SOURCE
OF DATA IN EDUCATIONAL RESEARCH
A.
Nature
of Data Sources
Research
findings are always based on the result of data analysis which is obtained from
data sources. It is important that researchers correctly select the data
sources and use the correct data collection instrument. When the data sources
are wrongly selected, the research data obtained from them (no matter how
appropriate the data collection instruments are, how correct the data
collection technique is) will be wrong, and the finding will have validity
problem.
Criteria
of data sources selection are different from one research to another research
depending on the design of the research.
The way to get correct data sources for Classroom Action Research is
different from the way to get correct data for an experimental research, and so
forth. Furthermore, Charles, C.M (in Latief 138) states that
“Sources
of data depend on the nature of the research. In Classroom action Research a
researcher is concerned only with a particular group in its entirety, such as
class, grade level, school. Where
research is concerned with a population so large that it cannot be investigated
in its totality, samples are a necessity. In research whose findings are
intended to be generalized to the population, it is necessary that manageable
samples be selected that accurately reflect the distribution of trait within
the population at large (Charles,
C.M, 1995: 96-97)
B.
Introduction
to Sampling
Subject : is an individual who participates in a
research study or is someone from whom data are collected. Each person who is
given a treatment and whose behavior is measured is considered to be a subject.
Population
: is a group of elements of cases,
whether individuals, objects, or events that conform to specific criteria and to
which we intend to generalize the results of the research.
Sample
: is
a group of elements or a single element from which data are obtained.
Therefore, based on the definition
above, we can draw the conclusion that the small group that is observed is
called a sample, and the larger group about which the generalization is made is
called a population.
C.
Rationale
of Sampling
If you can observe all instances of
a population with confidence, base conclusions about the population on these
observations is called perfect induction. However, if you observe only some
instances of a population as a whole, it is called imperfect induction. This
imperfect induction is the concept of sampling, which involves taking a portion
of the population, making observations on this smaller group, and then
generalizing the findings to the parent population, the larger population from
which the sample was drawn. Thus, samples must be representative if you are to
be able to generalize with reasonable confidence from the sample to the
population. An unrepresentative sample is termed a biased sample.
D.
Steps
in Sampling
There are several steps in
sampling, such as:
1. defining
the target population
2. defining
the accessible population
3. selecting
a representative sample
4. obtaining
an adequate sample
When the researcher wants to
conduct a research about the effectiveness of School Based Curriculum toward SMP
students’ achievement in Indonesia and the researcher want to use UAN as the
comparison, the researcher will define the population as all the Junior High
School students taking national English examination in Indonesia. Yet, that
population is still too big and not accessible; therefore the researcher has to
define an accessible population which has the same characteristics with the
population, so that the accessible population is a province/city that has the
similar average with the population. The accessible population can be defined
as Junior High School students taking national English examination in Malang.
If the researcher thinks that the accessible population is still too big, the
researcher may define the representative sample from the accessible population,
for instance, researchers randomly choose several SMP schools in Malang.
To have a representative sample,
the researcher is recommended to take the data randomly. The more sample taken, the more
representative the sample is. However, as we have discussed former, the
inappropriately selected sample can lead to invalid conclusion. Thus, care in
selecting sample is more important than increasing the size of sample.
E.
Types
of Sampling Procedures
v Probability
Sampling
It
is a method of sampling in which every member or element of the population has
an equal probability of being chosen in the sample.
The
four types of probability sampling most frequently used in educational
research, such as:
1.
Simple Random Sampling
In
simple random sampling every member of the population has an equal and
independent chance of being selected for the sample.
If
the population is small, a more practical technique can be used. Write the name
or the ID number of each student on a slip of paper, then mix the slips
thoroughly, and draw the slips as many as needed for the sample (Borg,
W.R.& Gall, M.D.1989:221).
The
simple random sampling technique is the best technique in assuring the
representativeness of the sample from the accessible population.
