SPSS Training Guide

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2. SPSS
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(Statistical package for social sciences)
Presented by:
Kabir Khan
LIMS-1911
Department of Library and Information Management
Superior University , Lahore

3. CONTENT
 Introduction
 General Capabilities
 Data Measurement & Data Analysis
 Types of Variables
 Level of measurement
 SPSS Tabs
 Graphical representation of data
 Descriptive measures
 Measure of Central Tendency/Location
 Measure of Dispersion
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4. INTRODUCTION
 It was Introduced in 1968
 It was originally developed to facilitate statistical analysis in the social
sciences.
 Purchased by IBM in 2009 for more than $1 billion dollars.
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5. GENERAL CAPABILITIES
 Comprehensive software for data management and data analysis
 General tabulated reports
 Produce charts
 Plot distributions and trends
 Conduct descriptive statistics
 Perform complex statistical analysis
* If you are good in formulation, then you can do most of the data
analysis in MS Excel 
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6. DATA MANAGEMENT
 Defining variables
 Coding variables
 Entering & editing data
 Creating new variables
 Representation of data into
graphs & diagrams
 Descriptive measures e.g central
tendency, variation
 Inferential measures e.g
hypothesis testing, regression,
ANOVA etc.
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Data Analysis

7. TYPES OF VARIABLES
 Quantitative Variables: which are further divided into discrete and
continuous variables.
I. Discrete variable can assume only certain values. There are gaps
between the values, e.g no. of children in the families of a certain
locality
II. Continuous variable can assume any value within a specific
range, e.g amount of rainfall.
 Qualitative variables, which are also known as categorical or non
numerical variables.
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8. LEVEL OF MEASUREMENT
 Nominal
 Ordinal
 Scale (ratio & interval)
* It should be noted that data can be entered manually and
data files can also be exported.
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SPSS has two tabs:
1. Data view
2. Variable view

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12. GRAPHICAL REPRESENTATION OF DATA
1. Scatter Plot
2. Bar diagram
3. Pie Diagram
4. Box Plot
5. Histogram
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13. 1. SCATTER PLOT
 In data analysis, the first step is finding the relationship
between the two variables. Scatter plot provide great help in
this context.
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14. Training Workshop by PASTIC
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Continued…

15. 2. BAR DIAGRAM
 A simple bar chart consist of horizontal or vertical bars of
equal widths and lengths proportional to the values they
represent.
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Simple bar diagram showing
the % of patients with
different clinical symptoms.

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Continued…
Multiple Bar diagram Component Bar diagram

17. 3. PIE DIAGRAM
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Pie diagram showing
the% costs of a
publishing house.

18. 4. BOX PLOT
Training Workshop by PASTIC
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19. Training Workshop by PASTIC
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Box-whisker plot is
showing that the
distribution is
symmetric.

20. 5. HISTOGRAM
 Histogram consists of a set of adjacent rectangles.
 Class boundaries are taken along x-axis.
 Frequencies are taken along y-axis.
 It should not be confused with HISTORIGRAM which is a graph of time series.
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21. Training Workshop by PASTIC
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22. DESCRIPTIVE MEASURES
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Descriptive
Measures
Averages
Measures of
Dispersion
Mean
Median
Mode
Range
Mean
Deviation
Variance & St
deviation

23. MEASURE OF CENTRAL TENDENCY/LOCATION
 When two or more data sets are to be compared, the visual
representation is not enough.
 So, a data set should be summarized in a single value.
 Such single value is known as AVERAGE or Measure of
central tendency.
 The most common types of averages are arithmetic
mean/mean, median, and mode.
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Continued…
1. Mean
The marks obtained by 9 students are 45, 32, 37, 46, 39, 36, 41, 48, 36. The
mean mark is given by 40 marks. What will happened if the marks of 10th student
90 is included?

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2. Median
The midpoint of the values after they have been arranged in ascending or descending
order.
Median is not affected by the extreme values.

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If the number of observations is odd:
If the number of observations is even:

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Median divides the ordered data into two equal parts. So, we have
Median = Second quartile Q2
Median can be calculated for all levels of data except nominal.

28. MEASURE OF DISPERSION
 To summarize a data set, measure of central tendency is not enough.
 Dispersion shows the variation in the data.
 A small value for a measure of dispersion indicates that the data are
clustered closely around the average.
 It means that, used type of average is reliable measure.
 We will discuss range, mean deviation, variance, and standard
deviation in this section.
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29. THANK YOU
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