Variables
Extraneous variables (EVs)
Any variable that may potentially interfere with the IV should be identified and controlled
for/removed at the start of the experiment. These variables are known as extraneous variables.
These variables are known as nuisance variables that do not vary systematically with the IV. In
other words these variables could not alone directly affect the DV but they could have an influence.
Types of Extraneous variables
1. Participant variables = Participant variables are individual differences between participants
such as levels of intelligence, age, gender, social class, fitness etc. Participant variables
cannot be used in repeated measures studies where the same participants does both
conditions.
Example: A study looking at the effects of drinking Red Bull on speed to complete a 5km run. If we
were to use independent groups we would have one group of participants who drink Red Bull any
then run 5km and another separate group of participants who do not drink Red Bull and then run
5km. Their times would be recorded. We may find that the group who drank Red Bull had a faster
time for the 5km but we cannot be sure these results are due to the Red Bull (IV) they could in fact
be influence by an extraneous variable such as the fact that all the participants in the Red Bull
condition were faster runners any-way. Therefore the IV (Red Bull v no Red Bull) has not fully
affected the DV (the time taken to run 5km) and the results are affected by an extraneous variable
(running ability).
2. Situational variables = These are outside influences on the experiment such as time of day,
weather, noise, type of room the experiment takes place in etc.
Example: In the same study as above, if we were to use a repeated measures design we would
have the participant run 5km on the morning after consuming Red Bull then that same person run
5km the next day, this time on the after-noon, after not consuming any Red Bull. We may find that
the participant was slower the following day when they did not consume any Red Bull However,
this may not actually be due to the IV (Red Bull v no Red Bull) affecting the DV (time taken to
complete 5km) but could be influenced by an extraneous variable such (time of the day) as they
could have been more tired in the second condition on the afternoon and this is why they were
slower.
Confounding variables
Definition = These are uncontrolled extraneous variables that have affected the DV other than the
IV. They could provide an alternative explanation for the change in the DV other than the IV. They
change systematically with the IV. In other words they change like the IV and therefore could cause
the change in the DV.
Example = A study looked at whether wine causes people to become more talkative, we could
have two conditions (wine and water) and count how many words each participant speaks in the
following 2 minutes. We may find that the first 10 participants who arrive for the water condition
happen to be very shy, introverted people. The next 10 people who arrive for the wine condition are
all quite extravert types, very loud and outgoing. This would be an unfortunate coincidence but this
coincidence now means we have ended up with a second unintended IV - personality.
So when the results are analysed and we find those who drank wine were chattier, we can't be
sure if this is because of the type of drink or the personalities of the participants.The problem was
that extraversion varied systematically with the IV (in other words, the extraversion/introversion
was split up just the same way the wine/water was) and this alone could explain changes in the
DV.
,Extraneous Variable
A 'nuisance' variable which may affect the results. Do not vary systematically with the IV.
Confounding Variable
An uncontrolled extraneous variable which may end up confounding the results of the experiment
because it could provide an alternative explanation for the results. Confounding variables vary
systematically with the IV.
Investigator effects
● Investigator effects are the ways in which researchers unconsciously influence the results
of research.
● It can include expectancy effects e.g. a researcher interviews an older person and expects
they will be more interested in topics relating to social care issues so then gives them a
higher rating.
● Other investigator effects include physical characteristics of investigators that may influence
results, such as age of ethnicity.
● Body language - for example, in the wine and water study, the researcher may be inclined
to smile more during a conversation with a participant who is in the wine condition because
you expect a greater level of chattiness. In this instance the researcher is affecting the
results.
● Investigators may be unconsciously biassed in their interpretation of data and find what
they expect to find so they pretend to have seen behaviour or give higher /lower ratings.
How to deal with investigator effects
1. Use a double-blind procedure: This involves neither participants nor investigators knowing
which condition participants are in by employing a research assistant who does not know
the aim of the study. This prevents researchers from unconsciously giving participant's
clues as to which condition they are in.
2. Standardisation: This involves ensuring all participants are subject to exactly the same
environment, information & experience in an experiment. Standardised instructions should
be read to each participant which explain exactly what will be done & eliminate investigator
effects e.g. all participants should be tested in the same room, by same researcher, hear
the same instructions, listen to the same type of music, look at the same type of pictures for
the same length of time etc.
3. Randomisation: The use of chance when designing research, allocating participants to
conditions and the order in which the conditions occur. E.g. Ps. Have to remember a list of
words. A generator would generate the order of the words rather than the researcher.
Demand characteristics
Demand characteristics are any cue from the researcher or from the research situation that may be
interpreted by the participant as revealing the purpose of the investigation. This may lead to a
participant changing their behaviour within the research situation. There are several features of
research studies that enable participants to guess what a study is about and what is expected of
them.
Such demand characteristics can involve participants:
● Guessing the purpose of the research and trying to please the researcher by giving what
they think is the 'right' result.
● Guessing the purpose of the research and trying to annoy the researcher by giving the
wrong results; this is called the 'Screw-you effect'.
, ● Acting unnaturally out of nervousness of fear of evaluation. These demand characteristics
mean that participant behaviour is no longer natural which therefore results in an
extraneous variable that may affect the DV.
How to deal with demand characteristics
Single blind procedure. This is where the participants have no idea which condition of a study they
are in e.g. in drug trials the participants would not know whether they were given a real drug or a
placebo drug (sugar pill).
What is operational using variables =
It is about ensuring that you make your DV measurable ( e.g. aggression levels) and the conditions
of the IV specific (e.g. 150ml cup of coffee vs. 150ml cup of water) so researchers know how to
repeat the study if they need to.
Aims, hypotheses, and variables
What is an aim? (Background)
Definition: An aim states the intent of the study in general. In other words, what it intends to
investigate.
Example: To investigate the effect of having a training partner on athletes' motivation levels.
The aim is then reflected in the hypothesis.
All psychological research will have an aim which is driven by theory. In psychology observations
are made about behaviours or events which in turn leads to theories that try to explain the
obser-vations. Researchers will then test the theories assumptions.
Example: athletes perform better when with a training partner and a theory proposes this is
because they are more motivated with peers around them.
What is a hypothesis?
A hypothesis is a clear testable prediction of the expected studies outcome which makes reference
to the IV and the DV. It is written as a statement not a question and is generated from the theory
being tested.
Example: Athletes who have a training partner are likely to score higher on a questionnaire
measuring motivation levels than athletes who train alone."
Variables
A variable is anything within a study that changes and does not stay constant, e.g. time taken to do
a task, anxiety levels and exam results.
There are a few different kinds of variables:
● The Independent Variable (IV): the IV is the variable which is directly manipulated by the
researcher. It is the thing which is being changed in an experiment. It will usually have two
conditions.
● The Dependent Variable (DV): is the thing being measured in the experiment and which
changes as a result of the IV being manipulated.
Example: Athletes who have a training partner are likely to score higher on a questionnaire
measuring motivation levels than athletes who train alone.
IV: training with partner or training alone
DV: Motivation score.
Hypothesis
There are several different types of hypothesis in psychology.
Extraneous variables (EVs)
Any variable that may potentially interfere with the IV should be identified and controlled
for/removed at the start of the experiment. These variables are known as extraneous variables.
These variables are known as nuisance variables that do not vary systematically with the IV. In
other words these variables could not alone directly affect the DV but they could have an influence.
Types of Extraneous variables
1. Participant variables = Participant variables are individual differences between participants
such as levels of intelligence, age, gender, social class, fitness etc. Participant variables
cannot be used in repeated measures studies where the same participants does both
conditions.
Example: A study looking at the effects of drinking Red Bull on speed to complete a 5km run. If we
were to use independent groups we would have one group of participants who drink Red Bull any
then run 5km and another separate group of participants who do not drink Red Bull and then run
5km. Their times would be recorded. We may find that the group who drank Red Bull had a faster
time for the 5km but we cannot be sure these results are due to the Red Bull (IV) they could in fact
be influence by an extraneous variable such as the fact that all the participants in the Red Bull
condition were faster runners any-way. Therefore the IV (Red Bull v no Red Bull) has not fully
affected the DV (the time taken to run 5km) and the results are affected by an extraneous variable
(running ability).
2. Situational variables = These are outside influences on the experiment such as time of day,
weather, noise, type of room the experiment takes place in etc.
Example: In the same study as above, if we were to use a repeated measures design we would
have the participant run 5km on the morning after consuming Red Bull then that same person run
5km the next day, this time on the after-noon, after not consuming any Red Bull. We may find that
the participant was slower the following day when they did not consume any Red Bull However,
this may not actually be due to the IV (Red Bull v no Red Bull) affecting the DV (time taken to
complete 5km) but could be influenced by an extraneous variable such (time of the day) as they
could have been more tired in the second condition on the afternoon and this is why they were
slower.
Confounding variables
Definition = These are uncontrolled extraneous variables that have affected the DV other than the
IV. They could provide an alternative explanation for the change in the DV other than the IV. They
change systematically with the IV. In other words they change like the IV and therefore could cause
the change in the DV.
Example = A study looked at whether wine causes people to become more talkative, we could
have two conditions (wine and water) and count how many words each participant speaks in the
following 2 minutes. We may find that the first 10 participants who arrive for the water condition
happen to be very shy, introverted people. The next 10 people who arrive for the wine condition are
all quite extravert types, very loud and outgoing. This would be an unfortunate coincidence but this
coincidence now means we have ended up with a second unintended IV - personality.
