Basic data operations in SPSS - ISS - University of Leeds

Information Systems Services 

Basic data operations in SPSS for 

Windows 17 

TUT 113 

Version 6 (November 2009)

Basic data operations in SPSS for Windows 17 

Version 6 (November 2009) 

Contents 

1 Introduction ................................................................................................................................... 4 

1.1 About data management ........................................................................................................ 4 

1.2 Requirements ........................................................................................................................ 4 

1.3 Documentation ...................................................................................................................... 4 

1.4 Getting Started ...................................................................................................................... 4 

Task 1 Download example data sets ........................................................................................ 4 

1.5 The SPSS Command Language ............................................................................................ 5 

1.6 The Syntax Window ............................................................................................................... 5 

Task 2 Using the Syntax Window ............................................................................................ 5 

1.7 The Syntax Reference Guide ................................................................................................. 8 

Task 3: Using the SPSS Syntax Reference Guide ..................................................................... 8 

2 Data Transformations ................................................................................................................... 9 

2.1 Creating New Variables ......................................................................................................... 9 

Task 4 Using Compute to create a new variable ...................................................................... 9 

2.2 Computing Counts Using COUNT........................................................................................ 12 

Task 5 Computing a Count ..................................................................................................... 12 

2.3 Recoding variables .............................................................................................................. 15 

Task 6 Recoding into the same variable ................................................................................. 15 

Task 7 Recoding into a new variable ..................................................................................... 17 

Task 8 Performing an automatic recode ................................................................................. 21 

2.4 Conditional transformations ................................................................................................. 23 

Task 9 Performing a conditional compute ............................................................................. 23 

3 Working with subsets of data ...................................................................................................... 26 

Task 10 Selecting a subset of cases .......................................................................................... 26 

Task 11 Deleting selected cases ............................................................................................... 28 

Task 12 Sub-group processing ................................................................................................. 29 

4 Merging SPSS data sets ............................................................................................................. 31 

Task 13 Adding cases ............................................................................................................... 31 

Task 14 Adding variables ......................................................................................................... 34 

Task 15 Finishing SPSS ........................................................................................................... 36 

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1 Introduction 

1.1 About data management 

The first step in performing a statistical analysis with SPSS is usually to define your data and enter your data 

into SPSS. If you are lucky, this is all that will be required before you are able to carry out your analysis. More 

commonly, however, you may find it necessary to manipulate the data before you can perform the required 

analysis. 

For example, it may be necessary to generate new variables from existing variables, or you may need to group 

the values of a variable into a small number of groups to facilitate the creation of simple summary statistics or 

simple graphics. 

You may also find that the structure of your original data does not conform to that required by some of the 

statistical or graphical procedures in SPSS and that the data needs to be re-shaped prior to analysis. 

This document will take you through some of the basic tasks of data manipulation that you may need to 

accomplish prior to performing statistical analyses in SPSS. The tasks have been designed in such a way that 

you are advised to complete a task and its exercises before proceeding to the next one. 

1.2 Requirements 

It is assumed that you already know how to login to the network and run the Microsoft Windows operating 

system. It is also assumed that you know how to run SPSS for Windows from the Windows desktop, create a 

new data sheet, enter text into a cell, run a simple analysis and print it out. If you do not yet know how to 

achieve these operations it will be necessary for you to read and work through the following document: 

Getting started with SPSS for Windows (BEG 14) 

1.3 Documentation 

If you require further information on the facilities in SPSS the following documents: 

Advanced data operations in SPSS for Windows (TUT 114) 

Simple statistical analysis in SPSS for Windows (TUT 115) 

Advanced statistical analysis in SPSS for Windows (TUT 116) 

All the documents referred to above are available as PDF files for printing from the ISS web site: 

http://iss.leeds.ac.uk/downloads/303/statistical_analysis 

References are made in this document to the SPSS Syntax Reference Guide. This is now available online as 

part of the SPSS Help system. 

1.4 Getting Started 

A variety of SPSS data sets will be used for these exercises. The data sets are stored in a zip file on the ISS 

website. The first task will be to copy the zip file into your own chosen directory and unpack the zip file. You will 

need the files in order to complete these exercises. 

Task 1 Download example data sets 

Activity 1.1 Open a web browser such as Internet Explorer and go to the URL: 





http://iss.leeds.ac.uk/downloads/303/statistical_analysis 

Activity 1.2 Scroll down to locate the item titled 

‘Basic data operations in SPSS for Windows 17’ 

double click on this item and click on Download on the screen that follows. 

Activity 1.3 From the Save As dialog box, choose a suitable directory to store the file in and click . 

Close the web browser. 

Activity 1.4 Go into Windows Explorer and double click on the zip file. Click File>Extract to unzip the data 

files. 

Activity 1.5 Locate the SPSS icon from the Statistics menu and double-click the icon to open SPSS. After a 

short period the SPSS data editor window will be displayed. 

1.5 The SPSS Command Language 

The use of a standard Windows-based interface for applications software allows transfer learning to take place 

when moving between different applications. Mastering one menu system makes it easy to master another. In 

addition the SPSS graphical user interface greatly simplifies the specification of analyses and other operations 

by relieving the user of the need to write ‘programs’. 

The SPSS graphical user interface caters for most of the functionality provided by SPSS. However, there are 

some operations that cannot be handled using this approach. For example, you may need to re-shape data 

input from an external source into a form more appropriate for analysis. For such operations it may be 

necessary to resort to the use of the SPSS Command Language. 