2.
Systematic Random
Sampling
In
systematic random sampling every n element is selected from a list of all
elements in the population, beginning with a randomly selected element.
If
out of 1000 population, 100 students are selected as the sample, than every 10th
student is selected. The starting point for the selection is chosen at random.
If the starting point selected randomly is 8, for example, then the following
10th students are selected; they are 18, 28, 38, 48, 58, etc until
100 sample are selected (Cohen, L., Manion, L.1994:87, Charles, C.M. 1993: 98,
Borg, W.R.& Gall, M.D.1989:224).
The
systematic random sampling technique involves a simple procedure of three steps,
they are:
1. Divide
the accessible population (e.g. 1000) by the number (e.g. 100) decided for the
sample (e.g. 1000:100=10)
2. Select
at random a number smaller than the number arrived at by the division (e.g.
<10)
3. Starting
from that number (e.g. 8) select every 10th name from the list of
the accessible population (8, 18, 28, 38, 48, 58, 68, etc. until 100 names are
selected for the sample.
3.
Stratified Random
Sampling
Stratified
random sampling is used when the researchers want to ensure that subgroups
within the population need to be represented proportionally in the sample.
For
example, because the proportion of the male population is approximately 40 %
and the female population is 60 % out of the accessible population then the
researchers select 40 % of the sample from male population and 60 % from female
population (Charles, C.M. 1993: 97). Sex is taken into consideration in the
sampling process if the researchers believe that the variable data are affected
by sex.
If
the researchers believe that the variable under study is also affected by level
of students’ intelligence (indicated by IQ test scores), then the population is
not only divided by sex but also divided by IQ levels (e.g. Students with High
IQ scores, students with Mid IQ scores, and students with Low IQ scores).
The
accessible population is then divided by sex into two sub groups (of male group
and female group) then each sub group is further sub-divided by IQ levels into
six sub groups (of Female students with High IQ scores, female students with
Mid IQ scores, female students with Low IQ scores, male students with High IQ
scores, male students with Mid IQ scores, and male students with Low IQ
scores)
This
stratified random sampling technique involves a procedure of dividing the
population into homogeneous groups, each group containing subjects with similar
characteristics (Cohen, L., Manion, L.1994:88, Borg, W.R.& Gall,
M.D.1989:224). If the homogeneous groups
are determined by sex and IQ level, then the steps to be taken for this
procedure are as follows:
1. Define
in what way the accessible population varies, e.g. in terms of sex and IQ
levels
2. Identify
the sub groups based on the variation of sex and IQ level (See Table 1)
3. Examine
the proportion of each sub group in the accessible population
4. Take
samples randomly for each sub group proportionally
Table
1: Stratified Sub groups
|
Sex
of the students
|
Levels
of Students’ IQ
|
||
|
High
IQ
|
Mid
IQ
|
Low
IQ
|
|
|
Male
|
1
|
2
|
3
|
|
Female
|
4
|
5
|
6
|
4.
Clustered Random
Sampling
When
the population is large and widely dispersed, gathering a simple random sample
poses administrative problems. Instead of travelling around a city to test all
high school students about their English achievement, we can select randomly a
specific number of schools and test all the students in those selected schools
(Cohen, L., Manion, L. 1994:88). Cluster sampling technique involves the random
selection of groups that already exists.
5. Stage
Random Sampling
In a research involving a large number of population
cluster sampling may take several stages. It is used when the population is
very complex. For example students of Senior High schools in East Java. The
students are organized in schools within cities in East Java. After that the
researcher starts selecting cities in East Java, then the schools within the
cities, then the classes within each school, then students in each class. So,
the sampling units/students are selected through different stages using a
combination of random sampling techniques.
v Non probability
sampling
Different
from probability sampling, non-probability sampling includes methods of
selection in which elements are not chosen by chance procedures whose findings
are intended to be generalized to the population. Its success depends on the
knowledge, expertise, and judgment of the researcher. Here, the researcher
involves non-random procedures for selecting the members of the sample. The
major forms of non-probability sampling are:
1.
