Let's run some correlation tests in SPSS now. For reasonable sample sizes, the central limit theorem ensures that the sampling distribution will be normal. This assumption is not needed for sample sizes of N = 25 or more.

normality: our 2 variables must follow a bivariate normal distribution in our population.

However, the statistical significance-test for correlations assumes

However, finding a strong correlation in this case is very unlikely and suggests that my population correlation wasn't zero after all.Ĭomputing and interpreting correlation coefficients themselves does not require any assumptions. So finding a non zero correlation in my sample does not prove that 2 variables are correlated in my entire population if the population correlation is really zero, I may easily find a small correlation in my sample. Sample outcomes typically differ somewhat from population outcomes.

a correlation of 1 indicates a perfect ascending linear relation: higher scores on one variable are associated with higher scores on the other variable.Ī correlation test (usually) tests the null hypothesis that the population correlation is zero.ĭata often contain just a sample from a (much) larger population: I surveyed 100 customers (sample) but I'm really interested in all my 100,000 customers (population).

However, there may be a (strong) non-linear relation nevertheless.

a correlation of 0 means there's no linear relation between 2 variables whatsoever.

a correlation of -1 indicates a perfect linear descending relation: higher scores on one variable imply lower scores on the other variable.

It's best understood by looking at some scatterplots. SPSS Correlation Analysis Tutorial report this ad By Ruben Geert van den Berg under CorrelationĪlso see Pearson Correlations - Quick Introduction.Ī (Pearson) correlation is a number between -1 and +1 that indicates to what extent 2 quantitative variables are linearly related.

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