Versions of SPSS prior to the emergence of the Windows operating system required users to specify all their 

requests by using SPSS programs written in the SPSS command language. In practice, this usually involved 

writing out programs on paper before attempting to load them into SPSS on a computer. Nowadays, most 

users ‘program’ directly by interacting with the SPSS graphical user interface. However, the SPSS Command 

Language remains at the heart of the SPSS system, despite the fact that specifications are made interactively. 

1.6 The Syntax Window 

To enable SPSS command language to be used SPSS uses a special window called the Syntax Window. 

Syntax windows can be used in one of two ways. An empty syntax window can be opened using File …New 

… Syntax enabling commands to be entered by the user. Or, after requesting an analysis using the menu 

interface, the commands underlying the analysis can be inspected, and possibly edited, by using the Paste 

button to display them in a syntax window prior to executing the request. 

Task 2 Using the Syntax Window 

Activity 2.1 Open the SPSS data file statan.sav. 

Activity 2.2 (i) From the Analyze menu, select Descriptives ... Frequencies. 





(ii) From the list of variables displayed select height as the variable for analysis. 

(iii) Untick the box labelled Display frequency tables. 

Activity 2.4 Click on the Statistics button. 

In the dialogue box displayed: 

(i) Select Quartiles, Std. deviation and Mean. 

(ii) Select Percentiles, enter 5 in the box alongside and click on Add. 

(iii) Enter 95 in the percentiles box and click on Add. 

(iv) Click on Continue. 

Activity 2.5 Click on the Charts button. 

In the dialogue box displayed: 

(i) Select Histograms and With normal curve. 

(ii) Click on Continue. 

Activity 2.6 Normally, once the specification of an analysis is complete, the usual action is to execute the 

analysis by clicking on the OK button. 

(i) Instead of clicking on OK click on the button labelled Paste. 

A Syntax Window, shown in Figure 1 below, will be displayed containing commands that SPSS 

has assembled to perform the Frequencies analysis you requested interactively. 

Figure 1: Syntax window containing Frequencies commands 

Note the generation of sub-commands corresponding to the various options specified in the 

dialogue windows. For example /HISTOGRAM NORMAL has been generated in response to 

selecting a histogram with a super-imposed Normal Curve in the charts options window. 

You are free to edit this program before running it. In fact, there are a few procedures in SPSS 

where some of the options can not be specified interactively and must be specified by editing a 

partially-complete program assembled in this way. 





Activity 2.7 To run the program select Run from the menu. (In some situations it may be appropriate to 

highlight a sub-set of commands for execution before selecting Run). The output obtained from 

executing this program is displayed in Figure 2. 

Figure 2: Output from Frequencies analysis 

A syntax window can also be obtained from the File menu by selecting File … New … Syntax. This allows 

you to by-pass the menu system and to take complete control over the writing of SPSS programs. This 

approach is most commonly used for writing non-standard input programs rather than for specifying analyses. 

One advantage of using the syntax window is that it allows you to save programs generated by SPSS for reuse 

at a later stage. Syntax files are stored with a file type of .sps. Re-using programs can save you time 

and effort by eliminating the need to repeat specifications using the menu system. They also serve as a 

precise record of the analysis carried out. This can be very helpful if problems arise in carrying out an 

analysis. 

Activity 2.8 Save the contents of the syntax window. 

(i) From the menu, select Window and *Syntax1 - SPSS Syntax Editor. The syntax 

window will come to the foreground. 

(ii) Select File … Save As and save the contents in file named testsyntax. 

(iii) Close the Syntax window using the X button at the top right of the window. 

Activity 2.9 Re-open the saved syntax file. 

(i) From the Data Editor menu, select File …Open…Syntax and re-open the syntax file. 

This can now be edited and/or re-executed as required. 

(ii) Close the Syntax window using the X button at the top right of the window. 





1.7 The Syntax Reference Guide 

The full syntax of the SPSS language is defined in the SPSS Syntax Reference Guide available from SPSS UK 

Limited. The guide is also available electronically as part of the SPSS software. 

Task 3: Using the SPSS Syntax Reference Guide 

Activity 3.1: Select Help… Command Syntax Reference from the Menu. 

Activity 3.2: From the index on the left- hand side of the display, Select Frequencies. 

You should see a definition of the syntax of the Frequencies command as shown in Figure 3. 

Figure 3: SPSS Online Help 

The emphasis throughout this text will remain on using SPSS interactively. However, to aid learning of the 

SPSS command language, the SPSS commands generated by the SPSS menu system will be shown 

following the description of the interactive method. These commands will be highlighted in the text using the 

symbol . 

For a number of topics however, the use of SPSS commands is essential. SPSS commands are also used 

occasionally in preference to the interactive style simply because they provide a clearer record of the operation 

being performed. 





2 Data Transformations 

It is common that the data currently available is not in the form required for analysis. For example, 

examination of the data may reveal the need to create new variables for analysis derived from values of one or 

more existing variables. If data values are to hand, these can be entered at the keyboard. However, if data 

values are to be derived from values of other variables, one of the data transformation facilities must be used. 

2.1 Creating New Variables 

The Compute facility on the Transform menu allows you to create new variables from values of existing 

variables. 

Task 4 Using Compute to create a new variable 

Objective Create a new variable based upon values of existing variables. 

