Data Analysis using SPSS: Part 1

Basics of Research and Statistics
Introduction to SPSS
Data Management using SPSS
Data Analysis using SPSS
Taddesse Kassahun
Email: tadestat@gmail.com
Cellphone: +251911835082
Department of Statistics
Addis Ababa University
Taddesse Kassahun Data Analysis using SPSS 1 / 135

Part One

Contents
1 Basics of Research and Statistics
Research Problems
Variables
Research Hypotheses and Questions
Collect the Data
Methods of Data Presentation
2 Introduction to SPSS
Start and Exit SPSS
SPSS Windows
Help in SPSS
The SPSS Menus and their Use
3 Data Management using SPSS
Entering Data into SPSS
Importing and Saving Data in SPSS
Editing Data
Data Rearrangement

Research Problems
Variables
Collect the Data
Research Problems
A research process begins with a problem of interest to
the researcher.
A research problem is a statement that asks about the
relationships between variables.
Statistical methods can be used to indicate solutions for
such research problems using:
What kind and how much data need to be collected?
How should data are organized and summarized?
How can we analyze data and draw conclusions from it?
How can we assess the strength of the conclusions and
evaluate their uncertainty?

Research Problems
Variables
Collect the Data
Variables
A variable is a characteristic of the participants or
situation for a given study which takes different values.
A variable must be able to vary or have different values.
Sex is a variable: it has two values (female or male).
Age is a variable: it has a large number of values.
Level of statistics knowledge is a variable (none, average,
sufficient).
In quantitative research, variables are divided into two:
independent variables and dependent variables.
SPSS uses the term values to describe the several options
or values of a variable.

Research Problems
Variables
Collect the Data
Variables ...
For example, a study is performed to investigate the eﬀect
of a treatment (one group is assigned to treatment group
while the other group doesn’t receive the treatment)
The independent variable (treatment type) has two values
or levels (treatment and no treatment).
Suppose now that we are interested in comparing two
treatments and include a third no-treatment group as a
control.
The study still has one active independent variable
(treatment type) but with three values or levels.
Variable = Treatment type; Variable values: 1 =
Treatment 1, 2 = Treatment 2, 3 = Control

Research Problems
Variables
Collect the Data
Variables...
Dependent variable is assumed to measure or assess the
eﬀect of an independent variable(s).
Dependent variables are often test scores, factory outputs,
ratings on questionnaires, readings from instruments, etc.
SPSS uses other terms for the dependent variable:
Dependent list, Test variable.
Dependent or independent variable can be either
qualitative or quantitative.

Research Problems
Variables
Collect the Data
Hypotheses and Questions
Research hypotheses are predictive statements about the
relationship between variables.
Research questions do not entail specific predictions and
are phrased in question format.
Example.
Question: Is there a difference in students’ scores on a
standardized test if they took two tests in one day versus
taking only one test on each of two days?
Hypothesis: Students who take only one test per day will
score better on standardized tests than students who
take two tests in one day.
Research questions can be difference, associational, and
descriptive.

Research Problems
Variables
Collect the Data
Statistics
The two broad areas of statistics to deal with research
questions: descriptive and inferential statistics.
Descriptive statistics is devoted to the summarization and
description of data.
Descriptive statistics presents data using graphs,tables,
and numbers such as averages, measures of variation, and
percentiles.
Inferential statistics consist of methods for drawing
generalizations and predictions about population based on
information obtained from a sample of the population.
The two primary methods for making inference are
estimation and hypothesis testing.

Research Problems
Variables
Collect the Data
Inference Process

Research Problems
Variables
Collect the Data
Scales of Measurement

Research Problems
Variables
Collect the Data
Statistics for diﬀerent types of research question

Research Problems
Variables
Collect the Data
Group comparison questions
Usually used for Randomized Experimental and
Comparative Approaches.
Values or categories of the independent variable are used
to categorize the participants into groups (e.g., high and
low).
Example. Do persons with low and high anxiety differ on
their average grades?
That is, will the average GPA of the high anxiety persons
be significantly different from the average GPA for low
anxiety persons?

Research Problems
Variables
Collect the Data
Associational Questions
Used for the Associational Approach, in which there are
two or more variables.
The scores on the independent variable are associated
with or related to the dependent variable scores.
Is the type of relationship positive or negative? How
strong is such relationship?
Example. Will students’ degree of anxiety be associated
with their overall GPA?
That is, will knowing students’ level of anxiety tell us
anything about their tendency to make higher versus
lower grades?

Research Problems
Variables
Collect the Data
Descriptive questions
Used for the Descriptive Approach.
For this type of question, scores on a single variable are
described in terms of their central tendency, variability, or
percentages in each category/level.
Example. What percentage of people in Addis Ababa
can utilize smart phones? What is the average time a
person wait for taxi in Arat Kilo to go to Piassa?

