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SPSS Tutorials: Computing Variables

Click If (indicated by letter E in the above image) to open the Compute Variable: If Cases window.

Compute Variable If Cases dialog window (SPSS version 25).

1The left column displays all of the variables in your dataset. You will use one or more variables to define the conditions under which your computation should be applied to the data.

2 The default specification is to Include all cases. To specify the conditions under which your computation should be applied, however, you will need to click Include if case satisfies condition. This will allow you to specify the conditions under which the computation will be applied to your data.

3The center of the dialog box includes a collection of arithmetic operators, Boolean operators, and numeric characters, which you can use to specify the conditions under which your recode will be applied to the data. There are many kinds of conditions you can specify by selecting a variable (or multiple variables) from the left column, moving them to the center text field, and using the blue buttons to specify values (e.g., “1”) and operations (e.g., +, *, /). You can also use the built-in functions in the Function Group list under the right column.

After you are finished defining the conditions under which your computation will be applied to the data, click Continue. Note that when you specify a condition in the Compute Variable: If Cases window, the computation will only be performed on the cases meeting the specified condition. If a case does not meet that condition, it will be assigned a missing value for the new variable.

Sours: https://libguides.library.kent.edu/spss/computevariables

SPSS Tutorials: Using SPSS Syntax

Getting Descriptive Statistics Using Syntax

Let’s now use syntax to run the same Descriptives procedure as before.

Reopen the Descriptives procedure. All of your previous settings should still be active. Instead of clicking OK, click Paste. This should open a new Syntax Editor file with the descriptives syntax in it. (Alternatively, you can create a new syntax file by clicking File > New > Syntax, and typing or copy/pasting the following syntax into that window.)

Notice that that the text in the syntax editor appears in certain colors, and some words become bold. These stylistic formats simply define different parts of the syntax command.


When you are finished typing the syntax, you need to tell SPSS to run the command by clicking the green arrow at the top of the window. Voila! We have produced the very same output using both drop-down menus and syntax.

Note:You can copy the syntax from an output window and paste it into a new Syntax Editor window to re-use, modify, and save the syntax. To copy syntax from the output (in the Output Viewer window), simply click the syntax, copy it, and paste it into a Syntax Editor window.

Modifying Syntax

Once you have syntax in the Syntax Editor window, you are free to modify the syntax and/or save it. For example, perhaps I decide that I want to look at some different variables, English and Writing, and I would like to get the range statistic instead of the minimum and maximum. I can easily modify the syntax I already have to accommodate these changes:


Now when SPSS runs the descriptives command, it shows the range, mean, and standard deviation for the variables English and Writing.

Sours: https://libguides.library.kent.edu/SPSS/Syntax
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SPSS Basic Operators

SPSS basic operators are mainly used with IF, DO IF and COMPUTE. They work mostly as you'd expect but they do have a couple of surprises in store. No worries, we'll walk you through. We'll demonstrate how to get things done on the last 5 variables in hospital.sav.

SPSS Data Hospital

Before jumping into SPSS operators, we'll first set 6 as a user missing value for all relevant variables by running missing values doctor_rating to facilities_rating (6).

SPSS Basic Operators

= or EQComparison operatorEqualTrue or false
<> or NE or ¬= or ~=Comparison operatorNot equalTrue or false
< or LTComparison operatorLess thanTrue or false
<= or LEComparison operatorLess than or equal toTrue or false
> or GTComparison operatorGreater thanTrue or false
>= or GEComparison operatorGreater than or equal toTrue or false
ANDLogical operatorEvaluate whether both conditions holdTrue or false
ORLogical operatorEvaluate whether one or both of the conditions holdTrue or false
NOTLogical operatorEvaluate whether condition does not holdTrue or false

True, False and Unknown

SPSS operators can return three values: true, false and unknown. SPSS uses 1 to indicate “true” and 0 to indicate “false”. This seemingly trivial fact opens up some surprising shortcuts. For example

compute flag_1 = doctor_rating = 5.

is perfectly valid syntax. It sets the values of flag_1 to

  • 0 for cases not having 5 on doctor_rating;
  • 1 for cases having 5 on doctor_rating;
  • a system missing value for cases having a missing value on doctor_rating.
SPSS Compute Comparison

After running this syntax on our data, we see the result in data view as illustrated by the screenshot below.

SPSS Dichotomizing Variables

Like so, this is a great shortcut for dichotomizing variables and we'll use it throughout this tutorial and many others.

Missing Values in SPSS Operators

SPSS operators will return a system missing value (meaning “unknown”) when a missing value is encountered in a basic operator. This holds for user missing values (which are not really missing or unknown) as well. A surprising result is that compute flag_2 = doctor_rating = 6. returns system missing values for cases having “6” (a user missing value) on doctor_rating. Even though it's clear that doctor_rating = 6 indeed for such cases, 6 being a missing value still causes SPSS to return “unknown” for this comparison.