Comments Compute can also be used to modify the value of an existing variable. 

Problem In the data file employeedata.sav, the variables jobtime and prevexp record the number of 

months employed and the number of months of previous work experience. 

We need to calculate the total number of years worked rounded to the nearest whole number. 

Activity 4.1 Open the SPSS data file employeedata.sav. 

Activity 4.2 Select Transform from the menu. 

The Transform menu, pictured in Figure 4, provides access to a range of tools for performing 

data transformations in SPSS. 

Figure 4: Transform menu 

Activity 4.3 Select Compute Variable from the menu. 

The Compute Variable facility is used to create new variables whose values can be expressed 

as a mathematical function of values of existing variables. 

Activity 4.4 In the Compute Variable dialog window, enter workyrs as the name of the Target Variable. 

Activity 4.5 Assemble the expression RND((jobtime + prevexp)/12) in the Numerical Expression box. 

To assemble the required expression, 

(i) Scroll down the Function group box and select Arithmetic. 





(ii) Having selected Arithmetic functions, scroll down the list of Functions and Special 

Activity 4.6 Click on . 

Variables and highlight the RND function. Click on the upward arrow key to select this 

function. 

(iii) Replace the symbol ?, representing the argument of RND, by the expression (jobtime + 

prevexp)/12. 

The term jobtime+prevexp represents the total number of months worked. Division by 12 

converts this into years. The RND function rounds the result to the nearest integer. 

On completion of the expression, the dialog window should resemble Figure 5. 

Figure 5: Compute dialog window 

The result is shown in Figure 6, below. 

Figure 6: Data Editor showing computed variable workyrs 

Note that if missing values had been present in either jobtime or prevexp, the result variable 

workyrs would have been assigned the system missing value. 

Figure 7 shows the SPSS commands used to perform the transformation. 

COMPUTE workyrs = RND((jobtime + prevexp) / 12) . 

EXECUTE . 

Figure 7: COMPUTE command 





2.1.2 SPSS functions 

Note the use of the command EXECUTE. This forces the transformation to be executed. 

Without this command, the execution of the transformation would be delayed until the next 

procedure is encountered. 

The RND function used in the previous example is one of many functions provided by SPSS. Table 1, below, 

lists some commonly used functions. 

Function Description 

ABS(arg) Returns the absolute value of arg. eg ABS(-3.5) = 3.5. 

CONCAT(str1, str2,…) Concatenates the text strings specified by str1, str2, … 

DATE.DMY(d,m,y) Returns a date value given day, month and year indicated 

by integer values in d, m and y. 

LN(arg) Returns the base-e logarithm of arg. (arg >0) 

MAX(value1,value2[,...]) Returns the maximum value of its arguments that have 

valid values. This function requires two or more 

arguments. 

MEAN(arg1,arg2[,...]) Returns the arithmetic mean of its arguments that have 


arguments, which must be numeric. 

MIN(value1,value2[,...]) Returns the minimum value of its arguments that have 


arguments. 

MOD(arg,modulus) Returns the remainder when arg is divided by modulus. 

eg MOD(13,12)=1 

RND(argr) Returns the integer that results from rounding arg. 

eg RND(2.4)=2 ; RND(2.5)=3 ; RND(-2.5)=-3 

SQRT(arg) Returns the positive square root of arg. 

SUBSTR(string,pos,len) Returns the substring of string of length len beginning at 

position pos. eg substring(‘JAN99’,4,2)=’99’. 

SYSMIS(numvar) Returns 1 or true if the value of numvar is system-missing. 

The argument numvar must be the name of a numeric 

variable in the working data file. 

TRUNC(arg) Returns the value of arg truncated to an integer (towards 

0). eg TRUNC(2.7)=2; TRUNC(-2.7)=-2. 

XDATE.MDAY(arg) Returns day number in month from a date specified by 

arg. 

XDATE.MONTH(arg) Returns month number from a date specified by arg. 

XDATE.YEAR(arg) Returns a four-digit year from a date specified by arg. 

YRMODA(y,m,d) Returns the number of days from October 15, 1582, to the 

date represented by the arguments y, m, and d. 

Table 1: A selection of SPSS functions 

For a complete list of functions available, consult the SPSS Online Help system or the SPSS Syntax Reference 

Guide. 





2.1.3 SPSS System Variables 

SPSS uses a set of special variables, called system variables, with reserved names to hold the values of 

commonly used data items. Their names begin with a dollar sign ($). 

Table 2 lists a selection of these variables and their values. 

Variable Description 

$CASENUM Records the number of cases read up to and including the current case. 

$SYSMIS System Missing Value 

$JDATE Current date in number of days since October 14, 1582 

$DATE Current date in the form dd-mmm-yy 

$DATE11 Current date in the form dd-mmm-yyyy 

$TIME Current date and time. Value is the number of seconds from midnight, 

October 14, 1582 to the time when the value of $TIME is used. 

Table 2: A selection of SPSS system variables 

Although their values can not be modified, system variables may be used in the same way as normal variables 

within data transformations. 

2.2 Computing Counts Using COUNT 

A common requirement is the need to form a count of the number of occurrences of a particular value across a 

list of variables. For example, patients’ medical data may contain variables recording the presence or absence 

of a host of symptoms. If the presence of a symptom is coded using ‘1’, the number of symptoms exhibited by 

the patient is just the number of variables with the value ‘1’. Although the Compute facility can deal with this 

problem, the Count facility provides a more efficient means of performing this task. 

