Spss basics tutorial

SPSS Step-by-Step
Tutorial: Part 1
For SPSS Version 11.5

SPSS Step-by-Step 3
Table of Contents
1 SPSS Step-by-Step 5
Introduction 5
Installing the Data 6
Installing files from the Internet 6
Installing files from the diskette 6
Introducing the interface 6
The data view 7
The variable view 7
The output view 7
The draft view 10
The syntax view 10
What the heck is a crosstab? 12
2 Entering and modifying
data 13
Creating the data definitions: the variable view 13
Variable types 13

4 SPSS Step-by-Step
Variable names and labels 15
Missing values 15
Non-numeric numbers, or when is a number not a
number? 15
Binary variables 15
Creating a new data set 16
Getting help in creating data sets and defining
variables 22
Creating primary reference lists 24
Frequencies 24
Descriptive statistics: descriptives (univariate) 25
Recodes and Transformations 26
Backup the original file 26
Recoding existing variables 27
Recode income data 27
Recoding variables revisited 37
The one exception in recoding variables 37
The other exception 37
3 Charting your data 39
Using the automated chart function 40
Using the Interactive Chart function 42
Creating a chart from scratch 45

SPSS Step-by-Step 5
1 SPSS Step-by-Step
Introduction
SPSS (Statistical Package for the Social Sciences) has now been in development for
more than thirty years. Originally developed as a programming language for con-
ducting statistical analysis, it has grown into a complex and powerful application
with now uses both a graphical and a syntactical interface and provides dozens of
functions for managing, analyzing, and presenting data. Its statistical capabilities
alone range from simple perentages to complex analyses of variance, multiple
regressions, and general linear models. You can use data ranging from simple inte-
gers or binary variables to multiple response or logrithmic variables. SPSS also
provides extensive data management functions, along with a complex and powerful
programming language. In fact, a search at Amazon.com for SPSS books returns
2,034 listings as of March 15, 2004.
In these two sessions, you won’t become an SPSS or data analysis guru, but you
will learn your way around the program, exploring the various functions for manag-
ing your data, conducting statistical analyses, creating tables and charts, and pre-
paring your output for incorporation into external files such as spreadsheets and
word processors. Most importantly, you’ll learn how to learn more about SPSS.

Installing the Data
6 SPSS Step-by-Step
Installing the Data
The data for this tutorial is available on floppy disk (if you received this tutorial as
part of a class) and on the Internet. Use one of the following procedures to install
the data on your computer.
Installing files from the Internet
Before you begin to download the files, create a new folder on your computer’s
hard disk named SPSSTutorialData. When you download the files, you’ll save
them in this directory.
1. Go to ftp.datastep.com. Do not type “www” or “http://”.
2. When prompted for a user name, enter: SPSS
3. For the password, enter: tutorial.
4. To download each file, click it once, press Ctrl-C or select Edit > Copy from the
menu.
5. Switch to a window for your computer and save the file in the directory named
SPSSTutorialData. To do so, open the folder and press Ctrl-V or select Edit >
Paste from the menu.
6. Once you have downloaded all the files, close the Internet browser.
Installing files from the diskette
1. With the diskette in the floppy drive, open a window for the C drive on your
computer (or any other drive where you want to save the data files).
2. Create a new folder named SPSSTutorialData.
3. Copy the files from the floppy disk to the SPSSTutorialData folder by copying
and pasting or by dragging.
Introducing the interface
When you use SPSS, you work in one of several windows: the data view, the vari-
able view, the output view, the draft output view, and the script view. Eventually
you’ll also use the syntax editor (think: code) to save or refine your queries.

SPSS Step-by-Step 7
The data view
The data view displays your actual data and any new variables you have created
(we’ll discuss creating new variables later on in this session).
1. From the menu, select File > Open > Data.
2. In the Open File window, navigate to C:SPSSTutorialDataEmployee data.sav
and open it by double-clicking. SPSS opens a window that looks like a standard
spreadsheet. In SPSS, columns are used for variables, while rows are used for
cases (also called records).
3. Press Ctrl-Home to move to the first cell of the data view.
4. Press Ctrl-End to move to the last cell of the data view.
5. Press Ctrl-Home again to move back to the first cell.
The variable view
‘At the bottom of the data window, you’ll notice a tab labeled Variable View. The
variable view window contains the definitions of each variable in your data set,
including its name, type, label, size, alignment, and other information.
1. Click the Variable View tab.
2. Review the information in the rows for each variable.
Note: While the variables are listed as columns in the Data View, they are listed as
rows in the Variable View. In the Variable View, each column is a kind of
variable itself, containing a specific type of information.
3. Click the Data View tab to return to the data.
4. Double-click the label id at the top of the id column. Notice that double-clicking
the name of a variable in the data view opens the variable view window to the
definition of that variable.
5. Click the data view tab again.
The output view
The output window is where you see the results of your various queries such as fre-
quency distributions, cross-tabs, statistical tests, and charts. If you’ve worked with
Excel, you’re probably used to seeing all your work on one page, charts, data, and
calculations. In SPSS, each window handles a separate task. The output window is
where you see your results.