Research Problems
Variables
Collect the Data
Role of Statistics in Research

Research Problems
Variables
Collect the Data
Basic concepts
Survey - a method of data collection with no special
control on one or more of the factors aﬀecting variable of
interest.
Two types of surveys: census (complete enumeration)
and sampling.
The two situations where census is mandatory:
If information is needed from each and every unit of the
study population ;
If a study is on rare events.
Sampling Unit: an element or a set of elements
considered for selection at some stage of sampling, e.g.,
animals, persons, households, etc.

Research Problems
Variables
Collect the Data
Basic concepts . . .
Sampling Frame: listing of all the things or sampling
units that makes up a given population.
Sample Design: A set of rules or procedures that
specify how a sample is to be selected.
Sampling Error: the diﬀerence between the true
population value and the statistic.
It can be minimized by increasing the size of sample.
Non-sampling Error: errors occurring due to data are
incorrectly collected, recorded or analyzed.
It may happen both in census survey and sample survey.

Research Problems
Variables
Collect the Data
Basic concepts . . .
Planning a research design is the foremost task before we
select the data collection instrument(s).
Select, modify or develop a data collection instrument:
questionnaires,structured interviews,
observations,standardized inventories, etc.
Pilot test and reﬁne instruments: It is desirable to try out
our instrument and directions with, at the very least, a
few colleagues or friends.
We should check our raw data after we collect it even
before it is entered into the computer.
We then assign numbers to the values or levels of each
categorical variable.

Research Problems
Variables
Collect the Data
Data in raw form are usually not easy to use for decision
making.
Data can be presented using
Table (of counts, percentages). It can be one way,
contingency or multi-way in general.
Chart (bar – simple, clustered,stacked; pie)
Graph (histogram, frequency polygons, line plot, stem
and leaf plot)
Statistical quantities such as mean, range, standard
deviation, etc.
The type of diagram/ graph to use depends on the
variable being summarized.

Research Problems
Variables
Collect the Data
Describing Data Numerically

Research Problems
Variables
Collect the Data
Statistical Estimation
Estimation is the process of determining a likely value for
a variable in the population based on information
collected from the sample.
Estimation is the use of sample statistics to estimate
population parameters.
Interest usually lies in looking at estimates of many
statistics — totals, averages, proportions etc. for diﬀerent
variables.
For example, a sample survey could be used to produce
estimates for the proportion of smokers among all people
aged 15 to 24 in the population.

Research Problems
Variables
Collect the Data
Point Estimate
A single numerical value used to estimate the
corresponding population parameter.

Research Problems
Variables
Collect the Data
Interval Estimate
An interval estimate provides how much uncertainty is
associated with a point estimate of a population
parameter.
An interval gives a range of values:
by taking into consideration variation in sample statistics
from sample to sample;
based on observation from 1 sample;
to provide information about closeness to unknown
population parameters.
It is stated in terms of level of conﬁdence.
If Pr(a < θ < b) = 1 − α, then the interval from a to b is
called a 100(1 − α)% conﬁdence interval of θ.

Research Problems
Variables
Collect the Data
Interval Estimate
The general formula for interval estimates (conﬁdence
intervals) is:
PointEstimate ± (Reliability Factor)(Standard Error)

Research Problems
Variables
Collect the Data
Interval Estimate
Conﬁdence intervals for µ and P:
σ2
known: Z is used as a test statistic.
σ2
unknown: t is used as a test statistic.

Research Problems
Variables
Collect the Data
Conﬁdence Interval for µ (σ2
Known)
Assumption:
Population is normally distributed.
Conﬁdence interval estimate is given by:
X − Zα/2
σ
√
n
< µ < X + Zα/2
σ
√
n
where Zα/2 is the normal distribution value for a
probability of α/2 in each tail.

Research Problems
Variables
Collect the Data
Conﬁdence Interval for µ (σ2
Unknown)
If the population standard deviation is unknown, we can
substitute it by the sample standard deviation (s).
This introduces extra uncertainty, since s is variable from
sample to sample.
So, we use the t distribution instead of the normal
distribution.
Assumption:
Population is normally distributed.
Conﬁdence interval estimate is given by:
X − tn−1,α/2
s
√
n
< µ < X + tn−1,α/2
s
√
n

Research Problems
Variables
Collect the Data
Confidence Interval for Population Proportion
Upper and lower confidence limits for the population
proportion are calculated with the formula
ˆp − Zα/2
ˆp(1 − ˆp)
n
< p < ˆp + Zα/2
ˆp(1 − ˆp)
n
where
Zα/2 is the standard normal value for the level of
confidence desired.
ˆp is the sample proportion
n is the sample size

Research Problems
Variables
Collect the Data
Interpretation of Confidence Interval
Suppose that a 95% confidence interval for the true
proportion of internet users in a city is:
0.16 < p < 0.33
An investigator is 95% confident that the true percentage
of internet users in the city is between 16% and 33%.
Although the interval from 0.16 to 0.33 may or may not
contain the true proportion, 95% of intervals formed in
this manner will contain the true proportion.

