1. Data Analysis
Anindita C. Rao

2. Data Analysis
D Data Analysis Results
A interpretation
T
A
C
O
L Getting data ready
L for analysis Feel for data Goodness of Hypothesis
E •Editing 1.Mean Data Testing
•Handling Blank 2.S.D. •Reliability •Appropriate
C
Reponses 3.Correlation •Validity statistical tools
T 4.Frequency
•Coding
I •Categorizing distribution
O •Creating data file
N •Programming

3. Data Preparation
• Getting data ready for analysis(SPSS)
• Editing
• Handling Blank Reponses
• Coding
• Categorizing
• Creating data file
• Programming

4. Coding & Categorization
• Usually a number to each response
• Coding of questionnaires
• If male=1 and if female=2
• Negatively worded questions
Some compromises with ethics helps you in
practical life Strongly disagree1-2-3-4-5-6-7Strongly agree
• EXCEL we designate specific columns to
specific questions and responses

5. Descriptive statistics
• Frequency(tabulation)
• Measure of central tendency(mean,
median & mode)
• Measure of Dispersion(range, variance,
standard deviation & interquartile range)

6. Inferential Statistics
• Correlation( Direction, Strength &
Significance) -1 to +1
• Parametric: Pearson’s correlation
• Non Parametric: Spearman’s Correlation
& Kendall’s rank correlation(ordinal)

7. Data Analysis
• Simple tabulation & Cross tabulation
• ANOVA
• Correlation & Regression
• Discriminant Analysis
• Factor Analysis
• Conjoint Analysis
• Multidimensional Scaling
• Cluster Analysis

8. Tabulation Variables
Male Female Total
25 35 60
Frequency
Family Background
Descriptive
Frequen Valid Cumulative
Statistics cy Percent Percent Percent
Valid Nuclear
72 60.0 60.0 60.0
Joint
48 40.0 40.0 100.0
Total
120 100.0 100.0

9. Cross Tabulation
Two variable interaction
Variable
1(nominal)
Variable
2(nominal)
Qualification * Centrality Crosstabulation
Count
Centrality
-2 0 1 2 3 4 Total
Qualification Doctorate
4 6 0 18 6 0 34
Post Graduate
0 4 3 20 14 8 49
Graduate
0 0 5 28 4 0 37
Total
4 10 8 66 24 8 120

10. Chi square
• To determine the systematic association
between two variables
• Null hypothesis: no association
• Expected cell frequencies comparison with
actual cell frequencies
• Greater the discrepancies, greater will be
chi square statistic

11. Chi square test
• Two nominal variables
• Cross tabulation
• Non parametric
• SPSS code
1. Analyze from SPSS bar
2.Analyze> Nonparametric test> Chi-square

13. Exercise
• In this case study , we are observing association between educational
background(independent variable) of the PGDBM students and their performance in the
terms of grade(dependent variable) secured. We want to test at 90% and 95% confidence
level, what is the level of significance of association.(refer to file in SPSS)
Educational Background Code
B.Com 1
B.E. 2
B.Sc. 3
BBA 4
B.A. 5
Grades as follows
Grade Obtained Grade Code
A 1
B 2
C 3

15. t test
• Significant mean difference between two
groups
• Nominal variable on interval or ratio scale
• (smokers & non smokers on extent of well
being)
• Sample size less than 30
• Df = N-1
• Mann Whitney U test

16. ANOVA
• Significant mean difference among
multiple groups
• Multiple regression
Variance caused by independent variable on
dependent variables simultaneously

17. Hypothesis Testing
• Errors
• Normal population
• Degrees of freedom
• One tailed or two tailed
• Single Population
• . p value

18. Univariate Techniques
Metric Data Nonmetric data
(interval or ratio) (nominal or ordinal scale)
One sample
Two or More Sample
•Frequency
One sample Two or More
•Chi square
samples
•T test
•K-S
•Z test
•Runs
Independent Related
•Binomial
•Two group t •Paired t
test test
Independent Related
•Z test
•Chi square •Sign
•One way
anova •Mann whitney •Wilcoxon
•Median •Mc Nemar
•K-S •Chi Square
•K-W Anova

19. Multivariate Techniques
Independence
Dependence
Techniques
Techniques
Interobject
One dependent More than one Variable
Similarity
variable dependent Interdependence
variable •Cluster
•Cross tabs •Factor Analysis
Analysis
•Cannonical
•Anova & correlation •MDS
Covariance
•Multiple
•Multiple discriminant
regression
•Discriminant
•Conjoint

20. Criterion(dependent)
One Two or More
Nomin Ordinal Interval No Ordinal Interval
al min
al
One Nominal Chi •Sign test Analysis of Multiple
Squar •Mann variance discriminant
e whinney analysis
•Krushal
Wallis Anova
Ordinal •Spearman
rank
Correlation
•Kendall’s
rank
correlation
Two Interval Anova Regression Ano Multiple
or Analysis va Regression
More Analysis
Nominal Friedman Anova Anova
two way Factorial
analysis Design
Ordinal