This document provides an introduction to SPSS (Statistical Package for Social Sciences) software. It discusses the history and ownership of SPSS, its use as a statistical analysis program, and an overview of its basic functions. Key features covered include opening and managing data files, descriptive statistics like frequencies and charts, data cleaning techniques for handling missing values, and methods for data manipulation such as recoding variables and creating new computed variables. The goal is to provide readers with foundational knowledge on using SPSS for statistical analysis in the social sciences.

1. Introduction to SPSS
(Version 16)
OMID MINOOEE
MBA (ABM),second semester
Institute of development study
Mysore university

2. content:
Spss
SPSS at a glance
Basic Structure of SPSS
Cleaning your data
Descriptive Statistics
Charts
Data manipulation
Other Resources

3. spss
SPSS is a computer program used for survey authoring
and deployment (IBM SPSS Data Collection), data
mining (IBM SPSS Modeler), text analytics, statistical
analysis, and collaboration and deployment (batch and
automated scoring services).

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

26. Thank you