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SPSS Overview

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SPSS, Statistical, Regression, ttest
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Content Preview
Prof. Kalyan Sengupta, PhD.
www.weanalyst.com 
 
SPSS 16 For Windows

1. Introduction of SPSS screen

Variables 

Case 
Data View 
Variables View 
 
Figure 1 : SPSS 16 Screen

2.
Opening a data file :

2.1. Menu Option: File | Open | Open Data (Opens File Dialog Box)

Change File of Type from here to select
non-SPSS file.Data file types which can
be opened in SPSS are
:

SPSS ( *.sav)
SPSS/PC+ (*.sys)
Sysstat (*.syd, *.sys)
SPSS Portable (*.por)
Excel file (*.xls, *.xlsx, *xlsm)
Lotus (*.w)
Sylk (*.slk)
Dbase file (*.dbf)
SAS (*.sas7bdat, *.sd7, *sd2, SSd01, *xpt)
Stata (*.dta)
Text (*.txt, *.dat)
 
 
                       Figure 2 : Open Data Screen

 
W e A n a l y s t  
PAGE ‐ 1 

Prof. Kalyan Sengupta, PhD.
www.weanalyst.com 
 

3. The Data Editor Screen: (Figure 1 : SPSS 16 Screen )
3.1. Looks like spreadsheet. Variables name at the top and data are placed
in rows and columns.
3.2. Each row represents a case.
3.3. Each column represents a variable.

4. Can define a variable by using
4.1. Data I Define Variable. Then enter Variable Name.
Use this to select and
unselect the data.
Left screen of data is not
selected where as right
screen is selected from
scan
Measurement Scale can be

Data can be numeric
changed

/string /date/currency etc.


Field width may be set
4.2. Click Continue to change data format
with the decimal places

Click
Labels to assign
descriptive variable and
value labels

Value labels may be
typed in as for example,
Employment Category for
each value, value label
may be added ego
1 ="Clerical"
2="Officer"
3="Manager”



 
W e A n a l y s t  
PAGE ‐ 2 

Prof. Kalyan Sengupta, PhD.
www.weanalyst.com 
 
4.3. Click Missing Values to specify codes for missing values. Data might
contain some missing values, as because

A respondent refused to answer

The question did not apply to that respondent.

The missing values are excluded from most calculations. In the
Missing Values window, can define

no missing values

Discrete missing values

5. Inserting New cases:

Select any cell in the row, below the positio
Select any cell in the row, below the
n in
positio

n in
whic
whi h you want to insert th
h you wa
e new
nt to insert th
ca
e new
se.
ca
Right click and click “Ins
Right click a
ert Case
nd click “Ins
” or
ert Case

” or
Data I Insert case
Edit I Insert case .
.
A new row is
A new ro
inse
w is
rted for th
inse
e ca
rted for th
se.
e ca
Select any cell in the column to the right of the
position where you want to insert the new
variable.


Right click and click “Insert

Variables” or
6. Inserting New Variables:
Edit | insert Variable.









 
W e A n a l y s t  
PAGE ‐ 3 

Prof. Kalyan Sengupta, PhD.
www.weanalyst.com 
 
7. Edit the data
Search for data:
Select any case or variable you want to search
Edit I find -> search dialog box.
Enter the data value you want to find
Click search for ward or search
backward.
Go to case: Edit I go to case ( Enter the row
number for the case and Click OK )


8. Compute a variable (Transform I Compute )
It can compute the values of a variable or a new variable. There are over 70 built-in
functions, which may be used during computation.


The formula of Numeric
Expression can be
composed from variable list
and the functions, Eg. Total
=Basic +DA + HRA

Logical functions can also be
inserted in computing a
Type name of the
variable.
single Target

Click If -> If cases dialog box
variable 
Click "Include if case'
satisfies condition:"
Types & Label button.

Compose the logical
Label is optional, which describes a variable.
expression

By default, new computed variables are

Eg. JobCat = 3 & sex = 1
numeric.

This can be changed to string if you are doing
string operation

Click OK after all Operation
 
W e A n a l y s t  
PAGE ‐ 4 

Prof. Kalyan Sengupta, PhD.
www.weanalyst.com 
 


9. Count Occurrences (Transform I Count Value within case):

Creates a variable that counts the occurrences of the same values in list of
variables for each case .For example reading of newspapers may be noted
in the survey by check boxes (Yes l No) for each one, say Statesman,
Telegraph, etc.

Enter a Target variable name

Select two or more variables of the same type (numeric or string)

Click define values and specify which value or values to be counted

If Cases Dialog Box (optional) for defining subset of cases.

