PSYC 2030 Chapter Notes - Chapter 9: Confidence Interval, Correlation And Dependence, Twin Study
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Chapter 9: Correlational Research
Correlation and Regression – The Basics
A correlation exists when two variables are associated or related to each other in some
fashion – in a positive correlation the relationship is such that a high score on one
variable is associated with a high score on the second variable as well as a low score on
one relates to a low score on the other – a negative correlation is an inverse relationship;
high scores on one variable are associated with low scores on the second variable and
Jon Stuart Mill’s Method of Concomitant Variation (correlation method) states that
changes in the value of one variable are accompanied by predictable changes in a second
Positive and Negative Correlation
An example of a positive correlation is study time and grades; the more time you spend
studying the better your grades will be – an example of a negative correlation is goofing-
off time and grades (GPA); the more time you spend goofing-off the worse your grades
The strength of a correlation is indicated by the size of a statistic called the correlation
coefficient, which ranges from -1.00 for a perfect negative correlation, through 0.00 for
no relationship, to +1.00 for a perfect positive correlation – the most common coefficient
is the Pearson’s r, for data measured on an interval or ratio scale.
Scatterplots indicate the strength of a correlation as well as provides a visual
representation of the relationship shown by a correlation.
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