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ANALYSES, PRESENTATION, AND INTERPRETATION OF DATA

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Analysis

Analysis is
the process of breaking up the whole study into its constituent parts of
categories according to the specific questions under the
statement of the problem. This is to
bring out into focus the essential features of the study.
Analysis usually precedes
presentation.

Example:
In the study of the teaching of science
in the high schools of Province A,
the whole study may be divided into its constituent parts
as follows according to the specific
questions:

1.Educational qualifications of the science teachers
2.Methods and strategies used in the teaching of science
3.Facilities available for the teaching of science
4.Forms of supervisory assistance
5.Differences between the perception of the teachers and
those of the
students concerning the teaching of science
6.Problems encountered in the teaching of science
7.Proposed solutions to the problems
8.Implications of the findings

Each constituent
part may still be divided into its essential categories. Example: The
educational qualifications of the teachers may further be
subdivided into the following:

1.Degrees earned in preservice education
2.Majors or specializations
3.Units earned in science
4.Teacher’s examinations and other examinations passed
5.Seminars, conferences, and other special trainings attended
for the
teaching of science
6.Books, journals, and other materials in science being read
7.Advanced studies

Number of years in science teaching

Then under degrees earned are

Bachelor of Arts
Bachelor of Science in Education
Master of Arts

The other constituent
parts may also be similarly divided and subdivided. The data
are then grouped under the categories or parts to which they
belong.

Classification
of data. Classification is grouping together
data with similar
characteristics. Classification is a part of analysis. The
bases of classification are the
following:

a.Qualitative (kind). Those having the same quality or are of the same
kind are grouped together. The grouping element in the examples
given under analysis is qualitative. See examples under analysis.

b.Quantitative. Data are grouped according to their quantity. In age, for
instance, people may be grouped into ages of 1014, 1519,
2024,
2529, etc.

c.Geographical. Data may be classified according to their location for
instance; the schools in the secondary level in Province
A may be
grouped by district, as District 1, District 2, District
3, etc.

d.Chronological. In this, data are classified according to the order of
their occurrence. Example: The enrolments of the high schools
of
Province A may be classified according to school years, as
for,
instance, enrolments during the school years 1985’86, 1986’87,
1987’88.

Crossclassification.
This is further classifying a group
of data into subclasses. This is
breaking up or dividing a big class into smaller classes.
For instance, a group of students
may be classified as high school students as distinguished
from elementary and college
students. Then they are further subdivided into curricular
years as first, second, third, and
fourth years. Each curricular year may still be subdivided
into male and female.

Arrangement
of data or classes of data. The bases of
arrangement of data or groups
of data are the same as those of classification.

a.Qualitative. Data may be arranged alphabetically, or from the biggest
class to the smallest class as from the phylum to specie
in classifying
animals or vice versa, or listing the biggest country to
the smallest one
or vice versa, or from the most important to the least important,
or
vice versa, etc. Ranking of students according to brightness
is
qualitative arrangement.

b.Quantitative. This is arranging data according to their numerical
magnitudes, from the greatest to the smallest number or vice
versa.
Schools may be arranged according to their population, from
the most
populated to the least populated, and so with countries,
provinces,
cities, towns, etc.

c.Geographical. Data may be arranged according to their geographical
location or according to direction. Data from the Ilocos
region may be
listed from north to south by province as Ilocos Norte, Abra,
Ilocos Sur
and La Union.

d.Chronological. This is listing down data that occurred first and last
those that occurred last or vice versa according to the purpose
of
presentation. This is especially true in historical research.
For instance,
data during the Spanish period should be treated first before
the data
during the American Period.

Classification,
crossclassification and arrangement of data are done for purposes of
organizing the thesis report and in presenting them in tabular
form. In tables, data are
properly and logically classified, crossclassified, and
arranged so that their relationships are
readily seen.

Groupderived Generalizations

One of the main
purposes of analyzing research data is to form inferences,
interpretations, conclusions, and/or generalizations from
the collected data. In so doing the
researcher should be guided by the following discussions
about groupderived
generalizations.

The use of
the survey, usually called the normative survey, as a method of collecting
data for research implies the study of groups. From the findings
are formulated conclusions
in the form of generalizations that pertain to the particular
group studied. These conclusions
are called groupderived generalizations designed to represent
characteristics of groups and
are to be applied to groups rather than to individual cases
one at a time. These are
applicable to all kinds of research, be they social, science
or natural science research. There
are several types of these but are discussed under four categories
by Good and Scates.
(Good and Scates, pp. 290298) The key sentences are of this
author.
1.Generally, only proportional predictions can be
made. One type of
generalization is that which is expressed in terms of proportion
of the cases in a group,
often in the form of probability. When this type is used,
we do not have enough information
about individual cases to make predictions for them, but
we can nevertheless predict for a
group of future observations. As to individual event, however,
we can say nothing;
probability is distinctly a group concept and applies only
to groups.

