Research methods - Experiments: Variables and Design Flashcards
Describe what experiments are used for and how they work
one variable is manipulated by the experimenter and the effect of the change on another variable is measured. If there is a change, there is said to be a cause and effect relationship between the two variables.
what are the three different types of experiments
laboratory experiments
field experiments
quasi experiments
what is an aim
a general statement of the purpose of the study and highlights what the researcher intends to investigate
define independent variables
the variable that the researcher manipulates
define dependent variables
the variable that the researcher measures the effect of the IV on
what must both variables be
fully operationalised
Why is it important that both variables are fully operationalised
Allows for research to be replicated in the future
Operationalise this: performance
Score out of 20
Define the extraneous variable
When something else has the potential of affecting the dependent variable that is not the independent variable
What are the four types of extraneous variable
Participant variables, situation variables , demand characteristics,
Researcher effects
Define participant variables
These are the characteristics of an individual which may affect the dependent variable eg age
Define situation variables
Features of the environment which may affect the dependent variable eg unexpected background noise
Define demand characteristics
If participants work out the aims of the research study, they may begin to behave in a certain way
Define researcher effects
The researcher may give away the aims of the research study eg body language
how can it be ensured that these extraneous variables do not affect out independent variable
control them - making the variables the same for everyone they affect.
the more extraneous variables we control, the more likely we are to establish cause and effect
what is a confounding variable
when researchers are unable to control an extraneous variable, making cause and effect near impossible to achieve
what is a hypothesis
a prediction that researcher make about what they think will happen in their study - it is a statement that demonstrates the relationship between the IV and the DV
what are the general rules when writing a hypothesis
future tense
include both conditions of the IV
include the DV
define null hypotheses
these predict that no difference will be found in the results between the conditions - the IV will have no effect on the DV
define experimental hypotheses
these predict that there will be a significant difference in the results between the two conditions
what are one-tailed (directional) hypotheses
they predict the direction of results e.g participants who drink coke will recall more words than participants who do not drink coke
what are two-tailed (non-directional) hypotheses
they do not predict the direction of results, but just state that there will be an effect
how would you know whether to write a one-tailed or two-tailed hypothesis
if previous research has been conducted and it tells us the direction of the results, then we should write a one-tailed hypothesis
if there is not previous research or the results of previous research are inconclusive then we should write a two-tailed hypothesis
what are the three experimental designs
independent measures
repeated measures
matched pairs
why is experimental design important
the validity of an experiment is directly affected by how it is constructed
describe the independent measures design
different ps take part in each condition of the independent variable. the ps remain independent from each other
describe the repeated measures design
all of the ps take part in both conditions of the independent variable
the ps repeat the experiment whilst taking part in all the conditions
describe the matched pairs design
each ps is matched with a ‘twin’ who is similar to them, and one ps from each twin pair takes part in each condition
ps are usually matched on characteristics that could affect the outcome of the experiment
what are order effects
ps responses are affected by the order of conditions they are exposed to
what is counterbalancing
a technique used to reduce order effects when using aa repeated measures design
how does counterbalancing work
it varies the order in which ps take part in each condition of the IV
ABBA as half the ps take part in condition A first, then do condition B and the other half do condition B first, then A
what are strengths of the independent measures design
reduces the possibility of demand characteristics as ps only take part in one condition of the IV so less likely to guess aim
avoids order effects
what are weaknesses of the independent measures design
participant variables may influence the results of the study as there are different ps with different characteristics in each condition
larger sample of ps required due to them only doing one condition
how to deal with weaknesses of independent measures design
ps should be randomly allocated to conditions ie put names/numbers into a hat
this means that ps variables should not cluster in one condition
what are strengths of the repeated measures design
reduces the effect of participant variables as ps take part in both conditions
fewer participants required
what are weaknesses of the repeated measures design
ps may be able to guess the aim of the experiment and so may display demand characteristics due to taking part in both conditions
order effects may impact outcome of study as ps take part in both conditions, they may become fatigued or bored and perform worse or may perform better due to practice
how to deal with weaknesses of the repeated measures design
order effects can be reduced with counter-balancing
what are strengths of the matched pairs design
reduces the effect of ps variables as ps are matched on key characteristics that researchers have identified could have an effect on the results
avoids order effects
what are weaknesses of the matched pairs design
will be difficult to control all ps variables as you can only match on variables identified as relevant AND there may be characteristics that cannot be matched
time consuming
how to deal with weaknesses of the matched pairs design
start with a large group of ps to increase the chances of being able to match ps on key variables