8 SPSS Step-by-Step
Try it:
1. From the menu, select Analyze > Descriptive Statistics> Crosstabs.
2. Click once on Employment, then click the small right arrow next to Rows to
move the variable to the Rows pane (Figure 1).
FIGURE 1. Moving a variable to the Rows pane
3. Click Gender, then click the small right arrow next to Columns to move the
Gender variable to the Columns pane. (Figure 2).

SPSS Step-by-Step 9
FIGURE 2. Selecting a column variable
4. Click Display clustered bar charts (Figure 3).
FIGURE 3. Selecting clustered bar charts
click here to display
the clustered bar
charts

10 SPSS Step-by-Step
5. Click OK. SPSS brings the output window to the front displaying two tables
and the clustered bar chart you requested. Take a moment to review the contents
of the tables and the chart. Notice that the red arrow next to the title Crosstabs
corresponds to the Crosstabs icon in the left pane of the window. The left pane
displays the contents of the right pane and is a convenient method of moving
around among the various output you’ll be generating.
Note: In some cases, you may see asterisks instead of numbers in a table cell.
Asterisks indicate that the current column is too narrow to display the com-
plete number. Widen the column by dragging its margin to the right.
The draft view
The draft view is where you can look at output as it is generated for printing. The
draft view does not contain the contents pane or some of the notations present in the
output pane.
Try it:
1. From the menu, select File > New > Draft Output. SPSS opens a Draft Output
window that contains its own menu.
2. From the menu, select Analyze > Descriptive Statistics > Crosstabs. Notice that
the dialog box opens with your previous selections.
3. Click OK. From here you can select charts or tables, copy them, and paste them
into other applications like spreadsheets or word processors.
Note: If you want to maintain the correct spacing of the tables, use a non-propor-
tional font like Courier New.
The syntax view
SPSS has never lost its roots as a programming language. Although most of your
daily work will be done using the graphical interface, from time to time you’ll want
to make sure that you can exactly reproduce the steps involved in arriving at certain
conclusions. In other words, you’ll want to replicate your analysis. The best method
of preserving the exact steps of a particular analysis is the syntax view. In the syn-
tax view, you’ll preserve the code used to generate any set of tables or charts.
Syntax is basically the actual computer code that produces a specific output. It
looks like this:

SPSS Step-by-Step 11
CROSSTABS
/TABLES=jobcat BY gender
/FORMAT= AVALUE TABLES
/CELLS= COUNT
/BARCHART .
In the code shown above, SPSS is instructed to create crosstabs, using the variable
jobcat, sorting the crosstabs by gender using a specific format, to put a count into
each cell, and then to create a corresponding barchart.
Note: Preserving the steps you take in arriving at a conclusion is especially impor-
tant if you are writing for publication, peer review, or any other situation in
which others might want to test your conclusions.
In the next steps, you’ll create a simple chart and frequency distribution, save the
syntax and then recreate the chart and frequency distribution by running the saved
syntax.
Try it:
1. From the menu, select Analyze > Descriptive Statistics > Crosstabs. Notice that
your previous selections are still present.
2. This time, instead of clicking OK, click Paste.
3. SPSS opens the Syntax Editor with the code you just pasted.
When you select Paste instead of OK in dialog boxes like the Crosstabs box,
the code generated by the function is pasted into the Syntax Editor. If you
wanted, you could generate all your output from the syntax window alone. Gen-
erally, however, you will probably work with your data and output until they are
just the way you want them, then repeat the steps you took and paste the code
into the syntax editor. You can then run the syntax at any time to recreate the
output.
4. If the cursor is not already located somewhere in the syntax, click anywhere in
the word CROSSTABS, then click the small right arrow on the toolbar or select
from the menu Run > Current. SPSS opens the draft output window with the
same chart you created using the menu commands. This time, however, you cre-
ated the output by running the syntax (code) you created with the Paste func-
tion. Notice that you can scroll up to the previous output you created using the
menu commands.

What the heck is a crosstab?
What the heck is a crosstab?
A crosstab (short for cross tabulation) is a summary table, with the emphasis on
summary. Here’s an example:
Notice that the rows contain one set of categories (employment category) while the
columns contain another (gender). In this crosstab, the cells contain counts, but in
others you can use percentages, means, standard deviations, and the like.
Here’s the important part: crosstabs are used for only categorical (discrete) data,
that is, groups like employment categories or gender. You can’t use a crosstab for
continuous data like temperature or dosage or income. BUT, you can change data
like temperature or dosage or income into categories by creating groups, like
income less than $25,000, income between 25000 and 49999, income 50000 or
higher. We’ll discuss these data conversions known as transformations or recodes
later. For now, you just need to understand that crosstabs deal with groups or cate-
gories.
Now that you’ve seen the various windows you’ll be using, we’ll move on to the
techniques you’ll use in SPSS for managing your data files.
Employment Category * Gender Crosstabulation
Count
206 157 363
0 27 27
10 74 84
216 258 474
Clerical
Custodial
Manager
Employment
Category
Total
Female Male
Gender
Total

2 Entering and modifying data
In this section, you’ll learn how to define variables and create a data set from
scratch.
Creating the data definitions: the variable view
It’s impossible to talk about SPSS (or any analysis program) without talking about
data and types of data. So here goes. Each particular type of information (such as
income or gender or temperature or dosage) is called a variable. You can have vari-
ous types of variables such as numeric variables (any number that you can use in a
calculation), string variables (text or numbers that you can’t use in calculations),
currency (numbers with two and only two decimal places) and variables with spe-
cific formats.
Variable types
SPSS uses (and insists upon) what are called strongly typed variables. Strongly
typed means that you must define your variables according to the type of data they
will contain. You can use any of the following types, as defined by the SPSS Help
file.

• Numeric. A variable whose values are numbers. Values are displayed in stan-
dard numeric format. The Data Editor accepts numeric values in standard for-
mat or in scientific notation.
• Comma. A numeric variable whose values are displayed with commas delimit-
ing every three places, and with the period as a decimal delimiter. The Data Edi-
tor accepts numeric values for comma variables with or without commas; or in
scientific notation.
• Dot. A numeric variable whose values are displayed with periods delimiting
every three places, and with the comma as a decimal delimiter. The Data Editor
accepts numeric values for dot variables with or without dots; or in scientific
notation. (Sometimes known as European notation.)
• Scientific notation. A numeric variable whose values are displayed with an
embedded E and a signed power-of-ten exponent. The Data Editor accepts
numeric values for such variables with or without an exponent. The exponent
can be preceded either by E or D with an optional sign, or by the sign alone--for
example, 123, 1.23E2, 1.23D2, 1.23E+2, and even 1.23+2.
• Date. A numeric variable whose values are displayed in one of several calendar-
date or clock-time formats. Select a format from the list. You can enter dates
with slashes, hyphens, periods, commas, or blank spaces as delimiters. The cen-
tury range for 2-digit year values is determined by your Options settings (from
the Edit menu, choose Options and click the Data tab).
• Custom currency. A numeric variable whose values are displayed in one of the
custom currency formats that you have defined in the Currency tab of the
Options dialog box. Defined custom currency characters cannot be used in data
entry but are displayed in the Data Editor.
• String. Values of a string variable are not numeric, and hence not used in calcu-
lations. They can contain any characters up to the defined length. Uppercase and
lowercase letters are considered distinct. Also known as an alphanumeric vari-
able.1
Because SPSS uses strongly typed variables, you have to make sure that all the data
in any field (variable) is consistent.
1. SPSS Base System Version 11.5 Help.

Variable names and labels
Forget all the nice conveniences you find in Windows and the Mac for handling
long, interesting file names, or even the leniency that some database applications
provide in naming fields. In SPSS, variable names are eight characters, period. And
no funny characters like spaces or hyphens. Here are the rules:
• Names must begin with a letter.
• Names must not end with a period.
• Names must be no longer than eight characters.
• Names cannot contain blanks or special characters.
• Names must be unique.
• Names are not case sensitive. It doesn’t matter if you call your variable CLI-
ENT, client, or CliENt. It’s all client to SPSS.
Missing values
If you do not enter any data in a field, it will be considered as missing and SPSS
will enter a period for you.
Non-numeric numbers, or when is a number not a number?
Some numbers aren’t really numbers. That is, they’re numbers but you can’t use
them in mathematical calculations. Take a phone number, for example, or an
account number or a zip code. You can sort them, but you can’t add or subtract or
multiply them. Well, you could, but the result would be meaningless. In essence,
these numbers are actually just text which happens to be numeric. A good example
is an address, which contains both numbers and text. We refer to these variables as
string or text variables.
Binary variables
Binary variables are a special subgroup of numeric variables. Sometimes you treat
them as strings and sometimes you treat them as numeric. For example, yes/no,
male/female, and 0/1 are all binary variables. That is, they have two and only two
possible values. Obviously, you can’t do a calculation on yes/no or male/female,
BUT and this is a very big and very important BUT, you can recode these variables
into numeric values, like assigning a value of 0 to female and 1 to male.

