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Chapter 9

# PSYC 2030 Chapter Notes - Chapter 9: Confidence Interval, Correlation And Dependence, Twin Study

by OC100587

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**preview**shows page 1. to view the full**4 pages of the document.**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

vise versa.

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

variable.

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

will be.

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

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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