There
are various statistical techniques for analysing data. To choose an appropriate
technique of statistical analysis in the challenging task to a research worker.
It has two main functions:
1. Interpretation of results, and
2. Presentation of data.
The
major types of tests are employed for analysing data so as to interpret the
results. There are:
(A)
Parametric statistics or tests, and
(B)
Non-parametric statistics or tests.
A
researcher has to select either of these approaches for analysing his own
research data. The
following
are the criteria for choosing an appropriate statistical approach.
(A) Considerations for Parametric Statistics
This
type of statistical analysis may be employed effectively in the following
conditions:
1. Probability or representative sample has been
employed in the investigation.
2. Variables of the study can be qualified at interval
scale.
3. Specific assumptions are fulfilled. The obtained
data are normally distributed or not free distribution.
4. The population of the study has been clearly
defined.
5. Objectives of the research study.
Under this approach the following statistical techniques are
employed :
(a) To study the
descriptive relationship of two or more variables:
a1 – Pearson’s product
moment method of correlation (two variables)
a2 – Multiple correlation
(more than two variables)
a3 – Partial correlation
(more than two variables)
a4 – Factor
analysis-extracting factors or estimating psychological or factorial validity
of tests.
(b) To analyse the
functional relationship of the variables:
b1 – Main effect of two
treatments ‘t’ test
b2 – Main effect of more
than two treatments F-test
b3 – Interaction effect
of two or more variables-Two or more ways analysis variance techniques
b4 – Gain or loss of more
than two treatments-Analysis of covariance and correlated ‘t’ test.
(B) Considerations for Non-parameteric Statistics
This
type of statistical analysis may be used effectively in the following
situations:
1. When non-probability sample is selected in the
research study.
2. The variables of the study are quantified at any
level of measurement, mainly, nominal and ordinal scale. It may be in the
discrete form.
3. No assumption is required for this approach.
4. Free distribution of data, may be skewed or may be
normally distributed.
5. Objectives of the study.
In this approach the following statistical techniques are
generally used :
(a) To study the
relationship of two or more variables:
a1 – Spearman Rho
correlation in small sample not in large sample for two variables. Data are
available at ordinal or internal scale.
a2 – x2 and contingency correlation. It is used when two or
more variables are taken. The data may be nominal or ordinal scale or interval
scale.
a3 – Analysis variance.
(b) To analyse the
difference between two or more groups:
b1 – Median test for
small test.
b2 – x2 test for large sample also for small sample.
b3 – Run test and U-test
when data are on ordinal scale.
b4 –
Sign test.
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