Task 5 Computing a Count 

Objective Create a count of the number of occurrences of a value in a set of variables. 

Problem The SPSS data file census.sav contains data derived from a national census. The variables 

natenvir, natheal, natcrime, natdrug, nateduc, natdef and natmass record the respondents’ 

views on the adequacy of the Government’s spending in several areas of national importance, 

such as Health and Education. 

The responses to these variables are coded as follows: 

Code Label 

0 Missing 

1 Too Little 

2 About Right 

3 Too Much 





A simple (albeit crude) statistic measuring the overall satisfaction with Government spending 

might be the count of the number of these variables for which the respondent replied ‘About 

Right’. This is easily obtained using the Count facility. 

Activity 5.1 Open the SPSS data file census.sav. 

Activity 5.2 Select Transform … Count Values within Cases from the menu. In the window displayed: 

(i) Enter spending as the name of the Target Variable. 

(ii) Enter Satisfaction with Government Spending as the Target Label. 

(iii) Highlight natenvir, natheal, natcrime, natdrug, nateduc, natdef and natmass in the 

list of variables and use the button to transfer them to the list of selected variables. 

The window should now resemble Figure 8. 

Figure 8: Specifying variables in the Count dialog box 

Activity 5.3 Click on to specify the values that contribute to the count. 

(i) Enter 2 (the code for ‘About Right’) in the Value box. 

Figure 9: Defining values for Count 

(ii) Click followed by . 

Activity 5.4 Click in the initial Count window to execute the request. Figure 10 shows the result. 





Figure 10: The result of the count operation 

The variable spending, added to the end of the list of variables, contains the appropriate count (width and 

decimal places have been changed to 1 and 0 respectively using the Variable View window since the value of 

spending is a number in the range 0 to 7). If required, value labels may be defined for spending using the 

Variable View window. 

We can examine the distribution of values of the variable spending using Frequencies or Graph. The results 

are shown in Figure 11. The list of frequencies reveals that only 0.5% of those surveyed feel that spending is 

‘About Right’ in all of the areas of Government spending. 

Figure 11: Frequency distribution and bar chart for spending 


COUNT spending = natenvir natheal natcrime natdrug nateduc 

natdef natmass (2) . 

VARIABLE LABELS 

spending 'Satisfaction with Government Spending' . 

EXECUTE . 

Figure 12: COUNT command 





2.3 Recoding variables 

There are several situations where modifications to the values of existing variables need to be made. 

Examples include correcting errors in data, changing the codes used for variables and forming categories 

based upon values of a given variable. 

SPSS offers two facilities for re-coding data: Recode and Automatic Recode. The Recode facility is the most 

versatile of the two facilities and allows variables to be re-coded either into the same variable or into different 

variables. Automatic Recode addresses the specific problem of replacing a set of character codes by a set of 

numeric codes. This can be useful as a preliminary to using some of the statistical modelling procedures which 

require categorical variables to be defined as numerical variables. 

The following three tasks illustrate both styles of Recode and the Automatic Recode facility. 

Task 6 Recoding into the same variable 

Objective Correct errors in a variable. 

Comment The Recode facility can be used to change values of an existing variable. This can be useful 

for correcting errors in the data. 

Problem Figure 13 shows the frequency distribution for the variable Zodiac. Note the single case with a 

code of 13. This is an incorrect value that needs correcting. 

Figure 13: Frequency table for the variable zodiac 

We can use Recode to change the value 13 to the SPSS system missing value. 

Activity 6.1 Open the SPSS data file census.sav if it is not already open. 

Activity 6.2 Select Transform>Recode>Into Same Variables. 

(i) Select Zodiac from the list of variables displayed and click on . 





Activity 6.3 Define old and new values. 

Figure 14: Recode into Same Variables dialog 

(i) In the area on the left labelled Old Value, enter the value 13 into the box labelled Value. 

(ii) In the area labelled New Value, select . 

(iii) Click on . The value 13 will disappear from the box on the left hand side and the 

mapping between the old and the new value will appear in the large box on the right 

hand side, as shown in Figure 15. 

Figure 15: Recode showing mapping of old value to new value 

Note the use of the keyword SYSMIS. This is a reserved name in SPSS and is used to 

represent the value of the SPSS system missing value. 

(iv) Click . 

Activity 6.4 Click to execute the re-code operation. 

Figure 16 shows the result of re-running Frequencies on the modified variable. 





Figure 16: Revised frequency table after recoding zodiac 

The entry corresponding to code 13 has been replaced by an entry labelled ‘System’ under the 

general heading ‘Missing’. Note the changes to the columns ‘Valid Percent’ and ‘Cumulative 

Percent’ resulting from the exclusion of this case from the analysis. 


RECODE 

zodiac (13=SYSMIS) . 

EXECUTE . 

Figure 17: RECODE into ‘Same Variable’ 

Task 7 Recoding into a new variable 

Objective Recode a variable into a new variable. 

Comment A very common requirement in data analysis is to be able define a new variable by re-coding a 

range of values of an existing variable into a single category. This can serve a number of 

purposes. It is often used as a method of reducing the dimensionality of the data allowing some 

analysis to be performed where otherwise no analysis would be possible. It is also used to alter 

the format of summary tables and displays. 

Problem The chart in Figure 18 shows a histogram obtained for the variable income. 

