In addition to free SPSS Tutorials, you can find interview questions, how to tutorials and issues and their resolutions.
Introduction of SPSS Tutorials
SPSS is software for editing and analyzing data. These data may come from basically any imaginable source: a customer database, scientific research, or even the server log files of a website. SPSS can open all file formats that are commonly used for structured data such as Excel, plain text files and relational (SQL) databases.
SPSS for Windows has the same general look a feel of most other programmes for Windows. Virtually anything statistic that you wish to perform can be accomplished in combination with pointing and clicking on the menus and various interactive dialog boxes. You may have noted that the examples in the Howell textbook are performed/analyzed via code. That is, SPSS, like many other packages, can be accessed by programming short scripts, instead of pointing and clicking. We will not cover any programming in this tutorial.
Presumeably, SPSS is already installed on your computer. If you don't have a shortcut on your desktop go to the [Start => Programs] menu and start the package by clicking on the SPSS icon.
Before proceeding I should say a few words about a very simple convention that will be used in this tutorial. In this point and click environment one often has to navigate through many layers of menu items before encountering the required option. In the above paragraph the prescribed task was to locate the SPSS icon in the [Start] menu structure. To get to that icon, one must first click on [Start] then move the pointer to the [Programs] options, before locating the SPSS icon. This sequence of events can be conveyed by typing [Start => Programs] .
In the Data View, the data are laid out in the standard rectangular format for statistical software. Each row represents a unit of observation, sometimes also referred to as a “record” or in SPSS as a “case.” The case (observation) number in the leftmost column is assigned automatically and is not saved as data. Each column represents a variable. All of the data in a column must be of the same “type,” either numeric or string (also called “character”).
Each data cell holds a data value. If data are missing, they are displayed as a period (“.”) or as a blank (“ “). Data values may be displayed as either the actual value or as a “formatted” value. For example, a data value about a person’s income might be 15000, while its formatted value might be shown as “$15,000.” Formats can also take the form of value labels, for instance, data recorded as 1’s and 2’s might be labeled as “Male” and “Female.” While formatting makes it easier to interpret results, it is important to remember that the data values are what SPSS actually processes. In particular, when you set up a command that requires you to specify one or more data values, you use values and not formatted values.
Before you enter your data, you will want to define your variables. You need to give each variable a label (up to 8 characters in length), provide a longer descriptive label, provide information about missing values for that variable, and value labels if it is a nominal variable.
At the bottom left of the screen you will see two tabs: one is called, "Data View" and the other is called "Variable View." Variable view should be in white. If not, click on it. Across the top line the column headings should read: Name, Type, Width, etc.
- To define a variable, from the Spss Data Editor window:
- -For example, if your variable is sex, it has two categories, male and female. You can assign numeric values to male and female, e.g., male=1, female=2.
- -To do this, click on the grey box on the right hand side of the box and a Value Labels dialogue box will appear.
-Click in the box labeled “value” and type in your first value, e.g., “1”
-Click in the box labeled “Value Label” and type in “Male”
-Go back to the box labeled “Value” and type in your next value, e.g., “2”
-Click on the box labeled “Value label” and type in “Female”
To add a variable
If you want to insert a variable that has been left out, click the cursor on the variable below the line you want to insert the variable
Click on "Data"
Click on "Insert Variable"
To delete a variable
If you want to delete a variable from your list of variables,
Click on the column you want to delete
Click on "Edit"Click on "Clear"
- To recode a variable (e.g., you have an item coded on a Likert Scale from 1=Strongly Disagree to 5=Strongly Agree and you want to recode the item so the scale runs from 1=Strongly Agree to 5=Strongly Disagree): From the Data Editor Window
- -Click on "Transform"
- -Click on "Recode into Different Variables..."
- Note: There is a danger to choosing the other option, "Recode Into Same Variables...". SPSS will change the values of your original variable. If you make a mistake, it is difficult to undo the change and make the necessary correction to the original data.
Select the variable you want to recode from the list on the left by clicking on it to move it to the box on the right. Next you must create a name for your new recoded variable. On the right side of the dialog box under "Output variable" click on the box labeled "Name" and type in the name of the new modified variable.Then click on "Old and New Values" and use the resulting dialog box (shown below) to begin the task of replacing 5's with 1's, 4's with 2's, etc., or whatever the recording calls for. Note that the original variable stays there unchanged, and a new variable is created with the new values.
