This document provides an overview of data analysis and decision making. It defines data analysis as reviewing large amounts of raw data to determine conclusions. There are two main types of data analysis: qualitative analysis, which examines categorical data through methods like interviews, and quantitative analysis, which analyzes numerical data using descriptive statistics. Decision making is choosing alternatives based on preferences. Tools for decision making include SPSS, Stata, and Mstat C. SPSS allows entry and analysis of data through functions like frequencies, measures of central tendency, and correlation. The document provides resources for learning SPSS.

1. Data Analysis for Effective
Decision Making
By:
Syed Sohail Ahmed
Assistant Professor
Email: ssoahmed@ssuet.edu.pk

2. Agenda
 What is data Analysis?
 What Is Decision Making?
 International tools for Decision
Making
 Use of SPSS in Data Analysis
 References

3. What is data Analysis?
 The term “data analysis” refers to
the process by which large
amounts of raw data is reviewed
in order to determine conclusions
based on that data

4. Types of Data Analysis
 The nature of data analysis varies,
and correlates to the type of data
being examined
 there are two broad categories:
 Qualitative analysis
 Quantitative Analysis

5. Qualitative analysis
 Qualitative analysis deals with the
analysis of data that is categorical in
nature. In other words, data is not
described through numerical values,
but rather by some sort of descriptive
context such as text.
 Data can be gathered by many
methods such as interviews, videos
and audio recordings, field notes, etc.

6.  Qualitative analysis can be
summarized by three basic principles
(Seidel, 1998):
 Notice things
 Collect things
 Think about things

7. Quantitative Analysis:
 Quantitative analysis refers to the
process by which numerical data is
analyzed, and often involves
descriptive statistics such as mean,
media, standard deviation, etc

8. Statistical Models
 Analysis of variables
 Data dispersion
 Analysis of relationships between variables
 Contingence and correlation
 Regression analysis
 Statistical significance
 Precision
 Error limits

9.  Know where to find the Information
and how to use it- That’s the secret of
Success
 By Albert Einstein

10. Benefits of Data Analysis
 Allows for the identification of important trends
 identify performance problems that require
some sort of action
 Can be viewed in a visual manner, which leads
to faster and better decisions(e.g Pie Chart)
 Better awareness regarding the habits of
potential customers
 It can provide a company with an edge over
their competitors

11. What Is Decision Making?
 Decision making is the study of
identifying and choosing
alternatives based on the values
and preferences of the decision
maker

12. Internationally recognized tools
for Decision Making
 STATA
 SPSS
 Mstat C

13. 13
Introduction to SPSS 16.0

14. 14
Outline
 Review of Concepts (stats and
scales)
 Data entry (the workspace and
labels)
 By hand
 Import Excel
 Running an analysis- frequency,
central tendency, correlation

15. Types of Variables
 What are variables you
would consider in buying a
second hand bike?
19
Row tree, D. (1981). Statistics without tears. London: Penguin
Books.
 Brand (Trek, Raleigh)
 Type (road, mountain, racer)
 Components (Shimano, no
name)
 Age
 Condition (Excellent, good, poor)
 Price
 Frame size
 Number of gears

16. 20
Types of Scales
 Nominal- objects or people are categorized
according to some criterion (gender, job
category)
 Ordinal- Categories which are ranked
according to characteristics (income- low,
moderate, high)
 Interval- contain equal distance between
units of measure- but no zero (calendar
years, temperature)
 Ratio- has an absolute zero and consistent
intervals (distance, weight)

17. Parametric vs Non-parametric
 Parametric stats are more powerful
than non-parametric stats- for real
numbers- T test
 Non-parametric stats are not as
powerful but good for category
variables - Mann-Whitney U (likert)
21

18. 22
The Workspace
Cases
Variables
Toggle between
Data and Variable
Views
Value Label

19. 23
Data Entry (by hand)
1. Click Variable View
2. Click the Row 1, Name cell and type Campus (no spaces allowed in
name)

20. 24
4. Type 2 for the value and dubai for the label- click Add and then OK
3. Click the Row 1, Values cell and type 1 for the value and abu dhabi for
the label- click Add
Data Entry (by hand)

21. 25
Data Entry (by hand)
5. Click the Row 2, Name cell and type TOEFL
6. Click the Row 2, Label cell and type Paper based TOEFL Scores

22. 26
Data Entry (by hand)
8. Click the Row 4, Name cell and type Gender
7. Click the Row 3, Name cell and type IELTS

23. 27
Data Entry (by hand)
9. Click the Row 4, Type cell and click String and click OK
10. Click the Row 4, Values cell and type m for the value and male for the
label- click Add

24. 28
Data Entry (by hand)
11. Type f for the value and female for the label- click Add and then OK
(notice the measure is now nominal)
12. Click Data View in the bottom left corner to start entering the data

25. 29
Data Entry (by hand)
13. Click on the cells and enter the data (either type numbers of select from
the dropdown menu)

26. 30
Data Entry (import from Excel)
14. Click Open- Data…
15. Change Files of type to Excel, then browse and open the file.

27. 31
Data Entry (import from Excel)
16. Select the worksheet, the range (if desired), and if to read variable
names- click OK
The data and variable names will
appear

28. 32
Running Analyses
17. With SPSS open, select file- Open- Data
18. Navigate to SPSS- Tutorial- sample_files- select demo, click Open

29. 33
Running Analyses (Frequency)
19. Select Analyze- Descriptive Stats- Frequencies
20. Select the desired variables and click the arrow to move them to the right
side

30. 34
21. Click Statistics
22. Select any stats that you want to see, click Continue
Running Analyses (Frequency)

31. 35
Running Analyses (Frequency)
23. Click Charts
24. Select the type of chart you want, click Continue, then OK

32. 36
Running Analyses (Frequency)
Result Tables and Graphs will appear

33. 37
Running Analyses (Central
Tendency)
26. Select the desired variables (household income) and click the arrow to
move them to the right side
25. Select Analyze- Descriptive Stats- Frequencies

34. 38
Running Analyses (Central
Tendency)
Results will appear
27. Select some measures of
central tendency and dispersion-
click Continue then OK

35. 39
Running Analyses (Correlation)
28. Click Analyze- Correlate- Bivariate
29. Move the two variables of
interest to the right side (age &
income), click OK

36. 40
Running Analyses (Correlation)
30. Results appear and tell us that the relationship is weak to
moderate and results are not due to chance

37. 41
Resources
 Texas A & M- a huge selection of helpful movies
http://www.stat.tamu.edu/spss.php
 UCLA- SPSS 12.0 Starter Kit (useful movies, FAQs, etc)
http://www.ats.ucla.edu/stat/spss/sk/default.htm
 Indiana University- Getting Started (useful instructions with
screenshots)
http://www.indiana.edu/~statmath/stat/spss/win/
 University of Toronto- A Brief Tutorial (screenshots, instructions
and basic stats)
http://www.psych.utoronto.ca/courses/c1/spss/page1.htm
 Central Michigan- Tutorials and Clips (movies, screenshots,
instructions- slow loading but good)
http://calcnet.mth.cmich.edu/org/spss/toc.htm
 SPSS Statistics Coach and Tutorial (under Help) as well as the ZU
library
 Online Statistics Textbook
http://www.statsoft.com/textbook/stathome.html