# Point biserial

## Correlation between continuous and categorical variables

The point biserial correlation is used to assess the relationship between

**a continuous variable and a categorical variable**. The point biserial correlation is very similar to the independent samples t-test. Indeed, the*p*-value yielded from a point biserial correlation will be the exact same as the*p*-value for an independent samples t-test if the two tests are performed on the same sample. Categorical variables that have more than two levels (polychotomous) cannot be analyzed using a point biserial correlation. Only dichotomous (two levels) categorical variables can be analyzed using the point biserial correlation.The Venn diagram below depicts the correlation of a categorical and a continuous variable. Point biserial is the correlation test used when testing the relationship between a categorical and a continuous variable.

### The steps for conducting a point biserial correlation in SPSS

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

2. Click

3. Drag the cursor over the

4. Click on

5. Click on the continuous outcome variable to highlight it.

6. Click on the

7. Click on the dichotomous categorical outcome variable to highlight it.

8. Click on the

9. Click

2. Click

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

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

**.**__B__ivariate5. Click on the continuous outcome variable to highlight it.

6. Click on the

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

8. Click on the

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

**OK**.### The steps for interpreting the SPSS output for a point biserial correlation

1. In the

If the

If the

**Correlations**table, match the row to the column between the two continuous variables. The**Pearson Correlation**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 continuous variables.If the

*p*-value is**MORE THAN .05**, then researchers have evidence that there is not a statistically significant association between the two continuous variables.**Higher coefficients**denote a**stronger magnitude**of relationship between variables.**Smaller 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 pre-configured database and data dictionary for point biserial.**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 Professionals

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