Introduction to SPSS Stats

Outline
• Introduction to SPSS
• Menu Interface
• Entering Data
• Reading Data
• Output Viewer
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SPSS Stats

INTRODUCTION
• SPSS is software for managing data and calculating a
wide variety of statistics
• It is frequently used in the social science.
• SPSS has three main windows, plus a menu bar at the top
• These allow you to (1) see your data, (2) see your
statistical output and (3) see any programming commands
you have written. Each window corresponds to a separate
type of SPSS file.
i. Data Editor (.sav files)
ii. Output Viewer (.spv files)
iii. Syntax Editor (.sps files)
SPSS Stats

MENU INTERFACE
• SPSS opens directly into an untitled Data Editor.
SPSS Stats
SPSS is software for managing data and
calculating a wide variety of
statistics
Data View – Used for entering, editing
and modifying data.
Very much like an excel spreadsheet

TOOLBAR
• File - Standard options for opening, saving, printing and
exiting
• Edit - Standard commands to undo, redo, cut, copy and paste
• View - Options for showing/hiding toolbars, displaying values
or their labels in Data Editor
• Data – Used to manipulate the data; sort, merge.. Etc
• Transform - Creation of new variables.
SPSS Stats

TOOLBAR (CONTINUE)
• Analyze - Heart of SPSS. This menu provides access to the statistical
procedures for analysing your data set. All the items on the analyze
menu have sub menus.
• Graphs - Provide options to create high quality plots and charts.
• Utilities - Used to display information on individual variables.
• Window - Provides option for switching between different SPSS
windows
• Help – Contains SPSS help system
SPSS Stats

TOOLBAR – HELP
• For your information, click “HELP” , and select
• Help|Topics.
Very useful giving information about how to carry out particular tasks.
• Help|Case Studies.
Provides hands-on examples of how to create various types of
statistical analyses and how to interpret the results.
• Help|Statistics Coach.
Designed to assist in data analysis by leading you through a series of
questions about your data and what you want to do with your data.
SPSS Stats

2 Types of View
2 Type of View
DATA VIEW
Almost like excel
spreadsheet
Data View – Used for
entering, editing and
modifying data.
VARIABLE VIEW
Used to define the type
of information that is
entered in to each
column in data view.
SPSS Stats

Data Editor : Data View
Data View – Used for entering, editing
and modifying data.
Very much like an excel spreadsheet
SPSS Windows

Cases represent individual respondents to a survey
Data Editor : Data View
SPSS Windows

Data Editor : Variable View
Used to define the type of information
that is entered in to each column in
data view.
SPSS Windows

• Variable Formats
– Click on the Variable View tab of the
Data Editor to edit or display
formats
• Name • Type • Width
• Decimals • Label • Values
• Missing • Columns • Align
• Measure
• Variable Labels
• Type in descriptive text that explains
what the variable measures
• Labels are provided to make the
variable names more informative
Formatting Your Variables From Variables View

STEP BY STEP : DATA ENTERING
• For this course, we focus about “Manually Enter Data”
Manually Enter Data
STEP 1: Define Variables
in Variable View
STEP 2: Enter data in
Data view
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NAME COLUMN
UNDERSTAND THE RULES
• The name must begin with a letter.
• Maximum of 8 characters and no spaces.
• Names must be unique.
• @ # _ or $ allowed.
• A full stop can be used but not as the last character, so best avoided.
• The space character and others such as * ! ? And ‘ are not allowed.
• Names are not case sensitive so ID, id and Id are identical.
• Certain SPSS keywords are no allowed as variable names they are:
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• In the Type column click on the button to reveal the
Variable type dialogue box and select the appropriate
variable type.
TYPE, WIDTH, DECIMAL COLUMN
Note that a common mistake made
by first-time users is to enter
categorical variables as type “string”
by typing text into the Data View. To
enable later analyses, categories
should be given artificial number
codes and defined to be of type
“numeric.”
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Width
• the width of the actual data entries.
• The default width of numerical variable entries is eight.
• be increased or decreased
Decimals
• the number of digits to the right of the decimal place to
be displayed for data entries.
• This is not relevant for string (will appear greyed-out 0)
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LABEL and Values column
Label
• a label attached to the variable name.
• They are helpful for reminding users of the meaning of
variables
• can be displayed in the output from statistical analyses.
Values
• labels attached to category codes.
• For example, data set included a categorical variable gender.
Clicking the  insert “0” in Value and insert “female” on
Label and click “add”. (try add male gender)
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MISSING, COLUMN, ALIGN, MEASURE
Missing
• missing value codes. SPSS recognizes the period symbol as indicating a missing value.
• used 99, 999 or 9999 etc.
Columns
• width of the variable column in the Data View.
• The default cell width for numerical variables is eight.
• Note that when the Width value is larger than the Columns value, only part of the data entry
might be seen in the Data View.
Align
• alignment of variable entries.
• The SPSS default is to align numerical variables to the right-hand side of a cell and string
variables to the left.
Measure
• measurement scale of the variable.
• For variables of type “numeric,” the default measurement scale
is a continuous or interval scale
• For variables of type “string,” the default is a nominal scale.
• For “ordinal,” is for categorical variables with ordered
categories but is not used by default.
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KEY IN DATA
• SPSS usually requires data in wide format
• One row per observation (e.g. one row per patient)
• Columns represent the different variables
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OUTPUT VIEWER EXAMPLE
• After key in all the data, and go trough the analysis, where results of
statistical analysis performed via analyze are displayed (will open
automatically when analysis is performed).
Spss
STATS