Figure 18: Histogram of the variable income 





Suppose that a chart showing fewer bars is considered more appropriate. We can use Recode, 

as follows, to define a new variable, incrange, each of whose values correspond to a range of 

income values. Observing that income values range from 24000 to 68200, we will use seven 

income ranges, each of width 10000. 

Activity 7.1 Select Transform>Recode>Into Different Variables … 

Activity 7.2 Define variable names and labels. 

(i) Select Income as the variable to be re-coded. 

(ii) In the box labelled Name, enter incrange for the name of the new variable. 

(iii) In the box labelled Label, enter Income Range. 

(iv) Click on to assign incrange as the name of the Output variable. 

The window should now resemble Figure 19. 

Figure 19: Recode into different variables 

Activity 7.3 Define the mappings between old and new values. 

(i) Click on . 

Note the different ways of specifying values to be re-coded. 

Figure 20: Defining old and new values 

You can specify individual values, system or user missing values or ranges of values as the 

values to be re-coded. In this example, we will re-code ranges of values of income into single 

codes 1,2,3 … 7. 





(ii) Select the Range specification labelled (currently greyed), and enter 

10000 in the box alongside. 

Enter 1 in the box labelled New Value and click on . 

‘Lowest thru 10000 -> 1’ will appear in the Old --> New box. This defines the first of our 

mappings. 

(iii) Select the first of the Range specifications and enter the values 10000 and 20000 in the 

boxes provided. 

Enter 2 in the box labelled New Value and click on . 

‘10000 thru 20000 -> 2’ will be added to the list of mappings. 

(iv) Define similar mappings for each of the income ranges 20000–30000, 30000–40000, 

40000–50000 and 50000–60000 using codes 3,4,5 and 6. 

(v) Define the final range using the Range specification labelled . 

Enter 60000 in the box alongside, enter 7 in the box labelled New Value and click on 

. 

The specification ‘60000 thru Highest -> 7’ will be added to the list of mappings. 

Note that the apparent ambiguity surrounding the mapping of the values which appear as both 

the upper bound of one range and the lower bound of the next range, (such as 10000), is 

resolved automatically by SPSS. SPSS assigns such values to the first of the two ranges in 

which the value appears. Hence 10000 will be mapped into the value 1. 

Figure 21 shows the status of the window after completing all the re-code specifications. 

Figure 21: Completed recode specifications 

(vi) Click on to return to the earlier window. 

Activity 7.4 Click to perform the re-code. 

The variable incrange will appear as a new column on the right of the data editor window. 





Activity 7.5 Use the Variable View window to define value labels for incrange as follows: 

Code Value label 

1 Legacy Dialogs 

…Bar on the variable incrange. Your graph should resemble Figure 22. 

Figure 22: Bar chart of the variable incrange 


RECODE income 

(Lowest thru 10000=1) (10000 thru 20000=2) 

(20000 thru 30000=3) (30000 thru 40000=4) 

(40000 thru 50000=5) (50000 thru 60000=6) 

(60000 thru Highest=7) INTO incrange . 

VARIABLE LABELS incrange 'Income Range'. 

EXECUTE . 

Figure 23: RECODE into ‘Different Variable’ 





Task 8 Performing an automatic recode 

Objective To recode a block of data in order to group some categories together. 

Comment Categorical variables may be coded using alphanumeric codes or numeric codes. For example, 

a variable such as gender may be coded using either ‘F’ and ‘M’ or numeric codes 1 and 2. For 

the purpose of obtaining descriptive statistics, the choice of coding would not usually be critical. 

However, some of the statistical procedures for modelling categorical data, such as the loglinear 

models procedure, require that categorical data be encoded using numerical codes. 

Whilst Recode could be used to change each alphanumeric code into a numeric code, this 

could be a tedious process if many values required re-coding. Instead, the Automatic Recode 

facility can be used. 

Problem In the data file census.sav, the variable gender is coded using ‘f’ and ‘m’. To change these into 

numeric codes, we need to create a new variable which is numeric (SPSS does not let you recode 

into the same variable and change the type of the variable). 

Activity 8.1 From the Transform menu select Automatic Recode. 

Activity 8.2 Select Respondent’s sex [gender] from the list of variables and transfer it to the Variable -> 

New Name box. 

Activity 8.3 Enter sex in the box below and click on the button. The completed dialog box 

should resemble Figure 24 below. 

Figure 24: Automatic recode of gender 

Activity 8.4 Click and inspect the output at the bottom of the output window. 

SPSS produces a summary table in the output window (similar to that shown in Figure 25) 

showing the old and new codes. 

Figure 25: Summary of automatic recode 





Note that the new variable, sex, has inherited the variable label and value labels of the old 

variable, gender. 


AUTORECODE 

VARIABLES=gender /INTO sex 

/PRINT. 

Figure 26: AUTORECODE 





2.4 Conditional transformations 

All the transformations described so far have been examples of unconditional transformations. These are 

transformations that affect every case in the data set in the same way. Situations arise, however, where the 

form of the transformation to be carried out varies from case to case. Such transformations are called 

conditional transformations. They can be applied to the data transformation commands Compute and Recode. 

All conditional transformations are specified using a logical expression. Usually, this expression is constructed 

using the dialog window appropriate to the transformation being carried out. Alternatively, they may be 

constructed using an IF statement as part of an SPSS program compiled within a syntax window. 

