research methods

1. The Research Process.
The Experimental method

2.  Statements made by psychologists about
behaviour need to have evidence to back
them up.
 Evidence has more credibility if it is
collected using recognised and/or scientific
methods.
 Common sense or intuition or “everyone
knows that” is not sufficient.

3.  The research process starts with an
observation or a question about an aspect of
behaviour.
 From these a theory is constructed which can
be developed into an hypothesis.
 The hypothesis is then tested to find out if it
is supported or refuted.

4. DATA THEORY HYPOTHESIS
REFINE RESEARCH TO TEST
THEORY HYPOTHESIS

5.  Control group - a group used as a baseline measure
against which performance of the experimental
group is judged.
 Single blind trial – an experiment where either the
people conducting the experiment or the
participants do not know which treatment the
participants receive.
 Double blind trial – an experiment where neither the
people conducting the experiment nor the
participants know which treatment the participants
have received.

6.  A true experiment has three key
features:
1. The researcher manipulates an independent
variable (IV) in order to investigate whether
there is a change in a second variable, known as
the dependent variable (DV)
2. All other variables, which might influence the
results, are either controlled, held constant or
eliminated. Unwanted variables are called
extraneous or confounding variables
3. Participants (Ps) are ideally allocated to the
experimental conditions randomly

7.  Laboratory experiment – conducted in a
artificial environment in an attempt to control
all relevant variables except the IV.
 Field experiment – behaviour is studied in a
natural environment, but the IV is still
manipulated and it’s effect on the DV measured.
 Quasi / natural experiment – the IV is not
directly manipulated by the researcher but
occurs naturally, i.e. gender, age, whether a
country has capital punishment or not.

8. Strengths Weaknesses
•High levels of control limit the •The environment is artificial
effects of confounding variables and so the results may lack
ecological validity
•Replication is possible to check
the study’s results •Participants’ behaviour may
change if they know they are
•It is possible to establish causal being studied – this is known as
relationships between variables. demand characteristics.
•Ethical issues are sometimes
raised as deception is used.

9. Strengths Weaknesses
•More natural environment raises •Lower levels of control make it
ecological validity more likely that extraneous
variables will affect the results,
•Demand characteristics are thus causal relationships are
avoided as participants are more difficult to establish.
usually unaware of the study
taking place. •Ethical issues can be raised as
participants do not know they
are being studied.

10. Strengths Weaknesses
•It is possible to use this method •There is no control over
to study relationships that it extraneous variables so it is very
would be unethical to difficult to identify causal
manipulate, e.g. The effect of factors.
capital punishment on serious
crime.

12. Experimental Design

13.  When deciding on an appropriate design, the researcher must
consider:
 the precise nature of the experimental task
 how to control the relevant variables
 the availability of participants
 Independent groups design
 different participants are used in each condition of the
experiment
 Repeated measures design
 the same participants are used in each condition of the
experiment
 Matched pairs design
 each participant in one group/condition is carefully
matched on all the variables considered to be relevant to
the investigation with a participant in another
group/condition

14. Design Strength Weakness
Independent No order effects Participant variables
Measures differ between
conditions
Need more
participants
Repeated Measures Need fewer Order effects –
participants practice or fatigue
No effect of effects.
participant variables
Matched pairs Limits some of the Matching participants
effects of participant can be difficult &
variables. time consuming

16. Hypotheses
Independent & dependent variables
Operationalisation

17.  An hypothesis is a statement that can be
tested to see if it is true.
 it is a prediction.
 There are two types:
a) the experimental / alternate hypothesis
b) the null hypothesis

18.  Thisis a general prediction. It does not give
enough detail on which to base an
investigation.
“Does eating cheese before going to bed
cause nightmares?” (research question)
“To investigate the effect of eating cheese
on nightmares” (aim of the study)

19.  This gives enough detail for the investigation
to be carried out.
 The component parts of the hypothesis are
operationalised – stated in terms which make
it clear how they will be measured.
“Eating 100g of cheddar cheese an hour
before going to bed will increase the
number of nightmares reported by
participants by a significant amount”

20. Two-tailed or non-directional One –tailed or directional
 This is used when it  This is used when
cannot be certain what previous research
the results will be. suggests what the
 A will be affected by B outcome will be.
 Look for the use of
 A will be changed by B adverbs such as
 There will be a more, faster, higher, de
difference between A crease, increase etc.
and B  A will be ...... than B
 There will be a  There will be a positive
relationship between A correlation between A
and B and B.

