to SPSS 10.0
to the information in this publication may be assumed for noncommercial
SPSS version 10.0
for Windows is a statistical software package that allows users to
create, access, manipulate, and analyze data.
purpose of this document is to introduce new users to the SPSS for
Windows environment. After reading this document, the student should
be able to:
application windows and use the menu system.
- Create data
- Access preexisting
- Create supporting
- Save and
Users of SPSS
for Windows should have experience operating within a Windows environment
(95, 98 or NT). In addition, an understanding of basic statistical
techniques and prior programming experience using other statistical
programs is beneficial.
To open SPSS,
do the following:
- Click on
the START button on the Window taskbar.
- Go to the
PROGRAMS folder tab, then the SPSS for Windows folder tab.
- Click once
to highlight the SPSS 10.0 for Windows icon to enter the program.
the SPSS icon on the Windows desktop.
Data Editor, Syntax Editor, and Output Viewer
of three parts: The Data Editor, the Syntax Editor, and the Output
Viewer: When you start SPSS, the Data Editor window opens by default.
It allows you to create your data set and perform statistical operations
The Data Editor
Editor fulfills the same function as the Data Editor. However, in
the Data Editor you manipulate your data set interactively by using
the pull-down menus. You use the Syntax Editor to achieve the same
results by writing the procedures in the SPSS programming language
(Figure 2). To open the Syntax Editor, go to the menu bar and click
on FILE, click NEW, and click SYNTAX.
The Syntax Editor
The Output Viewer
displays the results of statistical operations you perform on your
data. It pops up automatically once you run a statistical procedure
3: The Output Viewer
Menu Commands in the Data Editor
The menu options
you have vary, depending whether you are in the Data Editor, the
Syntax Editor, or the Output Viewer.
menu:This menu provides techniques for defining variables, inserting
variables or cases, sorting files, splitting files, merging data
sets, aggregating data, or using a select command to look at a subgroup
within the data file.
menu allows you to transform your data set on the basis of existing
variables. Among other things, you can recode your variables and
compute new variables from existing ones..
With the Analyze
menu you perform statistical operations on your data set, the output
of which will be displayed in the Output Viewer.
menu contains a number of graph options that allow you to visually
display descriptive statistics in the Output Viewer.
menu provides several miscellaneous functions. UTILITIES/VARIABLES
for example, allows you to view the properties of your variables
as you defined them on the Variable View tab in the Data Editor
(see Figure 4).
Use the Help
menu if you are looking for information on a specific subject. For
that purpose, go to HELP/TOPICS, and select the "Index"
tab. Then, type the word you are looking for on the white line and
click the "Display" button. To get a general idea of how
SPSS works, go to HELP/TUTORIAL. This function provides a step-by-step
introduction to the most important topics on SPSS.
you run statistical operations and need help understanding
your output, in the Output Viewer right-click on the table
containing your statistics and go to the "Results Coach"
option. This option will tell you what your table contents
Creating a New Data Set
In this section
you will learn the steps involved in creating your own data file,
using the Data Editor. Several steps are involved when it comes
to creating a new data set. First, you have to tell SPSS whether
to treat your variables as numeric or string variables, how many
decimals to record etc. You do this by defining your variables.
Once you have done that, you enter your actual data.
The Data Editor
window has two sheets: A Data View sheet, and a Variable View Sheet.
By default the Data View sheet opens whenever you open the Data
Editor. The Data View Sheet contains your actual data set. Here,
the variable names are displayed in the grey row right above line
1. Each white row represents a case, and each column represents
To define your
variables, click on the Variable View tab at the bottom of the screen.
The Variable View Sheet opens (Figure 4).
It allows you to name your variables, to identify missing values,
assign variable and value labels, adjust column width and alignment,
and format the column for the type of data you are planning to enter.
The Data Editor in Variable View
to info on the Utilities menu.
View, each variable takes up one row. The columns contain the formatting
Use the default SPSS variable name indicated by VAR00001 or
create your own name for the variable. The name is limited to
eight characters and can be any combination of letters and numbers
as long as it does not start with a number; e.g. AGE, SEX, INCOME.
Each variable name must be unique; no duplicate names are allowed.
In addition, spaces are not allowed and special characters (@,#,$,%)
and punctuation marks should be avoided.
are several options in defining the type of data. The two most
commonly used are numeric and string variables. Numeric (the
default) indicates to SPSS that the data entered for this variable
will only be numbers. If you wish to put names, words, or a
combination of numbers and letters, you must format the column
type as a string variable. Please note that SPSS does not allow
you to perform operations on variables that are defined as string.