Task 9 Performing a conditional compute 

Objective Apply a data transformation to a subset of data cases which satisfy a specified condition. 

Comment This requires a variation of the Compute command used in Task 4. 

Problem The data file employeedata.sav contains data relating to employees of a company. The 

variable salary records the employees’ current salaries. An enlightened new CEO is concerned 

that the mean salary of female employees is only about $26,000 compared with a figure of 

about $41,400 for men (see Figure 27 below). 

Figure 27: Salaries of employees broken down by gender 

The CEO decides to rectify this situation immediately by awarding a ten percent salary 

increment to all female employees, irrespective of the length of service of the employee or of 

any other factors such as job function. 

We can implement this modification using a conditional compute transformation 

Activity 9.1 Open the SPSS data file employeedata.sav. 

Activity 9.2 From the Transform menu select Compute Variable. 

Activity 9.3 Define the transformation to be applied 

(i) Enter salincr in the box labelled Target Variable. 

(ii) Assemble the expression 0.1*salary in the Numeric Expression box. 

The dialog box should resemble Figure 28. 





Figure 28: Conditional compute – defining the transformation 

(iii) Click on the button marked and select . 

(iv) Select gender from the list of variables and transfer it to the box on the right using the 

transfer arrow . 

(v) Complete the condition by typing =‘f’ after the word ‘gender’. 

The dialog box should now look like Figure 29. 

Figure 29: Conditional compute – specifying the condition 

(vi) Click on . 

If we were to click on at this stage, the value of salincr for male employees would be 

undefined. It would be assigned the system missing value. To avoid this, we could have 

performed an initial unconditional compute operation, by assigning a value of 0 to salincr for all 

employees. An alternative method is to use the Paste button and insert this extra transformation 

into the SPSS commands. 





Activity 9.4 Define the action required for cases which do not satisfy the criterion for an increment. This 

requires that we arrange for all males to receive an increment of 0. 

(i) Click on . 

An SPSS syntax window will be displayed containing the following commands: 

Figure 30: Conditional transformation commands generated by SPSS 

(ii) Directly before these two commands, insert the command: 

compute salincr = 0. 

The window should now look like Figure 31. 

Figure 31: Pre-setting values of computed variable to zero 

(iii) Select Run>All to execute the transformations. 

When these commands are executed, the value of 0 will first be assigned unconditionally to the 

variable salincr for each case. For females only, this value will be overwritten by the value 

calculated by the expression in the IF statement. 

Figure 32 shows a sample of the cases after running the transformation. The Variable View 

window was used to change the type of the variable salincr to Dollar and the width and 

decimal places to 8 and 0 respectively. 

Figure 32: Data window showing result of conditional transformation 





3 Working with subsets of data 

A common requirement in analysis is to be able to analyse subsets of data. 

SPSS allows selections to be made on either a temporary or permanent basis. Temporary selections are 

performed using a filter. When filtering is active, all cases remain in the data editor window but only those 

cases filtered are used for analysis. If permanent selection is used, cases not included in the selected subset 

are deleted from the data editor window. 

If only a few analyses are to be carried out on subsets, filtering would be the best approach. However, if 

extensive analysis is to be performed on a selected subset, it would be more efficient to use a permanent 

selection. If permanent selection is used, however, it is advisable to save a copy of the selected data to a new 

SPSS system file before proceeding with analysis. This will protect against the danger of saving the data to the 

original SPSS data file, with a resultant loss of data. 

Data selection is performed using the Select Cases facility on the Data menu. 

When the same analysis is required for more than one subset of data, the repeated use of the Select Cases 

facility can become tedious. If the data set contains a variable whose values identify the multiple subsets, the 

Split File facility can be used instead to reduce the effort in carrying out the analyses on multiple subsets. 

Task 10 Selecting a subset of cases 

Objective To select a subset of data cases for analysis. 

Comment This illustrates the use of a temporary selection or filter. 

Problem The data file census.sav contains information relating to individuals. It is required to restrict 

analysis to individuals who are unmarried and earning at least £20,000 per annum. 

Activity 10.1 Open the SPSS data file census.sav. 

Activity 10.2 Select Select Cases from the Data menu. The Select Cases dialog window will be displayed. 

Figure 33: Select Cases dialog window 

Note the different ways in which cases may be selected. 

The choice ‘If condition is satisfied’ restricts cases to those satisfying a logical condition 

entered by the user. The next choice, ‘Random Sample of Cases’, selects a random subset of 

cases based on a pseudo-random number generated by SPSS. The third option allows case 





selection to be based on a range of times or on a range of case numbers. The final option, ‘Use 

filter variable’, allows case selection to be based upon values of an existing variable. Cases 

with values other than 0 or missing will be selected 

Activity 10.3 Specify the criterion for selection. 

(i) Select the option . 

(ii) Click on . 

The criterion for selection to be used in this example is ‘unmarried and earning at least 20,000 

per annum’. In terms of SPSS variables this can be expressed as ‘Marital=5 and Income 

>=20000’. 

(iii) Use the window displayed to assemble the required logical expression. 

Figure 34: Select Cases dialog showing selection criterion 

(iv) Click on . 

Activity 10.4 Execute the transformation. 

(i) Click . 

Figure 30 shows the effect of applying the selection. 

Figure 35: Select Cases dialog showing selection criterion 

Cases that fail to meet the selection criterion are indicated by a diagonal line in the row labels of 

the Data Editor, as shown in Figure 30, and the status line also indicates that filtering is turned 

on. 

