Sampling design.pptx

Sampling may be defined as the method of getting
a representative portion of a population. The term
population is the aggregate total of objects,
persons, families, species or orders of plants or of
animals.

It is necessary whether a research design is
descriptive or experimental, especially if the
population of the study is too large. Through this,
the 4 M’s (Man, Money, Material, and machinery)
resources of the investigator are limited and it is
advantageous for him to use sample survey rather
than the total population.

However, the use of the total population is advisable
if the number of subjects under study is less than
100. But if the total population is equal to 100 or
more, it is advisable to get a sample.

1. It saves time, money, and effort. The researcher can
save time, money, and effort because the number of
subjects involved is small. There are only small number to
be collected, tabulated, presented, analyzed, and
interpreted, but the use of sample gives a comprehensive
information of the results of the study

2. It is more effective. Sampling is more effective if
every individual of the population without bias has an
equal chance of being included in the sample data are
scientifically collected, analyzed, and interpreted.

3. It is faster and cheaper. Since sample is only a “drop in
a bucket”, the collection, tabulation, presentation,
analysis, and interpretation of data are rapid and less
expensive because of the small number of subjects.
4. It is more accurate. Fewer errors are made due to the
small size of data involved in collection, tabulation,
presentation, analysis, and interpretation.

5. It gives more comprehensive information. Since
there is a thorough investigation of the study due to a
small sample, the results give more comprehensive
information because all members of the population
have an equal chance of being included in the sample.

1.Sample data involved more care on preparing
detailed sub classifications because of a small
number of subjects.
2. If the sampling plan is not correctly designed and
followed, the results may be misleading.

3. Sampling requires an expert to conduct the study in
an area. If this is lacking, the results could be
erroneous.
4. The characteristic to be observed may occur rarely
in a population, e.g., teachers over 30 years of
teaching experience.
5. Complicated sampling plans are laborious to
prepare

1.State the objectives of the survey;
2.Define the population;
3.Select the sampling individual;
4.Locate and select the source list of particular
individuals to be included in the sample.

5. Decide the sampling design to be used;
6.Determine the sample size by using the
formula:𝑆𝑠 =
𝑁𝑉+[𝑆𝑒2 1−𝑝 ]
𝑁𝑆𝑒+[𝑉2𝑝 1−𝑝 ]
where N is the population,
𝑆𝑠 is the sample size, V is the standard value (2.58)
of 1 percent level of probability with 0.99 reliability
level, and p is the largest possible proportion (0.50).

7. Select the method in estimating the reliability of
the sample either test-retest, spilt-half, parallel-half,
parallel-forms or internal consistency.
8. Test the reliability of the sample in a plot institution
and;
9. Interpret the reliability of the sample.

Determining a sample size is basically a researcher’s decision.
Some investigator have no idea of determining the sample size
in a given population scientifically, and arbitrarily select the
majority criterion(50 per cent plus one) as sufficient for their
study. Other’s would choose a population lesser or greater than
the majority criterion or 51% of population under study. But
these ideas are not scientifically-oriented.

As stated earlier, sampling is advisable if the
population is equal to 100 or more than 100, but it is
inapplicable to population less than 100. The use of
the total population is advisable when the
population is less than 100 due to categorization.

For instance, the study is on the problems met by
science and mathematics instructors and professors.
First, the subject are categorized as a whole; second,
they are categorized into science and mathematics
instructors and professors; third, into qualified and
nonqualified, and so on. It is desirable to have a bigger
number in each categorization of sample to arrive at
reliable results.

To have a scientific determination of sample size, the
following formula is suggested:
Ss=
𝑁𝑉+[𝑆𝑒2 1−𝑝 ]
𝑁𝑆𝑒+[𝑉2× 𝑝 1−𝑝 ]
Ss= sample Size
N= total number of population
V= the standard value(2.58) of 1 percent level of
probability with 0.99 reliability.
Se=Sampling error(0.01)
p= the largest possible proportion(0.50)

For an illustration of the foregoing formula, the steps
are as follows:
Step1:Determine the total population(N) as assumed
subjects of the study.
Step2:Get the value of V(2.58), Se(0.01), and p(0.50)
Step3:Compute the sample size using the formula 6.1

For instance, the total population is 500, the
standard value at 1 percent level of probability is 2.58 with
99% reliability and has a sampling error of 1% or 0.01 and
the proportion of a target population is 50% or 0.50. Then,
the sample size is computed as follows:
Given:
N=500

V=2.50
Se=0.01
p= 0.50
Ss=
𝑁𝑉+(𝑆𝑒)2×(1−𝑝)
𝑁𝑆𝑒+(𝑉)2×𝑝(1−𝑝)
Ss=
500 2.58 +(0.01)2×(1−0.50)
500 0.1 +(2.58)2×0.50(1−0.50)
Ss=
1290+0.0001×0.50
5+6.6564×0.50(0.50)

Ss=
1290+0.00005
6.6641
Ss=
1290.0000𝑠
6.6641
Ss=193.57 or 194
The sample size for a population of 500 is 194. This
sample (194) will represent the subject of the study. Table
6.1 shows the computed sample sizes for different
population (N) at 0.01 level of probability with 0.99
reliability to a proportion of 0.50.

N Sample Size N Sample Size
100 97 300 166
125 111 325 171
150 122 350 175
175 132 375 179
200 148 425 185
225 148 425 185
250 155 450 188
275 161 475 191
525 196 900 218
550 198 925 219
575 200 950 220
Table 6.1. Computed Sample Size for
Different Population(N) at 0.01 level of
probability to a proportion of 0.50

600 202 1000 221
625 204 1100 224
650 205 1500 232
675 207 1700 235
700 208 2000 238
725 210 2500 242
750 211 3000 244
775 212 3500 244
775 212 4000 248
825 215 4500 249
850 216 5000 250
875 217

The foregoing computed sample sizes in Table
6.1 appears that the smaller the population, the higher
the percentage of the sample size; the larger the
population, the lower is the percentage of the sample
size. In other words, population is inversely
proportional to the percentage of the sample size.

To prove, in a population of 100, the sample size
is 97. To get the percentage, the formula is:
%=
𝑆𝑠
𝑁
× 100
Where:
%=percent
Ss=sample size
N=total number of population

To substitute the formula, consider the following
computation:
%=
97
100
× 100
%=0.97 x 100
= 97%
Hence, percentage is not applicable in
determining sample size because population is
inversely proposal to the percentage of sample size.

Sampling design.pptx

More Related Content

Similar to Sampling design.pptx (20)

Recently uploaded (20)

Sampling design.pptx