SPSS EQ Operator

Using SPSS = operator is straightforward. In case of string variables, keep in mind that the string values must be quoted and the comparison is case sensitive. The syntax below gives two examples.

*1. Flag cases having 5 on doctor_rating.

compute flag_1 = doctor_rating = 5.

*2. Flag cases whose first_name = 'TIM'.

compute flag_2 = first_name = 'TIM'.

*3. Move cases called 'TIM' to top of file.

sort cases by flag_2 (d).

SPSS NE Operator

For SPSS <> operator, the same basics hold as for the = operator. A numeric and a string example are given below.

*1. Flag cases whose doctor_rating is not equal to their nurse_rating.

compute flag_3 = doctor_rating <> nurse_rating.

*2. Flag cases whose first_name is not 'TIM'.

compute flag_4 = first_name <> 'TIM'.

SPSS LT Operator

An example of SPSS < operator is shown in the syntax below.

*Flag cases whose doctor_rating is less than their nurse_rating.

compute flag_5 = doctor_rating < nurse_rating.

SPSS LE Operator

The syntax below demonstrates SPSS <= operator. Note that we can use a statistical function (in this case MEAN) in such a comparison.

*Flag cases whose average rating is less than or equal to 2.

compute flag_6 = mean(doctor_rating to facilities_rating) <= 2.

SPSS GT Operator

The example below uses a basic addition in a comparison using SPSS > operator. Do keep in mind that numeric functions return system missing values when one or more of their arguments are missing values.

*Flag cases whose doctor_rating + nurse_rating is greater than 8.

compute flag_7 = doctor_rating + nurse_rating > 8.

SPSS GE Operator

The syntax below demonstrates a basic comparison using the >= operator.

*Flag cases whose nurse_rating is at least two points higher than their doctor_rating.

compute flag_8 = nurse_rating - doctor_rating >= 2.

SPSS AND Operator

SPSS AND operator combines two logical expressions. It returns “true” if both of its arguments are true. The schedule below shows its outcomes when one or both of its arguments are unknown.

First argumentSecond argumentAND returns

*Flag cases whose doctor_rating and nurse_rating are both at least 4.

compute flag_9 = doctor_rating >= 4 and nurse_rating >= 4.

SPSS OR Operator

SPSS OR operator returns “true” if at least one of its arguments are true. The schedule below holds the outcomes when one or both arguments are unknown.

First argumentSecond argumentOR returns

If you need more than two OR operators in one command, ANY and RANGE are often better alternatives.

*Flag cases having a 5 on either doctor_rating, nurse_rating or both.

compute flag_10 = doctor_rating = 5 or nurse_rating = 5.

*Alternative with ANY. Note that missing values are handled slightly differently.

compute flag_11 = any(5,doctor_rating, nurse_rating).

SPSS NOT Operator

SPSS NOT operator reverses the outcome of other (combinations of) comparisons. An example is shown below.

*Flag cases who didn't rate anything with 5 points.

compute flag_12 = not(any(5,doctor_rating to nurse_rating)).

Sours: https://www.spss-tutorials.com/spss-basic-operators/
Recoding Variables in SPSS with a Specified Condition for Inclusion of Records

Logical expressions

Data transformation (IF), as well as case selection (SELECT IF) requires the specification of a : a case for which the expression is true is selected, a case for which the expression is false is rejected.

, like , can be quite simple or highly complex and can contain: variable names, constants, operators and functions.

Logical operators and functions

usually contain a logical operator, for instance:

Gender = 2True if gender equals 2
Age> 20True if a case has an age larger than 20
Age > 20 & Gender =2True if age is larger than 20 and at the same time Gender is equal to 2.
country = "CH"True if in the (string) variable country a case has a value of "CH" (note that the quotes are required for string values)

Generally speaking the form of a logical expression is:

<expression> <operator> <expression>

A <expression> can just be a variable name or a constant or an arbitrary complex <expression>. Use parentheses to clarify the hierarchy of operations in complex expressions.

The main logical operators are:

~=NENot equal
<LTless than
<=LEequal or less
>GTgreater than
>=GEgreater than or equal
&AND(logical) and
|ORLogical or

In addition to these elements, there are a number of useful logical functions, namely: [See the SPSS documentation for more.]

RANGE(exp,low,high)True if <exp> is between <low> and <high>. You can specify several pairs of <low><high> elements in the same function call.RANGE(AGE,20,40)
ANY(exp,val[,val])True if <exp> yields any of the listed <val> valuesANY(PARTY,1,4,6)
MISSING(var)True if a case is missing (user or SYSMIS)MISSING(PARTY)


Logical expressions in menus

When using menus you will need to enter logical expression into dialog boxes. Here is what you get when selecting the IF button to specify a conditional transformation.

A similar but dialog is shown by to select (filter) observations for analysis).