The SPSS commands used to perform this selection are shown in Figure 36. 





COMPUTE filter_$=(marital = 5 & income >= 20000). 

VARIABLE LABEL filter_$ 'marital = 5 & income >= 20000 (FILTER)'. 

VALUE LABELS filter_$ 0 'Not Selected' 1 'Selected'. 

FORMAT filter_$ (f1.0). 

FILTER BY filter_$. 

EXECUTE. 

Figure 36: Commands used to perform case selection using filtering 

Inspection of Figure 36 reveals that SPSS creates a special variable called filter_$, coded 1 or 

0 depending on whether or not the case meets the selection criterion. 

Activity 10.5 Inspect the result of the selection. 

(i) Scroll to last column in the Data Editor. 

(ii) Click on the icon to display value labels. Case number 22, which 

satisfied the criterion, will show the label Selected in the filter_$ column. 

Note that the filter variable filter_$ is a temporary variable and will not be saved on exit from 

SPSS. To keep the variable for future use, rename it using the Variable View window and save 

the data before exiting from SPSS. 

A case selection criterion specified in this manner will remain in force until a new specification is 

issued. 

Activity 10.6 Cancel the selection. 

(i) Click on the icon. 


The Select Cases dialog window will be re-displayed. 

(iii) Click on the radio button. 

(iv) Click . 

All cases will now appear as selected in the SPSS Data Editor. 

Task 11 Deleting selected cases 

Objective To delete cases from an active SPSS data set. 

Comment This use of Select Cases is more drastic in effect and results in the permanent deletion of data 

cases from the active data set. 

Activity 11.1 Click on the Dialog Recall icon and click on . The Select Cases dialog window 

will be re-displayed. 

Activity 11.2 Select . 

The previous selections will still be in place. 

Activity 11.3 Specify that permanent case selection is required. 

(i) Click on the radio button and click . 

The Data Editor will now contain just the 13 cases that satisfy the selection criterion. 





When permanent case selection is used in an SPSS, great care is needed to ensure that 

important data is not lost from physical data files. If, after making a permanent data selection, 

you were to use the Save command to save the remaining data cases without specifying a new 

file name, the original SPSS data file would be overwritten with the new reduced set of data with 

a resulting loss of data. If you do want to save the selected cases, use Save As instead to save 

the cases to a new data file. 

A similar danger arises if you decide to open a new data set. SPSS will ask you if you want to 

save the current data, the subset of 13 cases just selected. If you reply ‘Yes’, your original data 

file will be overwritten. This is illustrated in the next activity. 

Activity 11.4 Exercise caution when opening a new data file. 

(i) Select File>New>Data from the menu. 

At this point EXTREME CAUTION is required! 

If you were to reply ‘Yes’ to this, you would overwrite the original data file! If you did want to 

save this subset, you should reply Cancel and use File>Save As to save the data to a new file. 

Instead of saving this subset, revert to using the original data file. 


(iii) Select File>Open>Data and select census.sav. Click . 

The original data set, census.sav, will be re-displayed. 

Task 12 Sub-group processing 

Objective Perform the same analysis on sub-groups of data defined by values of a specified variable. 

Comment Split File provides a more efficient way of performing the same analysis on different subsets of 

data than the repeated use of Select Cases. 

Problem The file census.sav (which should be currently open following completion of the previous task) 

contains a variable polviews indicating the political views of the respondent. A second variable, 

genelec, indicates the party for whom the respondent voted in the 1992 general election. It is 

required to examine the distribution of votes for each political party by individuals sharing the 

same political views. 

Activity 12.1 Select Split File from the Data menu. 

Activity 12.2 Define the groups. 

(i) Click the radio button. 

(ii) Select Political views [polviews] from the list of variables and transfer it to the box 

labelled Groups Based on. The display should resemble Figure 37. 





Figure 37: Split File dialog box 

The default action is to sort the cases by the split file variable. If the data is already sorted, the 

sort can by avoided by clicking the ‘File is already sorted’ radio button. 

(iii) Click . 

The Split File On message appears on the status line of the Data Editor. 

Activity 12.3 To see the effect of selecting Split File processing, run a Frequencies analysis on the variable 

genelec. 

(i) Select Analyze>Descriptive Statistics>Frequencies. 

(ii) Select . 

(iii) Click on . 

A table of frequencies, similar to that in Figure 38, is produced for each category of political 

views. 

Figure 38: Frequencies on genelec with split file on polviews 

Activity 12.4 Before continuing, turn off split file processing: 

(i) Select Split File from the Data menu. 

(ii) Select and click . 

The full data set will now be available for subsequent analyses. 





4 Merging SPSS data sets 

Commonly, collections of data may be built up over time possibly involving more than one person in the 

process of data collection and data entry. For instance, if a survey is being carried out in the field, several 

interviewers may be involved and if each interviewer enters the data directly into an SPSS file the files may 

have to be merged. 

The Merge facility allows data from two files to be merged. It can be used either to combine cases from two 

files which share exactly the same variables (the Add Cases option) or to combine variables from two files (the 

Add Variables option). These two operations are mutually exclusive. 

If the two files to be merged contain exactly the same variables with exactly the same structure, the cases from 

the second file will be added at the end of the first file. 

If the files do not match exactly, SPSS will produce a list of unpaired variables. This list will contain: 

Variables from either data file that do not match a variable name in the other (in this case, pairs 

can be created from the unpaired set and these pairs can be included in the new merged file). 

