SPSS for Windows
Office of Information Technology (OIT)
Written by: William Dardick
SPSS for Windows
i. Data Editor
2. The Basics
b. Tool Bar
c. Opening Files
d. Saving Files
3. Working with Data
a. Date Entry
b. Data Transformation
c. Data Set Manipulation
4. Analyze Data
a. Descriptive Statistics
b. Compare Means
Introduction to SPSS version 13 (this document still holds for most of v 15)
The objective of this course is to teach the user basic knowledge of SPSS version 13 in a
windows setting. Most tasks in SPSS can be accomplished through the use of the pull-
down menu. The basic functions of SPSS will be focused on, such as entering or
importing data, creating variables, and transforming and analyzing data. For more
information on SPSS for windows please use the HELP menu or refer to the detailed
User and Applications guides created by SPSS for version 13 (SPSS Base Applications,
Base user, Regression Models, Interactive Graphics, Advanced Models). Your are also
well advised to consult statistical texts for questions regarding analysis.
The majority of work that will be done in SPSS for windows will be performed in the
Data Editor and the Viewer/Output Windows. SPSS syntax will not be focused on in this
course but a brief overview will be given to help build on future programming. It is not
necessary to know syntax to use SPSS for windows.
Dialog boxes, small windows without menus, will pop up when most menu functions are
used. Understanding these boxes will aid in the successful use of SPSS.
Bellow is the Data editor with the Open File dialog box. This is what the general
framework of SPSS for windows will look like.
When working with SPSS the two most important windows to understand are the Data
Editor and the Viewer.
The Data Editor looks like most other spreadsheets you may have encountered. It is
designed specifically for transforming and sorting data. When a data file is opened the
title of the Data Editor window will change to the files name. The Data Editor is where
you create new files or import existing files.
The Viewer is the output window for SPSS. It displays tables, charts and other graphics
that have been created through SPSS. Output in the Viewer window can be edited with
cut, paste and other common Edit options from the Edit menu. If print is selected from
the Viewer window the entire content of the window will print. You may select to print
specific sections by highlighting the output desired.
The Viewer window allows cutting and pasting of tables and charts to help assist you in
developing a report of results.
There are two ways to work in SPSS. This course focuses on the windows pull-down
version and will not be dealing with writing Syntax. Syntax is programming in SPSS. The
Syntax window allows you to save and write syntax commands.
To open a new Syntax file click on File, New, Syntax. To open existing Syntax files go
to File, Open, Syntax. The Open File will appear. Make sure Files of Type has
Quick Guide for learning Syntax
If you need to learn syntax for some procedure that you have performed in SPSS for
windows, you can always open up the SPSS journal. It keeps a record of everything you
have done during this session in syntax.
Go to the C drive open the Windows folder then open the temp folder. Type *.jnl
in the file name and open the SPSS folder. Copy and paste the syntax into a new
Another way to start learning syntax as you use the windows format is to set up your
output to display syntax.
The options Dialog window has several tabs. Select Draft Viewer. Check the
Display commands log. Click the OK button at the bottom of the box. Your
output will now display Syntax commands.
This option is particularly useful for learning data manipulation and analysis syntax.
The Main Menu
The Main Menu is located at the top of the window underneath the Title of the page. This
menu has a variety of functions.
The File menu gives access to open or close files, save files, change page set up or print
the information in the file being used.
The Edit menu has typical cut and paste commands
The Data menu can change the way your cases and variables are organized. You can
split, sort, and merge files in this menu.
Transform is a useful menu used to create new variables from old variables or
manipulate existing variables.
Analyze is the statistical menu. There are dozens of statistics to choose from in this menu.
T tests, Correlations and Regressions are just a few of your many options.
The Graphs menu can show data in a visual form. Some of its options are bar, line and
Utilities is a menu that hold information about the file or variables
The Window menu allows you to switch from one window to another
Help is a very useful menu for people learning SPSS or having difficulty with analysis. It
hold the table of contents, tutorials and Coaches for statistics and results.
The Tool Bar can be an easy way to perform many functions in SPSS. Becoming familiar
with the main menu is important, but there are some things that will be much quicker and
easier to do by selecting an icon on the tool bar.