Creating a new data set
Recoding binary variables is a critically important part of data analysis. Suppose,
for example, that all you want to know is whether a specific event occurred. For
example, suppose you want to know whether a client ever applied for welfare. As it
happens, the data set you’re using contains the date when each person applied for
welfare for the first time, if they ever did; if they didn’t the field is blank. Using this
date, you can create a new variable called, say, welfapp that contains a 1 if there is
any value in the first welfare application field and a 0 if there isn’t. Now you have a
simple value (0 or 1) that you can use for further calculations or subsetting.
Let’s get back to the male/female issue for a moment. Say you have recoded the
variable into a 0 for female and a 1 for male. If you calculate an average (mean) for
this variable, what you now have is a proportion. Say the average of your new vari-
able is .45. You now know that there are somewhat more men than women in your
population. In other words, you have calculated a proportion.
If you’re doing original research or, in our case, creating databases for clients, there
comes a time when you have to create your own data file from scratch. In this task,
you’ll create a new data set, define a set of variables, and then enter some data in
the variables. You’ll also create some automatic data entry constraints to improve
the accuracy of your data entry.
In this task, you will create four types of variables: numeric, date, string, and
binary.
1. From the menu, select File > New > Data. If you’re asked to save the contents
of the current file, click No.
2. When the new file opens in the Data View, click the Variable View tab at the
bottom of the window.
3. With the cursor in the Name column on the first row (referring to the name of
the variable) type:
clientid
4. In the Type column, click the build button (“build button” is actually a
Microsoft term, but since SPSS’s documentation doesn’t give the button a
name, we’ll use “build”) to open the Variable Type dialog box. (Figure 4)

FIGURE 4. Variable type dialog box
5. Select (click) String. Notice that you can now define the length of the variable
(Figure 5).
FIGURE 5. Defining the length of a string variable
6. Select all the text in the Characters field and type:
14
7. Click OK. The dialog box closes and the variable is now set to a length of 14
with no decimal places.
8. Press tab or Enter three times to move to the label column.
9. Type:
Client ID
This is the label that will appear on all output and in dialog boxes like those you
used in crosstabs and charts.

10. Press tab or Enter three times to move to the “columns” column. “Columns”
defines the width of the display of the variable, not its actual contents. The dis-
play width affects how the column will be displayed in output like crosstabs and
pivot tables.
11. Select all the text in the “columns” column and type:
14
12. Leave the remaining columns as they are, with left alignment and “nominal” as
the measure.
13. On the next row, click in the name column and type:
gender
14. Press tab or Enter to move to the next column.
15. Click the build button to open the Variable Type dialog box.
16. Select String and click OK to accept the width.
17. Click in the Label column for gender and type:
Gender
Notice that in the variable labels, you can use upper and lower case as well as
spaces and punctuation.
18. Press tab or Enter to move to the Values column.
19. Click the build button in the Values column to open the Value Labels window
(Figure 6).
FIGURE 6. Value labels window
Note: Variable labels determine how the name of the variable is displayed in out-
put. Value labels determine how each value is displayed. Thus, setting a

label of “Female” for “f” in the gender variable instructs SPSS to display
“Female” as a column heading for all cases with a value of f in gender.
20. In the Value field, type:
f
21. In the Value Label field, type:
Female
22. Click Add.
m
Male
25. Click Add.
26. The Value Labels window should now look like Figure 7.
FIGURE 7. Completed Value Labels window
27. Click OK.
28. On the next row, click in the Name column and type:
employed
29. Press tab or Enter or click in the Type field.
30. Click the build button to open the Variable Type window.
31. Employed is going to be a numeric, binary variable, so leave numeric selected,
but change Width to 1 and Decimal Places to 0.

32. Click OK.
33. Tab to or click in the Label field and type:
Employed year-end
34. Press tab or Enter to move to the Values field and click the Build Button.
1
Yes
37. Click Add.
0
No
40. Click Add.
41. Click OK.
42. On the next row, click in the Name field and type:
nextelig
43. Press tab or Enter or click in the Type field and click the build button to open
the Variable Type window.
44. Select Date by clicking it.
45. In the pane to the right, select the date format mm/dd/yyyy as in Figure 8.
FIGURE 8. Selecting a date format

46. Click OK.
47. Tab to or click in the Label field and type:
Next eligibility date
48. Tab to or click in the columns field and set the column with to 12.
49. From the menu, select File > Save As.
50. Navigate to the A: drive and name the file TestData.sav.
51. Click OK.
52. Click the Data View tab. Notice that you now have four columns in which
you’ll enter data for each record.
53. On the first row, click in the clientid column and type:
4839209
54. In the gender column, type:
f
55. Press tab. Notice that when you leave the field, SPSS updates the field to the
value label you assigned for f.
56. In the Employed field, click the drop-down arrow and select Yes.
57. In the nextelig field, type 4/1/2004.
58. Use the following table to complete the data entry for this file.
You have now seen how you can define variables, assign labels to both vari-
ables and values, and define constraints that will control the type of data that
can be entered. In future research, you’ll probably receive a file that has already
been entered in another application such as Access, Excel, or some other appli-
cation. If you’re doing your own data entry in SPSS, however, you should be
aware of its data entry capabilities.
clientid gender employed nextelig
5533990 m No 6/1/2004
5938209 f Yes 4/15/2004
9583902 m Yes 6/1/2004