Research Problems
Variables
Collect the Data
Hypothesis Testing
A statistical hypothesis is a statement concerning the
probability distribution of population parameters that are
inherent in a probability distribution.
In a hypothesis testing problem, a null hypothesis (H0)
& an alternative hypothesis (H1) are formulated.
The null and alternative hypotheses should be mutually
exclusive.
H0 : Population proportion of people satisﬁed with the
service = 0.7
H1 : Not H0
A hypothesis may be one sided, e.g., H1 : P > 0.7 or
two sided,e.g., P = 0.7.

Research Problems
Variables
Collect the Data
Hypothesis Testing
Two types of errors, namely Type I and Type II error
may be committed in testing a hypothesis.
Type I error is the error of rejecting the null hypothesis
when it is actually TRUE.
The probability of committing Type I error is denoted by
α, also called level of signiﬁcance.
A Type II error is the error of failure to reject the null
hypothesis when it is FALSE.
The probability of committing Type II error is denoted by
β.
1 − β is called the power of a test.

Research Problems
Variables
Collect the Data
Outline for Hypothesis Testing
1 From the (word) problem, determine the appropriate null
hypothesis & alternative hypothesis.
2 Identify the appropriate test statistic (a sample statistic
upon which we base our decision) and calculate its value
from the data.
3 Find the rejection region by looking up the critical value
in the appropriate table.
4 Observe whether the computed value of the test statistic
lies within the rejection region. If so, reject the null
hypothesis; otherwise, do not reject the null hypothesis.
5 Interpret the results

Research Problems
Variables
Collect the Data
Hypothesis Testing Process

Research Problems
Variables
Collect the Data
Level of Signiﬁcance and the Rejection Region

Research Problems
Variables
Collect the Data
p-Value Approach to Testing
p-value: Probability of obtaining a test statistic more
extreme (≤ or ≥) than the observed sample value given
H0 is true.
Also called observed level of signiﬁcance.
Smallest value of α for which H0 can be rejected.
Convert sample result (e.g.,X) to test statistic (e.g., Z
statistic).
Obtain the p-value
For an upper tail test:

Research Problems
Variables
Collect the Data
p-Value Approach to Testing
For a lower tail test:
For a two tail test:
p−value = 2×min{Pr(Z >
¯X − µ0
σ/
√
n
|H0), Pr(Z ≤
¯X − µ0
σ/
√
n
|H0)}
Decision rule: Reject the null hypothesis if p–value < α.

Start and Exit SPSS
SPSS Windows
Help in SPSS

Start and Exit SPSS
SPSS Windows
Help in SPSS
What is a statistical package?
A computer program/set of programs that provides
different statistical procedures within a unified
framework.
Currently owned and developed by IBM corporation, SPSS
stands for ”Statistical Product and Service Solutions”.
SPSS is a very powerful and user friendly program.
It uses both a graphical and a syntactical interface.
SPSS can take data from almost any type of file and use
them to generate tabulated reports, charts, and plots of
distributions and trends, descriptive statistics, and
conduct complex statistical analyses.

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Starting SPSS
You may use any one of the following options to start
SPSS.
1 Go to the Applications folder, and select SPSS from the
list of programs (or Start ⇒ All Programs ⇒ IBM SPSS
Statistics ⇒ IBM SPSS Statistics 20).
2 Double-click SPSS shortcut icon on the desktop. (if
present).

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Starting SPSS
If you select Run tutorial and click on OK, the tutorial
window will be opened.

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Starting SPSS
If you select the Type in data and click on OK, the data
editor window will be opened.

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Starting SPSS
If you select Run an existing query and click on OK, the
following window will be opened.

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Staring SPSS
If you select Create new query using database wizard and
click on OK, the following window will be opened.

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Exit SPSS
To exit the SPSS program:
click on the close button at the top right hand side
corner, or.
click on the File menu and choose the option Exit.

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Help in SPSS
SPSS Windows
The most common windows in SPSS are:
Data Editor
Output Viewer
Pivot Table Editor
Chart Editor
Syntax Editor
The file extension for Data Editor window is *.sav
The file extension for output viewer is *.spv.
The file extension for syntax editor is *.sps.
There are short cut toolbars which provide quick and easy
access to different facilities on each window.

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Data Editor Window
The data editor window is a spreadsheet like window to:
create a new data file and enter data;
display the contents of the working data file;
make changes to the existing data files and run
statistical analysis.
There are two views in this window, namely data view
and variable view

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Data Editor Window . . .
Unlike most spreadsheets, the Data Editor can only have
one dataset open at a time.
You can open multiple Data Editors at one time, each of
which contains a separate dataset.
The Data Editor contains several menu items that are
useful for performing various operations on your data.
Data Editor containing an example dataset.