21.  This assumes that there will be no effect in
the population from which the samples are
drawn.
 A will not be affected by B
 A will not be changed by B
 There will be no difference between A and B
 There will be no relationship between
A and B
 “Eating 100g of cheddar cheese an hour
before going to bed will have no
significant effect on the number of
nightmares reported by participants”

22.  Remember
“hypotheses don’t have to be true,
they just have to be possible”

23.  Thismeans ensuring that variables are in a
form that can be measured.
 Itcan be easy to do – e.g. % or seconds or
number of correct answers
 Itcan be very difficult – how can we measure
helpfulness or aggression or happiness?

24. “Drug A affects memory”
compared with? measured how?
how?
administered how?
when?

25. A variable is anything in an experiment
which can come in different forms or in
different values.
 There are four to remember:
a) Independent variables
b) Dependent variables
c) Extraneous variables
d) Confounding variables

26.  AnIndependent Variable (IV) is one
which is manipulated (changed) to do the
experiment.
A Dependent Variable (DV) is one which
is measured to obtain results.

27.  Extraneous variables (EV) are anything (other
than the IV) which could affect the results
(DV). As far as possible these are controlled for
fair testing.
 Confounding Variables are extraneous
variables which cannot be controlled for various
reasons:
a) it may not be possible to control them
b) it may be unethical to control them
c) it may not be known precisely what they
are.

28.  Participant variables
 Individual differences – the unique
characteristics of participants can act as an
extraneous variable.
 This can be addressed by using a repeated
measures or matched pairs design. If you have to
use independent measures then randomly
allocating participants to conditions goes some
way towards balancing out individual
differences.

29.  Participant variables.
 Demand characteristics – aspects of the research
situation can act as signals to participants about how
they should behave.
 Depending on the participants motivation, this can
have different effects, they can either behave
naturally, try to co-operate, behave negatively
(screw you effect) or try to present themselves in a
positive light (social desirability effects).
 Strategies to address participant reactivity include a
single blind technique, the use of placebos and the
use of standardised instructions & procedures.

30.  Research effects.
 Participants react differently to researchers because
of biosocial characteristics (i.e.age, gender,
ethnicity) and / or psychosocial characteristics (i.e
interpersonal skills). This can act as a source of bias.
 Researchers can also pay more attention to things
which fulfil rather than contradict their expectations;
this is known as investigator effects and, in itself,
can act as an extraneous variable.
 One technique that can be used to address this is the
use of a double blind procedure – where neither the
researcher nor the participants know the research
aim and / or which condition they have been
allocated to.

31.  Situational variables
 Laboratory settings give researchers high levels
of control but can also introduce other
extraneous variables e.g. Low ecological validity
- participants do not behave naturally in
artificial settings.
 However, in field settings there are many
potential extraneous variables that could
influence results and cause the wrong
conclusions to be drawn about the variables
under investigation.

32. Sampling

33.  One of the main aims of a research study is
to be able to generalise from small samples.
 Thereare three technical terms to think
about:
a) population
b) target population
c) sample

34. Definition Example
All the possible members of Single mothers
a group from which the
sample will be taken

35. Definition Example
The part of the population Single mothers with one
from which the sample is child, who live in
selected Cheltenham

36. Definition Example
The group selected from 20 single mothers with
the target population for one child in Cheltenham
experiment or study who answered the
advertisement

37. Samples need to be as representative as
possible so that we can use the findings from
them to generalise to the population without
being biased.
There are many ways of sampling, including:
a) random sampling
b) opportunity sampling
c) self-selected / volunteer sampling
d) stratified sampling

38.  Everymember of the target population has
an equal chance of being selected.
 Methods:
a) names in a hat
b) random number tables
c) computer generated random numbers

39. Strengths Limitations
Provides the best chance of May be impractical,
a mathematically unbiased particularly with large
representative sample. samples.
May get a skewed sample
that is unhelpful for the
investigation, even though
it is mathematically
unbiased.
E.g. the national lottery is random but can still produce
a sequence of 10,11,12,13,14.

40.  Selecting participants that are available at
the time and fit the criteria you are looking
for.
 Methods:
a) asking students in the common room
b) asking family or friends or people at work
c) asking people in the street.