Here are the standard Icons for the tool bar in SPSS Data editor.
To Open a file use the folder icon. It will bring up the open file dialog box.
To Save a file use the disk icon.
To Print the information in the window select the printer icon.
To Recall a dialog box, click the mini dialog box icon.
Undo is the curved arrow which points to your right, it takes you back one action.
Go to Chart selects a chart if available.
Go to Case allows you to select a case.
Variables allows you to locate, and gives you the information on a given variable.
Find allows you to find the next given score in a data set.
Insert Case allows you to insert a case into the data set.
Insert Variable allows you to insert a variable into the data set.
Split File gives you different ways to split the data file and to still perform analyses on
both sets of data.
Weight Cases gives cases different weights for statistical analysis
Select Cases can set up subgroups of cases
Value labels can assign descriptive values to each value in a variable.
Use Sets can restrict which variables are displayed in the dialog box.
Opening Data Files
Opening data in SPSS has been made relatively simple. By selecting:
you can easily access SPSS files.
The Open File dialog box opens and is set default to open SPSS(*.sav) data files.
You can open spreadsheets into SPSS with the Open File dialog box such as Excel or
Lodus with out complications. Txt/dat files are easy to open as well. The file wizard
opens here for a clear guide to import data.
Text files can be imported by selecting the text file desired. The Text Import Wizard will
appear and will assist opening the file. Understanding the format of the file is important
for the file to be properly read by SPSS. The file will be displayed at the bottom of the
When the Text Import Wizard opens the first page will ask if your file matches a
predetermined format. If you have previously saved a format from the text wizard and
wish to use the same format you would select this option and search for your format. To
create a format for you text select No and follow the steps presented by the Text Wizard
by selecting the Next option.
The Text Wizard will now walk you threw steps 2-6. If your variables are separated by
commas, Tabs, etc… select the Delimited option. If variables are aligned with fixed
columns use the fixed width option. Make sure to answer yes to the second question if
you have already named your variables in the text file.
The text file is previewed at the bottom of the window to assist with selecting options.
To open a database Select:
The Data Wizard window will appear and assist with the opening of the database.
Opening database files may require driver installation (if not already installed) or Login-
ID and Passwords.
Transferring Files from Access to SPSS.
Data base Wizard dialog box opens.
SELECT Add Data Source.
Dialog box odbc administrator opens
SELECT User DSN Tab
SELECT Add… button.
SELECT the MA Driver (*.mdb), Double click
Name the data source and describe it.
Database can help you find the location of the file. Select… button will open files.
Search the directories for access files.
SELECT a file and SELECT ok
The link should now be present under user data sources in the User DSN Tab. SELECT
You should now be back at the Data Wizard.
SELECT your newly created source.
SELECT next to choose which tables to add.
Drag the tables to add into the retrieve fields…
Limit Retrieved Cases if desired.
Define your variables.
Check your results and Select Finish.
SPSS requires a data set. If you are not importing data from another source or a previous
SPSS session, you will need to create a new data set. When your data set is complete or
you simple want to stop and come back to the same data later, you will need to save your
file. Saving a data editor file in SPSS. Go to:
The Save Data As dialog box will open. To save as a regular SPSS data file simple type
in the File name and click Save. It will be saved as a SPSS (*.sav) file.
You can also select other types of files to save the data.
The save as dialog box will open
Go to Save as Type and select another type of file to save the data in. If you know the
files ending, such as (*.xls) for excel you can simple type this at the end of your file
name. EX. SPSStestfile.xls
To save Output in the Viewer files first make sure you are in the viewer window.
Then you can save the same way as you did with the data editor file. Naming the output
will keep it from being saved as a default file name of Output#. The type of file should be
Viewer Files (*.spo)
Entering Variables and New Data
Starting with a brief understanding of variables can be helpful when working with a data
set. What is a variable? One of the most basic concepts to understand in research is that
of variables. A variable is a construct, characteristic, or property, capable of taking on
different values. Variables are traits that can vary from person to person or thing to thing.
“A variable is a symbol that can be replaced by any one of the elements of some specified
set. The particular set is called the range of the variable.”