Define a descriptive variable label, which appears in your statistical
reports and charts. It can be up to 120 characters long.
Define value labels, which can be up to 60 characters in length.
Value labels are especially useful when numeric codes are used
to represent non-numeric categories such as sex or race. NOTE:
Many procedures will not print the maximum number of characters
allowed for variable and value labels. The best approach is
to keep both variable and value labels as short as possible;
generally no more than 40 characters for variable labels and
20 characters for value labels.
Blank cells in the data set are automatically identified as
system-missing values and are represented by a period (.) on
the Data View sheet. Instead of leaving the data entry blank,
values are sometimes used to represent why data are not available.
An example would be a survey question that recorded "don't know"
or "refused to answer" responses by a specific value, such as
8 and 9. To exclude these two responses from your analysis you
can use the Missing Values option to identify them as user-missing
values. SPSS treats system-missing and user-missing values the
same; they are excluded from the data analysis.
the variable, you return to the Data View sheet by clicking
the data view tab at the bottom of the screen.
In Data View,
you enter your data just as you would in a spreadsheet program.
You can move from cell to cell with the arrow keys on your keyboard
or by clicking on the cell with the mouse. Once one case (row) is
complete, begin entering another case at the beginning of the next
row. You can delete a row of data by clicking on the row number
at the far left and pushing the delete key on your keyboard. In
a similar fashion, you delete a variable (column) by clicking on
the variable name so that the entire column is highlighted and pushing
the delete key.
To save a data
file select the FILE pull-down menu and then use the SAVE
option. Type the drive, folder, and file name and click OK
to save the file. This saves the data as an SPSS for Windows system
file and sets the file name extension as .sav. This file can only
be read by the SPSS for Windows program, unless the user is very proficient
in SAS. There are other options (under SAVE FILE as Type) which
allow you to save the data in ASCII, tab-delimited, SPSS/PC+ for DOS,
Microsoft Excel, and several other formats.
an SPSS data file, from the FILE menu single-click on the
option OPEN, then single-click on the option DATA.
This will produce the Open Data File dialog box.
For writing and explanatory ease, menu steps may be separated
by a slash from this point forward; e.g. FILE/OPEN/DATA.
Type in the
location and name of the data file or use Windows Explorer to locate
your file by establishing the drive and folder that contains your
files and selecting the file with a single click. In most cases
your file will be on floppy drive A:\ or network drive G:\, which
is the drive that has your login name as a label.
the file, click the OK button.
Importing from Excel
The SPSS Data
Editor can read files written in Excel 97. Just go to FILE/OPEN/DATA.
As the "Open File" dialog box pops up, select the appropriate drive
and folder. In the "Files of Type" field, make sure Excel is selected.
Highlight the file you wish to open, then click "Okay".
Freefield and Fixed Format Data - Which Is Which?
Some data sets
come as unformatted "text" files such as the ASCII files that can
be saved by text editors (e.g. WordPad or Notepad). Among these
data sets, two types can be distinguished:
- Data sets
that contain freefield data (Figure
5): Each case constitutes a line. The variables are recorded
in the same order for each case and separated from each other
by a delimiter, usually a blank or a comma.
- Data sets
that contain fixed format data (Figure
6): Here each case constitutes a line, and variables are recorded
in fixed columns. An example of this are ICPSR data sets (ICPSR
stands for "Inter-University Consortium for Political and Social
Research"). Because variables are located in fixed columns, you
do not have to import the entire data set, but can select the
variables of your choice by specifying the appropriate columns.
This is useful in cases when you are dealing with large data sets
that take up a lot of memory.
5: A Freefield Data Det - freefiel.txt
6: Fixed Format Data - speed.txt.
Importing Freefield Data
To import freefield
data, take the following steps: In the Data Editor, go to FILE/READ
TEXT DATA. In the "Open File" dialog box, select the text file
you wish to import and click "Open." The "Text Import Wizard" will
pop up and guide you through the remaining steps you need to follow.
In step 2 you will be asked "How are your variables arranged?" Be
sure to select the option "delimited." Once you have read your data
into the Data Editor, you can specify variable labels, value labels
etc. (Figure 7).
7: Importing freefiel.txt with the Text Import Wizard
Importing Fixed Format Data
Using the Data
You can import
fixed format data through the Data Editor. As before, go to FILE/READ
TEXT DATA. In the "Open File" dialog box, select the text file you
wish to import and click "Open." The "Text Import Wizard" will pop
up and guide you through the remaining steps you need to follow.