Getting help in creating data sets and defining variables
Getting help in creating data sets and defining
variables
Now that you have had some practice in creating a data set, let’s review the kind of
help SPSS provides to answer your questions.
1. From the menu, select Help > Topics.
2. On the Index tab, click in the field named: Type in the keyword to find:
3. Type:
variable attri
4. Double-click the highlighted text in the list (Variable attributes) to display the
topics found.
5. SPSS opens the Topics Found window with Applying Variable Definition
Attributes highlighted.
6. Click Display.
7. The Help window is updated to this topic (Figure 9).

Getting help in creating data sets and defining variables
FIGURE 9. Help window for Applying Variable Definition Attributes
8. Click the highlighted item Displaying or Defining Variable Attributes.
9. When the window is updated, click Show Me. SPSS now opens the tutorial
window that is included as part of the application. Any time you see Show Me
in a help window, you can click it to see that specific section of the tutorial.
10. Click the Next arrow a few times to see how the steps are illustrated.
11. Close the Show Me window (the Internet Explorer window) to return to the
standard help window.
12. Under Related Topics, click Value Labels. As you can see, you can follow the
links in the help file, start a new search, or select any of the topics listed on the
left to move around in the SPSS Help system.
13. Close the help window.
14. From the menu, select File > Open > Data.
15. Navigate to the Employee data.sav file on the floppy disk and open it.
click one of the highlighted topics for more information

Creating primary reference lists
There is one set of outputs you’ll create that is more important than anything else,
and that is the set of primary references. Primary references describe your overall
data set. In other words, how many in all? How many in each category? What are
the maximums and minimums? Means? Standard deviations? Here’s our rule: list
out the summaries and put them somewhere where you refer to them quickly.
You may not always want to print out all the details of your data set. For example,
printing out every single income for a data set of one million people, would not be
useful, economical, or nice to either your printer or the trees. So here are the basic
rules: print frequencies for categorical variables and descriptive (also called
univariate) statistics for continuous variables.
In this exercise, we’ll use the sample Employee dat.sav file.
Frequencies
1. If it’s not already open, open the Employee dat.sav file by selecting File > Open
and navigating to C:SPSSTutorialDataEmployee data.sav.
2. From the menu, select File > New > Draft Output.
3. From the menu, select Analyze > Descriptive Statistics > Frequencies.
4. Double-click Gender, Employment Category, and Minority Classification to
move them to the Variables list.
5. Click the check box labeled Display frequency tables.
6. Click Statistics.
7. Make sure all the check boxes are cleared (not checked).
8. Click Continue.
9. Click Charts.
10. If it is not already selected, select None by clicking it.
11. Click Continue.
12. Click OK.
14. Navigate to C:SPSSTutorialData and save the file as AllFreqs.
When you’re working with your own file, follow these steps, then print the output
so you’ll have it handy.

Descriptive statistics: descriptives (univariate)
The next step is to print the descriptive or univariate statistics for the continuous
variables.
2. From the menu, select Analyze > Descriptive Statistics > Descriptives.
3. Click Reset to clear any previous selections.
4. Double-click Current Salary, Beginning Salary, Months since Hire, and Previ-
ous Experience to move them to the Variables list.
5. Click Options.
6. In the Descriptives: Options window, click Mean, Std. deviation, Variance,
Range, Minimum, Maximum, Kurtosis, and Skewness (Figure 10).
FIGURE 10. Selected measures for Options
7. Click Continue.
8. Click OK.
9. When the resulting table is displayed, notice that the variables you selected are
listed as rows, while the statistics are listed in columns.
11. Navigate to C:SPSSTutorialData and save the file as AllDescriptives.
12. Notice that the statistic Range displays the distance between the minimum and
maximum.

Recodes and Transformations
When you are working with your own file, be sure to print this output and save it
someplace handy (we use binders for, well, just about everything). These two print-
outs, frequencies and descriptives give you a picture of the overall shape of your
data.
Sooner or later, no matter how carefully you planned your data design, you’ll prob-
ably want to work with some variables in different forms. If you collected income
or age data, for example, you might want to group the continuous variables into cat-
egories. Or you might want to create a variable that combines various conditions,
say, all minority managers by gender. This type of data manipulation is called
transforming or recoding. In this exercise, you’ll create several new variables, some
that indicate multiple conditions and some that recode continuous variables into
categorical variables.
Backup the original file
The first step before making any changes to your data file is: BACK UP YOUR
DATA. And the easiest way to back up your data is to save it under another name.
1. If you don’t have the data view open, select it from the menu by selecting Win-
dow > Employee data.sav - SPSS Data Editor.
2. From the menu, select File > Save As, then navigate to C:SPSSTutorialData.
3. Name the new file EmployeeData01 (Figure 11).

