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Felix Famoye

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Descriptive Statistics

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In this Tutorial: 





Analysis of Data

Descriptive statistical procedures consist of seven different types of analysis:

Descriptive Statistics includes the tools shown on the left. These are typical tools for exploring the descriptive summaries, frequencies, and cross-tabulation tables. These exploring tools along with graphical tools are not only useful for data exploration, but also are useful for data cleaning. Various data errors can be identified through these descriptive tools and can be corrected before any further analysis.

P-P plots and Q-Q plots are useful for checking the distribution assumption required by statistical techniques. 

The following three movie clips demonstrate some of these tools:

camera.gif (1166 bytes) MOVIE:  Frequencies and Descriptive camera.gif (1166 bytes)
camera.gif (1166 bytes) MOVIE: Explore Procedures camera.gif (1166 bytes)
camera.gif (1166 bytes) MOVIE: Crosstab Procedures camera.gif (1166 bytes)

Back to Statistical Procedures

In this on-line workshop, you will find many movie clips. Each movie clip will demonstrate some specific usage of SPSS.


This procedure gives frequency tables. It is usually used for categorical data. There are three submenus, which are:    


Statistics:  This submenu allows users to choose various descriptive measures such as average, median, standard deviation, percentiles, and so on


Charts: This submenu provides Bar charts and Pie charts for categorical data and histograms for quantitative data.


Format: This submenu allows users to define the format of the output.

Click here to watch Frequencies and Descriptive

This movie clip uses for this clip is the New Drug data set.  See the Data Set page for details.


This is usually for continuous (as known as interval or scale) data. This procedure gives options of computing descriptive measurements such as mean, variance, standard deviation, and so on.

Click here to watch Frequencies and Descriptive

The data set used for this clip is the New Drug data set.  See the Data Set page for details.


This procedure gives several graphical presentation and descriptive measurements. This is a quick tool for checking some trivial data errors. It has three submenus:


Statistics: This submenu gives descriptive measurements that are also available in the Descriptive Procedure. One can request for outliers.


Plots: This submenu gives several graphical plots for any selected variable. These plots include stem-leaf plot, histogram and normality plot. They are useful for checking if a variable satisfies normality assumption for further analysis.


Options: This submenu allows users to decide how the missing data will be treated.

Click here to watch Explore Procedures

This data set used for this clip is the Disease data set.  See the Data Set page for details.


This procedure is for exploring the relationship between two or more categorical variables in cross-tabulation  form. This procedure has three or four submenus. If you have license for Exact Tests, this will be the fourth submenu. The submenus are


Statistics: This submenu allows you to do exact tests.


Statistics: This submenu provides many statistical tests that are used for categorical data analysis, including chi-squared test, Spearman correlation and many others.


Cells: This submenu allows users to choose the information to display in the output pivot tables. For example, expected counts, row percentage and column percentage can be requested.


Format: This submenu allows users to choose the order of the category in the output.

Click here to watch Crosstabs Procedures

This data set used for this clip is the Tech Survey data set.  See the Data Set page for details.


This procedure provides summary statistics (e.g. median, mean, weighted mean, standard deviation, concentration index) for describing the ratio between two scale variables. It has one submenu.

Probability Plots:

P-P and Q-Q probability plots are used to determine whether the distribution of a variable in the data matches a given distribution. If the variable matches a test distribution, the probability plots cluster around a straight line. The test distributions include uniform, normal, Studentís t, gamma and beta. The P-P procedure plots a variableís cumulative proportions (or empirical cumulative distribution function) against the cumulative proportions of a test distribution. The Q-Q procedure plots the quantiles of a variableís distribution against the quantiles of a test distribution.


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This online SPSS Training Workshop is developed by Dr Carl Lee, Dr Felix Famoye , student assistants Barbara Shelden and Albert Brown , Department of Mathematics, Central Michigan  University. All rights reserved.