4. Ownership history:
Between 2009 and 2010, the premier vendor for SPSS was called PASW
(Predictive Analytics Software) Statistics. The company announced on
July 28, 2009 that it was being acquired by IBM for US$1.2 billion.[2] As of
January 2010, it became "SPSS: An IBM Company". Complete transfer of
business to IBM was done by October 1, 2010. By that date, SPSS: An IBM
Company ceased to exist. IBM SPSS is now fully integrated into the IBM
Corporation, and is one of the brands under IBM Software Group's
Business Analytics Portfolio, together with IBM Cognos.
5. Statistics program:
SPSS (originally, Statistical Package for the Social Sciences)
was released in its first version in 1968 after being developed
bynoH. Nie and C. Hadlai Hull. SPSS is among the most widely
used programs for statistical analysis in social science. It is
used by market researchers, health researchers, survey
companies, government, education researchers, marketing
organizations and others. The original SPSS manual (Nie,
Bent & Hull, 1970) has been described as one of "sociology's
most influential books
6. 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. Today we will be working on
SPSS 16.0.
7. How to open SPSS
Go to START
Click on PROGRAMS
Click on SPSS INC
Click on SPSS 16.0
8. Opening a data file
Click on FILE OPEN DATA
Click MY COMPUTER LOCAL DISK C:/
Click PROGRAM FILES SPSS
Click TUTORIAL SAMPLE FILES
Select CATALOG.SAV
9. Basic structure of SPSS
There are two different windows in SPSS
1st – Data Editor Window - shows data in two forms
Data view
Variable view
2nd – Output viewer Window – shows results of data
analysis
*You must save the data editor window and output viewer window
separately. Make sure to save both if you want to save your changes in
data or analysis.*
10. 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 satisfaction)
11. 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 indicates
that a value is
system-missing
by one period in
the data cell.
12. Cleaning your data – missing data
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…
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 want
to use half of a scale.
13. 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 data in
data view
14. 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 list.
15. Descriptive Statistics continued
Lets learn more about the number of
catalogs mailed (mail).
Click ANALYZE
Click DESCRIPTIVE STATISTICS
Click DESCRIPTIVES
Move MAIL 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 not always
make sense for every variable. For example, we should not be asking
for the mean of nominal variables like gender or race.
16. 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 graphs
into word and excel files.
17. Graphing Continued
There are other ways to make
graphs.
Click ANALYZE
Click DESCRIPTIVE STATISTICS
Click FREQUENCIES
Click services
Click CHART
Click BAR CHART
Click PERCENTAGES
18. Data manipulation – select cases
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
19. 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 are for
catalogs which mailed over 10,000 catalogs.
20. 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.
What did you find?
21. Data manipulation – recode a variable
Recoding allows a researcher to create a new variable
with a different set of parameters
click TRANSFORM
click RECODE INTO DIFFERENT VARIABLE
move mail over to
the right
create a name for
the new variable
mailcategories
click OLD AND
NEW VALUES
22. Data manipulation – recode a variable cont.
click RANGE to
create ranges of
old values
click VALUE to
create a new
value for that
range
23. Data manipulation in action!
Try recoding another variable on your
own.
Try finding the descriptive statistics of
your new variable.
24. Data manipulation – create a dummy variable
Dummy variables is a variable that has a value of
either 0 or 1 to show the absence or presence of some
categorical effect
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
click VALUE to set new
value to 0 or 1
25. What we have learned!
SPSS at a glance
Basic Structure of SPSS
Cleaning your data – missing data
Descriptive Statistics – frequencies,
descriptive statistics
Charts
Data manipulation – select cases,
recoding, dummy variables