Quality control
in manufacturing is an example. Based on the recognition that
products cannot be turned out as precisely as intended, but
that so long as a given
proportion of the cases fall within assigned limits of variation,
that is all that is expected. In
the biological field, certain proportions of offspring, inherit
certain degrees of characteristics
of parents, but individual predictions cannot be made. In
the social field, in insurance
especially, based on demographic and actuarial data, life
tables indicate life expectancies of
groups but nothing whatsoever is known about the life expectancy
of any particular
individual.

Here is another
example. Suppose in a certain school offering civil engineering, it is a
known fact that all through the years, bout 70% of its graduates
with an average of 2.0 or
its equivalent or higher pass the licensing examination for
civil engineers. On this basis, we
can predict that about 70% of the graduates of the school
with an average of 2.0 or higher
will pass the next licensing examination for civil engineers
but we cannot predict with
certainty the passing of a particular graduate even if his
average grade is 1.25.

2.The average can be made to represent the whole group.
A second type of
groupderived generalization results from using the average
as a representation of the
group of cases and offering it as a typical result. This
is ignoring the individuals comprising

the group or the variation existing in the group but the
average represents the whole group.
Generally, the mean and the median are used to denote the
averages of scale position but
other statistical measures such as the common measures of
variation, correlation,
regression lines, etc. are also structurally considered as
averages. These are group
functions conveying no sure knowledge about any individual
case in the group.

3.Full frequency distribution reveals characteristics
of a group. As a third type
of knowledge growing out of the study of the groups, we have the
fullfrequency distribution
– the most characteristics device, perhaps of all statistical work.
Perhaps, too the most
inferential characteristics of frequency distribution are shape and
spread. Frequency
distributions carry the implication of probability. One implication
is as follows. Suppose the
heights of a Grade I pupils are taken and then grouped into a class
frequency distribution,
using height as the trait or basis of distributions in groups. Then
the suppliers of chairs and
tables for the pupils will be able to know the number of chairs and
tables to suit the heights
of the pupils.

Here is another
example which enables us to know certain characteristics of a group.
Suppose a test is given to a group of students. Then their
scores are grouped into a class
frequency distribution. If the standard deviation, a measure
of variability, is computed and
it is unusually large, then we know that the group is heterogeneous.
If the standard
deviation is small, the group is more or less homogeneous.
If the distribution is graphed and
the curve is bellshaped, the distribution is normal, that
is, there is an equal number of
bright and dull students with the average in the middle.
If the curve is skewed to the right,
there are more dull students than bright ones, and if the
distribution is skewed to th left
there are more bright students than dull ones.

4.A group itself generates new qualities, characteristics,
properties, or aspects
not present in individual cases. For instance, there are many chairs in a room. The chairs
can be arranged in a variety of ways. However, if there is
only one chair, there can be no
arrangement in any order. Hence, order and arrangement are
group properties and they
represent relationships within a group, properties which
can arise only if there are two or
more cases.

Other group
properties that exist only in groups are cooperation, opposition,
organization, specialization, leadership, teaching, morale,
reciprocal sharing of emotions,
etc. which vanish in individual cases.

Two or more categories of generalization may be added at
this point.

1. A generalization can also be made about an individual
case. For instance, a
high school graduating student is declared valedictorian
of his class. We can generalize that,
that student is the brightest in his class. This is a groupderived
generalization because it
cannot be made if there is only one student. Here is another
example. A teacher declares
that Juan is the best behaved pupil in her class. This is
a groupderived generalization
because this statement cannot be made if there is only one
pupil. There are many instances
of this kind.

2.In certain cases, predictions on individual cases
can be made. It has been
mentioned earlier that, generally, only proportional predictions
can be made. However, in
correlation and regression studies, one variable can be predicted
from another. Take the
case of the civil engineering graduate taking the licensing
examination by the use of
regression equations. The accuracy of prediction is high
if (1) there is linearity in the
relationship of the two variables if graphed, (2) the distributions
in the two variables are
normal or not badly skewed, and (3) the spread or scatter
of the two variables is the same
for each column or row in the correlation table. The process
involves a complicated
statistical book especially that of Garrett, pp. 122146
for linear correlation and pp. 151165
for regression and prediction.
Prof. Erwin M. Globio, MSIT Thesis/Dissertation Adviser/Consultant Mobile: 09393741359  09323956678
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