Saving From Data Editor
• Although you can click directly on the Save button
in the Data Editor window a better approach is to select
File|Save As
• Using an incremental numbering system for file names
allows you to keep the most recent files and older copies.
C:USERSNETWORKING1.SAV
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Saving From Output Viewer
• In the output viewer window the output can also be saved
by choosing File|Save As
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Finishing with SPSS
• Bring the Data Editor window to the top and choose
File|Exit.
• Remember to save your worksheet &/or results first if
required.
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ENTERING DATA
1. File | Open an existing SPSS data document (.sav)
2. Manually Enter Data:
1. Define Variables in Variable View
2. Enter data in Data view

READING DATA
• Reading an SPSS Data File
• Reading Data From Excel
• Reading From Text File

1. SPSS can read Excel worksheets directly using the pull-down menu
File >>> Open.
2. Note that, the Excel file can have multiple sheets but SPSS can only
convert one worksheet at a time
3. It is advisable that the column labels (titles) follow the SPSS variable
naming convention , i.e. no blanks, not exceeding 64 characters, etc.
Otherwise, SPSS will truncate the names or provided default names
4. No blank lines are permitted between data values and after or before the
column labels (titles). Any blank lines would be assigned missing values
for the respective variables.
Reading in Data from Excel to SPSS

Warning:
•SPSS is much better at handling numeric variables than string variables
(categorical data entered as text).
•Therefore, if you want to transfer data from Excel to SPSS it is a good idea to
ensure that any categorical data (e.g. yes/no/don’t know, male/female, etc.) are
entered in Excel as numeric data (codes) rather than text.
•For example, you could always code ’No’ as 0 and ’Yes’ as 1, and so on.
5. Remove all main titles, comments or footnotes as SPSS
only recognises variable names and data values.
6. Value labels need to be defined in SPSS and some other
editing may need to be done after conversion

1. Open the file in Excel (data tahun 2000) to see the contents
2. The variable names are on the first line;
3. There are no empty lines in between the data lines;

Select This File
Step 1 : File >>>>> Open Data >>>> Select File “Data Penyiasatan 2000”

Select the first worksheet and
click OK

Click File >>>> Save As >>>> “Data Penyiasatan 2000”

Reading in Data from Text File to SPSS
1. If your data are in text files (such as in Notepad or WordPad)
you can convert to SPSS using the Read Text Data Wizard
2. See, below is the data file “tahap kepuasan perkhidmatan
kaunter di klinik” from text file.

Click File>>> Read Text Data…
Select the File “tahap kepuasan perkhidmatan kaunter di klinik”
1
2
3
4
Select This File
Click
Open

Check all the
data value
Click Next
5
6

Check All Option
Click Next
7
8

9 Click Next

10
Click Next

11 Click Next

12 Click Finish

The resulting SPSS Data Set. Save as Waiting.sav

Exercise By Using File “Healty Life Style.sav”
Specify Variable Name, Measure
Column 1 : Tahap Kepuasan
Column 2 : Masa Menunggu
Column 3 : Jantina

OPENING SPSS DATA SET
File >>> Open >>> Data
• Select the SPSS data set you want (extension .sav)
• Below, see the example for opening of file “Healty Life Style”
• There are 282 observations or cases (scroll down to the end of the data lines)

Variable View For File “Healty Life Style”

Specify the description of each variables
Variable View For File “Healty Life Style”

The independent samples t-test is used to test comparative research
questions
That is, it tests for differences in two group means or compares means
for two groups of cases.
Example:
Suppose the stats professor wanted to determine whether the average
score on Assignment 1 in one stats class differed significantly from the
average score on Assignment 1 in her second stats class.
Independent t-Test
Class 1 20.0 20.5 21.0 20.5 20.0
Class 2 24.5 23.5 20.0 20.0

