SPSS Tutorial — Basic Analyses

Frequencies

The frequencies procedure is primarily used for discrete data (e.g., nominal and ordinal data), although there are a number of options that are useful for scale level data.

This option brings up a dialogue box, and we need to move the variables of interest from the field on the left to the field on the right.

For nominal variables, for which further descriptives statistics are not appropriate (with the exception of the mode), we can skip the Statistics to obtain frequencies for each category.

For ordinal and scale variables, though, we will want to specify additional descriptive statistics to be calculated. These can be broken down into measures of central tendency (mean, median, mode, sum), variability (variance, standard deviation, range, minimum, maximum), and percentiles. This last category includes quartiles (25th, 50th, and 75th percentiles), cut-points for an arbitrary number of groups, and any arbitrary percentile.

Most options are selected simply by clicking on the box next to each item. For specific, arbitrary percentiles, select the option, type the desired percentile in the field to the right, and then click on the add button below:

Which results in the desired percentile being added to the list. Note that one can always delete or modify an entry. Also, more than one entry may be made.

These options generate the following output:

Descriptives

We can get many of these same statistics from the Descriptives item. The options available, however, are fine-tuned to scale level variables.

We first select the desired variables from the field on the left by moving them to the right.

Then, we click on the Options button to determine which statistics should be computed.

These options, then, generate the following output.

Compare Means

One final method for obtaining descriptive statistics focuses on generating statistics from multiple goups quickly and efficiently. This procedure is obtained from the Compare Means item of the Analyze menu, and then the Means item on the submenu.

The dialogue box requires that we select two variables: The dependent variable is the one on which the statistics are computed, and the independent variable list contains the discrete variables that characterize the different groups.

For example, if we want to compute average stress values based on pet ownership, the dialogue box would look like the following.

And the output would look like the following.

If we want to consider more than one different group, we can add layers to the independent variable list. For instance, we might want to compute means separately for men and women within each pet ownership group. We start by clicking on the Next button to add another layer

Doing so creates a field for the second layer, in which we specify the next grouping variable.

Then, we select the variable to be used in the second layer, in this case, gender.

These options, then, create a full table of means and standard deviations.

Lab Exercises

Lab Exercises

1. Create a data set for the following data:
   

   Group	Gender	Hw1	Hw2	Hw3
   expt	Male	92	84	93
   expt	Female	77	84	85
   expt	Male	87	86	81
   expt	Female	89	90	93
   expt	Male	64	73	78
   control	Female	81	84	93
   control	Male	83	90	91
   control	Female	84	88	86
   control	Male	82	80	78
   control	Female	96	91	88

2. Using the Frequencies option, find the mean, median, mode, quartiles, 95th percentile, variance, standard deviation, minimum, and maximum of Hw1, Hw2, and Hw3.
3. Using the Descriptives option, find the means and standard deviations of Hw1, Hw2, and Hw3.
4. Using the Compare Means — Means procedure, find the means on Hw1, Hw2, and Hw3 for everyone, for the experimental group, for the control group, for men, for women, and for all combinations of gender and group.