research

1. Analyzing Data
By Selliger and Shaomy

2. Data Analysis
 Data analysis refers to organize and summarize all the
data and hace results on the conclusion of the research
and are involve a variety of techniques for analyzing
data.

3. Quantitative Research
 In a quantitative research the data is in a numerical
form and statistics make the research more manageable
and efficient.

4. Qualitative Research
 The acquired data is non numerical and using
qualitative procedures have a heavy burden on the
research.

5. Pragmatic and non pragmatic
Statics.
 The pragmatic statics have a number of set assumptions
but also are more powerful than non pragmatic statics.
The non pragmatic statics are use for numina and
ordinal data but there are less powerful in sense that is
not possible to use them to establish hypothesis.

6. Analyzing Qualitative Research
Data
 In a qualitative research some data is collected by
certain procedures for example constructed
observations, open interviews and diaries. The data is
usually in the form of words and written documents.

7. Analyzing Descriptive Data.
 The data obtained from a descriptive research analyzed
the aids of the descriptive statics. The different types
of descriptive statics are central tendencies and
variability.

8. Analyzing Correctional Data.
 Correctional techniques are used for analyze the data
from a descriptive research. Also examines existing
relationships between variables. A correlation is very
useful for the different purposes of a research.

9. Analyzing Multivariate research
Data.
 This can be analyzed through a set of techniques where
the number of dependent variables ore one number of
independent variables are analyze tremendously.

10. The three multivariate research
data:
 Multi regression.
 Discriminant Analysis.
 Factor Analysis.

11. Multi Regression Analysis.
 This examines the relationship and the predicative
power of one or more independent variables.

12. Discriminant Analysis.
 This is concerned with the predication of membership in
one or more categories or a dependent variable from
scores on two or more independent variables.

13. Factor Analysis
 Helps the researcher make large sets of data more
manageable by identifying the factors that underline
the data.

14. Analysis 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 helps to determine how confident the
researchers can be with the differences found between
two groups.