LINDSEY BREWER
CSSCR (CENTER FOR SOCIAL SCIENCE COMPUTATION AND RESEARCH)
UNIVERSITY OF WASHINGTON
September 17, 2009
Introduction to SPSS
Topics we will cover today
SPSS at a glance
Basic Structure of SPSS
Cleaning your data
Descriptive Statistics
Charts
Data manipulation
SPSS at a glance
SPSS stands for Statistical Package for the
Social Sciences
SPSS was made to be easier to use then other
statistical software like S-Plus, R, or SAS.
The newest version of SPSS is SPSS 17.0.
How to open SPSS
Go to START
Click on PROGRAMS
Click on SPSS INC
Click on SPSS 16.0
The computers in the CSSCR lab typically have SPSS
Opening a data file
Click on FILE
OPEN
DATA
Click MY COMPUTER
LOCAL DISK C:/
Click PROGRAM FILES
SPSS
Click TUTORIAL
SAMPLE FILES
Basic structure of SPSS
There are two different windows in SPSS
1
st– Data Editor Window - shows data in two forms
Data view
Variable view
2
nd– Output viewer Window – shows results of data
analysis
*You must save the data editor window and output viewer
Data view vs. Variable view
Data view
Rows are cases
Columns are variables
Variable view
Rows define the variables
Name, Type, Width, Decimals, Label, Missing, etc.
Scale – age, weight, income
Nominal – categories that cannot be ranked (ID number) Ordinal – categories that can be ranked (level of
Cleaning your data – missing data
There are two types of missing values in
SPSS: system-missing and user-defined.
System-missing data is assigned by SPSS
when a function cannot be performed.
For example,
dividing a
number by
zero. SPSS
User-defined missing data are values that the researcher can tell SPSS to recognize as missing. For example, 9999 is a common user-defined missing value. To define a variable’s user-defined missing value…
Cleaning your data – missing data
Look at your variables in VARIABLE
VIEW
Find the column labeled MISSING
Find the variable that you would like to
work with.
Select that variable’s missing cell by
clicking on the gray box in the right corner.
click DISCRETE MISSING VALUES enter 9999 to define this variable’s
missing value
A range can also be used if you only
Cleaning your data – missing data cont.
When you have missing data in your data set, you
can fill in the missing data with surrounding
information so it does not affect your analysis.
click TRANSFORM
click REPLACE MISSING
VALUES
select the variable with
missing values and move it to the right using the arrow
SPSS will rename and create
a new variable with your filled in data.
click METHOD to select what
type of method you would like SPSS to use when replacing missing values.
click OK and view your new
Descriptive Statistics
Lets say we are interested
in learning more about the
number of customer service
representatives (
service).
Click ANALYZE
Click DESCRIPTIVE
STATISTICS
Click FREQUENCIES
Choose
service
from the
Descriptive Statistics continued
Lets learn more about the
number of catalogs mailed
(mail)
.
Click ANALYZE
Click DESCRIPTIVE STATISTICS
Click DESCRIPTIVES
Move
over with the arrow
Click OPTIONS – we can choose which statistics we are
interested in looking at
We should remember that these descriptive statistics will
Graphing Data
Click GRAPH
Click CHART BUILDER
Click HISTOGRAM
Put MEN on the X axis.
Click ELEMENT PROPERTIES.
Check the box labeled DISPLAY NORMAL CURVE. This will
impose a normal curve onto your graph. You can also change the style of your graph in this
element properties window.
You can copy and paste these
Graphing Continued
There are other ways to
make graphs.
Click ANALYZE
Click DESCRIPTIVE
STATISTICS
Click FREQUENCIES
Click
services
Click CHART
Click BAR CHART
By selecting cases,
the researcher can
select only certain
cases for analysis
click DATA
click SELECT
CASES
click RANDOM
SAMPLE OF CASES
select your
preferences
Data manipulation – compute new
variable
Computing new variables – create
a new variable from multiple variables
click TRANSFORM click COMPUTE
fill in the new target variable
TOTALSALES
fill in numeric expression =
men+women+jewel
create an IF statement by
clicking on the IF button
click INCLUDE IF CASE
SATISFIES CONDITION
enter condition MAIL>10000
This new variable
TOTALSALES
tells us what the total sales
Data manipulation in action!
Try creating another variable for
TOTALSALES2
for catalogs which
mailed under 10,000 catalogs.
Try comparing the descriptive statistics
of
TOTALSALES
and
TOTALSALES2.
Recoding allows a researcher to create a new
variable with a different set of parameters
click TRANSFORM
click RECODE INTO DIFFERENT VARIABLE
Data manipulation – recode a
variable
move
over
to the right
create a name
for the new
variable
mailcategories
click OLD AND
click RANGE
to create
ranges of old
values
click VALUE
to create a
new value for
that range
Data manipulation in action!
Try recoding another variable on
your own.
Try finding the descriptive
Dummy variables is a variable that has a
value of either 0 or 1 to show the absence or
presence of some categorical effect
Data manipulation – create a dummy
variable
To create a dummy
variable…
click TRANSFORM
click RECODE INTO
DIFFERENT
VARIABLE
click OLD AND NEW
VALUES
click RANGE to
create range of old
values