**
Graziano & Raulin**

**Research Methods (8th edition)**

In a computerized statistical analysis, descriptive statistics serve two purposes. The first is to describe the data, especially on those variables that will not be a part of the inferential statistical analyses. These might include the demographic characteristics of the sample, correlations of dependent variables with other variables measured, and the characteristics of the dependent variables themselves.

The second purpose is to find evidence of errors in the data entry process. No matter how diligent you are in checking your data during the entry process, it is relatively easy to input a data point incorrectly. In order to be successful at spotting these data entry errors before doing the inferential statistics, you must be familiar with the data and the variables being measured. Checking the maximum and minimum scores will often help you spot errors, such as by finding a score that is out of range (i.e., larger or smaller than it could possibly be). Of course, you must know what the largest and smallest possible scores could be to make this strategy work. Also look for scores that are highly unlikely although technically possible. If they show up, check the original data to make sure that such scores actually exist. Check to see that the mean is close to the middle of the numbers you remember seeing for a variable during the data collection and entry processes. Be particularly careful in checking variables that you may have computed--either before data entry or as part of the statistical analyses. Errors in the computations or the formulas given to the computer program are easy to make and will often result in clearly wrong answers that can be easily spotted if you are looking for them.

In this section we will show you how to set up some basic descriptive statistics for (1) categorical data and (2) score data. In each section, we will show how to generate both descriptive statistics and appropriate graphs.

Our examples will draw heavily on the data set entered previously and shown in Table 5.2 of the text. Before we do the analyses, we must select the data file that was previously prepared and saved. When you first start the SPSS program, it will open with a menu. One of the options in this menu is to open data bases that you created earlier. Alternatively, if we are already working in SPSS, we can open a data file by selecting the File menu, the Open submenu, and the Data choice on the Open submenu, which will give us this screen. We then select the file and click on OK to open it for the SPSS for Windows program.

Categorical data represents a classification of participants, and the appropriate summary statistics are frequencies. We compute summary statistics for categorical data in SPSS for Windows by selecting the Analyze menu, the Descriptive Statistics submenu, and the Frequencies option, which gives us this screen. We want to compute frequencies for the two categorical variables ("Sex" and "Party"). To do so, we highlight each of these variables in turn by clicking on them in the left box and moving them to the right box by clicking on the arrow button between the boxes. If we change our mind, we can move the variable back to the left box in the same manner.

Once both variables have been moved, we click on OK and the analysis is run, producing the output shown in this screen. This output lists both the frequency and percent of participants in each category. Note that on the left side of the screen is table of contents for the output. This structure provides easy access to different sections of complex statistical analyses. The current analysis is very simple, so the output barely takes a single screen, but many analyses will have pages of output, and this table of contents of the output can be very useful.

Like any data file, the output file can be saved using the save command on the File menu. It can also be printed by using the print command from the File menu.

Sometimes we want to tabulate frequencies for joint categories
(e.g., Female Democrats). To do this we use a procedure called *
crosstabs* (short for cross tabulation). We select the Analyze
menu, the Descriptive Statistics submenu, and the *Crosstabs*
option, which will give us this screen.

To do a cross tabulation of Sex by Party, we move one of these variables to the box marked "row(s)" and the other the box marked "column(s)" and press the OK button. This will produce the output shown here.

Finally, if we wanted to graph the data with a histogram, we select the Graphs menu, the Interactive submenu, and then click on the Bar option (in that order), which gives us this screen.

To produce a graph of the frequencies of political affiliations,
we drag and drop the variable Party to the to the *X*-axis of
the graph that is shown with the count on the *Y*-axis and
click on OK. This produces the Bar graph shown in
this screen.

Descriptive statistics for score data involve more than just the
frequencies of each score. We can produce such a frequency
distribution if we desire by using the procedure described above for
obtaining the frequency counts for our categorical variables. If we
want additional summary statistics, such as mean and variance, we
must use the *Descriptives* option.

Select the Analyze menu and Descriptive Statistics submenu, and
then select the *Descriptives* option. This process will give
us this screen.

Note that not all the variables are listed in the left box. The Descriptives procedure cannot be run on categorical data, and our alphabetical code for the "Sex" and "Party" variables implied that these were categorical variables. Hence, they were excluded from the list.

We will produce descriptive statistics for the variables "age," "income," and "voted" by moving them from the left box to the right box in the same manner as described previously. We could do the same for the "ID" variable, but since that variable is simply an identification number for each participant, the analysis would serve no purpose.

The *Descriptives* option will compute by default the mean,
standard deviation, and minimum and maximum scores for each variable
that we select. If we want to compute additional summary statistics,
we click on the Options button and select the additional summary
statistics we want. When we have identified the variables and
selected the summary statistics, clicking on OK will run the
analyses, producing this output.

We have prepared a series of animations that will walk you through the procedures discussed on this page. To run an animation, simply click on the title of the animation in the table below.

Note that we do not recommend that you try to run the animations if you have a slow connection, such as a dial-up connection. You will find that the animations take forever to load with a slow connection.

Opening a File in SPSS for Windows |

Descriptive Statistics for Categorical Data |

Doing a Cross-Tabulation |

Descriptive Statistics for Score Data |