Research Methods Flashcards
Dependent Variable (DV)
Measure of the behaviour we are interested in. On y-axis.
Independent Variable (IV)
Variable manipulated by experimenter, to see if it affects DV. On x-axis.
Operational defintion
Description of operations carried out by researcher to measure DV or to manipulate
IV. Helps others to replicate study, and helps us to remain objective and avoid biasing our results.
Reliability
Whether we get the same results if we measure the same variable again under the same
conditions
Validity
Whether our variable really measures what we meant it to.
Population
All the events, scores etc. we are interested in.
Sample
Representative subgroup drawn from population, preferably randomly. Used to draw conclusions
about whole population
Sampling error
Random samples drawn from the same population will give different results. Chance
variation. Unavoidable, but minimised by using large samples
Sampling bias
When a sample does not truly represent its parent population, usually because it was not
drawn randomly. E.g., a minority ethnic group may be underrepresented. Avoidable by random sampling.
Observational designs
Look for correlation between two DVs (strictly, there is no IV. Some sources, like
your lab manual, use IV slightly differently, to mean the variable that may cause changes in the DV. I prefer
the stricter definition that it is the variable the experimenter manipulates). Note that correlation doesn’t
always imply causation, so less powerful than experimental designs, but sometimes the only choice for
ethical or practical reasons.
Experimental designs
Manipulate IV and observe effect on DV. Can imply causation, if effect is
replicable. More powerful, but not always possible.
Cofounding variable
A variable other than our IV which might have been responsible for any change in
the DV that we observed. An alternative explanation for our results. Invalidates our experiment. Also just
called confound.
Controlling for potential cofounding variable
Hold them constant (esp. with external confounds, such
as time of day, or stimulus lists in a memory task). Randomize them (esp. with subject confounds, such as
individual differences in ability on a task)
Within-subjects design
Each subject is exposed to all levels of IV (all conditions). Comparison is between
each subject’s performance in several conditions. Internal (subject) confounds controlled, but could be
external (environmental) confounds
Between-subject design
Each subject only encounters one level of IV (one condition). Comparison is
between average performance of groups of different subjects in each condition. External variables can be
controlled, but could be subject confounds.
Matched-pair design
Each subject is only in one experimental condition, but his/her behaviour is
compared with a matched partner (according to subject confounds that might be important, like pre-existing
ability at the task) in the other experimental condition. Controls both external and internal confounds. Good
idea but not widely used.
Experimental group
Group that receives the intervention (e.g., a new drug).
Control group
Group that doesn’t receive the intervention, but is otherwise treated identically to
experimental group. Assess effect of intervention by comparing improvement of control with experimental
group.
Placebo
A sham drug (e.g., a sugar pill which looks like the real pill). Given to control group in drug
evaluations.
Single-blind design
Subjects don’t know whether they are in experimental or control group.
Double-blind design
Experimenter who regularly interacts with subjects doesn’t know which group they
are in either
Important message
Correlation does not imply causation!
