How does R compare to SPSS
Compare correlations statistically
Just like other statistics, correlations can be compared with one another. The calculation depends on the type of correlations and the sample. The three possible cases are discussed here and can be calculated directly.
Three different comparisons
The division primarily distinguishes whether the correlations both come from the same sample (i.e. are dependent) or from two different samples (i.e. are independent). In total, three different types of correlation comparisons can be made:
- compare two correlations from two different samples (independent)
- compare two correlations from a sample that have a third variable in common
- compare two correlations from a sample that no Have third variables in common
Usually we will have two correlations from two independent groups that we want to statistically compare. This can be the case, for example, if we want to compare the correlations from two different studies with one another or if the sample is independent of one another.
For example, we might want to compare the correlation coefficients from two studies on intelligence and life satisfaction. Both studies would indicate correlation coefficients for intelligence and life satisfaction, which we could statistically compare with specifying the sample size.
Dependent groups, with a third variable
Alternatively, we can also compare the correlation within a sample (dependent). One possibility here is that the two correlations are overlapping and therefore have one variable in common. For example, we might want to compare the correlations of intelligence and life satisfaction with the correlation of intelligence and income. Since the significance is based on the intercorrelation between life satisfaction and income, this correlation must also be specified as a parameter.
Dependent groups, no third variable
Finally we want to compare two correlations of dependent groups that have no third variable in common. For example, we might want to know whether the correlation between intelligence and income and between life satisfaction and expectations of self-efficacy are statically different. Since the significance depends on all intercorrelations involved, they must be specified as parameters.
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