An Introduction to SPSS

An Introduction to SPSS
Source: Johan Smits
Saxion Market Research

What is SPSS?
“Statistical Package for the Social
Sciences”
It is a software used for data analysis in
business research. Can be used for:
Processing Questionnaires
Reporting in Tables and Graphs
Analyzing: Means, Chi-square, Regression, …
and much more..

About SPSS Incorporated
SPSS Inc. is a leading worldwide provider
of predictive analytics software and
solutions.
Founded in 1968, today SPSS has more
than 250,000 customers worldwide,
served by more than 1,200 employees in
60 countries.

SPSS is now owned by
IBM
It is also known by the name PASW (Predictive
Analytics Software)

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.[3]
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.

We already know that a Research
Process consists of:
Problem definition
Research objectives
Desk Research
Field Research
Qualitative
Quantitative: constructing a questionnaire
Collecting and Analyzing data
Writing and Presenting the final research report

Translate the Questionnaire into
codes and enter data in SPSS
Questions in the questionnaire are mapped
into Variables in SPSS
SPSS comes into picture after data has
been collected by lets say: questionnaires

Important factors to consider before data
entry into SPSS
Question response formats
Scale characteristics
Levels of measurement

Question-response formats can be of the
following types:
Closed-Ended
Open-Ended with numerical response
Open-Ended with text response
Multiple response questions

Convert all these formats into numeric or
string (alphabet) data for entering into
SPSS..

Examples
Response-format :: Closed-Ended
How is your satisfaction with the customer
service of the staff of Suxes?
O Excellent
O Good
O Bad
O Very bad

Coding the answers
1 = Excellent
2 = Good
3 = Bad
4 = Very bad

Response-format :: Closed-Ended
11. Please indicate your gender.
O Female
O Male
Codes:
1 = Female
2 = Male

Open-ended with numerical response
What is your average expenditure in the
restaurant on a weekly basis?
……… euro per week
For how many years have you been
registered as a student at Pandion
University?
……… year(s)
Enter these types of data
As it is….

Open-ended with text response
I would like to have the assortment
extended with the following products:
…………………………………………
Processed by
 Coding manually afterwards or
 Typing the answers literally (text
variable)

Scale characteristics are of three types in
SPSS:
(Description)
(Order)
(Distance)
Nominal
Ordinal
Scale (also called as
interval or ratio)
Levels of Measurement

Coding data into the SPSS
Convert Questions  Variables
Name of the variable
Variable label
Value labels (data codes)
Level of measurement (Measure)

Some snapshots of the SPSS window:

The SPSS Data Editor
Data View

Variable View

Variable view
Name
Type (Numeric)
Label
Values (= the codes of the answers)
Measure (= Level of Measurement)

SPSS Menu’s
Analyze
Frequencies
Cross tabs
Tables




SPSS Menu’s
Graphs
Bar
Pie
Histogram
Line
Boxplot

SPSS Output
Separate file in Output Viewer
Inline Editing of Tables
Chart Editor for Graphs
Don’t forget to save
Data file
Output file

Part 1: Descriptive Statistics
PASW Statistics 17
(SPSS 17)
ITS Training Program
www.youtube.com/mycsula

Agenda
Manipulating Data
– Selecting Cases
– Splitting the File
Using Find and
Replace
– Finding Data
– Replacing Data
Reporting
– Copying and Pasting
into Word
• Introduction
– Research Stages
– Opening PASW
• Creating a Data File
– Defining Variables
– Entering Data
• Running Descriptive
Statistics
– Frequency Analysis
– Crosstabs

What is PASW?
Predictive
Analytics
Software

What is Statistics?
Statistics is a set of mathematical
techniques used to:
• Summarize research data.
• Determine whether the data supports the
researcher’s hypothesis.

Research Stages
1. Planning and Designing
2. Data Collecting
3. Data Analyzing
4. Data Reporting

Format of Questions
Fixed Response Open-Ended Response
e.g.
PROs
CONs
Easy to enter Easy to construct
Difficult to construct
Difficult to enter
Invalid responses
What is your gender?
a. Female b. Male
What is your gender?
( _____________ )

Running Descriptive Statistics
How to analyze data.
Descriptive
statistics are used for
summarizing
frequency or
measures of central
tendency.
Are the most
commonly used
statistics.

Frequency Analysis
Frequency shows the number of occurrences.
Also calculates measures of central tendency,
such as the mean, median, mode, and others.

Research Question #1
What kind of computer do people prefer to own?

