Although most probability functions are in the form of nice-looking probability density functions, probability density functions themselves tell us very little. This is because the probability of any given value for a continuous probability density function is zero, as can be shown through probability theory. For most practical purposes in using probability functions, cumulative probabilities are used, as they can yield actual numbers when taking in specific values. Calculating a cumulative probability in SPSS requires you to perform a calculation based on a probability density function.

Click on the Transform menu, and choose “Compute.”

Enter a variable from your data or a number in the “Target Variable” box.

Choose “CDF” in the “Function Group” selection box. The cumulative distribution function (CDF) is the function that computes the cumulative distribution.

Select the distribution. Recall that a cumulative probability represents the probability that a number chosen at random from a given distribution is smaller than a given variable. Choose a distribution that makes sense in terms of your data. For example, if you are analyzing the number of typos on a page, choose a Poisson distribution; if you are looking at individual differences within a population, choose Gaussian distribution.

Enter the parameters of the distribution. Each distribution has its own set of parameters. For example, the Gaussian distribution requires you to input a mean and standard deviation. If you do not have the true parameters for the distribution of your choosing, use estimates.

Run the function. The result will be the cumulative distribution. In mathematical terms, you calculated “P(x < a),” where “a” is the variable or number you entered.

#### References

- “Using SPSS for Social Statistics and Research Methods”; William Wagner; 2010

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