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Output Viewer Window
It displays the results of any statistical procedures you run
and other texts.
It has two panes: right-hand & left-hand.
Right-hand pane contains statistical tables, charts, and
text output.
Left-hand pane comprises a tree structure, which
provides an outline view of the contents.

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Pivot Table Editor Window
This window will allow you to edit the contents of a table
displayed by SPSS.
Double clicking a table on Viewer window results the
Pivot Table Editor window.

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Chart Editor Window
This window will help you edit charts produced by SPSS.
Double clicking a chart on the Viewer window gives this
window.
You can change the colors, select diﬀerent type fonts or
sizes, rotate axes, change the chart type, etc.

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Syntax Editor Window
Syntax is a computer code which produces a speciﬁc
output.
You may use this window if you wish to run SPSS
commands instead of clicking on the pull-down menus.
The Syntax Editor is recommended when there is a need
to repeat a procedure.
Use the normal SPSS menus to set up the basic
commands and paste them on Syntax Editor Window.

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Help in SPSS
Help is available in a number of diﬀerent ways such as:
Help menu. Every window has a Help menu on the
menu bar. The Topics menu item provides access to the
help system, where you can use the Contents and Index
tabs to ﬁnd topics. The Tutorial menu item provides
access to the introductory tutorial.

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Help in SPSS
Dialog box Help buttons. Most dialog boxes have a
Help button that takes you directly to a Help topic for
that dialog box. The Help topic provides general
information and links to related topics.
Statistics Coach. A wizard-like method for ﬁnding the
right statistical or charting procedure for what you want
to do.
Case Studies provides hands-on examples of how to
create various types of statistical analyses and interpret
the results.

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SPSS Menus
Menus are list of procedures that the user will find on top
of a computer screen.
The current version of SPSS has 12 main menus.
File ⇒ Edit ⇒ View ⇒ Data ⇒ Transform ⇒ Analyze
⇒ Direct Marketing ⇒ Graphs ⇒ Utilities ⇒ Add-ons ⇒
Window ⇒ Help.
File menu: to open, save, print and close files, provides
access to recently used files, etc.

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SPSS Menus
Edit menu: to do things like:
copy,
paste,
insert variables,
edit user preferences, etc.
View menu: to hide/ show:
the toolbar,
status bar,
gridlines, etc.
Data menu is useful to deﬁne variables and make
changes to the data ﬁle you are using.

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SPSS Menus
Transform menu is used to make changes to selected
variable(s) in the data ﬁle you are using. It includes
recoding existing variables & computing new variables.
Analyze menu is useful to perform statistical analyses
such as producing Reports, Calculating Descriptive
Statistics, as well as various statistical procedures such as
Regression and Correlation.
Direct Marketing menu provides tools designed to
improve the results of direct marketing campaigns by
identifying demographic, purchasing, and other
characteristics that deﬁne various groups of consumers.

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SPSS Menus
Graphs menu lets one make various types of plots from
a given dataset.
Utilities menu gives information about variables and
ﬁles.
Add-ons menu tells about other programs of the SPSS
family, such as Amos, Decision Time, Text Analysis,
services delivered, etc.
Window menu to manage SPSS window, split data, etc.
Help menu provides various help facilities.

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Typical SPSS Data Flow

Editing Data
Data Rearrangement
Working with the Data Editor
Data Editor window can be displayed in one of
the two views:
Data View
Variable View.
The Data View displays the contents of the
data file in the form of a spreadsheet.
Variable View defines all variables in data file.
Switching from one view to the other can be
done by clicking the appropriate tab (Data View
or Variable View). (see the picture on next
slide).

Editing Data
Data Rearrangement
Data Editor Window

Editing Data
Data Rearrangement
The Data View
To display the data & corresponding
variables.
Columns represent variables & rows
represent cases (observations).
Press Ctrl-Home to move to the ﬁrst cell.
Press Ctrl-End to move to the last cell.
Press Ctrl-Home again to move back to the ﬁrst cell.
Each cell in the grid contains the value of
one particular subject on one particular
variable.

Editing Data
Data Rearrangement
The Variable View
Allows the types of variables to be speciﬁed &
viewed.
Row is a variable and column is an attribute
associated with that variable
There are 11 characteristics to be speciﬁed in
the Variable View.

Editing Data
Data Rearrangement
The Variable View
1 Name: the chosen variable name.
It must begin with a letter and cannot end with a period.
It is inadvisable to end variable names with an
underscore.
Blanks and special characters (!, ?,” -,& and *) cannot
be used.
Each variable name must beunique, no duplications.
Reserved words:ALL, AND, BY, EQ, GE, GT, LE, LT,
NE, NOT, OT, TO, WITH cannot be used as variable
names.

Editing Data
Data Rearrangement
The Variable View
2 Type speciﬁes type of data such as numeric, dates,
currencies, etc. for each variable,
If you click on the Type column, the variable type sub
dialog box appears.