FIGURE 11. Naming the new file
4. Click Save. Notice that the title bar of your window now identifies the file as
EmployeeData01.sav. Now you can begin your transformations.
Recoding existing variables
Recoding refers to assigning codes (or different codes) to an existing variable.
NEVER ever, ever, EVER recode your variables into the same variable name (with
one exception). That way lies madness. And chaos. For one thing it deletes your
existing data. And for another it destroys the history of the data. Always create a
new variable to contain the new codes.
Recode income data
1. From the menu, select Transform > Recode > Into Different Variables to open
the Recode window (Figure 12).
Enter new name

FIGURE 12. The SPSS Recode window
2. Double-click Current Salary to move it to the Input Variable --> Output Vari-
ables pane.
3. Click Old and New Values to open window where you’ll create the new codes
(Figure 13).
FIGURE 13. Old and New Values window
4. Click the second Range radio button (lowest through) to activate its field (Fig-
ure 14).

FIGURE 14. Entering lowest income range
5. Click in the Lowest through range and type:
24999
6. Click in the Value field under New Value and type:
1
In the new field, any incomes less than 25000 will have a value of 1. (Figure 15)
FIGURE 15. Defining the new value for the lowest income range
click here to
activate field
enter maximum value for lowest range here

7. Click Add. Notice that the complete definition of old and new values appears in
the Old --> New pane (Figure 16). In the next steps you’ll define three more
income ranges.
FIGURE 16. Recode window with Old --> New values displayed
8. Under Old Value, click the first Range radio button to active the minimum and
maximum values (Figure 17).
FIGURE 17. Activating minimum and maximum range values
9. In the first range field, type:
25000
old and new values
displayed here
click here to activate
range fields

10. In the second range field, type:
49999
11. Under New Value, type:
2
Your window should now look like Figure 18.
FIGURE 18. Recode window with minimum and maximum range values
12. Click Add. Notice that the new definition is added to the Old --> New pane.
13. Under Old Value, click the third range radio button to activate the Range ...
through Highest field (Figure 19).
FIGURE 19. Activating the range through highest field
click here to activate
Range . . . through
Highest field

14. In the field to the left of through Highest type:
50000
3
16. Click Add. Again, the new definition is added to the Old --> New pane. In the
next steps, you’ll create a value to accommodate any odd values that might have
been entered into the file. Even if you’re sure there aren’t any, check anyhow.
17. Under Old Value, click the radio button for All other values. Notice that there
is no range to enter.
4
19. Click Add. Now all possible definitions are entered under the Old --> New pane
(Figure 20).
FIGURE 20. Completed definitions for income ranges
20. Click Continue. The Old and New Values window closes and the original
Recode into Different Variables window is displayed.
21. In the field for Output Variable Name type:
incrange
22. In the field for Output Variable Label type:
Income Range
complete list of
income range
definitions

The label is the name that will be displayed on all output, so you’ll want to
make sure it’s informative and correctly formatted.
23. Click Change. The new name is now listed in the Numeric Variable --> Output
Variable pane (Figure 21).
FIGURE 21. Recode window with output variable defined
24. Click OK. The Recode window closes and the data view is displayed. Notice
that there is now a new column on the right containing the new range codes.
(Figure 22)
FIGURE 22. Data view showing new variable
25. Noticed that the codes are displayed with two decimal places. These should be
simple integer codes, so in the next step you’ll change the format of the vari-
able.
original name and output name displayed here
new variable

26. Double-click the name incrange to open the Variable View with that variable
selected (Figure 23).
FIGURE 23. Variable view with incrange selected
27. Double-click the 2 in the Decimals field and type:
0
28. Press tab or Enter to leave the field.
29. Click the Data View tab to see the corrected data. In the next step, you’ll sort the
cases in ascending and descending order of incrange to see how the values were
applied and to see if there are any values that were not included.
30. Click anywhere in the incrange column.
31. From the menu select Date > Sort Cases to open the sort window (Figure 24).
FIGURE 24. Sort Cases window
32. Scroll down to Income Range and double-click it to move it to the Sort by pane.
33. Click OK. The cases are now sorted according to the lowest income range
value.
34. From the menu select Data > Sort cases. Notice that your previous choices are
still selected.
35. Click once on Income Range in the Sort By pane.
36. Click Descending under Sort Order.

