1.1.2 The main strength of self-report methods are that
they are allowing participants to describe their own
experiences rather than inferring this from observing
1.1.3 However participants may not respond
truthfully, either because they cannot
remember or because they wish to
present themselves in a socially
acceptable manner. Social desirability
bias can be a big problem with self report
measures as participants often answer in
a way to portray themselves in a good
1.1.4 Rating Scales
188.8.131.52 Likert scale
184.108.40.206.1 Strength - is that they can give us an idea about how
strongly a participant feels about something. This
therefore gives more detail than a simple yes no
answer. A further strength is that the data are
quantitative data which are easy to analyse
220.127.116.11.2 Weakness - with the scales for people to respond
towards the middle of the scale perhaps to make
them look less extreme. Participants may provide
the answers that they feel they should and
importantly as the data is quantitative it does not
provide in depth replies.
1.1.5 Fixed Choice Questions - Yes/No
1.2.1 A experiment is said to be reliable or consistent if the
study can produce similar results if used again
in similar circumstances. (ability to repeat)
1.3.1 This refers to whether a study measures or examines
what it claims to measure or examine. It is argued that
qualitative data is more valid than quantitative data.
1.4 Sampling - psychologist use sampling
techniques to choose people who are
representative of the population as a whole.
1.4.1 Opportunity Sampling - Taking the sample from
people who are available at the time the study is
carried out and fit the criteria your are looking for.
1.4.2 Self selected sampling - consists of participants becoming part of a
study because they volunteer when asked or in response to an
advert. It is useful as it is quick and relatively easy to do. It can also
reach a wide variety of participants. However, the type of participants
who volunteer may not be representative of the target population for
a number of reasons.
1.4.3 Random Sampling - This is a sampling technique which is
defined as a sample in which every member of the population
has an equal chance of being chosen. For example pull names
out of a hat
1.4.4 Stratified Sampling - Stratified sampling involves 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.
1.4.5 Snowball Sampling - Snowball sampling can be used if your population is not easy to
contact. You could ask a participant who fits your target population to tell their friends
about the study and ask them to get in touch with the researcher and so on.
2.1 Laboratory experiments
2.1.1 A laboratory experiment is an
experiment conducted under
highly controlled conditions.
2.1.2 By changing one variable (the
independent variable) while
measuring another (the dependent
variable) while we control all others,
as far as possible,
2.1.3 It is argued that laboratory experiments allow us to make
statements about cause and effect, because unlike
non-experimental methods they involve the deliberate
manipulation of one variable, while trying to keep all other
2.1.4 Demand characteristics - If a participant knows they are in an experiment
they may seek cues about how they think they are expected to behave.
2.1.5 Ethics - For example, experiments often involve
deceiving participants to some extent. However, it
is possible to obtain a level of informed consent
2.2 Field Experiments
2.2.1 real world situation
2.2.2 The independent variable
is still manipulated unlike
in natural experiments.
Field experiments are
usually high in ecological
validity and may avoid
demand characteristics as
the participants are
unaware of the
2.3 Quasi or natural experiments
2.3.1 A quasi experiment is where the independent variable
is not manipulated by the researcher but occurs
2.3.2 It is worth noting that quasi experiments are very common
in psychology because ethically and practically they are
the only design that can be used.
2.4 Experimental Design
2.4.1 Independent Measures Design
18.104.22.168 use two conditions with different particapnts
2.4.2 Repeated Measures Design
22.214.171.124 uses same participant in each condition
2.4.3 Matched Pairs Design
126.96.36.199 different participants in each group are as
simular as possible
2.5.1 It is important that the independent and dependent
variables are clearly stated in the hypothesis.
2.5.2 When a hypothesis predicts the expected direction of
the results it is referred to as a one-tailed hypothesis.
2.5.3 When a hypothesis does not predict the expected direction of the results it
is referred to as a two-tailed hypothesis.
2.5.4 The hypothesis that states the expected results is
called the alternate hypothesis because it is
alternative to the null hypothesis.
2.5.5 The null hypothesis is not the opposite of the alternate
hypothesis it is a statement of no difference.
2.6 Descriptive Statistics
2.6.1 Simply offer us a way to describe a summary of our
data. Inferential statistics go a step further and allow
us to make a conclusion related to our hypothesis.
You may be pleased to know that we will not be doing
inferential statistics until the second year.
2.8.1 In numerical order, middle number
2.9.1 Most Common
2.10.1 Bar Chart
2.10.2 Scatter Diagram
2.10.3 Box Plot
3.1 Observational studies -investigations where the researcher
observes a situation and records what happens but does not
manipulate an independent variable.
3.2 Tend to be high in ecological validity as there is no intervention
and if the observer remains undetected the method avoids
problems with demand characteristics.
3.3 Strength - observational studies is that they get to see how participants actually
behave rather than what they say they do. observational studies is that they offer
ways of studying behaviour when there are ethical problems with manipulating
variables. For example there will be less ethical issues.
3.4 Weakness - hard to repeat
3.5 Observations do not provide information about what participants are thinking or feeling.
3.6 Participant observation - is a type of observational study where the
observer is also a participant in the activity being studied. This type
of observation can be useful because it provides more insights about
behaviour but does have a problem that the observer may lose some
3.7 Structured observation - is where the researchers design a type of
coding scheme to record the participants' behaviour. Structured
observations generally provide quantitative data. Coding schemes
are ways of categorising behaviour so that you can code what you
observe in terms of how often a type of behaviour appears.
3.8 Controlled Observation - occurs when the researchers control some variables. These observations may be carried out
in laboratory situations or natural situations.
3.9 Sampling observational data
3.9.1 Event Sampling - researcher
recoding an event every time it
188.8.131.52 Strength - Wont miss anything that the participant does
184.108.40.206 Weakness - may become difficult to recorded every event that happens
3.9.2 Time Sampling - researcher decides
on a time for example 5 seconds and
then records what behaviour is
occurring a at that time.
220.127.116.11 Strength - Lots of depth Quantitative Data
18.104.22.168 Weakness - it is time consuming going through every piece of data
individually. Alsi difficult within a group to watch variety of people
3.10 Validity - If participants are aware they are being observed they may behave in the way they feel they should behave.
Validity could also be reduced by observer bias. That is the observer may be influenced by expectations and not record
objectively what happened
3.10.1 This then can be improved by putting information in categories so they are coded in a different or
clearer way. Observers could be kept unaware of the aims of the observation or more observers could
3.11 Reliability - A measurement is said to be reliable or consistent if the measurement can
produce similar results if used again in similar circumstances.
3.11.1 A common way of assessing the reliability of observations is to use inter-rater reliability.
This involves comparing the ratings of two or more observers and checking for
agreement in their measurements.
4.1 Correlation refers to a measure of how strongly two or more variables are related to each other.
4.1.1 A positive correlation means that high values of one variable are associated
with high values of the other. Or if you like, the variables increase together.
4.1.2 A negative correlation means that high values of one variable are associated with low
values of the other. Or if you like, as one variable increases the other decreases.
4.1.3 If there is no correlation between two variables they are said to be uncorrelated.
4.2 A correlation coefficient refers to a number between -1 and +1 and states how strong
a correlation is. If the number is close to +1 then there is a positive correlation. If the
number is close to -1 then there is a negative correlation. If the number is close to 0
then the variables are uncorrelated.
4.3 A hypothesis is a testable, predictive statement. The
hypothesis will state what the researcher expects to find out.
4.4 A hypothesis for correlation is a prediction of a relationship and not a difference or
cause and effect. Therefore you should never write a hypothesis for correlation that
includes the words difference, cause or effect
4.5 Descriptive Statistics
4.5.1 In correlational analysis the data is summarised by
presenting the data in a scattergraph (or scattergram)
4.5.2 It is important that the scattergram has a title and both axes are labelled.
4.6 Evaluation of Correlational Analysis
4.6.1 Correlations are very good for showing possible relationships between variables and some
times are the only practical or ethical way of carrying out an investigation.. Many researchers
use it at a starting point for research
4.6.2 However correlational analysis cannot demonstrate
a cause and effect relationship between variables.