Editing Data
Data Rearrangement
The Variable View
Numeric: the data type is number, e.g., 20, 30.99, etc.
Comma: Numeric values with a comma used as the
grouping separator and a period used as decimal
indicator. For example, 1,234.56.
DOT: Numeric values with a period used as the grouping
separator and a comma used as the decimal indicator. For
example, 1.234,56.
Scientiﬁc notation: A numeric variable whose values
are displayed with an embedded E and a signed
power-of-10 exponent., e.g., 145 as 1.45E+002
Date: Values are displayed in one of several
calendar − date or clock − time formats.

Editing Data
Data Rearrangement
The Variable View
Date: A numeric variable whose values are displayed in
one of several calendar − date or clock − time formats.
The century range for two-digit year values (e.g., 1916 -
16 and 2016-16 )can be customized.
Choose Options from the Edit menu and then click the
Data tab.

Editing Data
Data Rearrangement
The Variable View
Dollar: A variable displayed with a leading dollar sign ($)
Custom currency: A numeric variable whose values are
displayed in one of the custom currency formats that you
deﬁned on the Currency tab of the Options dialog box.
String: A variable whose values are not numeric and
therefore are not used in calculations.

Editing Data
Data Rearrangement
The Variable View
3 Width: Number of character places. The default is
eight.
4 Decimals: number of decimals in case of Numeric type.
5 Label: descriptive name of a variable, e.g., a variable
name can be OX and the corresponding label can be
Number of oxen owned by a farmer.
These descriptive labels are displayed in output.
6 Values: these are labels attached to category codes.
E.g., {Female = 0; Male = 1}
To assign a label:

Editing Data
Data Rearrangement
The Variable View
enter the value in the value text box,
enter the label in the label text box, then click on Add
Repeat these steps until you deﬁne labels for all values
Finally, click on OK.

Editing Data
Data Rearrangement
The Variable View
7 Missing: defines specified data values as user-missing
For example, to distinguish between data that are
missing because a respondent refused to answer and
data missing because the question didn’t apply to that
respondent.
Data values that are specified as user-missing are flagged
for special treatment and are excluded from most
calculations
Steps to Define Missing Values
Select the cell which is the intersection of missing value
attribute and the variable you need
Click the wizard button

Editing Data
Data Rearrangement
The Variable View
The Missing Values dialog box will be opened.
you can set up to 3 discrete missing values
Or range plus one discrete missing value.
Finally click on ok

Editing Data
Data Rearrangement
Variable View
8 Columns: the width of the variable column in Data
View.
When the Width value is larger than the Columns value,
only part of the data entry will be seen in the Data View.
9 Align: to make alignment of variable entries (Left, Right
or Center).
10 Measure: assigns measurement scale of variables. The
default depends on data type (Scale or Nominal)
11 Role: to assign the role a variable plays.
Input - variable will be used as an input (e.g., predictor
or independent variable).

Editing Data
Data Rearrangement
Variable View
Target - The variable will be used as an output
or target (e.g., dependent variable).
Both - variable will be used as both input and
output.
None - variable has no role assignment.
Partition - variable used to partition the data
into separate samples for training, testing, &
validation.
Split - Included for round-trip compatibility with
SPSS Modeler.

Editing Data
Data Rearrangement

Editing Data
Data Rearrangement
Entering Data
Data can be either entered directly or imported from a
number of sources, such as spreadsheet or text file.
Entering data directly:
The data editor window looks like a spreadsheet where
numbers and text can be entered directly.
Open a new data sheet (Ctrl+n or File > New > Data)
Try entering some number in the first column.
Type what you want in each cell, and hit the Enter key.
Now try to put some text into the same column and
press Enter.
The text disappears since SPSS has identified the
column as numeric.

Editing Data
Data Rearrangement
Entering Data
Put some text in the next column.
Try entering numbers in this column. You can, but you
will not be able to do any calculations.
Your new variables have been given the names VAR00001
and VAR00002. You can change the names by clicking on
Variable View tab.

Editing Data
Data Rearrangement

Editing Data
Data Rearrangement
Importing Data
To import an existing SPSS data data file, use File >
Open > Data.
To import data from other formats: File > Open >
Data > Click on the ’Files of type’ dialogbox and
choose the format (Excel, DB, Text, etc).
You can also simply copy and paste the data cells from
Excel into SPSS but you will have to label the columns.
Check whether text files may be delimited or not, excel
files may have so many sheets, whether the first row may
or may not contain variable names.

Editing Data
Data Rearrangement
Importing Data
Importing Data from Excel Files: File > Open > Data
> Choose Excel as ﬁle type
Select the ﬁle you want to import. Then click Open

Editing Data
Data Rearrangement
Importing Data
If there are initial empty rows/columns in the spreadsheet,
define the cells where the data are stored in the Range:
field e.g., A1:B8 (see fig. below)
Import Data from CVS File: CVS is a
comma-separated values file.
File >Open >Data > Choose All files as file type
Select the file you want to import. Then click Open

Editing Data
Data Rearrangement
Importing Data
Text Import Wizard shown below will be open.

