Psych 311 1nd Edition Lecture 19 Outline of Last Lecture I II III Pooled Variance Matched Subject Design Inferential Process Design Outline of Current Lecture I II III Correlation Pearson Correlation Coefficient r Formulas Current Lecture I Correlation Design two or more variables are measured and recorded for each individual these variables exist naturally researchers do not manipulate anything you want to determine if there is a relationship between these variables does not attempt to explain relationship and can t determine cause and affect Test HT that yields a Pearson Correlation Coefficient r numerical value that describes that relationship between variables provides info about two characteristics of the relationship 1 direction of relationship 2 strength of relationship Direction describes how the two variables are related reflected in sign of correlation coefficient r positive correlation the tendency for two variables to change in same direction as x increases so does y as x decreases so does y positive good negative correlation the tendency for two variables to change in the opposite direction if x increases y decreases negative bad Strength the strength of the relationship shows how closely related the two variables are These notes represent a detailed interpretation of the professor s lecture GradeBuddy is best used as a supplement to your own notes not as a substitute this is represented in the numerical value of r the larger the value for r regardless of sign the stronger the relationship r can range from 1 00 to 1 00 if r 0 then there is no relationship We can also see these relationships from scatterplots position of dots individuals on a scatterplot represents the relationship if no discernable pattern is present there is either a very weak relationship or none strength of relationship represented by how tightly clustered together these points are around imaginary line slope of invisible line tells you the direction of the relationship II Pearson Correlation Coefficient r A statistical method used to measure and describe the relationship between two variables Covary when changes in one variable are accompanied by consistent strength and predictable direction changes in other variables varying together III Formulas Sum of Products of Deviations SP SP X Mx Y My measures covariability for each participant you are computing deviation for x and y and multiplying them together to get total sum for all participants Sum of the Squared Deviations SS SS x M 2 measures variability for single variable Pearsons Correlation Coefficient r r x Mx y My SSx SSy r SP SSx SSy numerator the extent to which the variables covary carries sign denominator the extent to which the variables vary independently
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