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SPSS while highlight and run that command. If you would rather not mouse to the top of the syntax window, you can press Ctrl-R on your keyboard after highlighting the command or commands that you wish to run.
If you get strange error messages when you run your syntax, you may have run the syntax on the wrong data file.
If you did and you made changes to the data set that you did not want to make, simply close the data set without saving it. This brings up a good point: you should always work on a copy of your data, not on the original. Keep the original somewhere safe where it cannot be overwritten. If you make a mistake on the copy, it is no big deal; you just make another copy of the original and continue working on that.
SPSS has a command that allows you to control which data set is active via syntax cleverly called dataset activate. We will explore the dateset commands later in this seminar. To keep things simple, we are going to have only one data set open at a time in this seminar.
A comment on comments One of the most important things to remember when writing SPSS syntax is that all commands must end in a period. This includes comments, which you can use pretty much anywhere in your syntax file. If you do not have a license for all of the SPSS add-on modules, you will not be able to run all of the commands listed. There is usually a note at the top of the entry if the command is part of a particular add-on module. Perhaps the first thing that you need to know when using SPSS syntax is how to open a data file.
The command for opening an SPSS data file is get file followed by the path where the file is located and the name of the file to be opened. The path and file name should be enclosed in quotes, and you need to include the file extension, which is. If you have a. If you have an Excel file, you can type. One common issue is that Excel will allow you to do things that SPSS or any other statistical package will not. For example, you can put graphs and figures in the middle of your Excel spreadsheet, but such spreadsheets cannot be opened in SPSS.
Excel allows you to use spaces in variable names, and this is not permitted in SPSS. It is possible that the current version of SPSS may not be able to read in the current version of a Stata data file. Please note that when using the get stata command, you need to use the file keyword. When using the get sas command, you need to use the data keyword. SPSS can also open other kinds of files, such as. These are text files that have a comma or tab delimiter in other words, the values of the variables are separated by either a comma or a tab.
When reading a comma separated values. A note about quotes You can use either single or double quotes, as shown above, as long as they match obviously. Starting with SPSS version 21, you can password protect data files and output files. You can do this either through the point-and-click interface or with syntax. Syntax files cannot be password protected, so if you use syntax to set up the password protection, you or anyone else can see what the password is.
This is not necessarily a bad thing; if you forget what the password is, the file cannot be opened. If the password is lost the file cannot be opened. The password will also be displayed in the SPSS journal file. The password is limited to 10 characters, and it is case sensitive. The password must be enclosed in quotes and can include letters, numbers, symbols and punctuation.
In other words, all of the rules that apply to the handling of confidential information are the same whether you use password protection or not. SPSS can have many datasets open at once. If you have more than one dataset open, you need to be able to specify which dataset you want your syntax to manipulate. This can be done with the dataset commands. The dataset commands are a group of related commands that allow you to control the active dataset. This group of commands includes dataset activate , dataset close , dataset copy , dataset declare , dataset display and dataset name.
Your syntax will run on the active dataset. You can make any open dataset the active dataset simply by clicking on it. When using syntax, the dataset activate command will make the listed dataset the active dataset. You may need to use the dataset name command before the dataset activate command, so that you can name the dataset that you want to make active. The name you give the dataset will not change the name of the dataset that is stored on your hard drive.
We will see examples of these commands later in this presentation. The dataset copy command is very useful for making a backup copy of your dataset. We always recommend that researchers work on a copy of their dataset and keep the original in a safe place where it cannot be lost or overwritten. If a mistake is made on the copy of the dataset, another copy can be made and the syntax that you have written can be run to bring the new copy up to date.
The new file command clears the active dataset. You may want to use this command when you are generating data within an input program. We will start our presentation by creating some new variables. Two commands that you can use to create numeric variables are compute and if. SPSS will not create the new variables unless we issue either the execute command or a procedural command. Examples of procedural commands are list , regression and crosstabs.
The procedure does not have to use the newly created variable. In many of the examples below, the execute command is technically unnecessary because we issue the procedural command list immediately afterward.
We use the list command as a convenient way of showing the output on this webpage; in practice, you would probably just look at the Data Editor window. However, including the execute does not cause any problems, and it is handy to have in case you later change the syntax and remove the command that cause the execution of the compute commands. In the first example, we show how to make a new variable that is a copy of a variable that already exists in the data set.
