Chapter 6: Correlational Analysis: Pearson's r

Francis Galton

________ invented correlation but Karl Pearson developed it, discovering spurious correlations (a statistical relationship only-- not due to a real relationship between the two variables)

correlation coefficient

The strength or magnitude of the relationship between the two variables-- the test statistic, called the ________, varies form 0 (no relationship between the variables) to 1 (perfect relationship between the variables)

correlational relationship

A(n) ________ can not automatically be regarded as implying causation.

REMEMBER

________: Statistical significance does not necessarily equal psychological significance.

correlational analysis

The purpose of performing a(n) ________ is to discover whether there is a meaningful relationship between variables, which is unlikely to have occurred by sampling error (assuming the null hypothesis to be true), and unlikely to be spurious.

Bivariate Correlation

________: when we are considering the relationship between two variables.

Correlation Coefficient

________ (r): a ratio between the covariance (variance shared by the 2 variables) and a measure of the separate variances.

correlation coefficient

The ________ tells you how well the variables are related, and the probability value is the probability of that value occurring by sampling error.

Bivariate Correlation

when we are considering the relationship between two variables

Francis Galton invented correlation but Karl Pearson developed it, discovering spurious correlations (a statistical relationship only

- not due to a real relationship between the two variables)

the direction of the relationship

- whether it is positive, negative, or zero

the strength or magnitude of the relationship between the two variables

- the test statistic, called the correlation coefficient, varies form 0 (no relationship between the variables) to 1 (perfect relationship between the variables)

Perfect Positive Relationship

where all the points on the scattergram would fall on a straight line

Example

relationship between arousal and performance

full name

Pearsons product moment correlation

Correlation Coefficient (r)

a ratio between the covariance (variance shared by the 2 variables) and a measure of the separate variances

REMEMBER

Statistical significance does not necessarily equal psychological significance

Multiply 1/√(n

3) by 1.96

Zero-Order Correlation

correlation between two variables without taking any other variables into account

Partial Correlation

can be explained by lookin at overlapping circles of variance; a correlation between two variables, with one partialled out

If you look carefully, you can see that the variables that share most variance with each other have to do with quality of life

satisfaction with relationships and life

weak moderate

What kind of correlation is this? (choices: zero, weak, weak moderate, moderate, moderate strong, strong, perfect)

moderate negative

What kind of correlation is this? (choices: zero, weak positive, weak negative, weak moderate positive, weak moderate negative, moderate positive, moderate negative, moderate strong positive, moderate strong negative, strong positive, strong negative, perfect positive, perfect negative)

zero

What kind of correlation is this? (choices: zero, weak positive, weak negative, weak moderate positive, weak moderate negative, moderate positive, moderate negative, moderate strong positive, moderate strong negative, strong positive, strong negative, perfect positive, perfect negative)

moderate strong positive

What kind of correlation is this? (choices: zero, weak positive, weak negative, weak moderate positive, weak moderate negative, moderate positive, moderate negative, moderate strong positive, moderate strong negative, strong positive, strong negative, perfect positive, perfect negative)

moderate negative

What kind of correlation is this? (choices: zero, weak positive, weak negative, weak moderate positive, weak moderate negative, moderate positive, moderate negative, moderate strong positive, moderate strong negative, strong positive, strong negative, perfect positive, perfect negative)