2. The analysis and interpretation of data represent
the application of deductive and inductive logic to
the research process.
The data are often classified by division into,
subgroups, and are then analyzed and
synthesized in such a way that hypothesis may
be verified or rejected.
The final result may be a new principle or
generalization. Data are examined in terms of
comparison between the more homogeneous
segments within the group any by comparison
with some outside criteria.
3. NEED FOR ANALYSIS OF DATA OR
TREATMENT OF DATA
After administering and scoring research tools
scripts, data collected and organized. The
collected data are known as „raw data.‟
The raw data are meaningless unless certain
statistical treatment is given to them.
Analysis of data means to make the raw data
meaningful or to draw some results from the
data after the proper treatment.
4. Thus, the analysis of data serves the following
main functions:
1. To make the raw data meaningful,
2. To test null hypothesis,
3. To obtain the significant results,
4. To draw some inferences or make
generalization, and
5. To estimate parameters.
5. Analysis means “computation of certain
measures along with searching for pattern of
relationships that exist among data groups”
Depending on the measurement and sampling
procedures, the analysis of data are of two
types.
Statistical analysis (inferential)
Non-statistical (Descriptive)
6. Descriptive analysis is largely the study of
distributions of one variable.
This study provides us with profiles of
companies, work groups, persons and other
subjects on any of a multiple of characteristics
such as size. Composition, efficiency,
preferences, etc.”
Statistical analysis is always more precise and
objective.
Selection and choice of statistical tool depends
on three factors.
7. 1) DEPENDING ON TYPE OF MEASUREMENT
a) Nominal measurement
b) Ordinal measurement
c) Interval measurement
d) Ratio measurement
For nominal and ordinary measurement, we
commonly use nonparametric tests.
For interval and ratio measurement, we commonly
use parametric tests. Parametric statistics are
commonly used tests.
8. For application of parametric test the data should
fulfil two conditions:
A) Homogeneity of variance
B) variables involved must be true numerical
Nonparametric tests are also called “distribution-
free tests”. so these can be used in statistics
involved in nominal measurements and ordinal
measurements.
9. 2) DEPENDING ON NUMBER OF VARIABLES TO
BE ANALYZED:-
a) One variable - unidimensional analysis
b) Two variable – bivariate analysis
c) More than two variables – multivariate
analysis
If investigator is interested in describing a single
population, he should use “unidimensional analysis”.
If investigator wants to study interrelationship between
two variables he should use “bivariate analysis”.
10. If the investigator wants to study
interrelationship between more than two
variables, he should use Regression analysis and
multiple discriminant analysis.
It should be remembered that regression
technique is useful only if both variables
(independent variable and dependent variable)
are interval variables.
11. 3) DEPENDING ON TYPE OF ANALYSIS TO
BE DONE:-
a) Requires estimating a parameter
1. Point estimate (measures of
central tendency)
2. Interval estimate (measures of dispersion)
b) Testing of hypothesis
1.„t‟ test.
2. ANOVA test
3. chi-square test
12. Choice of statistical technique depends on the
type of “statistical inference” the researcher
wants.
Point estimate:-
A single statistic is used as an estimate of a
parameter ( mean, median, mode )
Interval estimate:-
An interval within which the true value of a
parameter of a population is stated to lie with a
predetermined probability on the basis of
sampling statistics. (also called range)
13. STATISTICAL ANALYSIS:-
It includes various ”tests of significance” and
“testing of hypothesis”
It is also useful in the estimation of population
values.
Ex.
Z test, „t‟ test, chi-square test etc.
Now there are two type of analysis.
1. Correlation analysis
2. Causal analysis
14. Correlation analysis:-
It is useful to study the correlation between two or
more variables and determining the amount of
correlation between two or more variables.
It is relatively more important in most social and
business researches.
Causal analysis:-
It is concerned with the study of how one or more
variables(independent variable) affect the changes in
another variable(dependent variable). This analysis
can be termed as “Regression analysis”
It is considered relatively more important in
experimental researches.
15. MULTIVARIATE ANALYSIS:-
A) Multiple regression analysis:
In this type of analysis we measure the changes
in dependent variable, with changes in two or
more than two independent variables.
This is done by finding a constant called
“regression coefficient”.
So the main objective of multiple regression
analysis is to make a prediction about the
dependent variable based on its “covariance”
with two or more independent variables.
16. B) Multiple discriminant analysis:
This analysis is used when there is a single
dependent variable, which needs to be classified
into two or more than two groups.
This analysis is used when we want to predict an
entity‟s possibility of belonging to a particular
group based on several predictor variables.
C) Multivariate analysis of variance
(Multi ANOVA):
ANOVA is the ratio of “variance among the
groups” to “variance within the groups”
17. Multi ANOVA is an extension of “ two-way
ANOVA”, wherein the ratio of among group
variance to within group variance is worked out
on set of variables.
When we want to use ANOVA or Multi ANOVA,
the data should meet three assumptions.
These are:
1) Selection of subjects on the basis of random
sampling.
2) Existence of homogeneity of variance between
groups.
3) Variables under study should follow normal
distribution.
18. D) Canonical analysis:
This analysis is useful in case of both measurable
and non-measurable variables.
for the purpose of simultaneously predicting a
set of dependent variables from their joint
covariance with a set of independent variables.