The left column is labeled "Old Value" Next to the word "Value" type in the first value of the variable you wish to change. For example, type in 5. Then click in the box on the right labeled "New value". Next to the word "Value" type in 1. Next, click on the "Add" button. You will see that in the box labeled "Old --> New:" it now says "5 --> 1". Repeat these steps for the other values you wish to recode, each time clicking on "Add". When you are done, click on "Continue". You will be presented with the Recode Into Different Variables dialog box.
Be sure to recode every single value that the variable takes on. Even if, as in the above example, 3 remains 3, be sure to recode the old value 3 into the new value 3. If you used a code for missing values, such as 9, be sure to recode your missing value into the same value.
You can also recode a range of values into a single new value. For example, on a Likert scale, let 1=strongly disagree, 2=disagree, 3=neutral, 4=agree and 5=strongly agree. Suppose you want to recode that into 3 values: 1=disagree, 2=neutral and 3=agree. To change strongly disagree and disagree into a single category coded "1," go to "Old Value" and click on "Range." Fill in the boxes below so that it reads "1" through "2". Under "New Value" write in "1." Click on "Add.
"Repeat this procedure to turn agree and strongly agree (i.e., values of 4 through 5) into 3. To change neutral=3 into neutral=2, repeat the procedure described above by clicking first on "Value" rather than "Range." When you are done, click on "Continue". You will be presented with the Recode Into Different Variables dialog box.Click "Change"Click "OK".
These core tutorials will help you to learn the fundamentals of Tableau.
For an in-depth understanding and practical experience, explore SPSS Training.
- To compute a new variable from existing ones (e.g. to create a self-esteem scale that is the mean of 8 self-esteem items), SPSS allows you to create a new variable based on a mathematical formula involving your original variables. From the Data Editor Window
- Click on “Transform”
- You will be presented with a dialog box listing the variables in your data set in the column on the left.
Conditional "IF" Statements
“If” statements are used to transform existing variables into new variables. For example, based on individuals’ scores on a Masculinity and a Femininity Scale, you might want to create a new variable that contains 4 categories (Masculine, Feminine, Androgynous, Undifferentiated). The four groups could be defined as follows: Masculine subjects score above the median on Masculinity and below the median on Femininity, Feminine subjects score above the median on Femininity and below the median on Masculinity, Androgynous subjects score above the median on both Masculinity and Femininity, Undifferentiated subjects score below the median on Masculinity and Femininity.
- To run: From the Data Editor Window
- Click “Transform”
- Click “Compute”
- The following Compute Variable dialog box will appear:
In the box labeled, “Target Variable” give a name to the new variable you want to create.
In the box labeled, “Numeric Expression” type 1 and Click “If...”
This will enable you to define your first group. The following Compute Variable: If Cases dialog box will appear:
Click “Include if case satisfies condition:”
In the box to the right, type a description of your first group. In the above example, the first group (Masculine) is made up of subjects with scores above the median=28 on Masculinity, and scores at or below the median=26 on Femininity.
Click on “OK”
You will be back to the Compute Variable dialog box.
Click on “OK”
A dialogue box asking whether you want to change the variable or not will appear.
Click on "OK"
REPEAT. You now have to repeat this entire procedure beginning with
Click on “Transform”
Click on “Compute”
In the numeric expression box the second time you type “2” and click on “If”
Type the description of your second group, Click on “Continue”, and when you return to the
Compute Variable dialog box, click “OK”.
REPEAT the entire procedure again for the remaining two groups.
After you have completed this procedure, you will see that the new variable you have just created appears at the end of your list of variables. You should now assign value labels for your new variable so that you will remember how you defined each group. Click on the new variable. Directions for how to assign value labels can be found under instructions for Defining Variables.
Features of SPSS
-Opening data files, either in SPSS’ own file format or many others;
-Editing data such as computing sums and means over columns or rows of data;
-Creating tables and charts containing frequency counts or summary statistics over (groups of) cases;
-Running inferential statistics such as ANOVA, regression and factor analysis;
-Saving data and output in a wide variety of file formats.