41. Strengths Limitations
Quick and convenient Biased on the part of the
researcher who will be
Economical selective in the choice of
participants.
Frequently used
Therefore it is likely to be
unrepresentative which
reduces the generalisability
of the findings.
E.g. asking fellow students in the common room to
answer a questionnaire about Saturday jobs.

42.  Sample selected on the basis of the
participants’ own action at arriving at the
sampling point.
 There are two types of self-selected sample:
a) volunteers
b) people in a particular place being
asked/tested about that place.

43. Strengths Limitations
Convenient Biased on the part of the
participant – volunteers are
Using volunteers can make different from non-
it easier to ensure ethical volunteers, and people
practice. choosing a particular place
e.g. a gym, may be
Not biased by the different from those who
researcher don’t, or who use it at a
different time of day.
Well motivated participants
This reduces
generalisability.
e.g. responding to an advertisement, being in the
common room when a study of usage of the common
room is being undertaken.

44.  Sample organised so that particular groups are
selected in proportion to their size in the
target population. Membership of each sub-
group is selected randomly.
 Method:
a) Count how many there are in each sub-group
(e.g. year groups in school) and the overall
total.
b) Use e.g. 10% of each sub-group for your
sample – picked randomly from the sub-group
list.

45. Strengths Limitations
Reasonably simple to do Can be time consuming
An effort has been made to Need access to the whole
make it as representative target population
as possible
The element of random
sampling increases the
generalisability
e.g. Using 20% each of first choice subject maths,
psychology, French and drama students in the sixth form
to complete a questionnaire on the usefulness of
General Studies.

46.  What factors influence the number of
participants used?
a) availability
b) topic being researched
c) expense
 Ifthe sample is too small it may be biased
and therefore unrepresentative.
 Ifthe sample is too large it may
smooth out interesting variations.

47. Types of studies
Types of data

48.  There are five more useful methodological
terms to learn.
 Cross-Sectional Studies
 Longitudinal Studies
 Snapshot Studies
 Qualitative Data
 Quantitative Data

49. Participants of different ages or ethnic groups or
nationality are studied at one point in time and compared
Strengths Limitations
Immediate results so Findings may quickly
convenient become out of date
No subject attrition Participant variables may
distort findings
Quick and cheap
Large sample needed
Cohort effect may bias
data

50. The same group of participants are investigated
several times over a long period of time.
Strengths Limitations
The same participants are Participant attrition
used which minimises
participant variables Difficult to generalise as
only one group
Gives the opportunity to
collect a lot of qualitative Difficult to replicate
as well as quantitative
data. Expensive
Can assess development May have to use many
different researchers –
inter-rater reliability can
be an issue

51. A study conducted at one point in time
Strengths Limitations
Quick to obtain results Cohort effects may reduce
validity
Provides a one-off view of
the immediate situation Participant variables may
distort findings
Findings may quickly
become out of date

52. Data in numerical form, the results of measurement
Strengths Limitations
Can be analysed using May be reductionist and/or
descriptive and inferential have low ecological validity
statistics
Numbers can be
Inferential tests give the emotionally “cold”
probability of the results
occurring by chance and May not tell you why/how
thus give the confidence the findings have occurred.
limits on the data and
whether the null hypothesis
is accepted or rejected
High reliability, objective
data

53. Data that is not in numerical form.
Strengths Limitations
May give information on Hard to analyse –
how/why the findings particularly statistically
occurred
May lack objectivity if the
Information is rich and researchers are too
detailed involved with their
participants
May have high ecological
validity Low reliability

54. Studies using correlational analysis

55.  Correlation is a technique for analysing data rather
than a research method.
 It usually involves collecting data by some other
means, i.e. observation / survey.
 The data are paired scores and the researcher
generally looks for linear relationships between
them.
 Such relationships can be illustrated visually in the
form of a scattergram and as a statistic called the
correlation coefficient (+1 to -1)
 The correlation coefficient indicates both the
direction and the strength of the relationship( the
number indicates the strength while the sign
indicates the direction)

56.  There are 3 types of Correlation Coefficients
correlation: +1.0 Perfect Positive
 Positive Correlation +0.8 Strong positive
 Negative Correlation +0.2 Weak positive
 Zero Correlation 0 Zero correlation
 Most correlations fall -0.2 Weak negative
somewhere between -0.8 Strong negative
these. For example:
-1.0 Perfect Negative