Crosstabs
Crosstabs are used
to examine the
relationship between
two variables.
It shows the
intersection between
two variables and
reveals how the two
interact with each
other.

What color do people prefer for their computer?

Improving Your Survey
What color do you like to have for your computer?
1. Beige 2. Black 3. Gray 4. White 5. Other _______

Selecting Cases
Filter out and
specify which
variable to use
for analysis with
the select
cases function.

Splitting the File
The split file function is used to compare the
responses or performance differences by groups
within one variable.

Is computer color preference different
between genders?

Part 2: Test of Significance
PASW Statistics 17
(SPSS 17)

Purpose of This Workshop
To show how PASW Statistics can help
interpret results obtained from a sample
and make inferences about the population.
SAMPLE POPULATION
Is it statistically significant?

Agenda
Using Null Hypothesis
Running Tests of Significance
Correlations
Paired-Samples T Test
Independent-Samples T Test
Running Multiple Response Sets
Frequency
Crosstabs
Merging Data Files

• A null hypothesis (H0) is a statistical
hypothesis that is tested for possible rejection
under the assumption that it is true.
• The purpose of most statistical tests is to
determine if the obtained results provide a
reason to conclude whether or not the
differences are the result of random chance.
• Rejection of H0 leads to the alternative
hypothesis H1.
Null Hypothesis

Null Hypothesis
The significance level (α) sets the
standard for how extreme data must
be before rejecting the H0.
To reject H0, data must meet a
significance level (α) of 0.05.
α = 0.05 means data would have
occurred by chance at most 5% of
the time.

• If p-value (sig.) ≤ α, then reject H0.
– Statistically significant
• If p-value (sig.) > α, then fail to reject H0.
– Statistically non-significant
Hypothesis Testing
Take note that the result is always stated in
relation to the null hypothesis, not the alternate.

Correlations
No Relationship
Y
X
Negative Relationship
Y
X
Y
X
Positive Relationship
A correlation is a statistical device that measures
the nature and strength of a supposed linear
association between two variables.

Correlation Coefficient
r = +
0.0 to 1.0
Direction
Magnitude
The strength of the linear relationship is
determined by the distance of the correlation
coefficient (r) from zero.

Is there a relationship between academic
performance and Internet access?
H0 = Internet access made no difference
H1 = Internet access made a different

Is there a relationship between academic
performance and Internet access?

T test
A T test may be used to compare two group
means using either one of the following:
• Within-participants design (a Paired-Samples
T Test)
• Between-participants design (an Independent-
Samples T Test)

Is there an instructional effect taking
place in the computer class?
H0: Instruction made no difference
H1: Instruction made a difference

Is there a difference in the average number of
seedlings grown in the light and those grown in the
dark?

Independent-Samples T Test
The first set of hypotheses is testing the variance,
while the proceeding set is testing for the mean.
The variances have to be equal before we can
determine if the means are equal.
H0: (µ (light) ≠ µ (dark)
H1: (µ (light) ≠ µ (dark)
H0: Variance (light) = variance (dark)
H1: Variance (light) ≠ variance (dark)

Is there a difference in the average number of
seedlings grown in the light and those grown in the
dark?
H0: No difference whether grown in the light or dark
H1: A difference when grown in the light versus dark

Running Multiple Response Sets
Multiple response sets are used when
respondents are allowed to select more than
one answer in a single question.
By running a frequency analysis, the result
provides an overall raw frequency for each
answer.
Crosstabs can also be used to examine the
relationship between the sets and other
variables.

Merging Data Files
Useful for users who store each of their topics in
separate files, and eventually need or want to
combine them together.
This allows users to import data from one file
into another.
Both sets of data (from each file) must contain a
common identifier for each of the cases that the
user wishes to combine.
An identifier identifies the correlating cases from
the additional data files.

Part 3: Regression Analysis
PASW Statistics 17
(SPSS 17)

To show users how PASW Statistics can
help in answering research questions or
testing hypotheses by using regression.
To provide users with step-by-step
instructions on how to perform regression
analyses with PASW Statistics.

Agenda
Using Simple
Regression
Scatter Plot
Predicting Values of
Dependent Variables
Predicting This Year’s
Sales
Using Multiple
Regression
Predicting Values of
Dependent Variables
Predicting This Year’s
Sales
Transforming Data
Computing
Using Polynomial
Regression
Regression Analysis
Editing Charts
Adding a Line
Manipulating X & Y Scales
Adding a Title
Adding Colors
Background Color

What Is Linear Regression?
Linear: Straight line.
Regression: Finds the model that
minimizes the total variation in the data
(i.e., the best fit).
Linear Regression: Can be divided into
two categories:
Simple regression
Multiple regression

What Is Polynomial
Regression?
Polynomial: A finite length expression
constructed from variables and constants.
Polynomial Regression: A special type
of multiple regression used to determine
the relationship between data (e.g., growth
rate, progression rate).