In the second example, we create a variable called newvar and set it equal to a constant. In our example, we set newvar equal to 0.
At this point, every case has a value of 0 for this variable. Next, we use some if commands to change the values of newvar according to the values in the variables num1 and num2. The point of the following examples is that you can make the rules for value assignments as complicated as you need them to be. You can use multiple and or or conditions in a single compute or if command. You can use either symbols or letters to specify equality or inequality.
Which you use is simply a matter of personal preference. Note that spacing between variables and symbols is also a matter of preference. You can use all sorts of math and functions when creating your variables.
We will show the use of many other functions a little later in this presentation. Another point to be made involves the use of parentheses. How you use parentheses can affect the resulting variable. Notice that the two if commands below are the same, except for the use of the parentheses.
The output of these two commands are put into different variables so that you can compare the results. The commands below are very similar to the commands above. The point of this example is to illustrate how SPSS processes the commands. In this example, the output of the two if commands are put into the same variable, newvar6 , so that you can see what is happening. SPSS has many functions that can be used to create new variables. We will look at a few below.
As you can see, you can use multiple functions to create a new variable. In the preceding examples, the naming of the variables has not been terribly good. We do not suggest that you name your variables newvar1 , newvar2 , etc. Rather, it is good practice to name your variables something meaningful and easy for you to type.
Variable names can be up to 64 bytes long approximately 64 characters long. However, please remember that you cannot use spaces in a variable name, and variable names should not have an underscore at the end, as these might conflict with variables created by commands or procedures. Variable names should also not end with a period, as the period is the command terminator in SPSS.
In other words, the period tells SPSS where each command ends. You can use any combination of upper and lower case letters in a variable name, but SPSS is case insensitive. The shift values command was introduced in SPSS version It is used to create new variables that have the shifted values of another variable in the active dataset.
As you can see in the third example, you can make multiple new variables in a single call to the shift values command. Note that the keyword variable cannot be shorten to var.
The create command uses functions to make new variables. A few of these functions, such as the lag function, are available with other commands. However, create has many functions that are unique to the command. In the first example, we use the lag function to create the new variable nvcr1 , which is num1 lagged by 1. In the next example, we create the new variables nvcr2 to nvcr5 as the lag of num1. The variable nvcr2 is lagged 2 steps, the variable nvcr3 is lagged 3 steps, and so on.
In the examples below, we use the csum and the diff functions. The csum function creates a cumulative sum, and the diff function gives the difference between values of the original variable. The degree of the difference must be specified. Binary variables are variables that have only two values, 0 and 1 and possibly missing. Here are two examples of methods that you can use to create binary variables. We can use the any function to recode certain values of a variable into 1 in the new variable nvany.
System variables are variables created during your current SPSS session that contain system-required information. There are eight system variables, but we will only mention two of them here. As we saw in the example above, you can assign system-missing values. System missing values are considered the lowest possible value in SPSS, which you may need to know when sorting a variable that contains system missing values.
A note about specifying missing values: When you are specifying missing values in variables that already exist in the dataset, use a function such as missing , sysmis , etc. The numeric command allows you to specify the format of the new variable. The numeric command creates an empty variable that you then populate using another command, such as the compute or if command. In the first example below, we create a numeric variable called nvn1. The PASW Statistics data sheet summarizes how this suite of products for statistical analysis helps organizations solve business and research problems easily and efficiently.
He has been using statistics software tools since the early 90s, and has been conducting training since He has been data mining and using IBM SPSS Modeler since its arrival in North America in the late 90s. The partitioning method randomly divides the data sets into three parts of training, testing, and validation. If the predictions are accurate than can be used to create a runtime predictive model. Retraining of models is done by changing the behavior patterns.
The most practical, hands-on approach with free retakes for life, forum support, manuals, shortcut sheets and the top trainers with years of certified training experience. Do your. Throughout the SPSS Survival Manual you will see examples of research that is taken from a number of different data files, survey.
To use these files, which are available here, you will need to download them to your hard drive or memory stick. Once downloaded you'll need to unzip the files. To do this, right click on the downloaded zip file and select 'extract all' from the menu. You can then open them within SPSS. This will allow you to search through the various directories on your computer to find where you have stored your data files. Find the file you wish to use and click Open.
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