Variables defined as numeric data in one file and string data in the other file. Numeric variables 

cannot be merged with string variables 

String variables of unequal width (in this case it would be necessary to modify the structure of 

such variables in one of the files). 

The new data file will contain all those variables that match exactly, all the new pairs selected, and any 

unpaired variables that have been matched to create new pairs. 

Any remaining unpaired variables which are included will contain missing data for the cases from the file that 

does not contain that variable. 

Before merging the files, any unwanted variables can be deselected. 

Task 13 Adding cases 

Objective To add data cases to the working file from another SPSS data file. 

Comment This illustrates the use of the Add Cases facility under Merge. 

Problem The data file possums.sav contains 462 data cases and 16 variables. The file possums1.sav 

contains 2 data cases for the same variables. It is necessary to combine the two sets of cases 

into one data file. 

Activity 13.1 Open the file possums1.sav from the directory in which your SPSS example data sets are 

stored. 

Activity 13.2 Select Data>Merge Files>Add Cases. The Merge Files menu will be displayed. 





Figure 39: Merge Files menu 

Activity 13.3 Choose the file possums.sav. Click on Open. 

Figure 40: Selecting the file to be merged 

The following dialog window will be opened. 

Figure 41: Variable specification in Merge 





Note that the left hand window of the dialog box has listed a pair of variables which do not 

match. The reason for this is that the declared width of the variable complics differs in the two 

files. 

Activity 13.4 Try to pair the variables by clicking on the first variable, complics



Note: If a column displays asterisks (*****), simply widen the column to see the data. This can 

be done by pointing the mouse to the boundary between the cells, and dragging to the right 

when the mouse pointer changes to a double headed arrow. 

Figure 44: Widening the column width 

A practical consideration in performing a merge is the order in which the two files are specified. 

They can be specified in either order, but the choice is important because it can affect the 

variable definitions assigned to common variables in the merged file. For example, if a numeric 

variable common to both files has a decimal places value of 0 in the active file and 2 in the file 

being added, the value assigned in the merged file would be 0. If the active file is our master 

file, then this might be acceptable. But if the file being added was our master file, then this 

would probably not be acceptable since it would mean that we are over-riding definitions which 

we have laid down in our master file. 

The conclusion might be, therefore, to always open the master file first and to add cases from 

the second file. Whilst this would prevent the possibility of master definitions being changed, it 

would also prevent us from changing any other definitions in the active file prior to the merge. 

Accordingly, in this example, we have chosen to open the file containing the new cases first so 

that we can change the active file prior to the merge. This approach does require, of course, 

that care is taken to ensure that master definitions are not inadvertently changed. The motto 

must therefore be: caution. 

Task 14 Adding variables 

Objective To add variables to the working file from another SPSS data file. 

Comment This illustrates the use of the Add Variables under Merge. 

Problem The data file possums1.sav contains 2 data cases and 16 variables. The file possums2.sav 

contains 3 new variables plus the common key variable patid with values 600 and 601. It is 

necessary to combine the two sets of variables into one data file. 

Activity 14.1 Open the file possums1.sav from the directory in which your SPSS example data sets are 

stored. 

Activity 14.2 Select Data>Merge Files>Add Variables. 

Activity 14.3 Choose the file possums2.sav. 

This file contains three variables id, height and weight with entries for patients with patid 600 

and 601. 

The following dialog window will be opened. 





Figure 45: Add Variables dialog 

The box labelled ‘Excluded Variables’ lists the variable patid which is common to both files. 

Activity 14.4 Select ‘Match cases on key variables in sorted files’, highlight the variable patid and select it 

as the key variable into the ‘Key Variables’ box. Click . 

A warning message, shown below, will be displayed warning you of the need for each file to be 

pre-sorted on values of the key variable(s). In this case, we are safe to proceed but in if your 

data is not pre-sorted, and you still choose to proceed, some information may be lost in the 

merge. 

Click . 

Figure 46: Warning to ensure data is sorted by key variables 

Activity 14.5 (i) Scroll to the right of the data view. The variables height and weight will appear at the 

right-hand end of the data vector. 

Figure 47: Merged data cases 

Activity 14.6 (ii) Use File>Save As to save the merged data to a new file (use the filename allvars.sav). 




Task 15 Finishing SPSS 

Objective Terminate the SPSS session. 

Comments You should always quit any computer program when you have finished your session. Never 

switch off the computer when SPSS is still running unless absolutely necessary as this will 

corrupt your spreadsheet files. Also never leave a computer whilst you are still logged on to it as 

others may use your account and could potentially damage your files. 

Activity 15.1 Select the Exit option from the File menu. 

Activity 15.2 SPSS will ask you if you want to save the contents of various windows before it lets you quit, so 

a dialogue box similar to Figure 48 appears: 

Figure 48: Saving the SPSS data 

Click either or depending on whether you want the data saved. If you choose to 

save it the file is saved to the same name on disk. Once the datasheet has been saved it can be 

reopened by SPSS and edited. 

You will also be asked if you want to save the contents of your output window – usually the 

numerical results of all your analysis work. Normally you would want to save this, but here you 

should click . 

Once you have done this SPSS will quit. If you click on in any of the boxes you will be 

returned to your unsaved work to continue.

Basic data operations in SPSS - ISS - University of Leeds

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