As you can see you can either type in the expression directly or compose it by using the various elements present: buttons to select operators etc, function lists to find and insert a function, and the variable list to find and insert variables.

Logical expressions and command language.

Several transformation commands use :

IF (<log-expression>) <variable> = <expression> DO IF (<log-expression>) SELECT IF (<log-expression>)
Sours: http://www.unige.ch/ses/sococ/cl/spss/trans/logexpression.html

Spss or in

NOTE:  This page was created using SPSS version 19.0.1.

There are a few reasons why you may not be able to see your variables in some of the SPSS dialog boxes.   One possible reason is that your variable is a string variable and the dialog box will only accept numeric variables.  Another possibility is that your variable has a different measurement scale than the dialog box will accept.  A third possibility is that your variable does not have the appropriate role.  We will discuss each of these issues and how to resolve them.

If you are using syntax, you need to know if the variable is numeric or string (and you may need to convert string variables to numeric format), but the measurement level and variable role settings do no matter.  These assignments can be modified using syntax (as shown below). 

String variables

Some dialog boxes show all of the variables in your data set, regardless of type of variable.  For example, you will see both numeric and string variables in the list of variables from which to choose.  Other dialog boxes, including the dialog box for the correlate command, will display only numeric variables.  Notice that although you can see the variable female in the data set, it does not appear in the list of variables that can be used in the correlation.


If we look at the Variable View window, we can see that the variable female is a string variable, even though it has values of "0" and "1".


There are several ways to convert string variables to numeric form.  For example, you could use the autorecode command, the recode command, or the numeric function with the compute command.  Below, we will use the autorecode command to convert the string variable female into the numeric variable female_num.

autorecode female / into female_num. exe.

Please see our Introduction to SPSS Syntax seminar (section 7) for a more thorough discussion of these commands and examples.  Once the variable has been made into a numeric variable, it will appear in the dialog box.

Measurement level

On the right side of the Variable View window, you will see a column titled Measure.  There are three possible settings for numeric variables:  nominal, ordinal and scale.  String variables can be either nominal or ordinal. 

The dialog boxes for automatic linear modeling, nptests (non-parametric tests) and genlinmixed use measurement level to determine which variables can be used in the various dialog boxes.


In the dialog box above, the yellow bars at an angel are scales and indicate that variable is a scale variable.  The three vertical bars indicate that the variable is an ordinal variable, and the three circles indicate that the variable is a nominal variable.

From the SPSS help file system:

Note: For ordinal string variables, the alphabetic order of string values is assumed to reflect the true order of the categories.  For example, for a string variable with the values of low, medium, high, the order of the categories is interpreted as high, low, medium, which is not the correct order.  In general, it is more reliable to use numeric codes to represent ordinal data.

When reading data into SPSS, the following conditions are used to determine the measurement level.

Copied from the SPSS help file located at .

ConditionMeasurement Level
All values of a variable are missingNominal
Format is dollar or custom-currencyContinuous
Format is date or time (excluding Month and Wkday)Continuous
Variable contains at least one non-integer valueContinuous
Variable contains at least one negative valueContinuous
Variable contains no valid values less than 10,000Continuous
Variable has N or more valid, unique values*Continuous
Variable has no valid values less than 10Continuous
Variable has less than N valid, unique values*Nominal

* N is a user-specified cut-off value. The default is 24.

You can change the measurement level of a variable in the Variable View window.  Alternatively, the variable level command can be used to change the measurement level of variables.  

variable level id race schtyp (nominal) /ses (ordinal) /female prog read write math science socst (scale).

Also, the cutoff value (how many unique values a variable must have to be given a level of scale) can be changed in the Options dialog box (Edit -> Options -> Data tab). 

Variable role

The variable role command was added to SPSS in version 18.  This setting can be seen in the variable View window on the far right.  It is used by the dialogs of some commands to pre-select variables for analysis.  You can modify a variable’s role either by changing it in the Variable View window or via syntax with the "variable role" command.  A variable’s role has no effect when running commands via syntax; it only matters when using the point-and-click interface of some of the newer commands in SPSS.

Some of the commands whose point-and-click interface uses the variable role are genlinmixed and automatic linear modeling.


Here is a brief summary of roles.  This information is taken from the Command Syntax Reference Guide entry for variable role.  By default, all variables are assigned the input role.

Inputpredictor/independent variable
Targetoutput/outcome/dependent variable
Bothboth input and output (both DV and IV)
Noneno role assignment
Partitionthe variable will be used to partition the data into separate samples for training, testing, and validation
SplitThis is used with IBM SPSS Modeler.  This is not a variable that will be used in to "split the file" in SPSS Statistics

The variable role command can be used to change the role of variables.

variable role /target write read /input math science ses schtyp /both socst.


Sours: https://stats.idre.ucla.edu/spss/faq/why-cant-i-see-my-variables-in-some-of-the-spss-dialog-boxes/
How to aggregate data in SPSS (Data Aggregation)

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