(William Hays Statistics for the social sciences 1973)
The sets, scores, numbers, or values of a variable can be of two types; Discrete or
continuous. A discrete variable can only have a finite set of scores. Continuous variables
are ordinal, interval or ratio in value. There is some measurable difference between cases
in this type of variable.
The most basic distinction drawn between variables is between dependent and
independent variables. When a researcher manipulates a variable it is said to be the
independent variable. The variable to be measured in the experiment is the dependent
variable. Another way of stating the difference between the two variable types is that the
independent variable is the cause and the dependent variable is the effect. In many
analyses the distinction can be seen through symbols. X is considered the independent
variable and Y is the dependent variable. They are also referred to as X being the
prediction variable and Y being the predicted variable.
Getting Started with Variables
Before entering your data into the Data Editor it is advised that you define your variables.
Defining Variables can be done rather effectively by going to the bottom of your data
Editor Page and selecting the Variable View Tab. You will see at the top of the window,
Name, Type, Width, etc…
Select on the first line and enter the variables name into the Name column. Variables
names can only be eight symbols long and cannot have a blank space.
By double clicking under the Type column, you can change the type of variable. A dialog
box will open that will allow you to select the variable type. The two most common types
are string and numeric. A string variable is qualitative and typical a word, such as a
persons name, religion or country. Numeric allows a variable to be used in all
transformations and analysis. Other types of variables, such as Date or Dollar can be
generated through this window.
The Width allows you to change the width of columns in the data set. SPSS has a default
of eight pixels.
You can select to place you will have Decimals round. The default for SPSS is two
decimal places for a numeric variable.
Labeling your variables is very important in research. Knowing what the variables name
stands for is not always intuitive. The Label option allows for detailed description of a
variable. This is particularly useful when more then one person are working on a data set
or there will be long periods of time between use of the data set. Going back to a data set
a year later can be frustrating if you did not label the original variables.
Values can be given to variables in order to compute information from what would
normally be a string variable. For example: One of your variables is gender but you
would like to do computations with gender as a numeric value. Select a value for Male
and a separate value for Female. In this case, Female will be 1 and Male will be 2. Under
the Values column in the variable view, select the variable you are going to use, in this
case Gender and select the Values box that references this variable. When the Values Box
for Gender is selected, double click the box with three dots that appears in the Values
column. A dialog box called Value Labels pops up. Select the box for Value and type in
1. Next to Value Label type ‘Female’. Select Add. Now when ever you have a 1 for this
variable it will add the value label Female. Do the same thing for Male with the value of
2 and select add. Select OK. Value labels have been added. Toggle to the data view and
select the Values Label Icon on the menu bar. You can select and deselect that icon to
display and remove value labels.
The variable view also can be used to indicate Missing vales. Select the Missing box for
the appropriate variable. Double click the grey box with three dots. The Missing Values
dialog box opens. Three options are given, no missing, select three missing values, or
select a missing value range plus one discrete value. The three discrete values can be any
numeric value, and string value of 8 characters or less or a blank string value. Select the
Discrete missing values button. Type in the appropriate value(s) into the box(s), select ok
and that value will be used as missing for all computations. You can use missing values
in conjunction with value labels to keep track of why a value is missing.
To change Columns size, either directly edit the number displayed or use the up or down
buttons displayed when the columns box is selected.
Alignment of text can be changed to left, right, center.
Scales of measurement (Measure)
Measurement allows you to pick from Scale (interval and ratio), Ordinal, and Nominal.
Nominal data is grouping data, ordinal data is ranked or ordered without even intervals
and scale data has equal intervals regardless of an absolute zero.
Scales of Measurement explained
When a number or name is assigned to an observation it becomes some form of
measurement. The type of measurement system used for observations can dictate what
types of statistical analysis can be performed on the data. In statistics there are four
separate scales of measurement.
The nominal scale is qualitative. Categories formed are mutually exclusive and
exhaustive. This means that no observation can fall into more then one category and that
there most be enough categories to include all of the observations. The type of variable is
considered categorical because it forms categories. The value difference between the
variables can not be said to be meaningful. The difference between genders is not
quantifiable. The qualities in the two genders may be different but they cannot be
When determining if a scale of measurement is nominal think about the
differences between values in a variable. If the differences are not able to be quantified or
have no meaningful mathematical distinction, you are probable working with a nominal
scale. Qualitative differences like location, m and m color, type of coffee, or movie, all
have differences that aren’t measurable. When comparing these groups we are doing little
more then naming them and using and putting observations in these named placeholders.
