The document discusses various techniques for analyzing different types of data in research. It describes statistical procedures like parametric and non-parametric statistics that have assumptions about the type of data. Qualitative data analysis involves deriving categories from the text or applying existing systems. Descriptive research uses frequencies, central tendencies, and variabilities to analyze data. Correlational research examines relationships between variables using correlations. Multivariate research analyzes multiple dependent and independent variables simultaneously using multiple regression, discriminant analysis, and factor analysis. Experimental research compares groups using t-tests and analyzes more than two groups with one-way ANOVA.

1. BY SELLIGER AND SHAOAMY
LENIS BEATRIZ MARQUEZ VIDAL

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
According to the reading , it says that there is a variety
of techniques are available for analyzing data

3. Statical procedures
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. One
of the assumptions of parametric statics is that the variable
mesured is normally distributed in the population.
The second assumptions is that the data represents an interval
or a ratio scale of mesurements.

4. Analyzing qualitative research
Well , talking about the qualitative research, let me
tell you that 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.
Two main kind of thechniques can be identified in
analyzing qualitative data : A) deriving a sey of
categories for dealing with text segments from the
text itself .this is a inductive procedure .B) an
ordering of systems of categories already exist al the
beggining of the process and the researcher applies
this system to the data .

5. 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.
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.

6. Analyzing correlational data
Correlations
Correlational techniques are used for analyzing
data obtained from descriptive research which,
examines existing relationships between variables,
with no manipulation of variables.
Something important to mention is that 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.

7. Analyzing multivariate research data
The data gathered 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.
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.

8. 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
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. Possible examples
of categories may be males/ females, monolingual/
bilingual learners or formal/ informal contexts .

9. Factor analyzis
Factor analyzis helps the researcher makes 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.
his type of analisis is base on the assumption that
variables measuaring the same factor will be highly
related while variables meassuring different factors will
have low correlation with one another .

10. Analyzis experimental research data
To analize the experimental let ‘s go to see an
example .
When two groups 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.

11. One way analyzis of variance (one way ANOVA )
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.
The analysis is performed in the variance of the
groups focusing on whether the varaibility between
the different groups is greater than the variability
within each gruop.