Most commonly in business research, two types
of hypotheses are tested.
• Relational Hypothesis- Examine how chang-
es of one variable vary with changes in other.
E.g. Advertisement increases sales.
• Hypothesis about differences- examine how
some variable varies from one group to an-
other. E.g. Stress level varies among working
women of IT and Non-IT sector.
For univariate analysis, one more type of hy-
pothesis is tested- hypothesis about differenc-
es from some standard. E.g. More than 50% of
males in Bangalore smoke.
• Univariate test for metric data can be done
through t test or z-test depends on sample
size and whether standard deviation of the
population is known or can be estimated from
sample.
• Univariate test for non-metric data can be
done through chi square, test, K-S or Runs
test to drawn inference about a population
depending on the nature of the sample.
Case
11
Bi-variate Analysis- Different types of statis-
tical techniques are used to analyse bi variate
data, based on- a) scale of measurement b)
type of distribution c) purpose of the test (sig-
nificant relation or significant differences). Nom-
inal and Ordinal scales are forming categorical
scale whereas Interval and Ratio scale comprise
Continuous scale. Categorical variables follows
non-normal distribution (Poisson, Binomial etc.)
and continuous variables may follow a normal or
non-normal (Poisson, Binomial etc.) distribution.
To check whether the variable follows a normal
or non-normal distribution, researcher can use
K-S test or Shapiro Wilk Test. Box plot of all the
observation of the variables also gives an idea
about the distribution.
Scale of measurement and type of distribution
are the two factors to be considered to decide
whether to use parametric or non-parametric
test. Please refer the diagram below for Bi-vari-
ate data analysis:
Figure: 1
X
Variable Y Variable Distri-
bu-tion of
Y variable Purpose Type of test Statistical Technique
1 Cat Cat Non Nor-
mal Sig. Differ-
ence Non Parametric
test Chi square or Z test
2 Cont. Cont. Normal/
Non-Nor-
mal Sig. Relation Parametric test/
Non-Parametric
test Pearson’s Correlation,
bi-variate regression/
Spearman’s Rank Corre-
lation
3 Cat
(X variable has 2 cate-
gories) Cont. Normal/
Non-Nor-
mal Sig. Differ-
ence Parametric test/
Non-Parametric
test T test (paired t test or
independent t test)/ Mann
Whitney test
4 Cat
(X variable has 3 or
more categories) Cont. Normal/
Non-Nor-
mal Sig. Differ-
ence Parametric test/
Non-Parametric
test ANOVA test/ Kruskal Wallis
test
DYNAMISM(E)