How these qualities are named is only important for categorical purposes. We can label
our m and m’s with numbers as long as there is the understanding that the numbers are
there to identify them. If we label m and m’s, 1 for red, 2 for green, and 3 for brown, it
does not mean that brown m and m’s have some greater value, or that the average of a red
and brown m and m is a green one. Qualitative values should never be manipulated in
this way when labeling them with numbers.
This does not mean that there are no ways to interpret the values of such
categorical data. There are statistics that are created for the soul purpose of analyzing
nominal data. As we will see later statistics such as chi-square can be used to interpret
The ordinal scale is quantitative. It has mutually exclusive categories that are
ranked in order of magnitude. When we rank order a variable we can tell some sort of
quantitative difference between variables, however, there is nothing specified in the
difference between scores to state that the difference between scores is equal. In a race,
the difference between first and second place, and third and forth place do not need to be
equal. They are simply ranked. First place is the fastest, then second then third. It doesn’t
matter if first and second almost tied and third was far behind them.
When data can be ranked, but values are not equal between intervals, as well as
having the properties of the nominal scale (mutual exclusive and exhaustive) it is ordinal
data. Think of your favorite foods, pick 5 and place them in order, the first one being
your absolute favorite food and the 5th being the least of your favorite foods. For
example, I might order mine, steamed crabs (1), barbeque ribs (2), clams casino (3),
Szechwan shrimp (4), and pizza (5). Now when I look at my list of foods I can’t say
exactly how much more I like steamed crabs then barbeque ribs. By adding pizza and
Szechwan shrimp I don’t get the value of clam’s casino. There may be a large gap
between steamed crabs and barbeque ribs, but almost no difference between the ribs and
the clams. These differences between values help me to place them in a greater then less
then order but it does not allow me to say anything quantitative about the magnitude
(size) of difference between scores.
Ordinal data can be discrete or continuous. Nonparametric statistics are useful for
Rank ordering can be informative, but sometimes you need to know something
more about your data. The interval scale has all of the properties of the ordinal scale as
well as having equal distance between scores. This means that the difference between any
two variables next to each other on the scale will be equal. The difference between 10
degrees Celsius and 11 degrees is the same as the difference between 100 and 101
degrees. The interval scale gives meaning to the magnitude between values.
The ratio scale has all of the properties of the interval scale but also has absolute
zero. This absolute zero means that with a rating of zero there is a complete absence of
this measurement. A zero on the Celsius scale does not mean you cannot have a colder
temperature. Kelvin temperature and weight in pounds are examples of absolute zeros. If
you have a weight of zero there is an absence of weight. Absolute zero also gives the
scale another quality. When you have 25 pounds and 50 pounds you can say 50 pounds is
twice as heavy as 25 pounds. You cannot say that 10 degrees Celsius is twice as could as
20 degrees Celsius.
A true zero means that there is no quantity of some particular trait. If you are
weightless in space your weight is zero. There can be a height of 10ft 1000ft and 0ft. If
you can remove all value from a measure and twice the value of one score means that it
has twice the quantity, then you are working with a ratio scale.
SPSS works like most spread sheets. To start entering data into SPSS go to your first
variable and first case. Type the datum into the outlined box. To move to the next case in
the same variable you may select enter or the directional arrow. To move across to
another variable select the directional arrow. You may always use the mouse to select any
box in the spreadsheet.
Now try opening a File. It will appear as an SPSS(*sav) File. To open:
Or you can use the open folder icon on the Tool Bar.
With a new file open you can edit the data simple by typing in new cases or variables into
the spreadsheet. If data in an old variable or case needs to be replaced simply select the
datum in question and type in a new value.
The transform option in the main menu is a very useful tool for data analysis. This option
allows for the creation of new variables or the transformation of old ones.
The Compute option allows for a multitude of transformations. Select the Compute
option from the Transform menu. The Compute Variable dialog box will appear on the
screen. It will look something like this:
Under Target variable you can create a new variable that is 8 characters or less. By
Clicking on the Type $ Label button you can give the variable Type and Definition as you
would when creating a variable in the data editor. All of your existing variables are listed
under the Type & Label button.
