SPSS 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 descriptive 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:
 

Descriptive Statistics

We can get many of these same statistics from the Descriptive 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.

Compare Means

One final method for obtaining descriptive statistics focuses on generating statistics from multiple groups 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 GPA values based on gender, 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, if you have information about student classification (freshmen, sophomore, junior, senior) we might want to compute means separately for men and women within each classification group. Let's say we now have this new variable in our initial data set.  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, classification.

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

Lab Exercises

1. Create a data set for the following data:
   
    

   Course Delivery	Gender	Test1	Test2	Final
   Online	Male	92	84	93
   Online	Female	77	84	85
   Online	Male	87	86	81
   Online	Female	89	90	93
   Online	Male	64	73	78
   Traditional	Female	81	84	93
   Traditional	Male	83	90	91
   Traditional	Female	84	88	86
   Traditional	Male	82	80	78
   Traditional	Female	96	91	88

2. Using the Frequencies option, find the mean, median, mode, quartiles, 95th percentile, variance, standard deviation, minimum, and maximum of Test1, Test2, and Final.
3. Using the Descriptive Statistics option, find the means and standard deviations of Test1, Test2, and Final. 
4. Using the Compare Means -- Means procedure, find the means on Test1, Test2, and Final  for everyone, for the online course delivery, for the traditional course delivery, for men, for women, and for all combinations of gender and course delivery method.