The document introduces SPSS, a statistical software package used for complex statistical analysis across various fields including social science research, marketing, and education. It describes the basic functions of SPSS including data management, univariate analysis to examine one variable, bivariate analysis of two variables, and multivariate analysis of three or more variables. Finally, it provides examples of common statistical tests that can be run in SPSS like frequencies, chi-square, correlation, t-tests, ANOVA, and regression.

1. Introduction to
SPSS
BY
DR.S.MALATHY
ASSISTANT PROFESSOR,
DEPARTMENT OF COMMERCE,
PSG COLLEGE OF ARTS AND SCIENCE

2. SPSS
 SPSS - Statistical Package for the Social Sciences, and it’s used
by various kinds of researchers for complex statistical data
analysis.
 The SPSS software package was created for the management
and statistical analysis of social science data.
 SPSS is used by market researchers, health researchers, survey
companies, government entities, education researchers,
marketing organizations, data miners, and many more for the
processing and analyzing of survey data.

3.  The most obvious use for SPSS is to use the software to run
statistical tests. SPSS has all of the most widely used statistical
tests built-in to the software.
 Therefore, you won't have to do any mathematical equations
by hand. Once you run a statistical test, all associated outputs
are displayed in the data output file.

4. USES FOR SPSS
 Data management
 Defining variables
 Coding values
 Entering and editing data
 Creating new variables
 Recoding variables
 Selecting cases

5.  Data analysis
 Univariate statistics - This type of data consists of only one
variable.- frequency - Mean, Median, Mode, Standard
Deviation, Variance, Max., Min.,
 Bivariate statistics - This type of data involves two different
variables. The analysis of this type of data deals with
causes and relationships and the analysis is done to find
out the relationship among the two variables. – Chi square
test
 Multivariate statistics -When the data involves three or
more variables, it is categorized under multivariate (more
than two – independent variable and One – dependent
variable) – Multiple Correlation, Multiple Regression,
ANOVA

6. Level of Measures
 Nominal. A variable can be treated as nominal when its values
represent categories with no intrinsic ranking (for example, the
department of the company in which an employee works).
 Ordinal. A variable can be treated as ordinal when its values
represent categories with some intrinsic ranking (for example,
levels of service satisfaction from highly dissatisfied to highly
satisfied).
 Scale. A variable can be treated as scale (continuous) when its
values represent ordered categories with a meaningful metric, so
that distance comparisons between values are appropriate.
Examples of scale variables include age in years and income in
thousands of dollars.

7. Click “Analyze,” then mouse over “Descriptive Statistics” and then click
“Frequencies.”
Select one or more variables in the left column of the “Frequencies” pop up
window and then click the center arrow to move them to the right hand
“Variable(s)” window.
Click the “Display Frequency Tables” check box and then click “OK.” .
Frequency Table

8. Chi Square Test
 Apply to two Categorical Variable (Nominal)
 Example : Gender and Problem with bank
Hypothesis :
Null Hypothesis : There is no significant association/relationship between
independent variable (Gender) and dependent variable(Problem with bank)
Alternative Hypothesis : There is significant association/relationship between
independent variable (Gender) and dependent variable(Problem with bank)
Less than 0.05 – Rejected
Null Hypothesis and
accepted Alternative
Hypothesis

9. Correlation
 Apply to Continuous Variables (Scale)
 Example – Height and Weight of the respondents
Hypothesis :
Null Hypothesis : There is no relationship between Height and Weight
of the respondents
Alternative Hypothesis : There is relationship between Height and
Weight of the respondents Less than 0.05 – Rejected
Null Hypothesis and
accepted Alternative
Hypothesis
High degree: If the coefficient value lies
between ± 0.50 and ± 1, then it is said to be a
strong correlation.
Moderate degree: If the value lies between ±
0.30 and ± 0.49, then it is said to be a medium
correlation.
Low degree: When the value lies below + .29,
then it is said to be a small correlation.
No correlation: When the value is zero.

10. T-Test/ANOVA
 t-test
Apply to two group (Gender – Variable, Male and Female –
Groups)
Example :
Null Hypothesis : The satisfaction score do not differ between
male and female respondents.
 ANOVA
Apply to more than two group (– Variable, Male and Female –
Groups)
Example : The satisfaction score do not differ between married,
unmarried and others.

11. Regression
 Apply to two variable (Dependent Variable – Continuous Scale
and Independent Variable – Scale, Nominal or Ordinal)
 To find the effect of personal Factors (Independent Variable) on
satisfaction score (Dependent Variable)