The dialog box has a calculator and a list of functions which can be used to compute new
Anxiety + Tension
This adds two variables together that are known to the data set to create a new variable.
This multiplies the variable by 10 and then subtracts 100.
Functions can be very useful in transforming your data into a new useful Variable.
Mean(test1, test2, test3)
This function would average across variables and compute a new variable that was the
mean for each case.
Count returns a one for an occurrence of a value and a 0 for the absence of the value in
the selected variable.
The recode option can transform variables into the same variable or a new variable.
Recoding into the same variable
This option allows you to eliminate the old variable and replace it with a new recoded
variable. To use this option select recode, into same variable, under the transform menu.
The dialog box will have the variables in your data set on the left. Simple select the
variable to recode and click on the arrow button. After selecting your variable click the
Old and New Values button, this will bring up a new dialog box to specify recoding.
Recoding into the different variable
The main difference with coding variables into different values is that you will retain the
original values in a separate variable. You must name the new variable, similar to the
compute function, and label the variable. Selecting the Old and New values button will
bring up a dialog box that will work in much the same way as it did in the Recoding into
same variable option.
Data set manipulations
The data set that you have may need to be manipulated in order for you to work with the
data. The Data menu allows several types of useful manipulations.
Splitting a data set can be accomplished by selecting the Data menu and clicking on
Split_File. The dialog box for split file appears. If you select compare groups, groups
will be split as directed by the grouping variable. You can use the organize output by
groups option to view the results of any procedure separately for each group.
Inserting new variables/cases can be accomplished through the data menu or by right
clicking on the mouse when selecting variables/cases.
You can choose to Sort cases under the Data menu by selecting this option. Choose from
ascending and descending order to sort your cases on a variable selected.
Merging files can be accomplished by selecting Merge Files under the data menu. You
can choose to merge variables or cases. Select a new SPSS file to merge with the current
Transposing variables and cases may come in handy for certain research questions or
when transferring data into SPSS from another system. Under the Data menu select
Transpose. A dialog box will appear and give options for selecting variables to be
SPSS offers powerful statistical procedures in a relatively easy to use framework. This
course will provide an introduction to some of these analyses. It is assumed that some
base level of knowledge in statistics is known prior to this course. A Statistic should be
used only when the researcher performing the analysis understands the analysis being
performed. Poor understanding of statistical techniques often leads to incorrect
interpretation of the results they yield.
This section begins with an introduction to basic descriptive statistics.
The middle road
Central tendency is the middle of the road score. The one typical score used to represent
the total scores is the central point. There are three methods to derive the central score of
a set of values; mode, median and mean.
The mode is the most frequently occurring value. You might use this for a quick look at a
data set to get a rough estimate of central values. Just by looking at a frequency
distribution you can determine the most frequently occurring value. It is possible to have
more than on mode for a distribution. When there are two modes the distribution is said
to be bimodal. As a measure of central tendency the mode is very limited for descriptive
purposes. Even thought the mode is the most stable measure of central tendency when
data is skewed, it is also the most susceptible to sampling error and variation, more so
then the other types of central tendency.
The median is the middle most score. When data is severely skewed you can get a good
measure of central tendency with the median.
The mean is the arithmetic average. It has the greatest amount of reliability. There will be
less variability from sample to sample amongst means then amongst other forms of
central tendency. Using the arithmetic Mean allows for the use of a wide variety of other
statistical applications. The mean is the most sensitive measure to outliers and
The mathematical definition of the mean is the sum of all the scores divided by the
number of scores.
X represents any score, Σ is the symbol for “the sum of”, N is the total number of scores
being summed for the set of values.
Effects of mode, median, mean when data is skewed. Mode stays constant, median stays
relatively stable, and the mean is most affected by skewed data and outliers. In the
theoretical normal curve all three of these measures will share the same value.