In step 2 select the option "Fixed Width," to answer the question
"How are your variables arranged?"
If you are dealing
with large data sets and want to import only certain variables,
you have to do that through the Syntax Editor in programming language.
The basic steps you have to follow are:
- Specify an
SPSS name for the data and tell SPSS where to locate the data
set. You do this with the FILE HANDLE command. Think of
the SPSS name for the data set as a working title. This working
title, which must not be longer than eight characters, tells SPSS
how to refer to the file it finds at the specified location on
your computer. This working title is only used by the SPSS Syntax
Editor. It has nothing to do with the actual file name of the
- Specify name
and column location for each variable you wish to import, and
determine its format (string or numeric? If numeric, how many
decimals?). You do this with the DATA LIST command.
- Specify variable
labels for your data (optional). You do this with the VARIABLE
- Specify value
labels (optional). You do this with the VALUE LABELS command.
- Tell SPSS
to execute steps 1 through 4. You do this with the EXECUTE
how these steps are carried out, we will import the data set speed.txt
(see Figure 6),
executing the steps just listed. The data set is a fixed format
text file which contains the records of sprinters: Their I.D. number,
their name, age, weight, speed and level of exhaustion. It is saved
on a floppy disk .
example / NAME='a:\speed.txt'.
Type the command
FILE HANDLE, followed by an SPSS name for your data set. Then insert
a slash and the NAME command. The file location has to be enclosed
by simple quotation marks.
As you can see, SPSS syntax uses slashes "/" and periods ".".
The slash is used to indicate subcommands (FILE HANDLE is
the command, NAME is the subcommand). The period indicates
the end of a command (including subcommands). In our case
the period signifies the end of the FILE HANDLE command.
SPSS syntax is not case sensitive. You do not need to use
caps to identify the commands, but it might want to do it
anyway to set the commands off.
Finally, be careful when typing your syntax. If your program
contains typing errors, it will not be executed.
LIST file = example/
Name 2-10 (A)
speed 17-19 (1)
LIST command is followed by the working title of the file -
in our case this is "example." Insert a slash after the working
title to indicate that variable names, locations and format are
next. Variable names - in our case "ID", "Name", "age", "speed",
names are followed by their respective column numbers. The variable
format follows in parentheses. If no format is specified, SPSS will
read the variable as numeric with zero decimals. The number "1"
tells SPSS to insert a decimal point before the last digit. "2"
would tell SPSS to insert a decimal point before the last two digits.
The letter "A" indicates that the variable in question is a string
Note that we
omitted the weight variable, which is recorded in columns 13 through
16, because for the purpose of our data analysis we do not need
it. The last variable is followed by a period to indicate the end
of the DATA LIST command.
ID "ID number of the sprinter"
Name "name of the sprinter"
age "age of the sprinter"
speed "speed in m/sec"
exhaust "Was sprinter exhausted?"
Type the command
followed. Then type the variable names, followed by the variable
labels in quotation marks. Insert a period at the end of the command.
exhausted 1 "yes"
You might want
to specify value labels for categorical data. In our example, we
do this for the variable "exhaust." Write out the name of the variable,
then list the numbers, followed by the labels in quotation marks.
Insert a period after the last variable for which value labels are
assigned, to indicate the end of the VALUE LABELS command.
Again, be sure
to insert a period after the EXECUTE command. To run the
program, highlight it and click on the "run" arrow on the tool bar.
SPSS will import the data set into the Data Editor. Save the SPSS
data set in a folder and under a file name of your choice.
8: The Syntax its Entirety
9: The Data Set in the Data Editor
In the Data Editor,
you can use the COMPUTE or the RECODE command to create
new variables from existing variables.
Transforming Variables with the Compute Command
option allows you to arithmetically combine or alter variables and
place the resulting value under a new variable name. As an example,
to calculate the area of shapes based on their height and width,
you compute a new variable "area" by multiplying "height" and "width"
with one another (Figure 10):
- Using the
menu system select TRANSFORM/COMPUTE, enter the target
variable name AREA and in the numeric expression box type HEIGHT*WIDTH.
- Click the
OK button to have the transformation run immediately or
the PASTE button to copy the proper commands to the Syntax
Editor. The OK button will only appear after a target variable
and numeric expression are provided.