10. Recording Value (Transform | Recode into Different Variables)



Can modify data values by recording
them. Useful for collapsing or
combining categories.
Select the variable you want to
recode
Enter an output (new) variable name

Click old and new values
You can recode Ole single values,
ranges of values and missing values

11. Rank Cases (Transform | Rank Cases)

This creates new values containing ranks, normal and percentile values for numeric variables .
Select one or more variables to rank
Optionally,

You can rank cases in ascending or descending order
Organize rankings into subgroups by selecting one or more values
Rank Cases – Tiles
This dialog box controls the method for assigning rankings to cases with the same
value on the original variable.
The following table shows how the different methods assign ranks to tied value 5 :


Value
Mean
Low High Sequential





10 1 1 1 1
15 3 2 4 2
15 3 2 4 2
15 3 2 4 2
16 5 5 5 3
20 6 6 6 4

 
W e A n a l y s t  
PAGE ‐ 5 

Prof. Kalyan Sengupta, PhD.
www.weanalyst.com 
 


12. Aggregate Data (Data I Aggregate)

Combines groups of cases into simple summery cases, and creates a new
aggregate. Cases are aggregated based on the values of one or more grouping
variables. The new data file contains one case for each group.
Steps:

Data I Aggregate Select break variable

Select one or more aggregate variable

Select an aggregate function for each aggregate variable

Enter new variable name and label

13. Split File ( Data I Split File )
This separates the data file into separate groups for analysis, based on some
group variable.
Steps:

Data I Split File

Select Compare Groups or Organize Output by Groups

Select Group Variable
14. Select Cases (Data | Select Cases)
This provides several 21 methods for selecting a
subgroup of cases based on criteria that include
Variables and Complex expressions.
You can also select a random sample of cases.
The criteria used to define a subgroup can
include variable values and ranges, case
numbers, arithmetic, logical expressions,
functions etc.
The unselected cases may be filtered or
deleted. The filtered cases remain In the data
file but are excluded from the analysis.
SPSS creates a Filtered Variable filter-$, to
indicate tile filter status. Filtered cases are also
indicated by a slash (/) through the row number.
To get them all back, select all classes.
Deleted cases are removed from the data file
and cannot be recovered, if you save the data
file after deleting the cases.
Click here to select the case on the basis of

Random sampling may be performed by
some condition
selecting Random Sample of Classes. Two
By clicking on “if” window which will come is
options of available:
shown in next figure

Approximately 1 0 % of all cases

Exactly 20 cases from the first 100 cases.


 
W e A n a l y s t  
PAGE ‐ 6 

Prof. Kalyan Sengupta, PhD.
www.weanalyst.com 
 
Select the criteria, Eg.
(gender = ‘m’ & Jobcat = 2)



15. Charts of SPSS (Charts | Legacy Dialogs)

Charts are obtained from graphs menu, bar, line, area, pie, histogram, scatter plots
are often used.

15.1. Bars
These may be drawn in three ways – single, cluster and stacked
Charts data may be selected with the option of
• Summaries for groups of cases
• Summaries for separate variable
• Values of individual cases

For a simple Bar chart, with summaries of group of cases, simply enter a variable
in the category axis, say, grade. The four bars corresponding to four .categories
will appear.

For a simple bar chart with summaries of a variable. add the summary parameter.
(say, mean age) . To do this, click on Option "Other Summary Functions" and add
age variable.

For a clustered Bar chart, with summaries of group of cases, for example, enter
Category axis as grade, and define cluster by sex.

For a cluster Bar chart, with summaries of separate variables, for example, enter
grade in the Category axis, and mean age, mean income in the Bars Represents
text box.
 
W e A n a l y s t  
PAGE ‐ 7 

Prof. Kalyan Sengupta, PhD.
www.weanalyst.com 
 

15.2. Line Chart
Three types of Line charts are available, -Simple, Multiple, DropLine. Selection of
simple and multiple charts are similar to those of bar charts. The DorpLine chart
indicates the positions of two or more groups on vertical lines.

15.3. Scatter Plots
The Highlight the relationship between two quantitative variables, by plotting the
actual values along two axes. They of1en allow to see the bivariate relationships,
such as a curvilinear pattern, that descriptive statistics do not reveal. To obtain
scatter plot:

15.4. Graphs | Scatter

The scatter plot dialogue box has four options: simple, overlay, matrix, 3-D.
For simple scatter plot, only enter x-axis and y-axis variables. Two other options:
Set Markers By: Select a variable to determine the categories that will be shown on
the chart, each value of the variable is B different color or marker symbol on the
scatter plot.

Label cases by : Select a variable to provide labe.ls for each marker. The value
label of each case is placed above the point on the scatter plot. If there is no value
label, the actual value \'\~Il be placed above the point.

15.5. Matrix scatter Plot
Select Two or more numeric variables to define cells of the matrix. There is one
row end column for each variable. Each cell contains a simple scatter plot of the
row variable 2nd !lie column variable.

15.6. Overlay Scatter Plots
Select two or more numeric x-y variable pairs. Each pair of variables is plotted on
the same scatter plot with a separate marker symbol. To swap the y-x variables,
select a pair and Click "Swap pair".

