## How do you interpret the Spearman correlation coefficient?

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The Spearman correlation coefficient, rs, can take values from +1 to -1. A rs of +1 indicates a perfect association of ranks, a rs of zero indicates no association between ranks and a rs of -1 indicates a perfect negative association of ranks. The closer rs is to zero, the weaker the association between the ranks.

**Is 0.5 A good correlation coefficient?**

Correlation coefficients whose magnitude are between 0.5 and 0.7 indicate variables which can be considered moderately correlated. Correlation coefficients whose magnitude are between 0.3 and 0.5 indicate variables which have a low correlation.

### Is a correlation coefficient of 0.7 strong?

Values between 0.7 and 1.0 (-0.7 and -1.0) indicate a strong positive (negative) linear relationship via a firm linear rule.

**Is a correlation coefficient of 0.6 strong?**

Correlation Coefficient = 0.8: A fairly strong positive relationship. Correlation Coefficient = 0.6: A moderate positive relationship.

## What are the assumptions of the Spearman correlation coefficient?

The assumptions of the Spearman correlation are that data must be at least ordinal and the scores on one variable must be monotonically related to the other variable. Effect size: Cohen’s standard may be used to evaluate the correlation coefficient to determine the strength of the relationship, or the effect size.

**Is .94 a strong correlation?**

Correlation coefficients whose magnitude are between 0.9 and 1.0 indicate variables which can be considered very highly correlated. Correlation coefficients whose magnitude are between 0.7 and 0.9 indicate variables which can be considered highly correlated.

### Is 0.65 A strong correlation?

For example, a correlation coefficient of 0.65 could either be interpreted as a “good” or “moderate” correlation, depending on the applied rule of thumb. It is also quite capricious to claim that a correlation coefficient of 0.39 represents a “weak” association, whereas 0.40 is a “moderate” association.

**What does a correlation coefficient of 0.9 mean?**

positive association

The magnitude of the correlation coefficient indicates the strength of the association. For example, a correlation of r = 0.9 suggests a strong, positive association between two variables, whereas a correlation of r = -0.2 suggest a weak, negative association.

## Is .4 a strong correlation?

In these kinds of studies, we rarely see correlations above 0.6. For this kind of data, we generally consider correlations above 0.4 to be relatively strong; correlations between 0.2 and 0.4 are moderate, and those below 0.2 are considered weak.

**What’s a good correlation coefficient?**

The values range between -1.0 and 1.0. A calculated number greater than 1.0 or less than -1.0 means that there was an error in the correlation measurement. A correlation of -1.0 shows a perfect negative correlation, while a correlation of 1.0 shows a perfect positive correlation.

### Is .69 a strong correlation?

The Spearman correlation coefficient, r s, can take values from +1 to -1. A r s of +1 indicates a perfect association of ranks, a r s of zero indicates no association between ranks and a r s of -1 indicates a perfect negative association of ranks.

**What is the difference between Didi and Spearman coefficient?**

di= difference in ranks of the “ith” element. The Spearman Coefficient,⍴, can take a value between +1 to -1 where, A ⍴ value of +1 means a perfect association of rank. A ⍴ value of 0 means no association of ranks.

## What is the Spearman’s coefficient for maternal age?

With these scales of measurement for the data, the appropriate correlation coefficient to use is Spearman’s. The Spearman’s coefficient is 0.84 for this data. In this case, maternal age is strongly correlated with parity, i.e. has a high positive correlation (Table 1).

**Is monotonicity the ultimate requirement for Spearman correlation coefficient?**

Although monotonicity is not the ultimate requirement for Spearman correlation coefficient, it will not be meaningful to pursue Spearman’s correlation without actually determining the strength and direction of a monotonic relationship if it was already known that the relationship between the variable is non-monotonic.