Dependent and
Independent Variables
Variables can be classified into two categories:
independent and dependent variables.
An independent variable is a variable that
influences the value of another variable.
A dependent variable is a variable whose
values are influenced by another variable.
This is influence, not cause and effect.

Scatter Plot
Before performing
regression, users need
to determine whether
a linear relationship
exists between the two
variables.
A scatter plot allows
users to examine the
linear nature of the
relationship between
two variables.
• If the relationship
does not seem to be
linear, then the result
may be a weak
regression model.

Scatter Plot
Create a scatter plot to determine if a
linear relationship exists between variables.

Using Simple Regression
Estimates the linear relationship between one
dependent (Y) and one independent (X) variable.
Linear Equation: Y = aX + b
 a: Slope of the line
 b: Constant (Y-intercept, where X=0)
 X: Independent variable
 Y : Dependent variable
Since we already know the values of X and Y,
what we are trying to do here is to estimate a
(slope) and b (Y-intercept).

Using Multiple Regression
Estimates the coefficients of the linear
equation, involving more than one
independent variable.
For example, users can predict a
salesperson’s total annual sales (the
dependent variable) based on independent
variables, such as age, education, and years
of experience.

Using Multiple Regression
Linear Equation: Z = aX + bY + c
 a & b: Slope coefficients
 c: Constant (Y-intercept)
 X & Y: Independent variables
 Z: Dependent variable

Computing
Most data transformations can be done with the
Compute command.
Using this command, the data file can be
manipulated to fit various statistical performances.

Using Polynomial Regression
Variable Meaning
a Constant
bj
The coefficient for the
independent variable to the j’th
power
ei
Random error term

Editing Charts
Adding a Best Fit Line at Total

Editing Charts – Manipulating Scales

Editing Charts – Title and Gridlines

Editing Charts – Adding Colors

Part 4: Chi-Square and ANOVA
PASW Statistics 17
(SPSS 17)

To show how PASW Statistics can help
answer research questions or test
hypotheses by using the Chi-Square test
and ANOVA.
To provide step-by-step instructions on how
to perform the Chi-Square test and ANOVA
with PASW Statistics.
To show how to import and export data
using Microsoft Excel and PowerPoint.
To show how to use scripting in PASW
Statistics.

Agenda
Using Chi-Square Test
Testing for Goodness-of-Fit
Using One-Way ANOVA
Using Post Hoc Tests
Using Two-Way ANOVA
Importing/Exporting Excel Spreadsheets
Using Scripting in PASW Statistics

 It analyzes data in order to examine if a
frequency distribution for a given variable
is consistent with expectations.
 Chi-Square test for Goodness-of-Fit
test: estimates how closely an observed
distribution matches an expected
distribution.
Using Chi-Square Test with Fixed
Expected Values

Weight Cases
Before a Chi-Square test is run, weight cases
should be used to identify and let PASW
Statistics know what the observed values are.

Using Chi-Square Test with a Contiguous
Subset

Using One-Way ANOVA
ANOVA: Analysis Of Variance.
One-Way ANOVA can be thought of as a
generalization of the pooled t test.
Produces an analysis for a quantitative
dependent variable affected by a single factor
(independent variable).
Instead of dealing with two populations, we
have more than two populations or treatments.

Using Post Hoc Tests
The null hypothesis in
ANOVA is rejected
when there are some
differences in μ1, μ2, …,
μx.
But to know where
specifically these
differences are, the
post hoc test is used.

Using Post Hoc Tests
LSD stands for List Squared Difference.

Using Two-Way ANOVA
A Two-Way Analysis of Variance
procedure produces an analysis for a
quantitative dependent variable affected
by more than one factor.
It also provides information about how
variables interact or combine in the effect.
Advantages:
More efficient
Helps increase statistical power of the result

Importing/Exporting Data
Data can be imported into PASW Statistics from
an Excel spreadsheet.
Data can be exported from PASW Statistics into
an Excel spreadsheet, PowerPoint slides, etc.

Using Scripting in PASW Statistics
Used to capture commands that are used
repeatedly.
This function simplifies working with
multiple analyses on a consistent basis.
Can use different data files as long as the
variables in the commands always have
the same name.

An Introduction to SPSS

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