The PASTE button allows you to develop a complete
program in the Syntax Editor that will list all the commands
used in your current SPSS session. This program can be saved
for future reference. .
The Compute Dialog Box
to "Using the IF Statement"
Transforming Variables with the Recode Command
option allows you to create discrete categories from continuous
variables (Figure 11). As an example, you
may want to change the height variable where values can range from
0 to over 100 into a variable that only contains the categories
tall, medium, and short.
The Recode dialog box
to "Using the IF Statement"
- Select TRANSFORM/RECODE/INTO
- A list of
variables in the active data set are provided. Select the variable
you wish to change by clicking once on the variable name and clicking
the arrow button.
- Click the
Output Variable box and enter a new variable name (8 characters
maximum) and click CHANGE.
- Select OLD
AND NEW VALUES. This box presents several recoding options.
You identify one value or a range of values from the old variable
and indicate how these values will be coded in the new variable.
After identifying one value category or range, enter the value
for the new variable in the New Value box. In our example,
the old values might be 0 through 10, and the new value might
be 1 (the value label for 1 would be "short", for 2 "medium",
for 3 "tall").
- Click ADD
and repeat the process until each value of the new variable is
properly defined (Figure 12).
- Click CONTINUE
to return to the Recode box and OK to return
to the Data Editor.
In dialog boxes that are used for mathematical or statistical
operations, only those variables that you defined as numeric
will be displayed. String variables will not be displayed
in the variable lists.
Recode: Old and new values
You also have the option of recoding a variable into the same
name. If you did this in the height example, the working data
file would change all height data to the three categories
(a value of 1 for "short"), 2 ("for medium", or 3 for "tall").
If you save this file with the same name, you will lose
all of the original income data . The best way to avoid
this is to always use the recode option that creates a different
variable. Saving the data file keeps the original income data
intact while adding the new categorized variable to the data
set for future use.
Using the "If" Statement in Transformations
IF statement in the Data Editor:
Within the COMPUTE and RECODE commands is an option to use an IF
statement. You can choose to only recode values if one of your variables
satisfies a condition of your choice. This condition, which is captured
by means of the "IF" command, can be simple (such as "if area=15).
To create more sophisticated conditions, you can employ logical
transformations using AND, OR, NOT.
- In the Compute
and Recode dialog boxes (Figures 10 and
11), click on the IF button.
- The Include
If Case Satisfies Condition dialog pops up(Figures 13
- Select the
variable of interest and click the arrow button.
- Use the key
pad provided in the dialog box or type in the appropriate completion
of the IF statement.
- When the
IF statement is complete, click CONTINUE.
The "Include Cases If" dialog box for the RECODE command
14: The "Include Cases If" dialog boxes for the COMPUTE command
IF statement in Syntax
For experienced users, the best method for using the IF statement
is to enter it directly into the Syntax Editor. Here are some examples
of appropriate syntax statements using the logical operators "and",
[Both conditions must exist for BUY to be assigned the value
IF (HEIGHT =10 AND WIDTH = 5) BUY=1.
[If any of the conditions exist, BUY will be assigned the value
IF (HEIGHT=10 OR HEIGHT=9 OR WIDTH=5) BUY=1.
[Reverses the outcome of an expression.]
IF NOT(HEIGHT=10 OR HEIGHT=9 OR WIDTH=5) BUY=2
The IF command
also can process the following mathematical operators (given here
EQ or = "equals"
LT or < "less than"
GT or > "greater than"
LE or <= "less than or equal to"
GE or >= "greater than or equal to"
NE or <> "not equal to"
Example: IF (HEIGHT LE 5) HEIGHT_B=1. EXECUTE.
For highlighting long programs in the Syntax Editor, use
Ctrl-Home to move to the beginning of the program
then Ctrl-Shift-End to highlight the entire program.
Analyzing Your Data
Once you have
opened or created a data set, you can immediately begin your analysis.
Under the ANALYZE menu there are several options available.
The DESCRIPTIVE STATISTICS option is discussed here, as it
is used most often in basic statistical analysis. Using the menu
bar select ANALYZE/DESCRIPTIVE STATISTICS. This provides
you with the choice between running frequencies, descriptives (mean,
median, mode etc.), crosstabs and summary statistics (under the
EXPLORE option). Within each choice you can select the type of statistics
you desire, format of the table, and for some statistical procedures
graphical representations of the data (use the CHARTS button).