37. Click OK. The cases are now sorted with the highest income range value listed
first. If there had been any entries that did not fall into the expected categories,
they would be listed first, having a value of 4.
Now let’s put the new variable to work and display the distribution of cases by
income range.
1. From the menu, select New > Draft Output.
2. From the menu, select Analyze > Descriptive Statistics > Crosstabs.
3. In the Crosstabs window, click once on Gender, then click the right arrow to
move it into the Rows pane.
4. Click once on Income Range, then click the right arrow to move it into the Col-
umns pane.
5. Click Cells to open the Crosstabs: Cell Display window (Figure 25).
FIGURE 25. Crosstabs: Cell Display window
6. In the Percentages pane, click the check box for Row. In this case, row percent-
ages will display the percent within gender in each income range. For counts,
make sure Observe is checked.
7. Click Continue.
8. Click OK. The Crosstabs window will close and the new crosstabs will be dis-
played in the draft output window.
Notice that the income ranges are listed as 1, 2, and 3. Not very informative. So
let’s go back and assign value labels for the new variable.
9. Close the draft output window without saving.
10. In the Data View, double-click the column heading for incrange to open the
Variable View for that variable.

11. In the Values column, click the build button to open the Value Labels dialog
box.
1
< $25,000
14. Click Add.
2
$25,000 - $49,999
17. Click Add.
3
$50,000 or more
20. Click Add.
4
Other
23. Click Add.
24. Click OK.
26. From the menu, select Analyze > Descriptive Statistics > Crosstabs.
27. Your previous selections are still available, so click OK.
In this exercise. you have worked through the entire process of recoding values into
a new target variable and practiced sorting the cases to find the minimum and max-
imum values of the new variable. Finally, you used the newly defined income

Recoding variables revisited
ranges to create a crosstab report showing the number and percent of employees
within each income category by gender.
Earlier, we mentioned that there is an exception regarding recoding into the same
variables.
The one exception in recoding variables
In fact, there is a case where you might want to recode a value into the same vari-
able, and that is the case of missing or unknown values.
BACK UP YOUR DATA FIRST!
In some cases, you might receive a data set which has missing or just plain odd val-
ues in one or more variables. (This will, of course, be a data set you received from
someone else, as you would never do anything so silly.) What you can then do is,
after identifying the odd values, recode them to a standard value that you will use to
represent missing data. You can use any value you want that does not already occur
as a valid value in the existing data. For example, suppose you have a field that was
supposed to be numeric but someone entered hyphens to indicate that they saw the
field but didn’t have any data to enter. (Don’t laugh. We’re working on a project
with just that problem.) You might then review all the data, find a value that does
not already exist as a valid value and change the hyphens to that value. For exam-
ple, if you work with legacy data sets, you’ll probably find a value of 9 or 99 used
to represent missing data.
The other exception
There are cases (we hope they’re rare) where you’ll want to recode unacceptable
values to a flag value that you can use in subsetting your data. For example, sup-
pose you’re surveying income and you find that values range from 0 to 55000,
except for ten values that are all greater than ten million. As it happens, you can’t
go back to confirm the odd values, but you don’t want to use them in your calcula-
tions. In that case, you might want to recode anything over, say, 60000 to a flag
value like 99999. In your calculations, you then have a standard value you can use
to exclude any outliers or suspicious data from your analyses. As usual, however,
be sure to back up your original file before recoding any variables.

On the whole, however, we prefer to recode into different variables.
BE SURE TO RECORD ALL CHANGES TO THE DATA SET.

3 Charting your data
Okay, you’ve done all the hard work, created the data collection instrument, con-
ducted the interviews or surveys, entered the data, cleaned the data and made any
necessary recodes or transformations, and now it’s time to find out what it all
means. One of the best ways to get an idea of what your data looks like (literally) is
to generate charts. Charts provide visual displays of comparisons and relationships.
They can also be extremely misleading in the hands of a bad analyst, so be careful.
If you’re going to be working with charts, two excellent sources of information and
guidelines are Edward Tufte’s work on visual representations and Gerald Jones’s
book How to Lie with Charts.1
A word on charts: keep them simple. The purpose of charts is to illuminate relation-
ships and comparisons, not to obscure them. Eschew what Tufte calls chart junk
and go for simple, clean, and clear designs. But that’s a whole other course.
SPSS excels at charts. Many of its functions exceed even those of Microsoft Excel
and Microsoft Access, which are pretty good on their own. SPSS provides three
methods of creating charts: you can use the automated chart function, you can use
the interactive chart function, or you can start with the blank chart and build it from
1. Jones, Gerald E., How to Lie with Charts, Sybex 1995. Tufte, Edward R., The Visual Dis-
play of Quantitative Information, Graphics Press, 1983, Envisioning Information, Graph-
ics Press, 1990, Visual Explanations, Graphics Press, 1997.

