This document discusses various techniques for analyzing quantitative and qualitative data in research. It outlines different statistical procedures that can be used depending on the type of data, such as descriptive statistics for descriptive research data, correlations for examining relationships between variables, and t-tests or analysis of variance for experimental data involving comparisons between groups. Both parametric and non-parametric statistical methods are covered. The document also addresses qualitative data analysis and multivariate analysis techniques like multiple regression, discriminant analysis, and factor analysis.

1. Analyzing Data
By Selliger and Shaomy

2. Analyzing Data
• Data analyzis refers to sifting, organizing,
summarizing and synthesizing the data so as
to arrive at the results and conclusions of the
research.
• A variety of techniques are available for
analyzing data

3. • In the Quantitative research the data is in
numerical form.
• Analyzing data with the aid of statistics usually
makes the research more manageable and
more efficient.
• In the Qualitative data analyzis techniques
deal with non-numerical data.
• Uising qualitative procedures, often puts a
heavier burden on the researcher.

4. • It is important to note that different statistical
procedures have certain requirements for their
use since certain techniques will only work with
certain types of data.
• Parametric statistics, although having a number
of set assumptions, are far more powerful than
non-parametric statistics.
• Non-parametric statistics, used for nomina and
ordinal data, have, in general, weaker
assumptions but they are also less powerful in
the sense that it is not possible to utilize them for
rejecting the null hypothesis at a given level of
significance.

5. Analyzing qualitative research data
• In qualitative research, where qualitative data
have been collected by procedures such as
unstructured observations, open interviews,
examining records, diaries, and other
documents, the data are usually in the form of
words in oral or written modes.
• The data still need to be analyzed
systematically, since they must lead to results
that others will accept as representative.

6. Analyzing descriptive research data
• Data obtained from descriptive research are
generally analyzed with the aid of descriptive
statistics .
• The types of descriptive statistics are
frequencies, central tendencies and
variabilities.
• Frequencies are used to indicate how often a
phenomenon occurs and they are based on
counting the number of occurrences.

7. • Central tendecy measures provide information
about the average and the typical behavior of
subjects.
• Variability provides information on the spread
of the behaviors or the phenomena among
the subjects of the research.

8. Analyzing correlational data
• Correlational techniques are used for analyzing
data obtained from descriptive research which,
examines existing relationships between
variables, with no manipulation of variables.
• A correlation is a statistical procedure which is
very useful for different purpuses in research and,
apart from examining relationships the reliability
and validity of data collection procedures and for
subsequent types of more advanced statistical
analyzis

9. Analyzing multivariate research data
• The data obtained from multivariate research,
can be analyzed through a set of techniques
where a number of dependent variables and
one or a number of independent variables are
analuzed simultaneously.
• There are three multivariate procedures:
Multiple regression, discriminant analyzis and
factor analyzis.

10. Multiple regression
• Through multiple regression analyzis it is
possible to examine the relationship and
predictive power of one or more independent
variables with the dependent variable.
• From multiple regression analyzis we can
obtain results showing which variables are
significant in their contribution to explaining
the variance in the dependent variable and
how much variance they contribute.

11. Discriminant analyzis
• Discriminant analyzis is concerned with the
prediction of memebership in one of two (or
more) categories of a dependent variable from
scores on two or more independent variables
distinguish between two or more categories of
the depedent variable.

12. Factor analyzis
• Factor analyzis helps the researcher make
large sets of data more manageable by
identifying a factor or factors that underlie the
data. It is different from multiple regression
and discriminant analyzis in that it does not
relate independent variables to a dependent
one, but rather operates within a number of
independent variables, without a need to have
a dependent variable.

13. Analyzis experimental research data
• When two groups, experimental and control,
are being compared, the researcher will use
the t-test which is capable of comparing two
groups on a given measure.
• The t-test is used to compare the means of
two groups. It helps determine how confident
the reseacher can be that the differences
found between two groups (experimental and
control) as a result of a treatment are not due
to chance.

14. • One way analyzis of variance is used to
examine the differences in more than two
groups. It specifically indicates how confident
the researcher can be that the differences, for
example, two experimental groups and a
control group as a result of a treatment are
not due to chance.
• Factorial analyzis of variance is capable of
analyzing the effect of different treatments in
more complex conditions.