To run frequencies,
select ANALYZE/DESCRIPTIVE STATISTICS/FREQUENCIES. The list
of variables for the active data set are provided . Highlight the
variable(s) of interest and click the arrow key to include the variable(s)
in your analysis (Figure 15). Once you
select the variables and indicate the type of statistics you want,
you have the choice of running the analysis immediately by clicking
OK or pasting the commands into the Syntax Editor to create
a program file.
15: Running frequencies for the variable height_b
to "Setting your Preferences"
As the program
begins to run, the Output Viewer will be activated. Once you are
in the Output Viewer, you can page through the output and print
the results. You can also insert text. To do that, place the insertion
point where you want the text box to appear, and go to INSERT pull-down
menu and select NEW TEXT.
To save your output
file go to the FILE pull-down menu and click on SAVE. The default
extension for an output file when saving is .spo, as opposed to the
.sav for data files, so files for the same program may be saved with
the identical names, different extensions.
Adding Graphics to Your Analysis
the Data Editor, you can request graphic operations as an
option directly through the GRAPHS menu. You can also
do it through the statistical operations you are running.
Let's take our example from Figure 15, where we ran the frequencies
for the variable HEIGHT_B. In the "Frequencies"
dialog box, click the "Charts" button. This will
call up a list of chart options. Check the "Bar Charts"
option and click "Continue" and then "OK".
The Output Viewer will display the frequencies for HEIGHT_B
not only in table format, but also as a bar chart
twice on the icon will take you to the Chart Editor,
where you can view and edit the graph (Figure
16). You can close the Chart Editor by clicking the "close"
button in the upper right corner of the window. This will
take you back to the regular Output Viewer. All the changes
you made to your graph in the Chart Editor will be saved if
you save your output file.
16: Frequency Bar chart for the variable height_b
Saving, Printing, and Exiting SPSS
To save data,
program syntax, output, or charts, select the FILE menu and
either use the option SAVE or SAVE AS. If the file
you wish to save has been saved before, the SAVE option will
automatically save it under the same name and file format in the
previously designated location.
If it has not
been saved before, the SAVE option will prompt you for a
name, directory, and disk location (drive). If the file has been
saved before but you wish to save it under a different name, location,
or format, use the SAVE AS option. Syntax files are saved
in ASCII format so they can be imported easily into other programs.
Output files are saved with the .spo extension, and can only be
opened in SPSS.The common file extensions used by SPSS are .spo
for output files and .sps or .txt for syntax files. A good rule
of thumb is to copy and paste desired output into Microsoft Word
before printing. If you do that, be sure to use the "Paste
Special" command in Word, rather than the simple "Paste
command". Then, when prompted for a pasting option, choose
"Paste as picture."
To print output,
program syntax, charts, or the data file, select the FILE
menu and then the option PRINT.
You exit SPSS
by selecting FILE/EXIT. If you have not saved the data, output,
or syntax files or they have changed since you last saved them,
you will be asked to save the files before exiting. You can either
save the files or decide to exit without saving.
Setting Your Preferences
after entering SPSS for Windows you should routinely check the OPTIONS
dialog box under the EDIT menu. This dialog box establishes
default settings for memory allocation, graph drawings, and output
formats. In particular, the output options should be set
so that your output includes commands, errors and warnings (to do
this, go to the "Viewer" tab, and on that tab to the box
that says "Initial Output State"). Ensure page size and
length are set to standard (on the Viewer tab go to the box that
says "Text Output Page Size").
that you need to know about is contained on the "General"
tab: The "Variable Lists" box on that tab allows you to
modify the way your variables are displayed on dialog box variable
lists (see Figure 15 for an example). You
can display your variables in alphabetical order or in the order
in which they appear in your data set. You can also display the
variable labels rather than the variable names. This option is particularly
important when you are dealing with large data sets, because if
the variables are not arranged in an orderly manner, finding them
on the dialog display list might take a long time.
A Practice Exercise
In this exercise,
we will enter the results of a very simple survey we conducted.
We conducted the survey because we want to know whether love for
baseball varies with age. In order to get at that information, we
asked 20 randomly chosen individuals about their age and about whether
or not they love baseball. If they loved baseball, we recorded their
answer as 1. If they did not love baseball, we recorded their answer
as 2. If they were not sure whether they loved baseball, we recorded
their answer as 3. If they refused to answer this question, we recorded
that as 4 for "no answer."
In order to
analyze the information our respondents gave us, we enter the data
into the SPSS Data Editor.