Using the automated chart function
scratch. For the charts you’ll be creating here, you’ll use all three methods to create
charts that illustrate gender distribution within job categories.
1. If it not already open, open the data file named Employee Data.sav in the folder
named SPSSTutorialData.
3. From the menu, select Graphs > Bar. (Figure 26)
FIGURE 26. Creating a simple bar chart
4. Make sure Simple is selected.
5. Click Define. SPSS then displays the Define Simple Bar window (Figure 27)
where you will begin to select the variables to be displayed.

FIGURE 27. Define simple bar window
6. Click once on Employment Category, then click the right arrow to move it to the
Category Axis field.
7. Click OK. SPSS displays the completed chart. Not too interesting, right? Let’s
make it a little more interesting.
8. From the menu, select Graphs > Bar.
9. This time, select (click) Clustered, then click Define.
category axis.
11. Click once on Gender, then click the right arrow to move it to Define Clusters
By.
12. Click OK. Much more interesting. In the next step, you’ll improve your chart
with explanatory titles.
13. From the menu, select Graphs > Bar.
14. This time, select (click) Clustered, then click Define.
category axis.
16. Click once on Gender, then click the right arrow to move it to Define Clusters
By.
17. Click Titles.

18. On the first line, type:
Job Category Distribution by Gender
19. On the first line under Footnote, type:
Print date: 3/16/2004
20. On the second line under Footnote, type:
Data source: SPSS sample data file, Employee dat.sav
21. Click Continue.
22. Click OK.
Using the Interactive Chart function
While the automated chart function is handy, it limits the degree to which you can
customize your chart. The interactive function provides more flexibility and power.
In the interactive chart window, you’ll drag variables to the axis where you want
them displayed.
1. From the menu, select Graphs > Interactive > Bar.
2. Click once on Employment Category and move it to the horizontal axis, as in
Figure 28.

FIGURE 28. Moving a variable to the horizontal axis
3. Click OK. Now you have a simple bar chart, more or less like the first one you
created. So let’s add some information.
4. From the menu, select Graphs > Interactive > Bar. Notice that when the window
opens, it still contains the information from your last chart.
5. This time, drag Gender to the field under Legend Variables called Color (Figure
29).
dragging a variable to the
horizontal axis

FIGURE 29. Dragging a variable to the legend color field
6. Click OK. Now you have a chart with employment category broken out by gen-
der. Now you could add several more grouping variables to the chart, but it
would begin to look pretty cluttered. To solve this problem, Tufte came up with
the idea of small multiples, the practice of displaying smaller charts with each
chart displaying only one of the grouping variables. In SPSS, such charts are
called panel charts.
7. From the menu, select Graphs > Interactive > Bar.
8. Notice that your previous choices are still displayed. This time, however, drag
Gender from the Legend Variables field down to the field for Panel Variables as
in Figure 30.
drag a variable to the
legend color field to add
a grouping variable to the
chart

FIGURE 30. Dragging a variable to the Panel Variables field
9. Click OK. The Draft Output window now displays two charts, one with
employment category distributions for women, the other with the same informa-
tion for men.
Creating a chart from scratch
In the Output window (not the Draft Output window) you can create a chart from
scratch, with complete control over every aspect of the graph.
1. From the menu, select File > New > Output.
2. From the menu in the Output window, select Insert > Interactive 2-D Graph.
SPSS displays the new, empty graph. (Figure 31)
drag one or more variables
to the Panel Variables
pane to create a separate
chart for each grouping
variable

FIGURE 31. Blank interactive graph
3. Move your cursor slowly over the various icons displayed on the graph. Notice
that SPSS displays a description of each icon.
4. Click the Assign Graph Variables icon (Figure 32).

FIGURE 32. Selecting the Assign Graph Variables tool
5. SPSS opens the Assign Graph Variables window in Figure 33.
FIGURE 33. Assign Graph Variables window
click here to assign
variables to the graph

6. From the left panel, drag Count [$count] to the field for the y axis (Figure 34).
FIGURE 34. Dragging a variable to the y axis
7. Now drag Employment Category to the x axis (Figure 35).
FIGURE 35. Dragging a variable to the x axis.
8. Finally, drag Gender to the field for Legend Variables Color (Figure 36).

FIGURE 36. Dragging a variable to the Legend Variables Color field
9. Click the X close button. Notice that the chart has the axis variables assigned
but still has no data.
10. From the menu, select Insert > Summary > Bar. Your initial chart is now com-
plete.
In fact, SPSS graphing functions go far beyond what we have explored here. You
can customize your chart by creating different types of charts (cloud, scatterplots,
line graphs, etc.) and by adding elements such as value labels, titles, notes, and
much more. Now that you have a general feeling for how graphs work in SPSS,
take some time to explore other functions. If you have any questions, be sure to
refer to the Help menu.

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