# Spearman's rho

## Correlation between two ordinal variables

Spearman's rho is the correlation used to assess the relationship between

**two ordinal variables**. Spearman's rho is a popular method for correlating unvalidated survey instruments or Likert-type survey responses. Spearman's rho is prevalent in the social sciences as most survey instruments use Likert-type or ordinal scales to allow participants to rate themselves along a continuum. Run skewness and kurtosis statistics on each variable's distribution. If the assumption of normality can be met, then more powerful biserial or Pearson correlations can be used instead of Spearman's rho.The Venn diagram below depicts the correlation of two ordinal variables. Spearman's rho is the correlation test used when testing the relationship between two ordinal variables.

### The steps for conduct a Spearman's rho correlation in SPSS

1. The data is entered in a within-subject fashion.

2. Click

3. Drag the cursor over the

4. Click on

5. Click on the first ordinal outcome variable to highlight it.

6. Click on the

7. Click on the second ordinal outcome variable to highlight it.

8. Click on the

9. Click on the

10. Click on the

11. Click

2. Click

**.**__A__nalyze3. Drag the cursor over the

**drop-down menu.**__C__orrelate4. Click on

**.**__B__ivariate5. Click on the first ordinal outcome variable to highlight it.

6. Click on the

**arrow**to move the variable into the**Variables:**box.7. Click on the second ordinal outcome variable to highlight it.

8. Click on the

**arrow**to move the variable into the**Variables:**box.9. Click on the

**Pearso**box to deselect it.__n__10. Click on the

**box to select it.**__S__pearman11. Click

**OK**.### The steps for interpreting the SPSS output for a Spearman's rho correlation

1. In the

If the

If the

**Correlations**table, match the row to the column between the two ordinal variables. The**Correlation Coefficient**is the actual correlation value that denotes magnitude and direction, the**Sig. (2-tailed)**is the*p*-value that is interpreted, and the**N**is the number of observations that were correlated.If the

*p*-value is**LESS THAN .05**, then researchers have evidence of a statistically significant bivariate association between the two ordinal variables.If the

*p*-value is**MORE THAN .05**, then researchers have evidence that there is not a statistically significant association between the two ordinal variables.**Higher rho coefficients**denote a**stronger magnitude**of relationship between variables.**Smaller rho coefficients**denote**weaker relationships**.**Positive correlations**denote a relationship that**travels at the same trajectory**. As one value goes up, then the other value goes up. Also, as one value goes down, then the other value goes down too.**Negative correlations**denote a relationship that**travels in different directions**. As one value goes up, the other value goes down. Also, as one value goes down, then the other value goes up.Click on the

**Download Database**and**Download Data Dictionary**buttons for a configured database and data dictionary for Spearman's rho.**Click on the****Adjusing for Multiple Comparisons**button to learn more about Bonferroni, Tukey's HSD, and Scheffe's test. Click on the**Validation of Statistical Findings**button to learn more about bootstrap, split-group, and jack-knife validation methods.## Hire A Statistician - Statistical Consulting for Students

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