First, we define
In the Data
Editor, click on the "Variable View" tab. First, let's
deal with the "age" variable. All the information defining
this variable is entered in the first row of the spreadsheet. For
name, we enter "age". For type, we click
on the button that appears when we access the cell. A dialog box
pops up. Because age is recorded as a number, we check the option
"Numeric." For width, we enter "2", because
all our respondents are between 0 and 99 years old, and therefore
we only need two digits (however, you can also choose any number
that is greater than that. The important thing is that you select
a number that is greater than or equal to the maximum number of
digits, decimals included, your values can assume). For decimals,
we select "0", because we recorded age as integers, and
we are not interested in displaying decimals. For label,
we enter "age of respondent". We skip the values
cell, because age is not a categorical variable, and therefore there
is no need to define value labels. We also skip the missing
cell, because for the age variable, there are not missing values.
Skip columns and align, and for measure, select
"interval", since age is an interval scale variable.
Now the variable
that records the respondents' love for baseball:
"Does respondent love baseball?"
Click on the button that appears when you select the cell. For
"value" type "1", for "value label"
type "yes", then click the add button. In the same manner,
you define "2" as "no", "3" as "not
sure", and "4" as "no answer". The reason
why we use value labels for the BASEBALL variable is that this
variable consists of categories (yes, no, don't know), that can
Enter "4". If a respondent gave no answer, his/her case
will be excluded in statistical analyses involving the BASEBALL
- Skip columns
As our next
step, we enter the data from the twenty questionnaires we had our
respondent fill out. To do that, click on the "Data View"
tab on the lower left of the screen. Now you see a line of grey
cells, with the first two reading "age" and "baseball",
respectively. These are the variable names we just defined.
white line represents one case, each respondent's answers are entered
into one line.
17: Entering the data into the Data Editor
- Now we save
the file on a floppy disk. Go to FILE/SAVE AS. In the dialog
box that pops up, browse for the a-drive. Choose a file name,
for example "exercise" and hit the SAVE button.
Now that you
have the data in SPSS, you can analyze it. First, let's transform
AGE into a variable consisting of discrete categories (AGE_REC),
which we can then cross-tabulate with the variable BASEBALL.
Go to TRANSFORM/RECODE/INTO
DIFFERENT VARIABLES. In the "Recode" dialog box, highlight
AGE, and put it into the Input->Output list by clicking on the
little arrow. Name the output variable "AGE_REC" and label
it "age recoded". Click on the "change" button,
then click on the button that says "Old And New Values".
The "Old and New Values" dialog box pops up. For "Old
Value", check the "Range" bullet, and enter "1"
through "15". For "New Value", enter "1"
and click on the "Add" button. For "16 through 35"
enter "2" and click on the "Add" button. In
a similar fashion, you recode the values "36" through
"99" into the new value "3". Then click on the
"continue" button. Then click the "OK" button.
A new variable
is added to your Data View. Since the new variable does not have
value labels yet, we go into Variable View and enter the value labels.
In the values cell, we assign the label "1-15"
to the value "1", the label "16-35" to the value
"2", and the label "36-99" to the value 3. In
the decimals cell we reduce the number of decimals to zero.
This makes it easier to read the data.
Now we can run
To do that,
go to ANALYZE/DESCRIPTIVE STATISTICS/CROSSTABS. For "Rows"
enter BASEBALL, for column enter AGE_REC. Click the "OK"
In the Output
Viewer, you can now see the results of your analysis (Figure
18). From the table you can see that the older the respondents
get, the less likely they are to answer the question "Do you
love baseball?" with "yes." Also, fifty percent of
those who answered "no", are in the 36-99 age bracket.
You could read this as tentative evidence for the hypothesis that
love for baseball varies with age.
Note that the
total number of cases included in the crosstabulation is 18, even
though we interviewed 20 individuals. The reason for this is that
two respondents refused to answer the question, and we coded that
refusal as a missing value, to be excluded from statistical analyses.
18: AGE_REC and BASEBALL crosstabulated
files:Standard DOS format that can be imported by other software
delimited: A special free-format data file where each variable
is separated by a comma.
format: Data for each variable are arranged in a specific
column and row of the data file.
- Free format:
Data for each variable are arranged and identified by their relative
position with the other variables.
variables: Data that are recorded in a numeric format (i.e.
variables: Data that are not recorded in a numeric format
Programming statements and commands which guide SPSS operations.
exellent and detailed introduction to SPPS 10.0, please consult
the SPSS tutorial, which you can find on the SPSS help menu.