15.7. 3-D Scatter plot
Select variables for y-axis, x-axis and z-axis. A 3-D diagram will be created.

15.8. Histogram
Enter the command graph | Histogram

Select a variable for histogram. You get bars showing the data divided into several
evenly spaced intervals. Height of each bar shows the number of cases in each
interval.

15.9. Pie Chart
This can be obtained for three different data sets as in case of bar chart

Summaries of groups of cases: This option will show slices for each group value.
Enter tile grouping variables, say grade; into “Define Slices By” text box.

This pie chart will show the case counts for each group values on the slices. In
case you want any summary variable to show, click “Other Summer Summary
Functions” and enter a summary variable (say, mean age).

Summaries of Separate Variables: For this you enter a list of summary variables to
indicate each slice.
 
W e A n a l y s t  
PAGE ‐ 8 

Prof. Kalyan Sengupta, PhD.
www.weanalyst.com 
 

Values of Individual Cases: This can be formed by selecting a numeric variable to
"Slice Represent”. Label the slice by case number.

15.10. Box
Plots
A Box Plot characterizes tile distribution of a variable, displaying its median and
quartiles. Special symbols identify the positions of outliers, if any. Box Plots may be
simple or clustered.

For a simple Box Plot, with summaries of groups of cases, enter the variable you
want to plot and enter the grouping variable in the category axis. There will be a
box for each value of the group.

For a cluster Box Plot, with summ8rie5 of groups of cases, enter the variable you
want to plot. The category variable and a variable to define cluster by (say, sex)
should also be defined. For each category, two boxes for male and female will be
shown.


16. To Obtain Descriptive and statistics (Analyze | Descriptive Statistics | Descriptive)
Select the variables of your analysis
Select Statistics you want to output
Select Charts to plot bar or pie or histogram,

17. To obtain Cross Tabulation (Analyze | Descriptive Statistics | Crosstabs)
Select one or more row variables and column variables
Optionally, select one or more layer variables
Set parameters of Statistics, Cells and Format.

18. To Obtain Means (Analyze | Descriptive Statistics | Means)
Select one or more variables
Optionally, select grouping variables (independent list)
Set options for Cell Statistics.

19. T - Test
19.1. To Obtain Independent Samples T – Test (Analyze | Compare
Means | Independent Samples T-Test)

Select one or more test variables

Select a grouping variable
Click "Define Groups" to specify two codes for the groups you want to
compare

19.2. To Obtain Paired Samples T-Test (Analyze | Compare Means |
Paired Samples T-Test)


Select a pair of variables you want to test
May click Options to control treatment of missing data and the level of
confidence internal.


19.3. To Obtain One Sample T-Test (Analyze | Compare Means | One
Samples T-Test)


Select one or more variables to be tested

Enter 8 numeric test value against whict1 eact1 sample mean is compared.

 
W e A n a l y s t  
PAGE ‐ 9 

Prof. Kalyan Sengupta, PhD.
www.weanalyst.com 
 

20. To Obtain One Way Analysis of Variance (Analyze | Compare Means | One Way
ANOVA)

Select one or more variables you want to test
Select a single grouping variable.

21. Correlation:

If two variables are suspected to be linearly related, then the question arises to
what extent they are related. Correlation co-efficient gives a measure of the degree
of linearity among two variables.

SPSS Step

Analyze I Correlate I Bivarate

Select Pearsonian and/or Kendall's t3U and/or Spearman's rank correlation

Decide which test are you going to perform; one-tailed or two-tailed.

Flag significant correlation to detect 'which correlation significantly differs from zero

Click "Options"

Select Statistics | Mean and S.D.

Click "OK".


22. Regression Analysis

If a set of variables (independent) are suspected to influence any other variable
(dependent) • then by a statistical technique we can find the relationship between
them by some optimal probabilistic way and use it to predict the value of the
dependent variable for a given set of values of the independent variables.

Linear Regression: This is the regression method where we assume linear
relationship between the variables. Linear regression can be fitted by two methods:
a) Enter or simple method
b) Stepwise method where the best set of repressors are used to predict the
dependent variable.

SPSS Steps

Open the Data Sheet
Select
from
menu
Analyze | Regression | Linear
Select one Dependent variable
Select independent variables
Select "Enter" method
Click "Plot" to produce partial plots
Click "Statistics" to obtain
• Regression coefficients
• Q Model fit, R squared value
Click "Save” store the following in the data sheet
• Unstandardized predicted value
• Unstandardized
residuals

Click "Options" to treat the missing values

Stepwise Regression: Same SPSS Linear regression with enter method, except
specify the method as Stepwise. In Options, F-to-change may be selected and
entered. But normally the default value is used.

 
W e A n a l y s t  
PAGE ‐ 10 

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