Correlation_Sp08_BB

# Correlation_Sp08_BB - Overview Correlation Visualizing...

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Correlation relationships among variables Overview: Correlation • Visualizing relationships: the scatterplot • Two methods of obtaining a measure of correlation: – Using covariance – Using z-scores • Assumptions, concerns, and limitations First approach to looking at relationships? Scatterplots An example: Shoe size and height Shoe size vs. Height 0 10 20 30 40 50 60 70 80 024681 0 1 2 Shoe Size Height Shoe Size Height 55 0 65 5 76 0 10 65 11 70 95 5 Steps to creating a scatterplot 1) Draw the axes and decide which variable goes on which axes 2) Determine the range of values to use for each variable and mark them on the axes 3) Mark a dot for each person’s pair of scores

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Types of correlations • No correlation • Linear – Positive correlations – Negative correlations • Curvilinear The Yerkes Dodson Curve (1909) Cautionary note… • Notice that ALL of these examples include two variables that are interval or ratio – There are other correlations for ordinal/nominal variables that will not be discussed in this class. • e.g., gender (M/F) and performance on a visuospatial task. Cautionary note… • Notice that ALL of these examples include two dependent variables. – Correlation is a descriptive statistics not an inferential statistics – Cannot infer causation Degree of correlation • Correlation coefficient = Pearson’s r • Indicates the direction and magnitude of a relationship between two variables. Magnitude • Values of r range from 0 to |1.00| – When r = 0, there is NO relationship – The closer to |1.00|, the stronger the relationship
Direction • Sign of r indicates whether correlation is negative or positive – Negative correlations = X Y (or vice-versa) – Positive correlations = X

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## This note was uploaded on 04/07/2008 for the course PSY 031 taught by Professor Dicorcia during the Spring '08 term at Tufts.

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Correlation_Sp08_BB - Overview Correlation Visualizing...

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