Measures of dispersion
A variable has scores that are likely to change. The characteristics of a single variable can
be as diverse as the number of subjects measured. When you take into account that there
can be thousands of variables measured from a single population or sample, each with its
own dispersion, it is often not satisfactory to simple calculate the mean and look at
If we take two samples from a population, both with 10 people in each sample, and both
with a mean score for height of 70 inches, we could conclude that these samples where
equal. However, if we look at the raw scores we may not have come to the same
conclusion. The first sample that was pulled from the population consists of 10 subjects,
each 70 inches tall. The second population is more diverse: 50, 55, 63, 68, 72, 74, 76, 78,
79, and 85.
Deviation Scores ∑(X − X ) = 0
Sum of Squares SS x = ∑ X − X ) 2
∑(X − X )
Variance S 2 =
∑(X − X )
Standard deviation S =
( Q3 − Q1 )
The Range measures the difference between the minimum and maximum scores.
The Analyze option on the main menu has many statistics that can be preformed on your
data. One of the most common options for the Analyze menu is the Descriptive
Statistics procedure. By opening up the Analyze menu you can select Descriptive
Statistics. Under this option you have four types of analyses that can be performed.
Looking at raw data can often be confusing. There is not much that can be said with
certainty by looking at raw unordered scores. The larger the data set the more difficult it
is to speak meaningfully about it. Today more so then any other time in history we have
hug data bases of data. Data bases in the millions are common place. The near future will
bring us into the billions as common place and on its coat tails the trillions. What if a data
base kept track of each order at every super market in the world? The data would only
stay in the Billions if it was tracked by person, and not order, the number of visits would
soon reach into the trillions. In the United States alone we could have 100 million people
stop into the super market three times a week for a year. Quickly summing up these
values, 3x52x100 million, we get 15.6 billion visits to the supper market that year.
Imagine the data set when the number of people doing something world wide is in the
billions and they do this several times a week or day.
Frequency distributions are one way of organizing data so observations about the data set
can be made. The frequency distribution shows the possible scores and the number of
observations for each score. When working with a large distribution it is sometimes
beneficial to group scores and look at the grouped distribution.
Select the first analysis labeled Frequencies. A dialog box will pop up with the heading
In the left box all of your variables from the data set will be present. By selecting one or
more of these variables and clicking the directional arrow shown in the window you can
place them into the Variable(s): section of the window. The variable is now ready to be
analyzed. Before doing so other options available to you in the frequencies window need
to be discussed.
Select Display frequency tables. This will provide a frequency table in your output or
Viewer window. On the bottom of your dialog box there are three options: Statistics,
Charts, and Format. Select Statistics. The Frequencies: statistics dialog box will open.
The box is composed of several sections.
First is the Percentile Values section. There are several options to divide data to obtain
specific percentages above and below specific points. The Quartiles option will divide the
output into 4 equal groups. Cut points allows for equal groups to be obtained other then
four. Individual percentiles can be selected by typing in the appropriate percentile in the
Percentile(s) option. Select Quartiles.
Central Tendency has four selections, Mean, Median, Mode and Sum.
The Desperation section is for measuring the amount of variance in the data set.
The Values are group midpoints selection is used only if the data is midpoint coded. This
selection will give estimates for the median and percentiles as if the data was ungrouped.
Distribution is tested by two statistics, Skewness and Kutosis. Skewness gives a measure
of symmetry for the data. Kurtosis measures to see if its peak is normal. In both cases a
normal distribution will have 0 Skewness and Kurtosis. A significant positive skew
indicates a long right tail in the distribution. When a data set is skewed negatively it has a
long left tail. A positive measure of kurtosis indicates clustering in the center with longer
tails than a normal distribution. A negative kurtosis has shorter tails and cluster less then
the normal curve.
When finished selecting statistics for the frequencies analysis click continue.
Now you are ready to run the analysis. Select OK.
The output will now appear in the Viewer window.
Next under the Descriptive Statistics option is Descriptives. By selecting descriptives
you will open a dialog box. The box is set up very similar to the frequency dialog box.
The main difference is the Options button.
The Display Order is the only new option in Descriptives. This option allows you to
display the order of the descriptives in the output as you choose.
Explore can be used to run descriptive statistics on data that is grouped. The Dependent
List holds the variables you want to explore. The Factor list creates the groups.
The option are not very different then the other descriptive options.