Editing Data
Data Rearrangement
Importing Data

Editing Data
Data Rearrangement
Importing Data
After importing data, it is good practice to
clean data (check any inconsistencies).
Use Analyze > Descriptive Statistics >
Frequencies for ech column.
Check the outputs to see if you have variables
with wrong and/ or missing values.

Editing Data
Data Rearrangement
Saving Data
To save data as SPSS file: Click on File> Save As >
Type in a suitable file name.
The Save as type should be set at SPSS (*.sav) option.
SPSS data can be saved as Excel file. File> Save As >
choose Excel from Save as type

Editing Data
Data Rearrangement
Printing a Dataset
Highlight the data that
will be printed. To print
all of the data, ignore this
step and continue to step
2.
Select Print from the File
menu. The Print dialog
box opens. Change the
options where appropriate.
Click OK.

Editing Data
Data Rearrangement
Deleting Entries
To delete an entry for a
cell, click on the cell and
press Delete.
Complete rows and
columns can be deleted by
clicking on the grey cell at
the top or left hand side
and pressing the Delete
key on the keyboard.

Editing Data
Data Rearrangement
Deleting Variables
Click on the top of the
column which contains the
variable to be deleted.
Hit the Delete key on the
keyboard or.
Right click at the top of
the column which contains
the variable to be deleted
and choose clear.

Editing Data
Data Rearrangement
Insert a Case (a row)
Click at the side of the
row below where you want
the new row to appear.
Use the insert cases icon
or
Right click at the side of
the row below where you
want the new row to
appear and use Insert
Cases.
Or use Edit > Insert
Cases

Editing Data
Data Rearrangement
Insert a Variable (a column)
Click on the top of the
column to the right of
where you want the new
column to appear.
Use the insert variables
icon or
Right click at the top of
the column to the right of
where you want the new
column to appear and use
Insert Variable.
Or use Edit > Insert

Editing Data
Data Rearrangement
Sorting Cases
SPSS can sort data by,
e.g., respondents’ date of
birth.
Go to Data > click on
Sort cases.
In the dialog box highlight
the respondent’s bdate
Click on the arrow to
transfer it to the Sort by
box.
Click on OK

Editing Data
Data Rearrangement
Sorting Variables
SPSS can sort variables
based on the 11
attributes, i.e, Name,
Type, etc.
Go to Data > click on
Sort Variables.
In the dialog box choose
the attribute.
Click on OK

Editing Data
Data Rearrangement
Splitting File
For analysis purpose, data
may be split into separate
groups based on the
values of one or more
grouping variables.
Go to Data > Split File
> Click on Compare
Groups.
Specify the grouping
variable, e.g., gender >
click on OK.

Editing Data
Data Rearrangement
Merging Files
Two possibilities for merging:
1 Merge files containing the same variables but different
cases (Add Cases)
2 Merge files containing the same cases but different
variables (Add Variables)
We use broadband 1 and broadband 2
datasets for illustration.

Editing Data
Data Rearrangement
Merging Files (Add Cases)
Have one of the files
open, and you can add
cases to it from an
external SPSS file.
Go to Data > merge
files > Add Cases....
Unpaired variables :
variables to be excluded
from the new merged data
file.

Editing Data
Data Rearrangement
Merging Files (Add Cases)
Variable from the working data file are identified with an
asterisk (*).
Variables from the external data file are identified with a
plus sign (+).
Variables in New Active Dataset: variables to be
included in the new merged data file. By default, all the
variable that match both name and data type are
included on the list.
If the same information is recorded under different
variable names in the two files, you can create a pair
from the unpaired variable list.
Select the two variables and click on Pair.

Editing Data
Data Rearrangement
Merging Files (Add Variables)
Have one of the ﬁles open.
Sort both data ﬁles using
the key variable (existing
in both).
Data > Merge Files >
Add Variables...
Choose the other dataset
from the dialog box and
click on Continue.
Click on Matched cases
on key variable ...
Transfer the key variableTaddesse Kassahun Data Analysis using SPSS 98 / 135

Editing Data
Data Rearrangement
Weighing Cases
Weight Cases gives cases diﬀerent weights for statistical
analysis.
The values of the weighing variable should indicate the
number of observations represented by single cases in the
data ﬁle.
Cases with zero, negative, or missing values for the
weighing variable are excluded from analysis
Consider No.ofcustomers as weight for the following.

Editing Data
Data Rearrangement
Weight Cases
We need to first create the three
variables.
To weight cases:Data >
Weight Cases > Weight
cases by
Select the weighting variable and
send to Frequency Variable: box
> Click on OK.
Once a weight is applied, it
remains in effect until another
weight variable is selected or
weighting is turned off.

