior general consent or c) retrospective
consent.
If deception is used, participants should be told immediately afterwards and given chance
to withdraw their data. The investigator should ensure that alternative procedures that
do not involve deception are unavailable.
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Adequate briefing/debriefing – all relevant details of the study should be explained
to participants before and after the study. Debrief is especially important if
deception has been used. Participants should ;leave the study feeling the same or
better about themselves as when they started.
Protection of participants – must protect participants from physical and mental
harm. Normally the risk of harm must be no greater than in ordinary life
Right to withdraw – participants should always be aware they can leave the study
at any time regardless of any payment and withdraw their data at any point.
Particular issue with observations.
Confidentiality – information should be kept confidential unless an agreement has
been made in advance. Use numbers instead of names (confidential – can be
traced back to names, anonymous – cannot be traced back)
Observations – should only be made in public places where people might expect to
be seen by strangers.
Giving advice – if evidence of a psychological or physical problem of which a
participant is apparently unaware is obtained the investigator has a responsibility
to inform the participant if he/she believes that by not doing so the participant’s
future well being may be endangered.
Colleagues – investigators share responsibility.
Before starting research psychologists should, seek peer guidance, consult likely
participants, follow BPS Code of Ethics, consider alternative methods, establish a cost
benefit analysis of short and long term consequences, assume responsibility and gain
consent from any ethical committee. If negative effects are found during the research it
should be stopped immediately and every effort should be made to correct adverse
consequences.
Research Method
Lab experiments
Field/natural experiments
Observations
Ethical Issue
 Participants feel pressure to act in a particular
way
 Reluctance to exercise right to withdraw
 Experimental situation can be stressful
 Informed consent difficult to get
 May be unaware of right to withdraw
 Debriefing is difficult
 If unaware of observation, issues of informed
consent, confidentiality and invasion of privacy
Correlational analysis
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Questionnaires/interviews
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Case studies
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Correlations may be interpreted incorrectly by
the public
Confidentiality must be maintained
Right to withdraw information on embarrassing
topics
Issues of confidentiality and invasion of privacy
Sampling
The sample should be representative of the target population. Generally the larger the
sample the better.
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Random sampling. Every member of the target population has an equal chance of
being selected. Use computer generated random lists or place all names in target
in a hat and pick out required number (still a chance sample could end up biased)
but is more likely to be representative and therefore results can be generalised. It
can be difficult to get details of the whole target population.
Opportunity sampling – select participants who are readily available and willing to
take part
Volunteer sampling – involves people volunteering to take part
Advantages of opportunity and volunteer sampling – easy, practical, cheap.
Disadvantages of opportunity and volunteer sampling – Sample likely to be biased
so findings may not be generalised to target population.
Demand characteristics
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Guessing the purpose of the research and trying to please the researcher by giving
‘right’ results
Guessing the purpose of the research and trying to annoy the researcher by giving
‘wrong’ results the ‘screw you’ effect
Acting unnaturally e.g. out of nervousness
Acting unnaturally in order to look good –social desirability bias
Single blind techniques reduce demand characteristics
Investigator effects
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Physical characteristics of researcher may influence results
Other characteristics such as accent, tone of voice, eye-contact, smiling etc. can
influence results
Investigator may be biased in their interpretation of the data
Double blind techniques reduce investigator effects
DATA ANALYSIS ANDD PRESENTATION
Qualitative data
Subjective
Imprecise measures used
Rich and detailed
Low in reliability
Used for attitudes, opinions, beliefs
Collected in ‘real life’ settings
Quantitative data
Objective
Precise measures used
Lacks detail
High in reliability
Used for behaviour
Collected in ‘artificial’ setting
Analysis and interpretation of quantitative data
Graphs – including bar charts, histograms and line graphs
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All graphs and charts must be fully labelled (title and axes)
Generally y axis height is three-quarters the x axis width
Only one graph or chart should be used to illustrate a set of data
Use an appropriate scale on the axes
Do not draw raw data. A chart or graph should be a summary of the data
Advantages
Median – central score in a
 Not affected by
list of rank –ordered scores
extreme ‘freak’
scores
 Usually easier to
calculate than mean
Mean – Add up all scores
and divide by total number
of scores
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Very sensitive and
most accurate
measure of central
tendency, works at
interval level of
measurement
Includes all the
information from
raw scores
Weaknesses
 Not as sensitive as the
mean as raw scores not
used in calculation
 Can be
unrepresentative
especially in a small
data set
 Less useful if some of
the scores are skewed
 The mean may not be
one of the original
scores
Mode – most common
number
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Not affected by
extreme scores
Can make more
sense
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Can have more than
one mode in a data set
Doesn’t take into
account the distances
between all values
Measures of dispersion - Measures of the variability or spread across scores.
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The Range – Take the lowest value from the highest value in a set of score and add
1. It is quick to calculate but can be distorted by extreme values
Interquartile range _ shows the middle 50% of a set of scores. It is easy to
calculate and is not affected by extreme values but it does not take into account
extreme scores and can be inaccurate if there are large intervals between scores
Standard deviation – this measures the spread of a set of scores from the mean.
The larger the standard deviation the larger the spread of scores. It is a more
sensitive dispersion measure as all scores are used in the calculation and allows for
the interpretation of an individuals score but it is harder to calculate and is less
meaningful if the data is not normally distributed.
Analysis and interpretation of correlational data
Scattergrams are useful to show at a glance how two variables are correlated. A statistical
test is needed to determine the exact nature of the correlation.
Analysis and presentation of qualitative data
Qualitative data involves people’s meanings, experiences and descriptions. It is
particularly good for researching attitudes, opinions and beliefs. It usually consists of
verbal or written descriptions. There are a number of ways it can be analysed:
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Content analysis – Common in media research. It requires coding units into which
the information can be categorised. (quantifying qualitative data)
Categorising – Grouping common items together
Quotations – Quotations are often used to bring research findings to life. They
should typify what others have said during the research
Qualitative data and naturalistic observations
Observer often records observation on tape as they observe, this can be coded or
categorised later. Mau use diary method and take notes during the observation (it can be
self reported)
Qualitative data and questionnaire surveys
Qualitative data is mainly collected from open ended questions. As the answer is in
participants own words it is less likely to be biased by the interviewer. Can be analysed
using content analysis, categorisation and/or use of quotes.
Qualitative data and interviews
Interviews are likely to be transcribed and then analysed using the qualitative techniques.
The interpretation of interview data is open to subjective interpretation.
Evaluation
Qualitative data analysis tends to be subjective, although this can be checked for both
reliability (through replication) and validity (triangulation).