So when the results are analysed and we find those who drank wine were chattier, we can't be
sure if this is because of the type of drink or the personalities of the participants.The problem was
that extraversion varied systematically with the IV (in other words, the extraversion/introversion
was split up just the same way the wine/water was) and this alone could explain changes in the
DV.
,Extraneous Variable
A 'nuisance' variable which may affect the results. Do not vary systematically with the IV.
Confounding Variable
An uncontrolled extraneous variable which may end up confounding the results of the experiment
because it could provide an alternative explanation for the results. Confounding variables vary
systematically with the IV.
Investigator effects
● Investigator effects are the ways in which researchers unconsciously influence the results
of research.
● It can include expectancy effects e.g. a researcher interviews an older person and expects
they will be more interested in topics relating to social care issues so then gives them a
higher rating.
● Other investigator effects include physical characteristics of investigators that may influence
results, such as age of ethnicity.
● Body language - for example, in the wine and water study, the researcher may be inclined
to smile more during a conversation with a participant who is in the wine condition because
you expect a greater level of chattiness. In this instance the researcher is affecting the
results.
● Investigators may be unconsciously biassed in their interpretation of data and find what
they expect to find so they pretend to have seen behaviour or give higher /lower ratings.
How to deal with investigator effects
1. Use a double-blind procedure: This involves neither participants nor investigators knowing
which condition participants are in by employing a research assistant who does not know
the aim of the study. This prevents researchers from unconsciously giving participant's
clues as to which condition they are in.
2. Standardisation: This involves ensuring all participants are subject to exactly the same
environment, information & experience in an experiment. Standardised instructions should
be read to each participant which explain exactly what will be done & eliminate investigator
effects e.g. all participants should be tested in the same room, by same researcher, hear
the same instructions, listen to the same type of music, look at the same type of pictures for
the same length of time etc.
3. Randomisation: The use of chance when designing research, allocating participants to
conditions and the order in which the conditions occur. E.g. Ps. Have to remember a list of
words. A generator would generate the order of the words rather than the researcher.
Demand characteristics
Demand characteristics are any cue from the researcher or from the research situation that may be
interpreted by the participant as revealing the purpose of the investigation. This may lead to a
participant changing their behaviour within the research situation. There are several features of
research studies that enable participants to guess what a study is about and what is expected of
them.
Such demand characteristics can involve participants:
● Guessing the purpose of the research and trying to please the researcher by giving what
they think is the 'right' result.
● Guessing the purpose of the research and trying to annoy the researcher by giving the
wrong results; this is called the 'Screw-you effect'.
, ● Acting unnaturally out of nervousness of fear of evaluation. These demand characteristics
mean that participant behaviour is no longer natural which therefore results in an
extraneous variable that may affect the DV.
How to deal with demand characteristics
Single blind procedure. This is where the participants have no idea which condition of a study they
are in e.g. in drug trials the participants would not know whether they were given a real drug or a
placebo drug (sugar pill).
What is operational using variables =
It is about ensuring that you make your DV measurable ( e.g. aggression levels) and the conditions
of the IV specific (e.g. 150ml cup of coffee vs. 150ml cup of water) so researchers know how to
repeat the study if they need to.
Aims, hypotheses, and variables
What is an aim? (Background)
Definition: An aim states the intent of the study in general. In other words, what it intends to
investigate.
Example: To investigate the effect of having a training partner on athletes' motivation levels.
The aim is then reflected in the hypothesis.
All psychological research will have an aim which is driven by theory. In psychology observations
are made about behaviours or events which in turn leads to theories that try to explain the
obser-vations. Researchers will then test the theories assumptions.
Example: athletes perform better when with a training partner and a theory proposes this is
because they are more motivated with peers around them.
What is a hypothesis?
A hypothesis is a clear testable prediction of the expected studies outcome which makes reference
to the IV and the DV. It is written as a statement not a question and is generated from the theory
being tested.
Example: Athletes who have a training partner are likely to score higher on a questionnaire
measuring motivation levels than athletes who train alone."
Variables
A variable is anything within a study that changes and does not stay constant, e.g. time taken to do
a task, anxiety levels and exam results.
There are a few different kinds of variables:
● The Independent Variable (IV): the IV is the variable which is directly manipulated by the
researcher. It is the thing which is being changed in an experiment. It will usually have two
conditions.
● The Dependent Variable (DV): is the thing being measured in the experiment and which
changes as a result of the IV being manipulated.
Example: Athletes who have a training partner are likely to score higher on a questionnaire
measuring motivation levels than athletes who train alone.
IV: training with partner or training alone
DV: Motivation score.
Hypothesis
There are several different types of hypothesis in psychology.