Editing Data
Data Rearrangement
Selecting Cases
In some situations, we may want
to look at part of a dataset only,
for example, the data for females
or males only.
There are three options for
selecting cases.
If condition is satisﬁed
Random sample of cases
Based on time or case range

Editing Data
Data Rearrangement
Selecting Cases: If condition is satisﬁed
Click Data > Select Cases >
If condition is satisﬁed >
click on the if... button >
Click on the arrow button to
move the variable.
Click on the mathematical sign
(<, >, +, ∗, etc.) from the
keyboard displayed on the
screen.

Editing Data
Data Rearrangement
Selecting Cases: Random sample of cases
To select the actual sample: use
Data> Select cases>random
sample of cases
Click on the Sample button.
Fill out the dialog box
appropriately.

Editing Data
Data Rearrangement
Selecting Cases: Based on time or case range
Case ranges are based on row
number as displayed in the Data
Editor.
Date and time ranges are
available only for time series
data with defined date variables.
To define date: Data > Define
Dates > Choose the date
format
Use Data>Select cases based
on time or case range >
Range...

Editing Data
Data Rearrangement

Editing Data
Data Rearrangement
Aggregating Data
Aggregate Data combines groups of cases into
single summary cases and creates a new
aggregated data file.
Cases are aggregated based on the value of one
or more grouping variables.
The new data file contains one case for each
group.
One may aggregate country data by region and
create a new data file in which region is the unit
of analysis.

Editing Data
Data Rearrangement
Aggregating Data
Data > Aggregate...
Select a break variable that
deﬁnes how cases are grouped to
create aggregated data.
Select aggregate variables to
include in the new data ﬁle.
Select an aggregate function for
each aggregate variable (MEAN
is the default).

Editing Data
Data Rearrangement
Multiple Response Sets
If a survey question can be answered multiple valid times,
such as questions which note ”Check all that apply”
Multiple variables are necessary to capture all the
responses.
Multiple responses are used in telecom industries, e.g.
multiple lines,
voice mail,
paging,
internet,
call waiting,
call forwarding,
electronic billing, etc.
Many customers use more than one service.

Editing Data
Data Rearrangement
The variables of a multiple response set are coded as
dichotomies or categories.
Dichotomy: A separate variable is created for each of the
valid responses (alternatives) to the question (Y/N,
present/absent, etc.).
Category : A separate variable is created for each
response (from a number of possible responses) given by
the survey taker.
Example. Which of the following services do you
subscribe? Upto 3 possible answers
Voice mail, paging, internet, call waiting, call forwarding,
electronic billing.

Editing Data
Data Rearrangement
Example . . .
Dichotomy: SIX variables, i.e., voice, pager, Internet,
callwait, forward, ebill with values Yes/No are created.

Editing Data
Data Rearrangement
Example . . .
Category: THREE variables, i.e., Response1, Response2,
Response3 with values voice, pager, Internet, callwait,
forward, ebill are created.

Editing Data
Data Rearrangement
To deﬁne a multiple
response set (dichotomy):
Data > Deﬁne Multiple
Response Sets...
Select two or more
variables. Type 1[for YES]
as the counted value of
the dichotomies
Enter a unique name for
each multiple response
set.

Editing Data
Data Rearrangement
Click Add to add the
multiple response set to
the list of deﬁned sets.
Use Analyze > Tables >
Custom Tables
The name of multiple
response set should
appear at the bottom of
the Table dialogue box.
Place it in the row and
click on OK.

Editing Data
Data Rearrangement
It is also possible to create
multiple response sets
using the Analyze Menu.
Use Analyze > Tables >
...
Use Analyze > Multiple
Response > Deﬁne
Variable Sets ...

Editing Data
Data Rearrangement
Duplicate Cases
One of the data entry
errors is entering the same
case more than once.
This can be identified
using: Data > Identify
Duplicate Cases >
Move the variable(s) to
Define matching cases
by: box
You will have a new
variable which identifies a
duplicate.

Editing Data
Data Rearrangement
Unusual Cases
There is a possi-
bility of having one or more
extremely large or small value
in a given dataset.
This can be identiﬁed
using: Data > Identify
Unusal Cases > Move
the variable(s) to the
Analysis Variables: box
You can also add a Case
Identiﬁer Variable:

Editing Data
Data Rearrangement
Validation Rule
A rule is used to determine whether a case is
valid. There are two types of validation rules:
Single-variable rules consist of a fixed set of
checks that apply to a single variable, such as
checks for out-of-range values.
Cross-variable rules are user-defined rules that
can be applied to a single variable or a
combination of variables. Cross-variable rules
are defined by a logical expression that flags
invalid values.

Editing Data
Data Rearrangement
Validation Rule
You can quickly obtain a
set of ready-to-use
validation rules by loading
predefined rules from an
external data file included
in the installation.
To define validation rules
Data > Validation >
Define Rules...
Select Single-Variable
Rules> enter the
minimum and maximum
values > Click on OK.Taddesse Kassahun Data Analysis using SPSS 118 / 135

Editing Data
Data Rearrangement
Validation Rule
To validate the rule, Data
> Validation >
Validate Data...
Move the variable to the
Analysis Variables:box
> Click on
Single-Variable Rules
tab > Check on the box
Apply under the Rules:
box > Click on OK.