Step 1: State the Null and Alternate Hypotheses
Ho = There is no difference between class 1 and class 2 on
Assignment 1.
Ha = There is a difference between class 1 and class 2 on
Assignment 1.
Is this a directional or nondirectional Ha?
Independent t-Test

Step 2: Input each student’s grade into SPSS, along with
which class they are in
Independent t-Test

Step 3: Run the Analysis.
Analyze  Compare Means  Independent Samples T-test
Test variable = Grade
Grouping variable = Class
Define Groups:
Type “1” next to Group 1
Type “2” next to Group 2
Click Continue
Click OK
Independent t-Test

Step 4: Make a decision regarding the null
Class 1 (M = 20.40, SD = 0.418)
Class 2 (M = 22.00, SD = 2.345)
Which row do we look at on the output?
Independent t-Test

Step 5: Levene’s Test for equal variances
Ho = The variances of the two variables are equal.
Ha = The variances of the two variables are not equal.
Independent t-Test
p = .000, which is <.05;
Therefore, we reject the null
and do not
assume equal variances!

Looking at the Equal variances not assumed row (the bottom row)
Independent t-Test

Make a decision regarding the null
t (3.153) = -1.347
p = 0.266
Using the level of significance = 0.05, do we reject or fail to
reject the null?
Independent t-Test

Remember
If p < 0.05, we reject the null
if p > 0.05, we fail to reject the null
According to SPSS, p = 0.266
0.266 > 0.05, therefore, we fail to reject the null!!
Independent t-Test

Step 5: Write up your results.
The null hypothesis stated that there is no difference between class 1
and class 2 on Assignment 1. An independent samples t-test revealed
that the average grades on Assignment 1 did not differ significantly
from Class 1 (M = 20.40, SD = 0.418) to Class 2 (M = 22.00, SD =
2.345), t (3.153) = -1.347, p = 0.266. Consequently, the researcher
failed to reject the null hypothesis.
Independent t-Test

Exercise:
Test whether is there evidence that the mean delivery time for the local
pizza restaurant is difference between the mean delivery time for the
national pizza chain.
Independent t-Test
Local National
16.8 22.0
11.7 15.2
15.6 18.7
16.7 15.6
17.5 20.8
18.1 19.5

Used to compare the means of two variables for a single group.
The procedure computes the differences between values of the two
variables for each case and tests whether the average differs from 0.
Paired Samples t-Test

Example:
A researcher wanted to know the effects of a reading program. The
researcher gave the students a pretest, implemented the reading
program, then gave the students a post test.
Pre Post
20.00 25.00
21.00 24.00
19.00 23.00
18.00 22.00
20.00 24.00
21.00 25.00

Step 1: State the Null and Alternate Hypotheses
Ho = There is no difference in students’ performance between the
pretest and the posttest.
Ha = Students will perform better on the posttest than on the pretest.
Is this a directional or nondirectional Ha?

NOTE for One-tailed Tests!!
Remember when we have a directional hypothesis, we conduct a one-
tailed test.
When we have a non-directional hypothesis, we conduct a two-tailed
test.
SPSS (unless given the choice) automatically runs a 2-tailed test, IF
you have a directional alternate hypothesis (and a 2-tailed test was
run), you MUST divide the p-value by 2 to obtain the correct p-value!

Step 2: Set up data

Step 3:
Analyze the Results  Analyze  Compare Means Paired Samples
t-Test
Paired variables: pre--post

Step 4:
Make a decision regarding the null
Pretest (M = 19.83, SD = 1.17)
Posttest (M = 23.83, SD = 1.17)
t(5) = -15.49
p< 0.001 (two-tailed)
What is the decision regarding the null?

We have a directional alternate, therefore we have to divide the p-value
by 2.
0.000/2 = 0
p < 0.001
What is the decision regarding the null?

Using the level of significance = 0.05, do we reject or fail to
reject the null?
If p < 0.05, we reject the null
if p > 0.05, we fail to reject the null
According to SPSS,
p < 0.001
0.001 < 0.05,
therefore, we reject the null!

Step 5:
Write up your results.
The null hypothesis stated that there is no difference in students’
performance between the pretest and the posttest. A paired samples t-
test revealed that students scored significantly higher on the
posttest (M = 23.83, SD = 1.17) than they did on the
pretest (M = 19.83, SD = 1.17), t(5) = -15.49, p < 0.001.
Consequently, the null hypothesis was rejected.

Exercise:
The following table gives the blood pressures of seven adults before
and after the completion of a special dietary plan. Can we conclude that
the mean blood pressures decrease after the completion of the
program.
Before After
210 193
180 186
195 186
220 223
231 220
199 183
224 233

Introduction to SPSS Stats

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