As
he plans the analysis of the data the researcher should consider two sections
of the research report in which statistics will be relevant. The first of these
is the section in which the data producing sample is described and in which may
also be compared to the selected sample and to the population. In describing
the sample, the basic descriptive statistics of the summary frequency
distribution and the appropriate measures of central tendency and variability
serve to provide the reader with some insight into the nature of the
respondents. Researches are interested in the usual demographic characteristics
such as gender, age, occupation and educational level, but in addition anyone
project will suggest other descriptive variables about which data should be
collected.

Provided
the data are available, the researcher should also employ inferential
statistics such as Chi-square or the t-test to determine whether or not his
data producing sample differs from his selected sample or population by
selecting which analysis he will prefer at his early stage, the researcher
structures the kinds of data he will need to produce about the population and
can incorporate the search for these data into his data gathering plan.

The
second section of the report in which statistical procedures plays role is in
the reporting of research results. The selection of these procedures should be
well structured by this point if the researcher has stated specific hypotheses
and research questions. The necessity to test the hypothesis provides guidance
to statistical procedures at the general level, with the decision as to the
level of data available providing the key to which specific procedures are to
employ. Thus, hypothesis which refers to the expected a relationship between
two variables, immediately indicates the need for a correctional analysis. Once
the researcher decides that the two variables will yield ordinal data, for
example, he can move directly to the specification of the rank order
correlation.

The
specification of statistical analysis at this stage of the research also
enables the researcher to estimate his data analysis cost in both time and
money and make whatever arrangements are necessary to reserve time on
data-processing facilities.

The elementary and
special statistical techniques of analsysis are as follows:

**1.**

**Elementary Statistical Techniques of Analysis**

Most
commonly used statistical techniques of analysis data are:

1.
Calculating frequency of distribution in percentages of items under study.

2.
Testing data for normality of distribution Skewness Kurtosis and mode.

3.
Calculating percentiles and percentile ranks.

4.
Calculating measures of central tendency-Mean, Median and Mode and establishing
Norms.

5.
Calculating measures of dispersion-Standard deviation, Mean deviation, quartile
deviation and range.

6.
Calculating measures of relationship-Coefficients of Correlation, Reliability
by the Rank difference and Product moment method.

7.
Graphical presentation of data-Frequency polygon curve, Histogram, Cumulative
frequency polygon and Ogive, etc.

While
analysis their data investigator usually makes use of as many of the above
simple statistical devices as necessary for the purpose for their study. There
are some other complicated devices of statistical analysis listed below which
researcher use in particular experimental or complex casual comparative studies
and investigations.

**2.**

**Special Statistical Techniques of Analysis**

The
following are the special statistical techniques of analysis:

1.
Test of students ‘

*t*’ and analysis of variance for testing significance of differences between statistics especially between Means.
2.
Chi-square test for testing null hypothesis.

3.
Calculation of Biserial ‘

*r*’ and Tetrachoric ‘*r*’ for finding out relationship between different phenomena in complex situations.
4.
Calculation of partial and multiple correlation and of Bivariate and
Multivariate Regression Equations for findings out casual relationship between
various phenomena involved in a situation.

5.
Factorial Analysis for the purpose of analysing the composition of certain
complex phenomena.

6.
Analysis of co-variance for estimating the true effect of the treatment after
adjusting the initial effect.

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