Convenience Sampling
Here,
a group of subjects selected because of availability
2.
Purposive Sampling
The
researcher selects particular elements from the population that will be
representative or informative about the topic. It is not widely used in
quantitative studies. In qualitative research, on the other hand, some type of
purposive sampling is almost always used.
3.
Quota Sampling
It
is used when the researcher is unable to take a probability sample but still
wants a sample that is representative of the entire/target population.
F.
Significant
Sample Size
Charles,
C.M. (in Latief: 142) states that “If the sample is large enough, the sample
tends to correspond fairly closely to the population”. In shortly, the bigger
the size of the sample, the more it tends to correspond fairly closely to the
population. This belief can be true of course if the sampling has been done
randomly. Nevertheless, large sample if improperly selected can lead to invalid
conclusion and so, sample size, in itself, is not a factor of major concern”.
Care in selecting the sample is more important than in increasing the size of
the sample”.
The
sample size becomes significant when the researchers become confident that if
he should draw a different sample of the same size and using the same procedure
he would obtain approximately the same results in his research (Borg, W.R.&
Gall, M.D.1989:215 in Latief 142).
G.
Sources
of Data in Experimental Research
In
experimental research, the researcher wants to implement a new instructional
strategy or a new educational product by comparing its results with another
group of equal level. Selecting a sample of 2nd year students in one
Junior High School to be treated in an experiment is too large and not
practical. Therefore, the experiment is conducted in two or more existing
classes in one school that have similar characteristic. (Latief: 143)
H.
Sources
of Data in Classroom Action Research
In
Classroom Action Research, a researcher begins the research by identifying the
problem occurred in the classroom. From
the classroom instructional problems, an innovative instructional strategy is
established to solve the problem. It becomes the product of CAR which is useful
for solving classroom. This product can be applied by any other classroom
teachers who have similar problems. So, the sources of data are the students
whose class is having problems to be solved through the research. There is no
need to think of the population and sampling in Classroom Action Research. (Latief:
143)
I.
Sources
of Data in Educational Research and Development
In
Educational Research & Development, a researcher aims to develop an
educational classroom product to be tried out in certain classrooms which
intended to use the products. The try-out aims at getting feedback in order to
revise the product. The research product then can be used to any other
classrooms similar to the classrooms where the try-out has been conducted.
Again, in Educational Research & Development, there is no need to think of
population and sampling. (Latief: 143)
J.
Sources
of Data in Qualitative Research
In
Qualitative research, the sources of data are assumed to be homogeneous. This
means that there is only one kind of the sources, so there is no need to think
of representativeness to be obtained using random sampling.
In
a historical study, for example, the researchers need data sources that are
believed to have the authority to give information needed as the data. The more
authoritative the sources are the more trusted the sources. The
authoritativeness of the sources is obtained by selecting the subjects based on
the researchers’ judgmental criteria. A set of criteria are determined to be
used as the basis of selecting the sources. The more criteria the sources meet,
the more authoritative the sources are. For a historical study on the brutal
killing in Indonesia related to Indonesian Communists party on September 30, 1965
(September 30, 1965 movements), for example, the authoritative sources of data
are the principal witnesses of the historical event, who are still alive, who
possess documents on the event (Borg, W.R., Gall, M.D. 1989 :817), smart enough
to recall the event, have neutral objective attitude to the event (not one of
the victims, not the one whose relatives got killed in the event, not the one
who hated the government), are willing to be interviewed, and other criteria
which are judged to help researchers select the right sources of data.
The finding based on the data from those
authoritative data sources are not to be generalized to a larger group of
population, it becomes the truth for all the members of the community. The
historical research finding on September 30, 1965 movements based on data from
principal witnesses of the event becomes trusted truth for all Indonesian
community.