Editing Data
Data Rearrangement
Validation Rule
To deﬁne validation rules
Data > Validation >
Deﬁne Rules...
Select Cross-Variable
Rules> enter the rules >
Click on New to add
more.> Click on OK.
To validate the rule, Data
> Validation > Validate
Data...> Cross-Variable
Rules > Click on OK.

Editing Data
Data Rearrangement
Compute Variable
It is possible to derive a new variable from existing
variables, e.g. by summing the values for several variables
together.
It is also possible to change an existing variable, e.g. by
multiplying it by another variable.
Use Transform > Compute Variable
In the Target Variable: box enter the name of the new
variable to be created.
As an example, in the data health funding.sav, let us
create a variable which increases the funding by 10%.

Editing Data
Data Rearrangement
Compute Variable

Editing Data
Data Rearrangement
Counting Occurrence
It is possible to count the occurrences of the same
value(s) in a list of variables for each case.
For example, You might have a satisfaction survey that
contains a lot of questions with the same scale.
You can use this procedure to calculate the total number
of Very Satisﬁed responses a respondent gave across a
number of questions
Another example, a survey might contain a list of
magazines with yes/no check boxes to indicate which
magazines each respondent reads.
The number of yes/no responses for each respondent
can be counted to create a new variable containing total
number of magazines read.

Editing Data
Data Rearrangement
Counting Occurrence
Use: Transform > Count Values within Cases...
Enter a target variable name.
Select two or more variables of the same type.
Click on Deﬁne Values and specify which value(s)
should be counted.
Other options include specifying a range of values or
specifying missing values as the values to count.

Editing Data
Data Rearrangement
Counting
As an example, let us count the
number of Yes (Y’s)
Magazine1 Magazine2 Magazine3
Y N Y
Y N N
Y Y N
N Y N
N Y N

Editing Data
Data Rearrangement
Recoding
Two options available for
recoding variables.
Recoding into same variable.
Creating a new variable.
Recoding into ame variable
Use: Transform > Recode
into Same Variables, move the
variable to be recoded into
Variables box.
Click on Old and New Values.
Write the values and click on the
add button.

Editing Data
Data Rearrangement
Recoding into Diﬀerent Variables
Use Transform > Recode into
Diﬀerent Variables
Move the variable to be recoded
to Variables box
Write the name of the output
variable and its label.
Type old and new values.
Click on the add button.
Check the option Output
variables are strings if you would
like to have a string variable.
Click on continue Taddesse Kassahun Data Analysis using SPSS 127 / 135

Editing Data
Data Rearrangement
Automatic Recoding
Automatic Recode converts
string and numeric values into
consecutive integers.
String values are recoded in
strict alphabetical order, with
upper-case letters preceding
lower-case.
The resulting new variable
retains the variable and value
labels of the old variable from
which it has been recoded.
Transform > Automatic

Editing Data
Data Rearrangement
Visual Binning
Taking two or more values and
grouping them into the same
category.
Create a categorical variable
from a scale variable.
Combine several response
categories into a single category.
Use Transform > Visual
binning.
Select the variables to bin
Click on continue and write the
binned variable name.Taddesse Kassahun Data Analysis using SPSS 129 / 135

Editing Data
Data Rearrangement
Optimal Binning
Optimal binning is useful to
optimally bin scale variables
without specifying class number
or width.
The possible number of classes
and width are determined by
optimizing the scale variable
with respect to a nominal
variable.
Use Transform > Optimal
Binning

Editing Data
Data Rearrangement
Ranking Cases
Cases can be ranked using
Transform > Rank Cases...
Move the variable into
Variable(s) box
Move a variable to the option
By if more than one variable are
going to be considered.

Editing Data
Data Rearrangement
Date and Time Wizard
This wizard simpliﬁes a number
of common tasks associated
with date and time variables.
Calculating the diﬀerence
between two dates.
Extracting part of a date or time
variable
Unit to be extracted can be:
year, months, weeks, days,
hours, minute, second, etc.
Use Transform > Date and
time wizard

Editing Data
Data Rearrangement
Date and Time Wizard
Example. Calculate the number of days between Bdate
and Cdate.
Bdate CDate
25.10.1900 03.06.1915
11.05.1902 11.05.1917
09.06.1986 10.09.2005
11.07.1991 15.11.2012

Editing Data
Data Rearrangement
Replace Missing Values
Creates new variables from
existing ones by replacing
missing values with estimates
computed with one of several
methods.
Use Transform > Replace
Missing Values...
Select the variable that contains
missing value.
Chose one method of estimation
(e.g., mean of nearby points)
and click on OK

Editing Data
Data Rearrangement

Data Analysis using SPSS: Part 1

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Data Analysis using SPSS: Part 1