Statistics Resources

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Point Biserial

The Point-Biserial Correlation is a special case of the Pearson Correlation and is used when you want to measure the relationship between a continuous variable and a dichotomous variable, or one that has two values (i.e. male/female, yes/no, true/false).

Assumptions

1. No outliers (continuous variable) - assessed through a visual examination of the scatterplot
2. Approximately normally distributed (continuous variable)
3. Homogeneity of variance of the continuous variable between both groups of the dichotomous variable - assessed through Levene's Test

Running Point-Biserial Correlation in SPSS

1. Analyze > Correlate > Bivariate
2. Move variables of interest to the "Variables" box.
3. Ensure "Pearson" is the only option selected for the test.
4. You may use the "Options" button to select descriptive statistics you wish to include as well.
5. Click "OK" to run the test.

Interpreting the Output

As with the Pearson and Spearman results, SPSS will generate the results in a matrix. You can ignore any boxes that show a "1" as the correlation value as these are simply the variable correlated with itself. These values will form a diagonal across the matrix that can be used to help you focus on the correct values. You only need to explore the correlation values on half of the matrix. APA Style uses the bottom half.

With the release of SPSS 27, users now have the option to only produce the lower half of the table, which is in line with APA Style and makes it easier to identify the correct correlation values.

Reporting Results

When reporting the results of the correlation analysis, APA Style has very specific requirements on what information should be included. Below is the key information required for reporting the Point Biserial Correlation results. You want to replace the red text with the appropriate values from your output.

rpb(degrees of freedom) = the rpb statisticp = p-value

Example:

A point-biserial correlation was run to determine the relationship between income and gender. There was a negative correlation between the variables, which was statistically significant (rpb(38), p - .023).

Notes:

• When reporting the p-value, there are two ways to approach it. One is when the results are not significant. In that case, you want to report the p-value exactly: p = .24. The other is when the results are significant. In this case, you can report the p-value as being less than the level of significance: p < .05.
• The r statistic should be reported to two decimal places without a 0 before the decimal point: .36
• Degrees of freedom for this test are N - 2, where "N" represents the number of people in the sample. N can be found in the correlation output.