57.  Strengths  Weaknesses
 Correlational analysis is  It is not possible to infer
useful for revealing cause & effect as there is
patterns in data where no manipulation of the IV
manipulation would be  Even when relationships are
unethical (e.g. In found it is not always
attachment studies) possible to understand why
 Looking at scattergram the relationship has
illustrations of data can occurred, other
address some the (unidentified) variables may
weaknesses identified such be responsible for the
as when outliers are pattern observed.
present or when the  Data can be skewed by
relationship is not linear. outliers
 Curvilinear relationships
can be missed

58. Observational techniques

59.  In observation research, behaviour is observed and
recorded and there is usually no deliberate manipulation
of variables.
 Observational research can differ in several important
ways, depending on:
 the setting of the study, e.g. naturalistic or laboratory-
based
 the role of the researcher, e.g. participant or non-
participant
 the amount of structure imposed, e.g. use of a coding
system to record instances of behaviour.

60.  Naturalistic – involves the recording of
spontaneously occurring behaviour in the
participant’s own natural environment
 Controlled – involves the recording of
spontaneously occurring behaviour but under
conditions contrived by the researcher
(i.e.Bandura)

61.  Strength – high ecological validity, especially if
the observer is hidden.
 Strength – can be more ethical than
manipulating behaviour as in field experiments
 Weakness - Cannot infer cause & effect as there
is no control over confounding variables
 Weakness – Replication is difficult as there are
so many uncontrolled variables.
 Weakness – some studies raise ethical issues like
invasion of privacy.

62.  Strength – More control over the
environment usually leads to more accurate
observations
 Strength – This type of observation is more
replicable as the environment is more
controlled
 Weakness - If the subject is aware they are
being observed you can get participant
reactivity
 Weakness - Lower ecological validity than
naturalistic observations and a higher
probability of demand characteristics.

63.  Structured observation – usually involves
the use of a pre-determined system for
assessing behaviour, e.g. recording behaviour
using a pre-determined time schedule and/or
using a behaviour checklist.
 Unstructured observation – this involves
trying to record as much behaviour as
possible, e.g. using video recording.

64.  Strengths – checks can be made on inter-
observer reliability using pilot studies.
 Strengths - Data is usually easier to analyse
using quantitative methods
 Weakness – behaviour that occurs outside of
the pre-determined time schedule can be
missed.
 Weakness – behaviours may occur that do
not fit into pre-determined categories and
are therefore not recorded.

65.  Strength– This is the most useful method
when the behaviour being observed may be
unpredictable
 Weakness – Results in large amounts of data
that can be difficult to analyse.
 Weakness – behaviours that are most
prominent are easily noticed whereas more
important behaviours may be less visible or
obvious to the observer.

66.  The researcher can either become a member of
the group being studied or observe from the
outside.
 Strengths of participant observation – Very high
ecological validity, especially if the researcher is
undisclosed
 Extremely detailed data can be gained using this
method
 Weakness of participant observation - Difficult
for the researcher to remain objective and
impartial
 Ethical problems arise when using an undisclosed
researcher

67.  Time sampling – Behaviour is recorded at
discrete time intervals. i.e. every 30 seconds
 Advantages – Reduces the amount of time
spent in sampling (easier to manage), which
may improve accuracy
 Disadvantage – Behaviours may be missed if
they occur between the discrete time
intervals.

68.  Event sampling – Key behavioural events are
recorded every time they occur.
 Advantages – Reduces the chances that
behaviours of interest will be missed
 Disadvantages – Other behaviours that are
important but were not anticipated may be
missed because they are not on the
behaviour checklist.

69.  Behaviour checklists / frequency grids – nominal data is
scored in a tally chart for a range of behaviours, this
produces quantitative data that is more easily analysed.
1. Each category must be clearly defined so that it is understood in the
same way by each observer.
2. Each category should be mutually exclusive
3. There should be enough categories to code all observed behaviours
4. The system should be easy to use
 This will raise inter-observer reliability – the degree to
which observers record the same things when doing the
same observations.
 A pilot study should be under taken to test all aspects of
the procedure, location and recording materials to ensure
that they are suitable.

70.  Weaknesses of coding schemes for
behavioural categories:
1. Observers can be affected by personal bias
when recording behaviours.
2. Participant reactivity can occur if
participants know they are being observed.
3. Coding schemes categorise human
behaviour into a series of categories, thus
can be considered reductionist.