The Crosstabs option allows you to count the frequencies that occur in any number of
cells within a larger table. One of the options in the statistics dialog box allows you to
compute a chi-squared along with the analysis.
There are some other useful analysis procedures that can be run through Explore.
The next selection in the Analyze menu is Compare Means. Select Compare means.
There are three types of t tests to choose from as well as a means option and One-Way-
ANOVA. The type of research comparison will determine what type of t test should be
performed. We will focus on t tests.
The one sample t test is used to compare the means of one variable to a predetermined
test value either known or hypothesized. The objective with a one sample T test is used to
see if a single variable differs from some specified constant. The T test is used with a
single mean and is non-directional. The statistics will state difference between the mean
and the constant.
Select One sample T Test.
The One-Sample T Test window will appear on the screen. Select the variable to be
analyzed and set the test value for which you are testing the variable. The options button
will allow you to change the Confidence Interval from 95 to whatever value is desired.
Missing values allows for the removal of cases for each variable measured or for
Click OK to run the T-test.
Other T tests follow a similar pattern for analysis.
The independent-samples T Test will compare means for two groups. This analysis
should be used with two separate groups that have been randomly assigned.
This time go to the Analyze menu…
Compare Means and select independent-samples T Test.
The window that appears is very similar to the One-Sample T test window.
The main difference here is the grouping variable section. The grouping variable should
be dichotomous or categorical as to divide the cases in the variables into groups. The
options for this T test are the same as for the others.
A Paired-Samples T Test is used when there are two variables for the same group being
compared. This test computes the difference between the two values to see if there is a
significant difference from a score of zero. This is often the appropriate test for a one
group pre-post test design.
t-test in more detail
There are three types of t-tests; independent, dependent, and single sample.
The t distribution is related to the z distribution.
X − µx X − µx
n( n − 1)
X − µx
n( n − 1)
The independent t-test is used when you have two separate groups that you want to
compare. The Best example of this t-test is the true experiment. Does the mean of one
variable in one condition differ from a variable in another condition.
(X −Y )
SS x + SS y 1 1
( nx − 1) + ( n y − 1) nx n y
( X − Y ) − (u x − u y ) hyp
SS x + SS y 1 1
( nx − 1) + ( n y − 1) nx n y
The next type of analysis that will be discussed is the correlation.
Under the Analyze menu open the correlate option and choose bivariate.
The bivariate correlation dialog box will open. Several options in the main dialog box
will be presented to you. Correlation Coefficients give you three options; Pearson,
Kendall’s tau-b, and Spearman. If the variables you are correlating are continuous and
have a normal distribution the Pearson correlation coefficient can be selected.
Test of significance gives a two tailed and one tailed option.
Flag significant correlations is a good option to help select relationships that are
At the bottom of the dialog screen is an option bottom. When opened a new dialog box
will appear. Selecting the Statistics (means and SD, Cross products) allow you to
compute other information with the correlation that will appear in the viewer window.
Missing Values gives options for handling missing data.
Partial correlations are computed the same way except they allow you to control the
influence of other variables on the variables being correlated.
Equations for correlations
Y = A + BX
∑ ( X − X )(Y − Y ) r=
N ∑ XY − ( ∑ X )( ∑ Y )
( SS ) ( SS )
x y [N ∑ X 2
− (∑ X ) N ∑Y 2 − (∑Y )
A simple linear regression is similar to a correlation, except it has the advantage of
prediction. The regression statistic can be computed by selecting Analyze, Regression,
Linear. Selecting a variable to place in the Dependent box and one or more variables to
be placed in the Independent Box can now perform a linear regression. The statistics
option can help in the selection of output for the regression.
The Statistics option allows you to select what you want to appear in the output Viewer.
Nonparametric tests and other Analyses
Nonparametric tests can be invaluable when variables do not fit the normal curve or in
some other violate assumptions of parametric statistics. Probable the most frequently
used nonparametric test is the Chi-Square.
A Chi-square test answer questions about data in the form of frequencies. It compares the
values observed to vales that would be expected. Ex. If 50% of your population is male
and 50% is female you expect a sample to have the same approximate frequency. If your
sample of 100 people yielded 75 female and 25 male you could test this using chi-square.
