This LibGuide contains written and illustrated tutorials for the statistical software SPSS.
SPSS is a user-friendly program that facilitates data management and statistical analyses. The first section of this tutorial will provide a basic introduction to navigating the SPSS program.
- Interacting with SPSS
This tutorial covers the various screens of SPSS, and discusses the two ways of interacting with SPSS: through the drop-down menus, or through syntax.
- The Data View Window
Variables are observable and measurable traits of interest. Cases are records of information on one or more variables. This tutorial discusses how Cases and Variables are oriented in the Data View window.
- Using SPSS Syntax
SPSS syntax is a programming language unique to the SPSS environment. It allows you to fine-tune statistical analysis and data manipulation in ways that would be tedious, difficult, or impossible to do through the drop-down menus. This tutorial covers the basics of understanding SPSS syntax.
In order to use SPSS, you need data. You can either create data or import data. Creating data means that you will enter the variables and values manually into SPSS to create a new dataset. Importing data means that you will use an existing data file that someone has already prepared and simply import it into SPSS. We cover both data creation and data importation in the following sections.
- Data Creation in SPSS
This tutorial covers how to create a new dataset in SPSS by manually entering data. Also covered is the difference between row numbers (which are a part of the spreadsheet) and ID variables (which are a part of the dataset and act as case identifiers).
- Importing Data into SPSS
This tutorial describes how to import data stored in an external file into SPSS.
- Creating and Deleting Cases
This tutorial demonstrates how to manually add a new case to an SPSS dataset, and how to manually delete an existing case from an SPSS dataset.
- Creating and Deleting Variables
This tutorial demonstrates how to manually insert a new variable into an existing dataset, and how to delete a variable from an existing dataset.
Variables are a key part of all datasets. They signify how your research was conducted, and dictate what types of analysis methods are appropriate. For this reason, it is important to know what variables are; how to define, compute, and recode them; and how to work with special types of variables, such as dates. This section of the tutorial will cover each of these topics.
- Variable Types
A variable's type determines if a variable numeric or character; quantitative or qualitative. It also dictates what type of statistical analysis methods are appropriate for that data. This tutorial covers the variable types that SPSS recognizes.
- Date-Time Variables
Date-Time variables in SPSS are handled differently than other types of variables. This tutorial covers how SPSS treats Date-Time variables, and also covers the Date and Time Wizard.
- Defining Variables
Variable definitions include a variable's name, type, label, formatting, role, and other attributes. This tutorial covers two different ways to define variable properties in SPSS, especially custom missing values and value labels for categorical variables.
- Computing Variables
The "Compute Variable" command allows you to create new variables from existing variables by applying formulas. This tutorial shows how the "Compute Variable" command can compute a variable using an equation, a built-in function, or conditional logic.
- Recoding (Transforming) Variables
Recoding a variable can be used to transform an existing variable into a different form based on certain criteria. This tutorial covers the "Recode into Different Variable" and "Recode into Same Variable" commands.
- Automatic Recode
If you have a string variable and have used blanks to indicate missing values, you may notice that SPSS does not automatically recognize the blank observations as missing. To fix this, you'll need to use Automatic Recode. More broadly, Automatic Recode is also used to quickly convert a string categorical variable into a numeric categorical variable.
Data manipulation refers to the management of a dataset. Managing a dataset often includes tasks such as sorting data, splitting data into separate samples, merging multiple sources of data, and restructuring data. It may be necessary to complete any (or all) of these tasks before you can explore and analyze data, so it is important to know how to manipulate data to fit your needs.
- Sorting Data
Sorting a dataset rearranges the rows with respect to one or more variables. This tutorial discusses how to sort data using the drop-down menus in SPSS.
- Splitting Data
In SPSS, the "Split File" command can be used to organize statistical results into groups for comparison. Split File is used when you want to run statistical analyses with respect to different groups, but don't necessarily want to separate your data into two different files. This tutorial shows you how to use the Split File command in SPSS and what situations it is useful in.
- Merging Data
(Coming Soon) Sometimes, you may be working with data that has been stored in different files. This tutorial talks about several ways of merging datasets.
- Weighting Cases
Sometimes, you don't have raw data available to you -- you may only have a frequency table indicating the types of responses and how many times they occurred. Alternatively, you may be working with a dataset that contains a weighting variable. This tutorial shows how to use the Weighting Cases command in SPSS to handle both of these situations.
- Partitioning Data
(Coming soon) The Select Cases procedure is used when you want to create a new dataset by extracting cases from an existing dataset. Unlike the Split File option, Select Cases affects the data itself, rather than the output. This tutorial covers several applications of Select Cases.
Our tutorials reference a dataset called "sample" in many examples. If you'd like to download the sample dataset to work through the examples, choose one of the files below: