correlations Flashcards
correlations
illustrates the strength and direction of an association between two or more co-variables
correlations are plotted on a scatter gram
one co-variable is represented on the x axis and the other on the y axis
each point of dot on the graphs is the x and y position of each co vairiable
types of correlation - positive
example - caffeine is related to high anxiety
positive correlation would be if we plotted the data on a scatter gram and saw and high arch going to the right
meaning the mote caffiene people drink the higher their level of anxiety
types of correlation - negative
example - how many hours of sleep they have over the same period during the caffeine
drinking lots of caffeine often disrupts sleep patterns so perhaps the more caffeine someone drinks the less sleep they have
negative correlation - as one variable rises the other one falls
upwards arch to the left
types of correlation - zero correlation
with he same example they might ask participants to also record how many dogs they see in the street that same week
there is no relationship between the number of caffiene drinks someone has and the number of dogs rather see in the street
no correlation shows random dots with no patterns
the difference between correlations and experiments
in an experiment the researcher contorls or manipulates the independent variable (IV) in order to measure the effect on the dependent variable (DV)
as a result those deliberate change in one variable it is possible to infer that the IV caused any observed changed int he DV
in contrast correlation there is no such manipulation of one variable and therefore it is not possible to establish cause and effect between one co-variable and another
even if we found a strong positive correlation between caffience and anxiety level we cannot assume that caffience was the cause of the anxiety
strengths
correlations are a useful preliminary tool for research
by assessing the strenght and direction of a relationship they provide a precise and quantifiable measure of how two variables are related
this many suggest ideas for possible future research if hairballs are strongly related or deomnstarate an interesting pattern
correlations are often used as a starting point to assess possible patterns between variables before researchers commit to an experimental study
strength
correlations are relatively quick and economical to carry out
there is no need for a controlled environment and no manipulation of variables is require
data collected by others (secondary. data ) such as government,ent stats can be used which means correlations are less time0consuming than experiments
limitations
as a result of the lack of experimental manipulation a dn control within a correlation studies can only tell us how variables are related but not why
correlations cannot demonstrate cause and effect between vauirbles and therefore we do not know which co-variable is causing the other rot change
example - we cannot conclude that drinking caffience causes anxiety it may be that people who are already anxious drink more caffiene as a result
so establishing the direction of effect is an issue
limitation
it may also be the case that another i tested variable is causing the relationship between two co variables we are interested in
an intervening variable also known as a third variable problem
perhaps people who have high-pressured jobs and hence spend a lot of time feeling ancipis drink a lot of caffiene because they work long hours and need to remain alert
thus the key unaccounted for variable here is job type which in effect is casuding the relationship between the two co-variables