To perform the Chi-squared analysis, select Analyze from the main menu, go to
nonparametric tests and select Chi-squared. Select your test variable. Compute by
There are many other analyses available to you on SPSS for windows. Understanding the
proper use of the statistical technique that is being used is important when performing
research any type of research. Statistics such as Multiple Regression, Discriminate
analysis and Factor Analysis, need to be understood before being used. If you do not
understand the analysis on the way in, you will not understand the results when they
Raw data can often be confusing and hard to manage. Being able to view the patterns of
your data is helpful both with determining a type of analysis to use and in interpretation
of the data set. Graphs are useful as a visual toll for the researcher and for the target
audience of the researcher.
One of the easiest graphs to develop is the bar graph. SPSS provides three basic types of
bar graphs: Simple, Clustered and Stacked.
A simple bar graph can be obtained by selecting Graph and selecting Bar. The Bar
Charts dialog box will open. There are three types of bar charts to choose from: simple,
clustered and stacked. Select Simple and click the define button.
Line charts are similar to Bar charts.
Area charts shades the area under the graph.
Pie charts divide the variable into slices.
High low charts display several variables with error bars.
Select Bar from the Graph Menu
Select Simple and click define
Basic Bar: place the variable of interest in the Category axis
Select pie from the graph menu.
Select summaries for groups of cases and click define.
Basic Pie: place the variable of interest in the Define slices by box
Select Histogram from the graph menu.
Basic Histogram: Add variable of interest to the Variable box.
Check the Display normal curve box.
Select scatter from the graph menu.
Select simple scatter and click define.
Select a variable for the X axis and another one for the Y axis
One of the most useful features on SPSS is the Help menu. You can gain access to some
very powerful learning tools by clicking on the Help option on the main menu. Every
window in SPSS has a Help menu. When using Help from a main widows (data editor,
viewer, syntax) access to several options will be available. The first four options in the
help menu can help you get started in SPSS and further your abilities with the product.
The first option available in the Help menu is Topics. By selecting Topics in the Help
menu you gain access to Content, Index, and Find Tabs. Content allows for selection of
Help topics. Index provides a search option as well as browse option topics. The Find tab
can look for instances of the word, or part of word that is typed in the window.
The second Help option is the tutorial. It is set up like the Topics window except the
content section has tutorials to help you use SPSS.
The Statistics Coach option can help walk you through analysis steps and Help in
selecting the appropriate statistic. As with any analysis if you are uncertain of the correct
analysis to use check with an expert. Remember that the accuracy of a statistic relies on
the proper information being run.
You may need the CD-ROM to access Syntax Guide in the Help menu. This Help tool
can be useful if you ever need to write syntax in SPSS.
SPSS is available on-line and can be reached be clicking on the SPSS Home Page option
in the Help menu. The URL for SPSS is http://www.spss.com/
Results Coach can be accessed on the viewer window by double clicking on a chart or
table and selecting Results Coach from the Help menu. If you are having difficulty
interpreting your data this may be of assistance to you. If you are still uncertain of your
results and how to interpret them please consult with an expert.
Help can be used during any analysis or procedure in SPSS that opens up a new window.
This will open up a window with help on the procedure you are currently working on.
You are now ready to start exploring SPSS.
Restructuring Your Data
Version 11 of SPSS has a very useful tool for the restructuring of data. Under the data
menu, select restructure. The restructure data wizard will open. This new wizard is
convenient to windows users. Before this option was created the only two ways to
restructure your data was to write a syntax code or to manually manipulate the data in the
The restructure wizard allows for most types of data restructuring. The first option
available is to stack variables.
The second option is to split existing variables into multiple variables.
The last option is to transpose variables. This option takes you to the transpose option
under the data menu.
The analyze menu has a report option that can be used to simplify data and provide
There are several options to get information about variables extracted from SPSS. First
You can use the File information option. Select Utilities, File Info will be in this menu,
Select this option. The SPSS Viewer will display All file information about variables.
This report can be cut and past into Microsoft word or excel for editing.
The second option for extraction variable information is to simple go to the variable view
tab and cut and past information into a spread sheet like excel.
You can also use the display Data Info… option under File. This does the same thing as
File information, except you can specify information on unopened files.